key variables in psychology research

Understanding Variables in Psychology: A Comprehensive Overview

Variables play a crucial role in psychology research, shaping the outcomes of studies in various ways. In this comprehensive article, we will explore the different types of variables, including independent, dependent, confounding, and extraneous variables.

We will also delve into how variables are measured in psychology, the importance of variables in research, and common misinterpretations to avoid. We will discuss ethical considerations in variable manipulation, how to avoid confounding variables, and provide examples of variables in psychology studies.

Join us as we unravel the complex world of variables in psychology.

• Variables in psychology refer to factors that can be measured and manipulated in research.
• Understanding the types of variables and how they are measured is crucial for accurate and ethical research.
• It is important to recognize and control for extraneous variables in order to accurately interpret study results.
• 1 What Are Variables in Psychology?
• 2.1 Independent Variables
• 2.2 Dependent Variables
• 2.3 Confounding Variables
• 2.4 Extraneous Variables
• 3.1 Self-Report Measures
• 3.2 Behavioral Measures
• 3.3 Physiological Measures
• 4 Why Are Variables Important in Psychology Research?
• 5 How Do Variables Affect the Outcome of a Study?
• 6.1 Correlation Does Not Equal Causation
• 6.2 The Third Variable Problem
• 6.3 The Importance of Control Variables
• 7 Ethical Considerations in Variable Manipulation
• 8 How to Avoid Confounding Variables in Research
• 9 Examples of Variables in Psychology Studies
• 10.1 What are variables in the context of psychology?
• 10.2 Why is it important to understand variables in psychology?
• 10.3 What are the two types of variables in psychology?
• 10.4 What is the difference between a continuous and a categorical variable?
• 10.5 How are variables measured in psychology?
• 10.6 What are potential confounding variables in psychology?

What Are Variables in Psychology?

Variables in psychology refer to characteristics or phenomena that can be measured, observed, or manipulated in research studies within the field of psychology.

One of the key types of variables used in psychological research are the independent variables . These are the factors that the researcher manipulates or controls to see how they affect the dependent variable . The dependent variable, on the other hand, is the outcome or behavior that is being measured and tested in response to changes in the independent variable. By defining and operationalizing these variables, researchers can design experiments and studies to test hypotheses and understand human behavior better.

Types of Variables

In psychology, various types of variables are distinguished, including independent variables, dependent variables, confounding variables, and extraneous variables, each playing a crucial role in research design and analysis.

Independent variables, often denoted as X , are the variables that are manipulated or controlled by the researcher to observe their effects on the dependent variable. These variables are essential for testing causal relationships in a study.

On the other hand, dependent variables, commonly represented as Y , are the outcomes or responses that are measured based on the changes in the independent variable. Confounding variables, also known as third variables, can distort the statistical relationships between the independent and dependent variables, leading to inaccurate conclusions. Extraneous variables are factors that might influence the results but are not the main focus of the study.

Independent Variables

Independent variables in psychology are the factors manipulated or controlled by researchers to observe their effects on dependent variables, contributing significantly to experimental research in psychology.

By systematically changing or setting these independent variables, researchers can determine how variations in these factors lead to changes in the dependent variables. This process allows researchers to investigate the causal relationships between different variables, helping establish cause-and-effect connections in psychological phenomena.

For example, in a classic psychology experiment, the independent variable could be the type of therapy (such as cognitive-behavioral therapy vs. psychoanalytic therapy) provided to participants, while the dependent variable could be the reduction in symptoms of anxiety. By manipulating the independent variable (the type of therapy), researchers can assess its impact on the dependent variable (symptom reduction) to draw conclusions about the efficacy of different therapeutic approaches.

Dependent Variables

Dependent variables in psychology are the outcomes or responses that are measured or observed based on changes in independent variables, providing insights into the impact of experimental manipulations on psychological phenomena.

Understanding dependent variables is essential for researchers to determine the statistical relationship between different factors. These variables are crucial in establishing a cause-and-effect connection, which is often measured using techniques such as Pearson’s r correlation coefficient.

Operationalizing dependent variables involves defining measurable criteria to quantify and analyze the outcomes accurately. Researchers carefully design experiments to ensure that dependent variables are validly and reliably measured, leading to meaningful conclusions regarding the causal relationships explored in the study.

Confounding Variables

Confounding variables in psychology are external factors that influence the relationship between the independent and dependent variables, posing challenges to the validity and interpretation of research findings.

These variables can muddle the true effect of the independent variable on the dependent variable, leading to skewed or inaccurate results. Common examples of confounding variables include participant characteristics, environmental conditions, and extraneous variables that were not accounted for during the study.

To identify and control for confounding variables, researchers often employ various methods such as random assignment, matched-pair designs, and statistical techniques like analysis of covariance. These strategies help isolate the true impact of the independent variable and ensure that the results accurately reflect the relationship being studied.

Extraneous Variables

Extraneous variables in psychology are additional factors that may unintentionally affect the results of a study if not properly controlled or accounted for, emphasizing the importance of rigorous research design and methodology.

Controlling extraneous variables is crucial as they can introduce errors and bias, leading to inaccurate conclusions. Researchers aim to minimize their impact through various strategies such as randomization, matching, and statistical control.

For example, in a study investigating the impact of a new teaching method on student performance, extraneous variables like student motivation or prior knowledge could confound the results. By carefully considering and controlling these factors, researchers can enhance the reliability and validity of their findings.

How Are Variables Measured in Psychology?

In psychology, variables are measured using various methods such as self-report measures , behavioral measures , and physiological measures to capture different aspects of human behavior and mental processes .

One common method for measuring variables in psychology is through self-report measures, where individuals provide information about their thoughts, feelings, and behaviors through surveys or questionnaires.

Behavioral measures involve observing and recording a person’s actions or reactions in a controlled setting to assess specific behaviors and responses.

Physiological measures, on the other hand, include techniques like brain imaging or heart rate monitoring to objectively measure bodily changes in response to stimuli.

Operationalizing variables is a critical step in measurement, where abstract concepts are defined in measurable terms to ensure consistency and objectivity.

Sampling methods play a crucial role in influencing the measurement of variables by determining the representativeness of the chosen sample and affecting the generalizability of research findings.

For example, using Pearson’s r, a statistical measure of correlation, helps in understanding the relationship between variables and assessing the strength and direction of their association.

When studying causal relationships in psychology, researchers must carefully consider the design of their studies and employ appropriate measurement techniques to establish causation between variables.

Self-Report Measures

Self-report measures in psychology involve collecting data directly from participants through surveys, questionnaires, or interviews, offering insights into individuals’ perceptions, attitudes, and behaviors.

One of the key advantages of self-report measures is the ease of administration and cost-effectiveness compared to other data collection methods in psychological research. Researchers can efficiently gather large amounts of data from a diverse range of participants without the need for extensive resources or specialized equipment. This method also allows for the exploration of subjective experiences, emotions, and opinions that may not be observable through external observations or objective assessments.

Behavioral Measures

Behavioral measures in psychology focus on observing and recording participants’ actions, responses, and performance in controlled experimental settings, providing objective data on behavior and cognitive processes .

One key distinction in psychological research lies in understanding the role of participant and situational variables. Participant variables refer to characteristics inherent in the individual being studied, like age, gender, or personality traits, which can influence behavior. On the other hand, situational variables involve aspects of the environment or experimental conditions that may impact behavior, such as noise level, temperature, or the presence of others.

For instance, in a study examining the effect of music on concentration, the type of music (situational variable) and the individual’s focus ability (participant variable) could both affect the results. By carefully controlling and measuring these variables, researchers aim to draw reliable conclusions from their observations.

Physiological Measures

Physiological measures in psychology involve assessing biological responses such as brain activity, heart rate, or hormonal levels to understand the physiological basis of behavior and mental processes.

These measures provide valuable insights into the underlying mechanisms of cognitive functions and emotional responses. For instance, electroencephalography (EEG) is frequently used to monitor brainwave patterns, offering clues about attention, memory, and emotional regulation. Similarly, galvanic skin response (GSR) can reveal levels of arousal and emotional engagement by measuring changes in sweat gland activity. By leveraging these physiological markers, researchers can uncover subtle nuances in human behavior that may not be apparent through self-report measures alone.

Why Are Variables Important in Psychology Research?

Variables play a crucial role in psychology research as they allow researchers to investigate relationships, identify patterns, and draw conclusions based on empirical data collected through controlled experiments and observational studies.

In psychological research, variables serve as the building blocks that enable scientists to explore the complexities of human behavior and mental processes. These variables can be categorized as independent variables, which are manipulated to observe their effects on dependent variables, providing insights into cause-and-effect relationships.

For example, in a study examining the influence of sleep duration on memory retention, sleep duration would be the independent variable, while memory retention would be the dependent variable. By systematically altering the sleep durations and measuring the corresponding changes in memory performance, researchers can establish a statistical relationship and potentially infer a causal link.

Through the careful selection and manipulation of variables, researchers can unravel intricate psychological phenomena, such as the impact of environmental factors on cognitive development or the relationship between stress levels and emotional well-being. By controlling for various variables and conducting rigorous analyses, psychologists can advance our understanding of human behavior and contribute valuable insights to the field.

How Do Variables Affect the Outcome of a Study?

Variables influence the outcome of a study by shaping the data collected, determining the validity and reliability of findings, and enabling researchers to draw meaningful conclusions based on the operational definitions applied to each variable.

When discussing the impact of variables on research outcomes, it is crucial to distinguish between independent and dependent variables. The independent variable is the one that is manipulated or controlled by the researcher, while the dependent variable is the outcome that is measured. For example, in a study investigating the effect of a new drug on blood pressure, the independent variable would be the administration of the drug, and the dependent variable would be the change in blood pressure measurements.

These variables play a critical role in defining the relationship between different elements in a study, such as the sample and population. The sample refers to the specific group of participants or subjects from which data is collected, while the population represents the larger group that the sample is intended to represent. Understanding how variables interact within these contexts helps researchers interpret their results accurately and make informed decisions based on the findings.

Common Misinterpretations of Variables in Psychology

Misinterpretations of variables in psychology often stem from assumptions of causation based on correlation , overlooking the complexities of research methodology, confounding variables, and the distinction between cause and effect relationships.

One common misconception is the belief that correlation implies causation. It’s crucial to understand that just because two variables are correlated, it doesn’t necessarily mean that one causes the other. This cause and effect fallacy can lead to inaccurate conclusions and misguided interventions.

Researchers must maintain methodological rigor by controlling for extraneous variables that could influence the results. Without proper controls, the validity of the findings may be compromised, impacting the reliability and generalizability of the study.

Correlation Does Not Equal Causation

One common misinterpretation in psychology is assuming causation from correlation, failing to recognize that correlation between variables does not necessarily imply a causal relationship, highlighting the importance of rigorous analysis and controlled experiments.

Correlation versus causation fallacy is a prevalent error in scientific research and everyday reasoning. This misleading assumption occurs when individuals wrongly infer a causal connection simply based on observed correlation without considering other possible factors. For instance, a study may find a strong positive correlation between ice cream sales and drowning incidents; however, this does not mean that buying ice cream directly causes more drownings. Statistical relationships can be easily misinterpreted as causal effects, emphasizing the significance of experimental designs in research to establish true cause-and-effect relationships.

The Third Variable Problem

The third variable problem in psychology refers to the scenario where a third unaccounted variable may be influencing the observed relationship between two variables, complicating the interpretation of results and research conclusions.

For instance, imagine a study that aims to investigate the correlation between sleep quality and memory performance. The researchers unintentionally overlook the impact of stress levels on both variables. In this case, stress could be the elusive third variable that skews the relationship between sleep quality and memory performance, leading to inaccurate conclusions.

To address this issue, researchers employ various strategies such as conducting controlled experiments, using statistical techniques like regression analysis to account for potential third variables, and designing studies with tight controls to isolate the main variables under investigation.

The Importance of Control Variables

Control variables are essential in psychology research as they help isolate the effects of the independent variable on the dependent variable by minimizing the influence of extraneous factors and ensuring valid comparisons between groups or conditions.

By controlling for these extraneous variables, researchers can maintain the internal validity of their experiments, making the results more reliable and trustworthy. For example, in a study examining the effects of a new teaching method on student performance, factors such as prior knowledge, socioeconomic status, or motivation levels could significantly impact the outcome. By controlling these variables, researchers can attribute any observed differences in performance to the teaching method itself, thus strengthening the overall validity of the findings.

Ethical Considerations in Variable Manipulation

Ethical considerations in variable manipulation refer to the responsible handling of data, participant information, and research procedures to ensure the protection of participants’ rights, privacy, and well-being, particularly in studies involving sensitive or qualitative data .

Researchers must navigate a complex landscape when manipulating variables, balancing the need for scientific rigor with moral principles. Informed consent is a cornerstone of ethical research, requiring individuals to voluntarily agree to participate after understanding the study’s purpose and potential risks.

Confidentiality plays a vital role in protecting participants’ identities and personal information, safeguarding against unintended disclosures. Data protection measures, such as encryption and secure storage, are essential safeguards against unauthorized access or misuse of research data.

How to Avoid Confounding Variables in Research

Avoiding confounding variables in research involves meticulous study design, careful data analysis, and the implementation of control measures to minimize the impact of extraneous factors on the observed relationships between variables, ensuring the validity and reliability of research findings.

One key strategy to prevent confounding variables is randomization, where participants are allocated randomly into different groups. By doing so, researchers can help ensure that any variations in results are attributed to the intervention being studied rather than external influences.

Blinding techniques can be used to prevent bias by keeping participants or researchers unaware of certain aspects of the study. Thorough data analysis, including statistical techniques like regression analysis and stratification, is essential for identifying and accounting for potential confounders that could skew results.

Examples of Variables in Psychology Studies

Examples of variables in psychology studies encompass a wide range of phenomena, from participant characteristics and environmental factors to cognitive processes and emotional responses, each contributing to the rich tapestry of data analyzed to derive meaningful research findings and statistical insights.

For instance, participant characteristics may include age, gender, education level, and personality traits, all of which can influence study outcomes. On the other hand, environmental factors such as time of day, lighting conditions, and noise levels can impact how participants respond to experimental tasks.

Cognitive processes like attention, memory, and problem-solving abilities are crucial variables that researchers manipulate or measure to understand human behavior better.

Emotional responses play a significant role in various studies, with variables like anxiety, happiness, and stress influencing decision-making and social interactions. By carefully considering and manipulating these diverse variables, researchers can tease out intricate patterns and draw robust conclusions from their data.

Frequently Asked Questions

What are variables in the context of psychology.

Variables in psychology refer to any measurable or observable factors that can influence behavior or outcomes in psychological research. These can include emotional states, personality traits, environmental factors, and more.

Why is it important to understand variables in psychology?

Variables play a crucial role in psychological research, as they allow researchers to systematically investigate the relationship between different factors and behaviors. Understanding variables can help researchers draw more accurate conclusions and make informed decisions based on their findings.

What are the two types of variables in psychology?

There are two types of variables in psychology: independent variables and dependent variables. Independent variables are manipulated by the researcher and are thought to have a causal effect on the dependent variable, which is the behavior or outcome being measured.

What is the difference between a continuous and a categorical variable?

A continuous variable is one that can take on any value within a specific range, such as age or height. A categorical variable, on the other hand, is one that has distinct categories or groups, such as gender or socioeconomic status.

How are variables measured in psychology?

Variables in psychology can be measured through various methods, including self-report measures, behavioral observation, biological assessments, and more. The chosen measurement method will depend on the specific research question and the type of variable being studied.

What are potential confounding variables in psychology?

Confounding variables are factors that can unintentionally influence the relationship between the independent and dependent variables. These can include extraneous variables that the researcher did not account for, leading to inaccurate conclusions. It is essential for researchers to control for these variables in their studies.

Dr. Sofia Alvarez is a clinical psychologist with over a decade of experience in counseling and psychotherapy. Specializing in anxiety disorders and mindfulness practices, she has contributed to numerous mental health initiatives and workshops. Dr. Alvarez combines her clinical expertise with a passion for writing to demystify psychology and promote mental wellness. She believes in the power of therapeutic storytelling and advocates for mental health awareness in various online platforms and community forums.

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2.1 Basic Concepts

Learning objectives.

• Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
• Explain the difference between a population and a sample.
• Describe two basic forms of statistical relationship and give examples of each.
• Interpret basic statistics and graphs used to describe statistical relationships.
• Explain why correlation does not imply causation.

Before we address where research questions in psychology come from—and what makes them more or less interesting—it is important to understand the kinds of questions that researchers in psychology typically ask. This requires a quick introduction to several basic concepts, many of which we will return to in more detail later in the book.

Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations. For example, the height of the students in a psychology class is a variable because it varies from student to student. The sex of the students is also a variable as long as there are both male and female students in the class. A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable is a quality, such as sex, and is typically measured by assigning a category label to each individual. Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

Sampling and Measurement

Researchers in psychology are usually interested in drawing conclusions about some very large group of people. This is called the population . It could be American teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher. But they usually study only a small subset or sample of the population. For example, a researcher might measure the talkativeness of a few hundred college students with the intention of drawing conclusions about the talkativeness of men and women in general. It is important, therefore, for researchers to use a representative sample—one that is similar to the population in important respects.

One method of obtaining a sample is simple random sampling, in which every member of the population has an equal chance of being selected for the sample. For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intended to vote for. Unfortunately, random sampling is difficult or impossible in most psychological research because the populations are less clearly defined than the registered voters in a city. How could a researcher give all American teenagers or all children with autism an equal chance of being selected for a sample? The most common alternative to random sampling is convenience sampling, in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students). The obvious problem with convenience sampling is that the sample might not be representative of the population.

Once the sample is selected, researchers need to measure the variables they are interested in. This requires an operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. When a variable has been measured for a particular individual, the result is called a score, and a set of scores is called data. Note that data is plural—the singular datum is rarely used—so it is grammatically correct to say, “Those are interesting data” (and incorrect to say, “That is interesting data”).

Statistical Relationships Between Variables

Some research questions in psychology are about one variable. How accurate are children’s memories for being touched? How talkative are American college students? How common is it for people to be diagnosed with major depressive disorder? Answering such questions requires operationally defining the variable, measuring it for a sample, analyzing the results, and drawing conclusions about the population. For a quantitative variable, this would typically involve computing the mean and standard deviation of the scores. For a categorical variable, it would typically involve computing the percentage of scores at each level of the variable.

However, research questions in psychology are more likely to be about statistical relationships between variables. There is a statistical relationship between two variables when the average score on one differs systematically across the levels of the other. Studying statistical relationships is important because instead of telling us about behaviors and psychological characteristics in isolation, it tells us about the causes, consequences, development, and organization of those behaviors and characteristics.

There are two basic forms of statistical relationship: differences between groups and correlations between quantitative variables. Although both are consistent with the general definition of a statistical relationship—the average score on one variable differs across levels of the other—they are usually described and analyzed somewhat differently. For this reason it is important to distinguish them clearly.

Differences Between Groups

One basic form of statistical relationship is a difference between the mean scores of two groups on some variable of interest. A wide variety of research questions in psychology take this form. Are women more talkative than men? Do children using human figure drawings recall more touch information than children not using human figure drawings? Do people talking on a cell phone have poorer driving abilities than people not talking on a cell phone? Do people receiving Psychotherapy A tend to have fewer depressive symptoms than people receiving Psychotherapy B? Later we will also see that such relationships can involve more than two groups and that the groups can consist of the very same individuals tested at different times or under different conditions. For now, however, it is easiest to think in terms of two distinct groups.

Differences between groups are usually described by giving the mean score and standard deviation for each group. This information can also be presented in a bar graph like that in Figure 2.2 “Bar Graph Showing the Very Small Difference in the Mean Number of Words Spoken per Day by Women and Men in a Large Sample” , where the heights of the bars represent the group means.

Figure 2.2 Bar Graph Showing the Very Small Difference in the Mean Number of Words Spoken per Day by Women and Men in a Large Sample

Based on data from “Are Women Really More Talkative Than Men?” by M. R. Mehl, S. Vazire, N. Ramirez-Esparza, R. B. Slatcher, and J. W. Pennebaker, 2007, Science, 317, p. 82.

Correlations Between Quantitative Variables

A second basic form of statistical relationship is a correlation between two quantitative variables, where the average score on one variable differs systematically across the levels of the other. Again, a wide variety of research questions in psychology take this form. Is being a happier person associated with being more talkative? Do children’s memories for touch information improve as they get older? Does the effectiveness of psychotherapy depend on how much the patient likes the therapist?

Correlations between quantitative variables are often presented using scatterplots . Figure 2.3 “Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms” shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 2.3 “Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms” represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

Figure 2.3 Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms

The circled point represents a person whose stress score was 10 and who had three physical symptoms. Pearson’s r for these data is +.51.

The strength of a correlation between quantitative variables is typically measured using a statistic called Pearson’s r . As Figure 2.4 “Range of Pearson’s “ shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s r is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line.

Figure 2.4 Range of Pearson’s r, From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship)

Pearson’s r is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 2.5 “Hypothetical Nonlinear Relationship Between Sleep and Depression” , for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

Figure 2.5 Hypothetical Nonlinear Relationship Between Sleep and Depression

Correlation Does Not Imply Causation

Researchers are often interested in a statistical relationship between two variables because they think that one of the variables causes the other. That is, the statistical relationship reflects a causal relationship. In these situations, the variable that is thought to be the cause is called the independent variable (often referred to as X for short), and the variable that is thought to be the effect is called the dependent variable (often referred to as Y ). For example, the statistical relationship between whether or not a depressed person receives psychotherapy and the number of depressive symptoms he or she has reflects the fact that the psychotherapy (the independent variable) causes the reduction in symptoms (the dependent variable). Understanding causal relationships is important in part because it allows us to change people’s behavior in predictable ways. If we know that psychotherapy causes a reduction in depressive symptoms—and we want people to have fewer depressive symptoms—then we can use psychotherapy to achieve this goal.

But not all statistical relationships reflect causal relationships. This is what psychologists mean when they say, “Correlation does not imply causation.” An obvious example comes from a study in Taiwan showing a positive relationship between the number of electrical appliances that people use and the extent to which they use birth control (Stanovich, 2010). It seems clear, however, that this does not mean that owning electrical appliances causes people to use birth control, and it would not make sense to try to increase the use of birth control by giving people toasters and hair dryers.

There are two reasons that correlation does not imply causation. The first is called the directionality problem . Two variables, X and Y , can be statistically related because X causes Y or because Y causes X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the third-variable problem . Two variables, X and Y , can be statistically related not because X causes Y , or because Y causes X , but because some third variable, Z , causes both X and Y . For example, the fact that people with more electrical appliances are more likely to use birth control probably reflects the fact that having more education or income causes people to own more appliances and causes them to use birth control. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier.

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated, when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As we will see later in the book, there are various ways that researchers address the directionality and third-variable problems. The most effective, however, is to conduct an experiment. An experiment is a study in which the researcher manipulates the independent variable. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor addition to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships. We will have much more to say about experimental and nonexperimental research later in the book.

Key Takeaways

• Research questions in psychology are about variables and relationships between variables.
• Two basic forms of statistical relationship are differences between group means and correlations between quantitative variables, each of which can be described using a few simple statistical techniques.
• Correlation does not imply causation. A statistical relationship between two variables, X and Y , does not necessarily mean that X causes Y . It is also possible that Y causes X , or that a third variable, Z , causes both X and Y .
• Practice: List 10 variables that might be of interest to a researcher in psychology. For each, specify whether it is quantitative or categorical.
• Practice: Imagine that you categorize people as either introverts (quieter, shyer, more inward looking) or extroverts (louder, more outgoing, more outward looking). Sketch a bar graph showing a hypothetical statistical relationship between this variable and the number of words people speak per day.
• Practice: Now imagine that you measure people’s levels of extroversion as a quantitative variable, with values ranging from 0 (extreme introversion) to 30 (extreme extroversion). Sketch a scatterplot showing a hypothetical statistical relationship between this variable and the number of words people speak per day.

Practice: For each of the following statistical relationships, decide whether the directionality problem is present and think of at least one plausible third variable:

• People who eat more lobster tend to live longer.
• People who exercise more tend to weigh less.
• College students who drink more alcohol tend to have poorer grades.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn & Bacon.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How the Experimental Method Works in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

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The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

• Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
• Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
• Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
• Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables. 

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

• Identifying a problem to study
• Devising the research protocol
• Conducting the experiment
• Analyzing the data collected
• Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

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Schweizer M, Braun B, Milstone A. Research methods in healthcare epidemiology and antimicrobial stewardship — quasi-experimental designs . Infect Control Hosp Epidemiol . 2016;37(10):1135-1140. doi:10.1017/ice.2016.117

Glass A, Kang M. Dividing attention in the classroom reduces exam performance . Educ Psychol . 2019;39(3):395-408. doi:10.1080/01443410.2018.1489046

Keskin M, Ooms K, Dogru AO, De Maeyer P. Exploring the cognitive load of expert and novice map users using EEG and eye tracking . ISPRS Int J Geo-Inf . 2020;9(7):429. doi:10.3390.ijgi9070429

Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

Haynes IV J, Frith E, Sng E, Loprinzi P. Experimental effects of acute exercise on episodic memory function: Considerations for the timing of exercise . Psychol Rep . 2018;122(5):1744-1754. doi:10.1177/0033294118786688

Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

Schumpe BM, Belanger JJ, Moyano M, Nisa CF. The role of sensation seeking in political violence: An extension of the significance quest theory . J Personal Social Psychol . 2020;118(4):743-761. doi:10.1037/pspp0000223

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Chapter 2: Getting Started in Research

Basic Concepts

Learning Objectives

• Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
• Explain the difference between a population and a sample.
• Describe two basic forms of statistical relationship and give examples of each.
• Interpret basic statistics and graphs used to describe statistical relationships.
• Explain why correlation does not imply causation.

Before we address where research questions in psychology come from—and what makes them more or less interesting—it is important to understand the kinds of questions that researchers in psychology typically ask. This requires a quick introduction to several basic concepts, many of which we will return to in more detail later in the book.

Research questions in psychology are about variables. A  variable  is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. A  quantitative variable  is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable  is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

Sampling and Measurement

Researchers in psychology are usually interested in drawing conclusions about some very large group of people. This is called the  population . It could be Canadian teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher. But they usually study only a small subset or  sample  of the population. For example, a researcher might measure the talkativeness of a few hundred university students with the intention of drawing conclusions about the talkativeness of men and women in general. It is important, therefore, for researchers to use a representative sample—one that is similar to the population in important respects.

One method of obtaining a sample is simple random sampling , in which every member of the population has an equal chance of being selected for the sample. For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intended to vote for. Unfortunately, random sampling is difficult or impossible in most psychological research because the populations are less clearly defined than the registered voters in a city. How could a researcher give all Canadian teenagers or all children with autism an equal chance of being selected for a sample? The most common alternative to random sampling is convenience sampling , in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students). Of course, the obvious problem with convenience sampling is that the sample might not be representative of the population.

Once the sample is selected, researchers need to measure the variables they are interested in. This requires an  operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. When a variable has been measured for a particular individual, the result is called a score, and a set of scores is called data. Note that  data  is plural—the singular  datum  is rarely used—so it is grammatically correct to say, “Those are interesting data” (and incorrect to say, “That is interesting data”).

Statistical Relationships Between Variables

Some research questions in psychology are about one variable. How common is it for soldiers who have served in the Canadian Forces to develop post-traumatic stress disorder (PTSD) after returning from a deployment in a war zone? How talkative are Canadian university students? How much time per week do school children spend online? Answering such questions requires operationally defining the variable, measuring it among a sample, analyzing the results, and drawing conclusions about the population. For a quantitative variable, this would typically involve computing the mean and standard deviation of the scores. For a categorical variable, it would typically involve computing the percentage of scores at each level of the variable.

However, research questions in psychology are more likely to be about statistical relationships between variables. There is a  statistical relationship between two variables when the average score on one differs systematically across the levels of the other (e.g., if the average exam score is higher among students who took notes longhand instead of by using a laptop computer). Studying statistical relationships is important because instead of telling us about behaviours and psychological characteristics in isolation, it tells us about the potential causes, consequences, development, and organization of those behaviours and characteristics.

There are two basic forms of statistical relationship: differences between groups and correlations between quantitative variables. Although both are consistent with the general definition of a statistical relationship—the average score on one variable differs across levels of the other—they are usually described and analyzed somewhat differently. For this reason it is important to distinguish them clearly.

Differences Between Groups

One basic form of statistical relationship is a difference between the mean scores of two groups on some variable of interest. A wide variety of research questions in psychology take this form. Are women really more talkative than men? Do people talking on a cell phone have poorer driving abilities than people not talking on a cell phone? Do people receiving Psychotherapy A tend to have fewer depressive symptoms than people receiving Psychotherapy B? Later we will also see that such relationships can involve more than two groups and that the groups can consist of the very same individuals tested at different times or under different conditions. For now, however, it is easiest to think in terms of two distinct groups.

Differences between groups are usually described by giving the mean score and standard deviation for each group. This information can also be presented in a bar graph  like that in Figure 2.1, where the heights of the bars represent the group means.

Correlations Between Quantitative Variables

A second basic form of statistical relationship is a correlation between two quantitative variables, where the average score on one variable differs systematically across the levels of the other. Again, a wide variety of research questions in psychology take this form. Is being a happier person associated with being more talkative? Do people who are narcissistic tend to take more selfies? Does the effectiveness of psychotherapy depend on how much the patient likes the therapist?

Correlations between quantitative variables are often presented using scatterplots . Figure 2.2 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 2.2 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. A  negative relationship  is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s  r . As Figure 2.3 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. The website Interpreting Correlation , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 2.4, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

Correlation Does Not Imply Causation

Researchers are often interested in a statistical relationship between two variables because they think that one of the variables causes the other. That is, the statistical relationship reflects a causal relationship. In these situations, the variable that is thought to be the cause is called the  independent variable (often referred to as  X  for short), and the variable that is thought to be the effect is called the  dependent variable  (often referred to as  Y ). For example, the statistical relationship between whether or not a depressed person receives psychotherapy and the number of depressive symptoms he or she has reflects the fact that the psychotherapy (the independent variable)  causes  the reduction in symptoms (the dependent variable). Understanding causal relationships is important in part because it allows us to change people’s behaviour in predictable ways. If we know that psychotherapy causes a reduction in depressive symptoms—and we want people to have fewer depressive symptoms—then we can use psychotherapy to achieve this goal.

But not all statistical relationships reflect causal relationships. This is what psychologists mean when they say, “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [1] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier.

For some excellent and funny examples of correlations that almost certainly do not show causation, enjoy the strange correlations found at Spurious Correlations  (Figure 2.5 provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated, when a careful reading of the articles shows that it has not because of the directionality and third-variable problems. One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offence later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As we will see later in the book, there are various ways that researchers address the directionality and third-variable problems. The most effective, however, is to conduct an experiment. An experiment is a study in which the researcher manipulates the independent variable. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships. We will have much more to say about experimental and non-experimental research later in the book.

Key Takeaways

• Research questions in psychology are about variables and relationships between variables
• Two basic forms of statistical relationship are differences between group means and correlations between quantitative variables, each of which can be described using a few simple statistical techniques.
• Correlation does not imply causation. A statistical relationship between two variables, X and Y, does not necessarily mean that X causes Y. It is also possible that Y causes X, or that a third variable, Z, causes both X and Y.
• Practice: List 10 variables that might be of interest to a researcher in psychology. For each, specify whether it is quantitative or categorical.
• Practice: Imagine that you categorize people as either introverts (quieter, shyer, more inward looking) or extraverts (louder, more outgoing, more outward looking). Sketch a bar graph showing a hypothetical statistical relationship between this variable and the number of words people speak per day.
• Practice: Now imagine that you measure people’s levels of extraversion as a quantitative variable, with values ranging from 0 (extreme introversion) to 30 (extreme extraversion). Sketch a scatterplot showing a hypothetical statistical relationship between this variable and the number of words people speak per day.
• People who eat more lobster tend to live longer.
• People who exercise more tend to weigh less.
• College students who drink more alcohol tend to have poorer grades.

Media Attributions

• Nicholas Cage and Pool Drownings © Tyler Viegen is licensed under a CC BY (Attribution) license
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A quantity or quality that varies across people or situations.

A quantity that is typically measured by assigning a number to each individual.

A quality that is typically measured by assigning a category label to each individual.

A very large group of people.

A small subset of a population.

A probability sampling method in which each individual in the population has an equal probability of being selected for the sample.

A definition of the variable in terms of precisely how it is to be measured.

Occurs when the average score on one variable differs systematically across the levels of the other variable.

A figure in which the heights of the bars represent the group means.

A graph which shows correlations between quantitative variables; each point represents one person’s score on both variables.

Higher scores on one variable tend to be associated with higher scores on the other variable.

Higher scores on one variable tend to be associated with lower scores on the other variable.

A statistic measuring the strength of a correlation between quantitative variables ranging from -1.00 (strongest negative relationship) to +1.00 (strongest positive relationship), with 0 showing no relationship between variables.

The variable of a statistical relationship that is thought to cause the other variable.

The variable that is thought to be the effect of the independent variable.

Two variables can be statistically related because X causes Y or Y causes X.

Two variables may be statistically related, but both may be caused by a third and unknown variable.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Variables in Research – Definition, Types and Examples

Variables in Research – Definition, Types and Examples

Table of Contents

Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

• Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
• Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
• Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
• Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
• Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
• Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
• Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

• Age : This is a continuous variable that represents the age of an individual in years.
• Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
• Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
• Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
• Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
• Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
• Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
• Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
• Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
• Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

• Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
• Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
• Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
• Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
• Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

• To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
• To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
• To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
• To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

• Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
• Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
• Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
• Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
• Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

• Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
• Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
• Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
• Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
• Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

• Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
• Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
• Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
• Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
• Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Ch 2: Psychological Research Methods

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

• Explain the steps of the scientific method
• Describe why the scientific method is important to psychology
• Summarize the processes of informed consent and debriefing
• Explain how research involving humans or animals is regulated

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

The Scientific Process

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Why the scientific method is important for psychology.

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent  form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing  upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

Introduction to Approaches to Research

• Differentiate between descriptive, correlational, and experimental research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
• Describe the strength and weaknesses of archival research
• Compare longitudinal and cross-sectional approaches to research
• Explain what a correlation coefficient tells us about the relationship between variables
• Describe why correlation does not mean causation
• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark  and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize  the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research  is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition  rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does  not  indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants  are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample   (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

• Define reliability and validity
• Describe the importance of distributional thinking and the role of p-values in statistical inference
• Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
• Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Dig deeper:  everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

• Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
• Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
• Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
• Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

• Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
• Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample  that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment  tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

• This was a 14-year study conducted by researchers at the National Cancer Institute.
• The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
• The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
• About 52,000 people died during the course of the study.
• People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
• The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
• Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
• This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

• Video about p-values:  P-Value Extravaganza
• Interactive web applets for teaching and learning statistics
• Inter-university Consortium for Political and Social Research  where you can find and analyze data.
• The Consortium for the Advancement of Undergraduate Statistics
• Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
• Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

• Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
• Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
• Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and  materials  as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
• Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
• Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

• define and apply the scientific method to psychology
• describe the strengths and weaknesses of descriptive, experimental, and correlational research
• define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

• Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read   Imagining Ted Bundy  (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

• Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

• Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
• Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers   recruit a sample of participants and track them for an extended period of time.

• In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study:  researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

• In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design:  two different variables are measured to determine whether there is a relationship between them.

• Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
• Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

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Chwalisz, K., Diener, E., & Gallagher, D. (1988). Autonomic arousal feedback and emotional experience: Evidence from the spinal cord injured. Journal of Personality and Social Psychology, 54, 820–828.

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Fanger, S. M., Frankel, L. A., & Hazen, N. (2012). Peer exclusion in preschool children’s play: Naturalistic observations in a playground setting. Merrill-Palmer Quarterly, 58, 224–254.

Fiedler, K. (2004). Illusory correlation. In R. F. Pohl (Ed.), Cognitive illusions: A handbook on fallacies and biases in thinking, judgment and memory (pp. 97–114). New York, NY: Psychology Press.

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Jenkins, W. J., Ruppel, S. E., Kizer, J. B., Yehl, J. L., & Griffin, J. L. (2012). An examination of post 9-11 attitudes towards Arab Americans. North American Journal of Psychology, 14, 77–84.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

• The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
• Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
• While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
• The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2: Concepts, Constructs, and Variables

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• Anol Bhattacherjee
• University of South Florida via Global Text Project

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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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Psychological Measurement

19 Understanding Psychological Measurement

Learning objectives.

• Define measurement and give several examples of measurement in psychology.
• Explain what a psychological construct is and give several examples.
• Distinguish conceptual from operational definitions, give examples of each, and create simple operational definitions.
• Distinguish the four levels of measurement, give examples of each, and explain why this distinction is important.

What Is Measurement?

Measurement  is the assignment of scores to individuals so that the scores represent some characteristic of the individuals. This very general definition is consistent with the kinds of measurement that everyone is familiar with—for example, weighing oneself by stepping onto a bathroom scale, or checking the internal temperature of a roasting turkey using a meat thermometer. It is also consistent with measurement in the other sciences. In physics, for example, one might measure the potential energy of an object in Earth’s gravitational field by finding its mass and height (which of course requires measuring  those  variables) and then multiplying them together along with the gravitational acceleration of Earth (9.8 m/s2). The result of this procedure is a score that represents the object’s potential energy.

This general definition of measurement is consistent with measurement in psychology too. (Psychological measurement is often referred to as psychometrics .) Imagine, for example, that a cognitive psychologist wants to measure a person’s working memory capacity—their ability to hold in mind and think about several pieces of information all at the same time. To do this, she might use a backward digit span task, in which she reads a list of two digits to the person and asks them to repeat them in reverse order. She then repeats this several times, increasing the length of the list by one digit each time, until the person makes an error. The length of the longest list for which the person responds correctly is the score and represents their working memory capacity. Or imagine a clinical psychologist who is interested in how depressed a person is. He administers the Beck Depression Inventory, which is a 21-item self-report questionnaire in which the person rates the extent to which they have felt sad, lost energy, and experienced other symptoms of depression over the past 2 weeks. The sum of these 21 ratings is the score and represents the person’s current level of depression.

The important point here is that measurement does not require any particular instruments or procedures. What it  does  require is  some  systematic procedure for assigning scores to individuals or objects so that those scores represent the characteristic of interest.

Psychological Constructs

Many variables studied by psychologists are straightforward and simple to measure. These include age, height, weight, and birth order. You can ask people how old they are and be reasonably sure that they know and will tell you. Although people might not know or want to tell you how much they weigh, you can have them step onto a bathroom scale. Other variables studied by psychologists—perhaps the majority—are not so straightforward or simple to measure. We cannot accurately assess people’s level of intelligence by looking at them, and we certainly cannot put their self-esteem on a bathroom scale. These kinds of variables are called  constructs  (pronounced  CON-structs ) and include personality traits (e.g., extraversion), emotional states (e.g., fear), attitudes (e.g., toward taxes), and abilities (e.g., athleticism).

Psychological constructs cannot be observed directly. One reason is that they often represent  tendencies  to think, feel, or act in certain ways. For example, to say that a particular university student is highly extraverted does not necessarily mean that she is behaving in an extraverted way right now. In fact, she might be sitting quietly by herself, reading a book. Instead, it means that she has a general tendency to behave in extraverted ways (e.g., being outgoing, enjoying social interactions) across a variety of situations. Another reason psychological constructs cannot be observed directly is that they often involve internal processes. Fear, for example, involves the activation of certain central and peripheral nervous system structures, along with certain kinds of thoughts, feelings, and behaviors—none of which is necessarily obvious to an outside observer. Notice also that neither extraversion nor fear “reduces to” any particular thought, feeling, act, or physiological structure or process. Instead, each is a kind of summary of a complex set of behaviors and internal processes.

The Big Five

The Big Five is a set of five broad dimensions that capture much of the variation in human personality. Each of the Big Five can even be defined in terms of six more specific constructs called “facets” (Costa & McCrae, 1992) [1] .

Table 4.1 The Big Five Personality Dimensions

The  conceptual definition  of a psychological construct describes the behaviors and internal processes that make up that construct, along with how it relates to other variables. For example, a conceptual definition of neuroticism (another one of the Big Five) would be that it is people’s tendency to experience negative emotions such as anxiety, anger, and sadness across a variety of situations. This definition might also include that it has a strong genetic component, remains fairly stable over time, and is positively correlated with the tendency to experience pain and other physical symptoms.

Students sometimes wonder why, when researchers want to understand a construct like self-esteem or neuroticism, they do not simply look it up in the dictionary. One reason is that many scientific constructs do not have counterparts in everyday language (e.g., working memory capacity). More important, researchers are in the business of developing definitions that are more detailed and precise—and that more accurately describe the way the world is—than the informal definitions in the dictionary. As we will see, they do this by proposing conceptual definitions, testing them empirically, and revising them as necessary. Sometimes they throw them out altogether. This is why the research literature often includes different conceptual definitions of the same construct. In some cases, an older conceptual definition has been replaced by a newer one that fits and works better. In others, researchers are still in the process of deciding which of various conceptual definitions is the best.

Operational Definitions

An  operational definition  is a definition of a variable in terms of precisely how it is to be measured. These measures generally fall into one of three broad categories.  Self-report measures  are those in which participants report on their own thoughts, feelings, and actions, as with the Rosenberg Self-Esteem Scale (Rosenberg, 1965) [2] . Behavioral measures  are those in which some other aspect of participants’ behavior is observed and recorded. This is an extremely broad category that includes the observation of people’s behavior both in highly structured laboratory tasks and in more natural settings. A good example of the former would be measuring working memory capacity using the backward digit span task. A good example of the latter is a famous operational definition of physical aggression from researcher Albert Bandura and his colleagues (Bandura, Ross, & Ross, 1961) [3] . They let each of several children play for 20 minutes in a room that contained a clown-shaped punching bag called a Bobo doll. They filmed each child and counted the number of acts of physical aggression the child committed. These included hitting the doll with a mallet, punching it, and kicking it. Their operational definition, then, was the number of these specifically defined acts that the child committed during the 20-minute period. Finally,  physiological measures  are those that involve recording any of a wide variety of physiological processes, including heart rate and blood pressure, galvanic skin response, hormone levels, and electrical activity and blood flow in the brain.

For any given variable or construct, there will be multiple operational definitions. Stress is a good example. A rough conceptual definition is that stress is an adaptive response to a perceived danger or threat that involves physiological, cognitive, affective, and behavioral components. But researchers have operationally defined it in several ways. The Social Readjustment Rating Scale (Holmes & Rahe, 1967) [4] is a self-report questionnaire on which people identify stressful events that they have experienced in the past year and assigns points for each one depending on its severity. For example, a man who has been divorced (73 points), changed jobs (36 points), and had a change in sleeping habits (16 points) in the past year would have a total score of 125. The Hassles and Uplifts Scale (Delongis, Coyne, Dakof, Folkman & Lazarus, 1982) [5]  is similar but focuses on everyday stressors like misplacing things and being concerned about one’s weight. The Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983) [6] is another self-report measure that focuses on people’s feelings of stress (e.g., “How often have you felt nervous and stressed?”). Researchers have also operationally defined stress in terms of several physiological variables including blood pressure and levels of the stress hormone cortisol.

When psychologists use multiple operational definitions of the same construct—either within a study or across studies—they are using converging operations . The idea is that the various operational definitions are “converging” or coming together on the same construct. When scores based on several different operational definitions are closely related to each other and produce similar patterns of results, this constitutes good evidence that the construct is being measured effectively and that it is useful. The various measures of stress, for example, are all correlated with each other and have all been shown to be correlated with other variables such as immune system functioning (also measured in a variety of ways) (Segerstrom & Miller, 2004) [7] . This is what allows researchers eventually to draw useful general conclusions, such as “stress is negatively correlated with immune system functioning,” as opposed to more specific and less useful ones, such as “people’s scores on the Perceived Stress Scale are negatively correlated with their white blood counts.”

Levels of Measurement

The psychologist S. S. Stevens suggested that scores can be assigned to individuals in a way that communicates more or less quantitative information about the variable of interest (Stevens, 1946) [8] . For example, the officials at a 100-m race could simply rank order the runners as they crossed the finish line (first, second, etc.), or they could time each runner to the nearest tenth of a second using a stopwatch (11.5 s, 12.1 s, etc.). In either case, they would be measuring the runners’ times by systematically assigning scores to represent those times. But while the rank ordering procedure communicates the fact that the second-place runner took longer to finish than the first-place finisher, the stopwatch procedure also communicates  how much  longer the second-place finisher took. Stevens actually suggested four different levels of measurement (which he called “scales of measurement”) that correspond to four types of information that can be communicated by a set of scores, and the statistical procedures that can be used with the information.

The  nominal level  of measurement is used for categorical variables and involves assigning scores that are category labels. Category labels communicate whether any two individuals are the same or different in terms of the variable being measured. For example, if you ask your participants about their marital status, you are engaged in nominal-level measurement. Or if you ask your participants to indicate which of several ethnicities they identify themselves with, you are again engaged in nominal-level measurement. The essential point about nominal scales is that they do not imply any ordering among the responses. For example, when classifying people according to their favorite color, there is no sense in which green is placed “ahead of” blue. Responses are merely categorized. Nominal scales thus embody the lowest level of measurement [9] .

The remaining three levels of measurement are used for quantitative variables. The  ordinal level  of measurement involves assigning scores so that they represent the rank order of the individuals. Ranks communicate not only whether any two individuals are the same or different in terms of the variable being measured but also whether one individual is higher or lower on that variable. For example, a researcher wishing to measure consumers’ satisfaction with their microwave ovens might ask them to specify their feelings as either “very dissatisfied,” “somewhat dissatisfied,” “somewhat satisfied,” or “very satisfied.” The items in this scale are ordered, ranging from least to most satisfied. This is what distinguishes ordinal from nominal scales. Unlike nominal scales, ordinal scales allow comparisons of the degree to which two individuals rate the variable. For example, our satisfaction ordering makes it meaningful to assert that one person is more satisfied than another with their microwave ovens. Such an assertion reflects the first person’s use of a verbal label that comes later in the list than the label chosen by the second person.

On the other hand, ordinal scales fail to capture important information that will be present in the other levels of measurement we examine. In particular, the difference between two levels of an ordinal scale cannot be assumed to be the same as the difference between two other levels (just like you cannot assume that the gap between the runners in first and second place is equal to the gap between the runners in second and third place). In our satisfaction scale, for example, the difference between the responses “very dissatisfied” and “somewhat dissatisfied” is probably not equivalent to the difference between “somewhat dissatisfied” and “somewhat satisfied.” Nothing in our measurement procedure allows us to determine whether the two differences reflect the same difference in psychological satisfaction. Statisticians express this point by saying that the differences between adjacent scale values do not necessarily represent equal intervals on the underlying scale giving rise to the measurements. (In our case, the underlying scale is the true feeling of satisfaction, which we are trying to measure.)

The  interval level  of measurement involves assigning scores using numerical scales in which intervals have the same interpretation throughout. As an example, consider either the Fahrenheit or Celsius temperature scales. The difference between 30 degrees and 40 degrees represents the same temperature difference as the difference between 80 degrees and 90 degrees. This is because each 10-degree interval has the same physical meaning (in terms of the kinetic energy of molecules).

Interval scales are not perfect, however. In particular, they do not have a true zero point even if one of the scaled values happens to carry the name “zero.” The Fahrenheit scale illustrates the issue. Zero degrees Fahrenheit does not represent the complete absence of temperature (the absence of any molecular kinetic energy). In reality, the label “zero” is applied to its temperature for quite accidental reasons connected to the history of temperature measurement. Since an interval scale has no true zero point, it does not make sense to compute ratios of temperatures. For example, there is no sense in which the ratio of 40 to 20 degrees Fahrenheit is the same as the ratio of 100 to 50 degrees; no interesting physical property is preserved across the two ratios. After all, if the “zero” label were applied at the temperature that Fahrenheit happens to label as 10 degrees, the two ratios would instead be 30 to 10 and 90 to 40, no longer the same! For this reason, it does not make sense to say that 80 degrees is “twice as hot” as 40 degrees. Such a claim would depend on an arbitrary decision about where to “start” the temperature scale, namely, what temperature to call zero (whereas the claim is intended to make a more fundamental assertion about the underlying physical reality).

In psychology, the intelligence quotient (IQ) is often considered to be measured at the interval level. While it is technically possible to receive a score of 0 on an IQ test, such a score would not indicate the complete absence of IQ. Moreover, a person with an IQ score of 140 does not have twice the IQ of a person with a score of 70. However, the difference between IQ scores of 80 and 100 is the same as the difference between IQ scores of 120 and 140.

Finally, the  ratio level  of measurement involves assigning scores in such a way that there is a true zero point that represents the complete absence of the quantity. Height measured in meters and weight measured in kilograms are good examples. So are counts of discrete objects or events such as the number of siblings one has or the number of questions a student answers correctly on an exam. You can think of a ratio scale as the three earlier scales rolled up in one. Like a nominal scale, it provides a name or category for each object (the numbers serve as labels). Like an ordinal scale, the objects are ordered (in terms of the ordering of the numbers). Like an interval scale, the same difference at two places on the scale has the same meaning. However, in addition, the same ratio at two places on the scale also carries the same meaning (see Table 4.1).

The Fahrenheit scale for temperature has an arbitrary zero point and is therefore not a ratio scale. However, zero on the Kelvin scale is absolute zero. This makes the Kelvin scale a ratio scale. For example, if one temperature is twice as high as another as measured on the Kelvin scale, then it has twice the kinetic energy of the other temperature.

Another example of a ratio scale is the amount of money you have in your pocket right now (25 cents, 50 cents, etc.). Money is measured on a ratio scale because, in addition to having the properties of an interval scale, it has a true zero point: if you have zero money, this actually implies the absence of money. Since money has a true zero point, it makes sense to say that someone with 50 cents has twice as much money as someone with 25 cents.

Stevens’s levels of measurement are important for at least two reasons. First, they emphasize the generality of the concept of measurement. Although people do not normally think of categorizing or ranking individuals as measurement, in fact, they are as long as they are done so that they represent some characteristic of the individuals. Second, the levels of measurement can serve as a rough guide to the statistical procedures that can be used with the data and the conclusions that can be drawn from them. With nominal-level measurement, for example, the only available measure of central tendency is the mode. With ordinal-level measurement, the median or mode can be used as indicators of central tendency. Interval and ratio-level measurement are typically considered the most desirable because they permit for any indicators of central tendency to be computed (i.e., mean, median, or mode). Also, ratio-level measurement is the only level that allows meaningful statements about ratios of scores. Once again, one cannot say that someone with an IQ of 140 is twice as intelligent as someone with an IQ of 70 because IQ is measured at the interval level, but one can say that someone with six siblings has twice as many as someone with three because number of siblings is measured at the ratio level.

• Costa, P. T., Jr., & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological Assessment, 4 , 5–13. ↵
• Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press ↵
• Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. Journal of Abnormal and Social Psychology, 63 , 575–582. ↵
• Holmes, T. H., & Rahe, R. H. (1967). The Social Readjustment Rating Scale. Journal of Psychosomatic Research, 11 (2), 213-218. ↵
• Delongis, A., Coyne, J. C., Dakof, G., Folkman, S., & Lazarus, R. S. (1982). Relationships of daily hassles, uplifts, and major life events to health status. Health Psychology, 1 (2), 119-136. ↵
• Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 386-396. ↵
• Segerstrom, S. E., & Miller, G. E. (2004). Psychological stress and the human immune system: A meta-analytic study of 30 years of inquiry. Psychological Bulletin, 130 , 601–630. ↵
• Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103 , 677–680. ↵
• Levels of Measurement. Retrieved from http://wikieducator.org/Introduction_to_Research_Methods_In_Psychology/Theories_and_Measurement/Levels_of_Measurement ↵

Is the assignment of scores to individuals so that the scores represent some characteristic of the individuals.

A subfield of psychology concerned with the theories and techniques of psychological measurement.

Psychological variables that represent an individual's mental state or experience, often not directly observable, such as personality traits, emotional states, attitudes, and abilities.

Describes the behaviors and internal processes that make up a psychological construct, along with how it relates to other variables.

A definition of the variable in terms of precisely how it is to be measured.

Measures in which participants report on their own thoughts, feelings, and actions.

Measures in which some other aspect of participants’ behavior is observed and recorded.

Measures that involve recording any of a wide variety of physiological processes, including heart rate and blood pressure, galvanic skin response, hormone levels, and electrical activity and blood flow in the brain.

When psychologists use multiple operational definitions of the same construct—either within a study or across studies.

Four categories, or scales, of measurement (i.e., nominal, ordinal, interval, and ratio) that specify the types of information that a set of scores can have, and the types of statistical procedures that can be used with the scores.

A measurement used for categorical variables and involves assigning scores that are category labels.

A measurement that involves assigning scores so that they represent the rank order of the individuals.

A measurement that involves assigning scores using numerical scales in which intervals have the same interpretation throughout.

A measurement that involves assigning scores in such a way that there is a true zero point that represents the complete absence of the quantity.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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14 Key Terms for Psychological Research

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Research Methods Key Term Glossary

Last updated 22 Mar 2021

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This key term glossary provides brief definitions for the core terms and concepts covered in Research Methods for A Level Psychology.

Don't forget to also make full use of our research methods study notes and revision quizzes to support your studies and exam revision.

The researcher’s area of interest – what they are looking at (e.g. to investigate helping behaviour).

A graph that shows the data in the form of categories (e.g. behaviours observed) that the researcher wishes to compare.

Behavioural categories

Key behaviours or, collections of behaviour, that the researcher conducting the observation will pay attention to and record

In-depth investigation of a single person, group or event, where data are gathered from a variety of sources and by using several different methods (e.g. observations & interviews).

Closed questions

Questions where there are fixed choices of responses e.g. yes/no. They generate quantitative data

Co-variables

The variables investigated in a correlation

Concurrent validity

Comparing a new test with another test of the same thing to see if they produce similar results. If they do then the new test has concurrent validity

Confidentiality

Unless agreed beforehand, participants have the right to expect that all data collected during a research study will remain confidential and anonymous.

Confounding variable

An extraneous variable that varies systematically with the IV so we cannot be sure of the true source of the change to the DV

Content analysis

Technique used to analyse qualitative data which involves coding the written data into categories – converting qualitative data into quantitative data.

Control group

A group that is treated normally and gives us a measure of how people behave when they are not exposed to the experimental treatment (e.g. allowed to sleep normally).

Controlled observation

An observation study where the researchers control some variables - often takes place in laboratory setting

Correlational analysis

A mathematical technique where the researcher looks to see whether scores for two covariables are related

Counterbalancing

A way of trying to control for order effects in a repeated measures design, e.g. half the participants do condition A followed by B and the other half do B followed by A

Covert observation

Also known as an undisclosed observation as the participants do not know their behaviour is being observed

Critical value

The value that a test statistic must reach in order for the hypothesis to be accepted.

After completing the research, the true aim is revealed to the participant. Aim of debriefing = to return the person to the state s/he was in before they took part.

Involves misleading participants about the purpose of s study.

Demand characteristics

Occur when participants try to make sense of the research situation they are in and try to guess the purpose of the research or try to present themselves in a good way.

Dependent variable

The variable that is measured to tell you the outcome.

Descriptive statistics

Analysis of data that helps describe, show or summarize data in a meaningful way

Directional hypothesis

A one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

Dispersion measure

A dispersion measure shows how a set of data is spread out, examples are the range and the standard deviation

Double blind control

Participants are not told the true purpose of the research and the experimenter is also blind to at least some aspects of the research design.

Ecological validity

The extent to which the findings of a research study are able to be generalized to real-life settings

Ethical guidelines

These are provided by the BPS - they are the ‘rules’ by which all psychologists should operate, including those carrying out research.

Ethical issues

There are 3 main ethical issues that occur in psychological research – deception, lack of informed consent and lack of protection of participants.

Evaluation apprehension

Participants’ behaviour is distorted as they fear being judged by observers

Event sampling

A target behaviour is identified and the observer records it every time it occurs

Experimental group

The group that received the experimental treatment (e.g. sleep deprivation)

External validity

Whether it is possible to generalise the results beyond the experimental setting.

Extraneous variable

Variables that if not controlled may affect the DV and provide a false impression than an IV has produced changes when it hasn’t.

Face validity

Simple way of assessing whether a test measures what it claims to measure which is concerned with face value – e.g. does an IQ test look like it tests intelligence.

Field experiment

An experiment that takes place in a natural setting where the experimenter manipulates the IV and measures the DV

A graph that is used for continuous data (e.g. test scores). There should be no space between the bars, because the data is continuous.

This is a formal statement or prediction of what the researcher expects to find. It needs to be testable.

Independent groups design

An experimental design where each participants only takes part in one condition of the IV

Independent variable

The variable that the experimenter manipulates (changes).

Inferential statistics

Inferential statistics are ways of analyzing data using statistical tests that allow the researcher to make conclusions about whether a hypothesis was supported by the results.

Informed consent

Psychologists should ensure that all participants are helped to understand fully all aspects of the research before they agree (give consent) to take part

Inter-observer reliability

The extent to which two or more observers are observing and recording behaviour in the same way

Internal validity

In relation to experiments, whether the results were due to the manipulation of the IV rather than other factors such as extraneous variables or demand characteristics.

Interval level data

Data measured in fixed units with equal distance between points on the scale

Investigator effects

These result from the effects of a researcher’s behaviour and characteristics on an investigation.

Laboratory experiment

An experiment that takes place in a controlled environment where the experimenter manipulates the IV and measures the DV

Matched pairs design

An experimental design where pairs of participants are matched on important characteristics and one member allocated to each condition of the IV

Measure of central tendency calculated by adding all the scores in a set of data together and dividing by the total number of scores

Measures of central tendency

A measurement of data that indicates where the middle of the information lies e.g. mean, median or mode

Measure of central tendency calculated by arranging scores in a set of data from lowest to highest and finding the middle score

Meta-analysis

A technique where rather than conducting new research with participants, the researchers examine the results of several studies that have already been conducted

Measure of central tendency which is the most frequently occurring score in a set of data

Natural experiment

An experiment where the change in the IV already exists rather than being manipulated by the experimenter

Naturalistic observation

An observation study conducted in the environment where the behaviour would normally occur

Negative correlation

A relationship exists between two covariables where as one increases, the other decreases

Nominal level data

Frequency count data that consists of the number of participants falling into categories. (e.g. 7 people passed their driving test first time, 6 didn’t).

Non-directional hypothesis

A two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

Normal distribution

An arrangement of a data that is symmetrical and forms a bell shaped pattern where the mean, median and mode all fall in the centre at the highest peak

Observed value

The value that you have obtained from conducting your statistical test

Observer bias

Occurs when the observers know the aims of the study study or the hypotheses and allow this knowledge to influence their observations

Open questions

Questions where there is no fixed response and participants can give any answer they like. They generate qualitative data.

Operationalising variables

This means clearly describing the variables (IV and DV) in terms of how they will be manipulated (IV) or measured (DV).

Opportunity sample

A sampling technique where participants are chosen because they are easily available

Order effects

Order effects can occur in a repeated measures design and refers to how the positioning of tasks influences the outcome e.g. practice effect or boredom effect on second task

Ordinal level data

Data that is capable of being out into rank order (e.g. places in a beauty contest, or ratings for attractiveness).

Overt observation

Also known as a disclosed observation as the participants given their permission for their behaviour to be observed

Participant observation

Observation study where the researcher actually joins the group or takes part in the situation they are observing.

Peer review

Before going to publication, a research report is sent other psychologists who are knowledgeable in the research topic for them to review the study, and check for any problems

Pilot study

A small scale study conducted to ensure the method will work according to plan. If it doesn’t then amendments can be made.

Positive correlation

A relationship exists between two covariables where as one increases, so does the other

Presumptive consent

Asking a group of people from the same target population as the sample whether they would agree to take part in such a study, if yes then presume the sample would

Primary data

Information that the researcher has collected him/herself for a specific purpose e.g. data from an experiment or observation

Prior general consent

Before participants are recruited they are asked whether they are prepared to take part in research where they might be deceived about the true purpose

Probability

How likely something is to happen – can be expressed as a number (0.5) or a percentage (50% change of tossing coin and getting a head)

Protection of participants

Participants should be protected from physical or mental health, including stress - risk of harm must be no greater than that to which they are exposed in everyday life

Qualitative data

Descriptive information that is expressed in words

Quantitative data

Information that can be measured and written down with numbers.

Quasi experiment

An experiment often conducted in controlled conditions where the IV simply exists so there can be no random allocation to the conditions

Questionnaire

A set of written questions that participants fill in themselves

Random sampling

A sampling technique where everyone in the target population has an equal chance of being selected

Randomisation

Refers to the practice of using chance methods (e.g. flipping a coin' to allocate participants to the conditions of an investigation

The distance between the lowest and the highest value in a set of scores.

A measure of dispersion which involves subtracting the lowest score from the highest score in a set of data

Reliability

Whether something is consistent. In the case of a study, whether it is replicable.

Repeated measures design

An experimental design where each participants takes part in both/all conditions of the IV

Representative sample

A sample that that closely matched the target population as a whole in terms of key variables and characteristics

Retrospective consent

Once the true nature of the research has been revealed, participants should be given the right to withdraw their data if they are not happy.

Right to withdraw

Participants should be aware that they can leave the study at any time, even if they have been paid to take part.

A group of people that are drawn from the target population to take part in a research investigation

Scattergram

Used to plot correlations where each pair of values is plotted against each other to see if there is a relationship between them.

Secondary data

Information that someone else has collected e.g. the work of other psychologists or government statistics

Semi-structured interview

Interview that has some pre-determined questions, but the interviewer can develop others in response to answers given by the participant

A statistical test used to analyse the direction of differences of scores between the same or matched pairs of subjects under two experimental conditions

Significance

If the result of a statistical test is significant it is highly unlikely to have occurred by chance

Single-blind control

Participants are not told the true purpose of the research

Skewed distribution

An arrangement of data that is not symmetrical as data is clustered ro one end of the distribution

Social desirability bias

Participants’ behaviour is distorted as they modify this in order to be seen in a positive light.

Standard deviation

A measure of the average spread of scores around the mean. The greater the standard deviation the more spread out the scores are. .

Standardised instructions

The instructions given to each participant are kept identical – to help prevent experimenter bias.

Standardised procedures

In every step of the research all the participants are treated in exactly the same way and so all have the same experience.

Stratified sample

A sampling technique where groups of participants are selected in proportion to their frequency in the target population

Structured interview

Interview where the questions are fixed and the interviewer reads them out and records the responses

Structured observation

An observation study using predetermined coding scheme to record the participants' behaviour

Systematic sample

A sampling technique where every nth person in a list of the target population is selected

Target population

The group that the researchers draws the sample from and wants to be able to generalise the findings to

Temporal validity

Refers to how likely it is that the time period when a study was conducted has influenced the findings and whether they can be generalised to other periods in time

Test-retest reliability

Involves presenting the same participants with the same test or questionnaire on two separate occasions and seeing whether there is a positive correlation between the two

Thematic analysis

A method for analysing qualitative data which involves identifying, analysing and reporting patterns within the data

Time sampling

A way of sampling the behaviour that is being observed by recording what happens in a series of fixed time intervals.

Type 1 error

Is a false positive. It is where you accept the alternative/experimental hypothesis when it is false

Type 2 error

Is a false negative. It is where you accept the null hypothesis when it is false

Unstructured interview

Also know as a clinical interview, there are no fixed questions just general aims and it is more like a conversation

Unstructured observation

Observation where there is no checklist so every behaviour seen is written down in an much detail as possible

Whether something is true – measures what it sets out to measure.

Volunteer sample

A sampling technique where participants put themselves forward to take part in research, often by answering an advertisement

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Longitudinal Job Crafting Research: A Meta-Analysis

• Review Article
• Open access
• Published: 06 May 2024

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• Likitha Silapurem   ORCID: orcid.org/0000-0002-9070-7995 1 ,
• Gavin R. Slemp 1 &
• Aaron Jarden 1  

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This study updates and extends upon previous meta-analyses by examining the key antecedents and outcomes within the longitudinal job crafting literature. Using a robust statistical approach that disattenuates correlations for measurement error, we further extend past work by exploring the moderating effect of time on the relationship between job crafting and its key correlates. A systematic literature search gathered all current longitudinal research on job crafting, resulting in k  = 66 unique samples in the current analysis. Random-effects meta-analysis was conducted for overall job crafting and also for each individual facet of job crafting dimensions. Results showed that both overall job crafting and the individual facets of job crafting had moderate to strong, positive correlations with all variables included in this analysis, except for burnout and neuroticism which were negatively associated. A similar pattern of findings was largely present for all individual facets of job crafting. The exception to this was decreasing hindering demands crafting that had weak, negative associations with all correlates examined, except for burnout where a moderate, positive association was found. Findings from the moderation analysis for work engagement, job performance, and job satisfaction showed that although there was a clear downward trend of correlational effect sizes over time, they did not reach significance. The current study contributes to the job crafting literature by advancing previous meta-analyses, demonstrating the effect that job crafting has on positive work outcomes for both the employee and organisation over time. We conclude by exploring the implications for future research and practice.

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Job Crafting Interventions: Do They Work and Why?

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Job crafting (Tims & Bakker, 2010 ; Wrzesniewski & Dutton, 2001 ) is a job design approach that employees use to modify their work so that it better aligns with their values, motives, and needs, thereby promoting wellbeing and flourishing at work (Frederick & VanderWeele, 2020 ). In the face of ongoing organisational change and uncertainty, specifically through modern advancements which have fostered a more digitalised and flexible workplace, recent research has shown that employee-driven job redesign behaviours such as job crafting, offer a promising practical alternative to previously used traditional employer-led, top-down job re-design approaches (Lichtenthaler & Fischbach, 2019 ; Rudolph et al., 2017 ). Previous reviews have synthesized the existing research on job crafting to understand the benefits and implications of job crafting for both employees and organisations (Lichtenthaler & Fischbach, 2019 ; Rudolph et al., 2017 ). However, we estimate that as much as 50–55% of the current literature on job crafting is cross-sectional, thus not making it possible to empirically discern whether job crafting is a temporal antecedent or an outcome of the variables examined.

In addition to experimental research, which is often time-consuming and expensive to conduct, longitudinal research is one possible methodological design that can be used to establish associations between variables over time, and determine the temporal order between variables, providing a stronger base for causal inference. To date, there have been two meta-analyses that aggregate longitudinal research on employee job crafting behaviour (Frederick & VanderWeele, 2020 ; Lichtenthaler & Fischbach, 2019 ). These reviews combine the literature on job crafting within different work contexts (i.e., the settings or factors that influence the nature of work) and examine the effect this has on work content (i.e., factors that are controlled by the employee including the job demands and resources). Yet these reviews either contain effects that are biased downward by measurement error (Lichtenthaler & Fischbach, 2019 ), or contain effect sizes that are difficult to interpret due to the statistical aggregation across multiple different research designs (e.g., experimental, time-lagged designs; Frederick & VanderWeele, 2020 ). Thus, the first objective of the current study is to build upon the extant longitudinal job crafting literature and use psychometric meta-analysis to establish effect size estimates that are more easily interpretable. Second, we aim to examine the moderating effect of time on the relationship between job crafting and key correlates. In doing this, we provide stronger evidence to draw temporal inferences between job crafting and the commonly measured variables in the literature, and furthermore, provide insight into the long-term correlates of job crafting, an area which has received limited attention.

The remainder of the introduction is structured as follows. First, we outline the key conceptualisations present in job crafting research. Next, we review the current job crafting literature including existing meta-analytical reviews of job crafting. Finally, we examine time lag as a moderator in this analysis, that then leads to the aims and main research questions for this analysis.

1 Theory and Research Questions

1.1 job crafting.

Job crafting sits within the field of positive psychology (Seligman, 2002 ), specifically positive organisational scholarship (Wrzesniewski, 2003 ) which focuses on interventions aimed at promoting and enhancing employee wellbeing, rather than preventing and/or treating illbeing. Positive psychological interventions like job crafting, aim to cultivate positive feelings, behaviours and cognitions in participants to have an overall positive effect on wellbeing (Sin & Lyubomirsky, 2009 ). Although different approaches exist within the job crafting literature, the two most common approaches are the role-based approach (Wrzesniewski & Dutton, 2001 ) and the resources-based approach (Tims & Bakker, 2010 ). In the role-based approach, Wrzesniewski and Dutton ( 2001 ) reviewed the literature on proactive job behaviour and suggested that employees can make changes to their work environment in three ways: 1) by altering the scope, number, sequence, or type of tasks, known as task crafting ; 2) by adapting the quality and/or amount of interaction and human connection at work known as relational crafting ; and 3) by reframing how they perceive the tasks within it, known as cognitive crafting . Tims and Bakker ( 2010 ) utilized the job demands and resources model (JD-R; Bakker & Demerouti, 2007 ) to define job crafting. This model suggests that employees seek to increase their job resources at work, while working to reduce problematic job demands. They refined this idea into four dimensions: 1) increasing structural job resources crafting (e.g., crafting more autonomy, and opportunities to develop oneself); 2) increasing social job resources crafting (e.g., crafting more social connections and support from colleagues); 3) increasing challenging job demands crafting (e.g., crafting more tasks and job responsibilities); and 4) decreasing hindering job demands crafting (e.g., crafting ways to have fewer emotional and cognitive demands). While there is much overlap between both perspectives, the omission of cognitive crafting from the Tims and Bakker’s ( 2010 ) model, as well as the lack of distinction between expansion (i.e., expanding the scope of the job by increasing job resources) and contraction (i.e., narrowing the scope of the job by decreasing demands) oriented behaviours regarding Wrzesniewski and Dutton’s ( 2001 ) task, relational and cognitive crafting, has let to further refinements and the theoretical integration of both models (Bindl et al., 2019 ; Zhang & Parker, 2019 ).

More recent models of job crafting are underpinned by regulatory focus theory (Higgins, 1997 ), and are thus known as, ‘promotion and prevention crafting’ (PPC; Bindl et al., 2019 ; Zhang & Parker, 2019 ) or as ‘approach and avoidance crafting’ (Bruning & Campion, 2018 ). Promotion-focused job crafting (i.e., altering the job to increase positive outcomes), is geared toward pleasure attainment and employees obtaining and creating favourable outcomes to bring about change (Higgins, 1997 ; Lichtenthaler & Fischbach, 2019 ). Such an approach aligns with increasing job resources, challenging job demands, and expansion-oriented task, relational and cognitive crafting aspects of previous job crafting models (Slemp & Vella-Brodrick, 2013 ). Prevention-focused job crafting (i.e., altering the job to reduce negative outcomes) is associated with the avoidance of pain, and reflects people’s need for safety, security and the avoidance of negatives states where needs are not satisfied (Higgins, 1997 ; Lichtenthaler & Fischbach, 2019 ). Prevention crafting aligns with decreasing hindering job demands, and contraction forms of task, relational and cognitive crafting (Bindl et al., 2019 ; Lichtenthaler & Fischbach, 2019 ). By combining the previous conceptualisations of job crafting, this new model of promotion and prevention crafting encompasses both the tangible and intangible changes that employees can create in work boundaries. This approach allows for a clearer definition of job crafting behaviour, thereby creating a stronger model for future research.

Despite the varying conceptualisations, published literature on job crafting has grown considerably over the last ten years. Much of this growing interest is due to the substantial research showing the benefit of job crafting on individual work outcomes such as work engagement, job satisfaction, wellbeing (Rudolph et al., 2017 ), job performance (Bohnlein & Baum, 2020 ) and burnout (Lichtenthaler & Fischbach, 2019 ). While certain work characteristics such as work experience (Niessen et al., 2016 ) and job autonomy (Sekiguchi et al., 2017 ) may present more opportunities for employees to craft their job and thus facilitate job crafting behaviour, typically, research has shown that the positive effect of job crafting is consistent across a range of organisational contexts and cultures, suggesting that all employees may be able to benefit from crafting their job despite their occupation or working condition. Furthermore, the collective evidence suggests that job crafting is associated with benefits not just for individual job crafters, but also work teams and organisations. For instance, studies have shown that team job crafting is positively associated with individual performance (Tims et al., 2013 ), team performance (McClelland et al., 2014 ), and team innovativeness (Seppala et al., 2018 ). Similarly, job crafting is positively associated with organisational citizenship behaviour (Guan & Frenkel, 2018 ; Shin & Hur, 2019 ), organisational commitment (Wang et al., 2018 ), and negatively associated with turnover (Esteves & Lopes, 2017 ; Vermooten et al., 2019 ; Zhang & Li, 2020 ), which, if we accept these reflect causal relationships, can lead to substantial financial benefits for organisations (Oprea et al., 2019 ).

Despite such research findings, much of the current literature on job crafting is cross-sectional, which poses some challenges for the field (Rindfleisch et al., 2008 ). Although cross-sectional research is beneficial in giving us a snapshot of the association between variables (Levin, 2006 ), there are two primary limitations that pertain to this study design. First, it is more prone to common method variance (CMV; i.e., the systematic method error due to the use of a single rater or source; Podsakoff et al., 2003 ), which means effect sizes are generally biased upwards. Second, due to a single measurement at one point in time, cross-sectional data places constraints on the ability to infer the direction of associations (Rindfleisch et al., 2008 ). Our estimates reveal that around 52% of the current research on job crafting is cross-sectional in nature, suggesting that concerns regarding the lack of interval validity in cross-sectional research leading to potentially inflated associations, may be applicable to the job crafting literature (Ostroff et al., 2002 ). For instance, despite overwhelming evidence showing a positive, moderately strong relationship between job crafting and work engagement in cross-sectional research (Rudolph et al., 2017 ), there have been smaller and more varied effects among intervention studies (Oprea et al., 2019 ), and randomized control trials (Sakuraya et al., 2020 ). Thus, to confirm findings from cross-sectional research and to attain a more accurate representation about how job crafting is associated with its key correlates, insights from more sophisticated research designs that help reduce these limitations are needed.

One of the main ways that researchers recommend reducing the threat of CMV bias is by gathering data over multiple time periods (Ostroff et al., 2002 ; Podsakoff, 2003 ; Rindfleisch et al., 2008 ). Fortunately, there have been numerous individual longitudinal studies conducted within the job crafting literature to determine the key outcomes of job crafting using such designs. Although there are some exceptions, findings amongst longitudinal studies also lend support to the hypothesis that job crafting predicts future positive work outcomes, such as person-job fit, meaningfulness, flourishing, job performance, and job satisfaction (Cenciotti et al., 2017 ; Dubbelt et al., 2019 ; Kooij et al., 2017 ; Moon et al., 2018 ; Robledo et al., 2019 ; Tims et al., 2016 ; Wang et al., 2018 ). There are currently two meta-analyses that have examined longitudinal literature on job crafting (see Frederick & VanderWeele, 2020 ; Lichtenthaler & Fischbach, 2019 ). Lichtenthaler and Fischbach ( 2019 ) found that prevention-focused job crafting at Time 1 had a positive relationship with work engagement and a negative relationship with burnout at Time 2. They also found an inverse relationship with prevention-focused job crafting, which showed a negative relationship with work engagement, and positive relationship with burnout at Time 2. Using Tims and Bakker’s ( 2010 ) framework, Frederick and VanderWeele ( 2020 ), found a positive relationship with job crafting at Time 1 and work engagement at Time 2.

However, while this evidence is promising, these reviews contain methodological limitations that warrant a new meta-analysis to help overcome them. For instance, the statistical analysis used to calculate the meta-analytic correlations in Lichtenthaler and Fischbach ( 2019 ) does not correct for measurement unreliability in the predictor or criterion, which may lead to a downward bias in effect sizes (Schmidt & Hunter, 2015 ; Wiernik & Dahlke, 2020 ). Similarly, Frederick and VanderWeele’s ( 2020 ) meta-analysis is limited insofar as it included both observational longitudinal studies, as well as intervention studies, in the same analysis. Because results from different study designs tend to differ systematically, this may lead to increased, as well as artificially introduced, heterogeneity (Higgins et al., 2019 ). Thus, such approaches may serve to establish effect size estimates that are some what difficult to interpret. In meta-analyses, it is important to take account of the fact that experimental studies, such as randomized control trials (RCTs) of intervention studies, generate effect sizes in fundamentally different ways than do observational studies (Borenstein et al., 2009 ; Deeks et al., 2019 ). For instance, effects from RCTs (for quasi-experimental trials) are generated by comparing the degree to which groups change over time, whereas observational studies generate effects by quantifying the strength of association between variables. Another notable difference is that in intervention studies, participants are actively trained in job crafting behaviours, whereas in observational studies, participants are untrained and observed in their natural work setting (Borenstein et al., 2009 ). Because both research designs are used to examine fundamentally different types of research questions, we suggest that it a new meta-analysis is needed so that so that study designs can be separated during the analysis (Higgins et al., 2019 ).

In sum, these issues suggest that a new investigation on longitudinal studies is needed to determine true effect sizes within this literature. In doing this, improved accuracy on the actual strength of association between job crafting and its key antecedents and outcomes will be identified.

1.2 Time Lag as a Possible Moderator of Effects

In addition to building upon previous meta-analyses, we also aim to generate insight into the effect of time on the relationships between job crafting and its key outcomes. Previous research suggests that, in addition to reducing CMV, longitudinal research allows one to examine the way in which the relationships change as a function of time (Ford et al., 2014 ). This has long been recognized in the job crafting literature (e.g., Oprea et al., 2019 ; Rudolph et al., 2017 ), yet there has been little consideration to the effect of time lags on effect sizes. Such a situation is unfortunate as time lags over which variables are measured are very likely to influence effect size magnitudes. Thus, to better understand whether job crafting is stable over time in longitudinal research, the optimal time lag must be considered. Without this insight, drawing conclusions about the stability of job crafting over time may be inaccurate without also taking into the account the specific time period in which stability is measured. Similarly, in cases where experimental manipulation is not possible, a proxy for understanding causal processes shows that one variable (e.g., job crafting) is able to predict later levels of a presumed outcome (e.g., work engagement; Card, 2019 ). An understanding of these predictive relations over time are critical goals in cumulative science, which we address here.

Given the above, the aim of the current study was to address two research questions:

Research Question 1 : What are the most prevalent antecedents and outcomes of the longitudinal job crafting literature, and how strongly is job crafting related to these variables?

Research Question 2 : To what extent does time lag influence the strength of relationships between job crafting and its key antecedents and outcomes?

In addressing these research questions, we aim to overcome existing methodological limitations present in the literature. In contrast to existing meta-analyses which use longitudinal studies and examined work engagement, burnout and performance we contribute to advancing knowledge by examining the key antecedents and outcomes of job crafting that have been studied in the literature to date, rather than focusing on a select few. Finally, in order to further advance the field of job crafting, we examine the moderating effect of time lag.

2.1 Literature Search Strategy

The search strategy was developed to retrieve all possible sources on job crafting, from which only longitudinal studies were included in the current meta-analysis. In line with best practice guidelines for systematic reviews and meta-analyses (Appelbaum et al., 2018 ; Rudolph et al., 2020 ; Siddaway et al., 2019 ), we employed a variety of search strategies to systematically search for studies that examined the longitudinal antecedents and consequences of job crafting. First, to retrieve both published and unpublished sources, we conducted a literature search using eight databases that covered both published and unpublished literature: Education Resources Information Centre (ERIC), Business Source Complete (searched through Ebscohost), Web of Science Core Collection, Medline, PyscINFO, Open Dissertations, ProQuest Theses and Dissertations, and Scopus. Our search covered all years to April, 2020. In line with literature search strategies recommended by Harari et al. ( 2020 ), we consulted a university librarian to help select the most relevant databases and also to select search terms for our literature search.

In line with current conceptions of job crafting (Slemp & Vella-Brodrick, 2013 ; Tims et al., 2012 ; Wrzesniewski & Dutton, 2001 ), we ran the search using the following base terms: “job”, “task”, “relational”, “cognitive”, “increasing structural demands”, “increasing social job resources”, “increasing challenging job demands”, “decreasing hindering job demands,” which were combined with “crafting” using the Boolean operator “AND”. This was done to ensure that the search only returned sources that had at least one of the base terms, as well as “crafting” in the title, abstract or key words. Second, we searched Google Scholar for sources that cited papers that are associated with the most commonly used measures of job crafting (Slemp & Vella-Brodrick, 2013 ; Tims et al., 2012 ). Third, we examined the reference lists of key meta-analyses for relevant longitudinal studies.

In total, our search process returned 1,692 sources, from which 883 remained after duplicates were removed (see Fig.  1 ). We first assessed the relevance of the source using the title and abstract, and subsequently further screened sources using the full text and our inclusion criteria (outlined below). This process yielded 320 empirical studies on job crafting, of which 68 were identified as longitudinal studies that met our inclusion criteria. Because Schmidt and Hunter ( 2015 ) suggested we need to identify any studies that may have duplicate sampling, as meta-analytic procedures are sensitive to the violation of the assumption of sample independence, in the final step of our screening we used the recommended procedures outlined in Wood ( 2008 ) to identify studies with duplicate samples. In doing this, we identified seven studies which used duplicate samples (e.g., Kim & Beehr, 2018 , 2019 , 2020 ). Where necessary, if the studies that identified as duplicates examined the same outcomes, we only included the earliest or ‘original’ sample in our analysis, as recommended by von Elm et al. ( 2004 , p. 975). However, we included duplicates in some analyses if there was no overlap in the variables studied. Overall, after applying these criteria we were left with 64 studies, three of which were unpublished ( k  = 66 independent samples, combined N  = 27,195).

Systematic search flow diagram (PRISMA)

2.2 Inclusion Criteria

Studies were included in the meta-analysis if they satisfied the following criteria: (a) the study directly measured and reported overall job crafting or a facet of job crafting (i.e., task crafting, relational crafting, cognitive crafting, increasing structural resources, increasing social resources, increasing challenging demands, decreasing hindering demands, increasing quantitative job demands, and physical job crafting), and the relevant antecedent or outcome; (b) the study was written in English; (c) the study contained primary data; (d) the full text was readily available; (e) job crafting was empirically measured using a known job crafting scale (see Table 1 in Rudolph et al., 2017 for a list of known measures in the job crafting literature); (f) the study has at least two waves of measurement; and (g) the study has at least a one month time lag between waves of measurement. This last criterion was set on the basis of Dormann and Griffin ( 2015 ), who used cross-lagged regression coefficients of both direct and reciprocal relationships, to determine the optimal time lag that will have the highest chance to detect an effect size if one exists for two waves of measurement. Findings from this study showed that ‘shortitudinal’ research with around approximately 1 month time lag (24 days), was found to be the minimal optimal length. Thus, studies with shorter time lags, such as daily diary studies and shorter longitudinal research, were excluded from the study.

2.3 Study Coding and Data Transformations

All studies were manually coded using an established coding template. The studies were coded for (a) the sample size, (b) the zero-order correlation coefficient ( r ) between job crafting and the relevant correlate variable, (c) the reliability of job crafting measure ( Rxx ), (d) the reliability of correlate measure ( Ryy ), (e) the scale used to measure job crafting, (f) the country in which the study was published, (g) the publication status of the paper (published vs unpublished), (h) the number of waves of measurement, (i) the time lag between waves (in months), (j) occupational status of participants, and (k) the study design.

Where necessary, we used the data transformation procedures described by Schmidt and Hunter ( 2015 ) to establish composite correlations for studies that only reported correlations at a facet level, but not the corresponding aggregate to reflect the overall construct. We did this by combining the correlation coefficients using their intercorrelations between the variable facets. We also established corresponding composite reliabilities using Mosier ( 1943 ) formulas. We calculated the composite to determine the overall job crafting construct. Based on regulatory focus theory, we did this to establish composites for approach and avoidance crafting, using a similar approach to Lichtenthaler and Fischbach ( 2019 ). Taking this approach meant that decreasing hindering resources crafting was excluded when calculating the overall job crafting composite. Unlike promotion-oriented forms of job crafting, decreasing hindering resources crafting is labelled a prevention form of job crafting, and often viewed as a protective mechanism when job demands are high (Demerouti, 2014 ). As such, the direction of association between the correlates of decreasing hindering resources crafting is often the inverse when compared to promotion crafting. Thus, combining these facets in the same analysis would result in downwardly biased aggregated effect sizes. Where necessary, we also established composite correlations when studies reported only facets of the antecedent or outcome variables.

2.4 Meta-Analytic Procedure

To conduct our meta-analysis, we used the psychometric meta-analysis approach, proposed by Schmidt and Hunter ( 2015 ). We conducted our analyses with R Studio (Version 1.4.1717) using the “psychmeta” package (Dahlke & Wiernik, 2019 ) and the unbiased sample variance estimator. We first calculated a sample-size weighted mean observed correlation between each facet of job crafting and each correlate variable. Next, we estimated the population correlation ( \(\overline{\uprho }\) ) using artifact (reliability) distributions, which are reported in our supplementary file .

Schmidt and Hunter’s ( 2015 ) approach to meta-analysis is based on the random effects model. The advantage of the random effects model is that it allows parameters to vary across studies and does not have strong assumptions about the homogeneity of effect parameters. Thus, random effect meta-analyses determine the mean effect size from an assumed distribution of effect sizes (Borenstein et al., 2010 ; Hedges & Vevea, 1998 ; Schmidt & Hunter, 2015 ), which leads to more accurate and generalizable estimates of effect sizes and confidence intervals (CI; Field, 2003 ; Schmidt & Hunter, 2015 ). While the random effects model requires at least three studies, the psycmeta package in R will aggregate effects when two studies are available, so we also report a meta-analytic correlation for all outcomes that had a minimum of two studies available, and also generated the 95% confidence intervals for each estimate.

Heterogeneity was assessed using \(S{D}_{\uprho }\) and the 80% credibility interval (CV). \(S{D}_{\uprho }\) is the corrected standard deviation of the true score correlation and, greater values of \(S{D}_{\uprho }\) suggest greater heterogeneity in meta-analytic associations (Schmidt & Hunter, 2015 ). We also generated the 80% credibility interval (CV), which provides an estimate of heterogeneity around each effect size. The CV is interpreted such that 80% of the distribution of true correlations (the \(\overline{\rho }\) distribution) falls within this interval (Schmidt & Hunter, 2015 ).

We summarize the meta-analytic findings by reporting (a) the number of studies in the analysis ( k ), (b) the combined sample size ( N ), (c) the “bare bones” meta-analytic correlation (Schmidt & Hunter, 2015 ) which is the meta-analytic correlation corrected only for sampling error ( \(\overline{r }\) ), (d) the observed standard deviation ( \(S{D}_{r}\) ), (e) the residual standard deviation ( \(S{D}_{res}\) ) for the meta-analytic correlation, (f) an estimate of the true correlation that is corrected for both sampling and measurement error ( \(\overline{\uprho }\) ), (g) the variance in this estimate ( \(S{D}_{\uprho }\) ), (h) the observed standard deviation of corrected correlations ( \(S{D}_{{r}_{c}}\) ), (i) the 95% CI and, (j) the 80% CV for the true correlation parameter. To interpret our effect sizes, we used Bosco et al.’s ( 2015 ) empirically established correlational benchmarks for applied psychological research. Specifically, we used benchmarks of | r |= 0.07, 0.16, and 0.32 to indicate the lower-bound thresholds of weak, moderate, and strong, which correspond to the 25th, 50th and 75th percentile, respectively.

2.4.1 Moderator Analysis: Time Lag

In line with our second aim, we conducted moderator analyses using meta-regression to examine whether time lag was related to study-level effect sizes. Time lag was coded as the time (in months) between the measurement of job crafting, or a facet of job crafting, and the correlate of interest. We concluded that the moderator was significant if the 95% CIs for the regression coefficients did not encompass zero (Borenstein et al., 2009 ).

2.5 Publication Bias Analysis

Although active steps were taken to locate and obtain unpublished data sources, it is possible that our findings could still be subject to publication bias. Thus, in line with recommendations by Field et al. ( 2021 ) we conducted a thorough analysis to assess whether publication bias was present in our findings, which we considered in several ways. First, we visually analysed funnel plots which displayed effect sizes plotted against their standard errors. The distribution of effect sizes on the funnel plots will be asymetrical in the case of biased literatures, which is often caused by having ‘missing’ published studies containing nonsignificant or small effects. Second, we also used Egger’s regression test of funnel plot assymtery to assess the symmetry of the funnel plot (Egger et al., 1997 ). Finally, Duval and Tweedie’s ( 2000 ) trim and fill procedure was used to examine the extent to which ‘missing’ studies would affect the original effect estimates. To ensure that a reasonable distribution of published and unpublished studies were considered, and also because findings from the trim and fill method may be biased for small sample sizes, only variables where k  > 10 were considered for the analyses. To run these analyses, we used the free, open source software Meta-Sen ( https://metasen.shinyapps.io/gen1/ ), which provides a comprehensive software platform to examine for sensitivity analyses and outlier-induced heterogenity. All analyses using Meta-Sen are included in our supplementary file .

3.1 Antecedents of Job Crafting

As indicated in Table  1 , overall job crafting exhibted near-zero, non-significant meta-analytic correlations with most of the demographic variables, except for education ( \(\overline{\rho }\) = 0.10 [95% CI = 0.05, 0.14]; k  = 17; N  = 7,809), work hours ( \(\overline{\rho }\) = 0.21 [95% CI = 0.15, 0.28]; k  = 3; N  = 1,013), and experience ( \(\overline{\rho }\) = 0.27 [95% CI = 0.19, 0.35]; k  = 2; N  = 627), which showed a moderate positive relationship. By contrast, personality factors were moderate to strong antecedents of job crafting, with proactive personality having a positive, strong relationship ( \(\overline{\rho }\) = 0.37 [95% CI = 0.15, 0.58]; k  = 6; N  = 2,161), and neuroticism having a moderate, negative relationship ( \(\overline{\rho }\) = -0.19 [95% CI = -0.40, 0.02]; k  = 3; N  = 864), on job crafting behaviours. Generally, moderate to strong correlations were observed for different work contexts, except for burnout ( \(\overline{\rho }\) = -0.16 [95% CI = -0.17, -0.16]; k  = 2; N  = 2,320) which had a moderate, negative relationship with job crafting. Of these, positive leadership ( \(\overline{\rho }\) = 0.68 [95% CI = 0.47, 0.90]; k  = 6; N  = 4,193), HR flexibility ( \(\overline{\rho }\) = 0.48 [95% CI = 0.31, 0.65]; k  = 3; N  = 1,294), and work engagement ( \(\overline{\rho }\) = 0.46 [95% CI = 0.42, 0.50]; k  = 3; N  = 1,018) were the strongest antecedents of job crafting. Strong positive correlations were also observed between all motivational factors and job crafting.

Similar to overall job crafting, near-zero, non-significant correlations were found between demographic variables and task, relational and cognitive crafting (see Table  2 ). The exception to this was the correlation found between age (relational crafting: \(\overline{\rho }\) = -0.07 [95% CI = -0.10, -0.05]; k  = 3; N  = 1,802; cognitive crafting: \(\overline{\rho }\) = 0.07 [95% CI = -0.01, 0.14]; k  = 3; N  = 1,802) and gender (relational crafting: \(\overline{\rho }\) = 0.06 [95% CI = 0.02, 0.10]; k  = 4; N  = 2,048; cognitive crafting: ( \(\overline{\rho }\) = 0.06 [95% CI = 0.05, 0.07]; k  = 3; N  = 1,802), which exhibited a weak negative, and a weak positive relationship, respectively. These findings suggest that females engaged in more relational and cognitive crafting than men. Social support was a strong, positive antecedent to job crafting behaviour for both task ( \(\overline{r }\) = 0.35, k  = 1; N  = 253) and relational crafting ( \(\overline{r }\) = 0.50, k  = 1; N  = 253). Similarly, self-esteem also exhibited a strong, positive association for both task ( \(\overline{r }\) = 0.44, k  = 1; N  = 138) and cognitive crafting ( \(\overline{r }\) = 0.54, k  = 1; N  = 138). Motivation had a strong positive association with task crafting ( \(\overline{r }\) = 0.34, k  = 1; N  = 426), and similar findings were also found between interdependence and relational crafting ( \(\overline{r }\) = 0.32, k  = 1; N  = 253). Neuroticism had a moderate, negative association with cognitive crafting ( \(\overline{r }\) = -0.10, k  = 1; N  = 253).

All demographic variables had non-significant associations with the facets of job crafting defined by Tims and Bakker ( 2010 ) (see Table  3 ). Work engagement was one of the strongest antecedents across all facets of job crafting, and had positive associations with structural resources crafting ( \(\overline{\rho }\) = 0.45 [95% CI = 0.39, 0.51]; k  = 8; N  = 4,570), social resources crafting ( \(\overline{\rho }\) = 0.31 [95% CI = 0.27, 0.35]; k  = 3; N  = 2,607), and challenging demands crafting ( \(\overline{\rho }\) = 0.54 [95% CI = 0.48, 0.61]; k  = 3; N  = 2,607). By contrast, there was a moderate negative relationship between work engagement and hindering demands ( \(\overline{\rho }\) = -0.27 [95% CI = -0.31, -0.15]; k  = 3; N  = 2,608). Burnout also had consistently moderate, negative associations with all of Tims et al.’s ( 2012 ) facets of job crafting (structural resources crafting: \(\overline{\rho }\) = -0.21 [95% CI = -0.31, -0.12]; k  = 2; N  = 2,320; social resources crafting: \(\overline{\rho }\) = -0.10 [95% CI = -0.15, -0.04]; k  = 2; N  = 2,320; challenging demands crafting: \(\overline{\rho }\) = -0.17 [95% CI = -0.23, -0.11]; k  = 2; N  = 2,320), except for hindering demands crafting which exhibited a strong, positive association with burnout ( \(\overline{\rho }\) = 0.40 [95% CI = 0.30, 0.51]; k  = 2; N  = 2,320). Workaholism exhibited low to moderate, positive associations with all of Tims et al.’s ( 2012 ) facets of job crafting (structural resources crafting: \(\overline{\rho }\) = 0.11 [95% CI = 0.07, 0.15]; k  = 3; N  = 2,607; social resources crafting: \(\overline{\rho }\) = 0.09 [95% CI = 0.01, 0.18]; k  = 3; N  = 2,607; challenging demands crafting: \(\overline{\rho }\) = 0.22 [95% CI = 0.16, 0.29]; k  = 3; N  = 2,607), however this association was not significant for hindering demands crafting ( \(\overline{\rho }\) = 0.13 [95% CI = -0.02, 0.27]; k  = 2; N  = 2,320). Moderate, positive associations were observed between psychological capital and social resources crafting ( \(\overline{\rho }\) = 0.20 [95% CI = 0.00, 0.19]; k  = 2; N  = 1,935). and challenging demands crafting ( \(\overline{\rho }\) = 0.25 [95% CI = 0.13, 0.37]; k  = 2; N  = 1,260).

3.2 Outcomes of Job Crafting

Of note in Table  1 is the strong positive association between job crafting and different job attitude outcomes, with work engagement ( \(\overline{\rho }\) = 0.46 [95% CI = 0.40, 0.52]; k  = 14; N  = 6,493) and meaningfulness ( \(\overline{\rho }\) = 0.52 [95% CI = 0.22, 0.83]; k  = 3; N  = 957) showing the strongest effects. In contrast, a moderate negative meta-analytic correlation emerged between burnout and job crafting ( \(\overline{\rho }\) = -0.22 [95% CI = -0.27, -0.18]; k  = 4; N  = 1,523). A strong, moderate, positive association was found between job crafting and performance ( \(\overline{\rho }\) = 0.28 [95% CI = 0.22, 0.34]; k  = 15; N  = 6,193). Very strong positive correlations were found between job crafting and self-efficacy ( \(\overline{\rho }\) = 0.58 [95% CI = 0.45, 0.71]; k  = 3; N  = 947) and psychological capital ( \(\overline{\rho }\) = 0.44 [95% CI = 0.29, 0.58]; k  = 2; N  = 1,289).

Generally task, relational, and cognitive crafting had moderate to strong positive associations with job attitudes, with need-supply fit and work-family balance having the strongest associations with relational crafting (work-family balance: \(\overline{r }\) = 0.19, k  = 1; N  = 1,411) and cognitive crafting (need-supply fit: \(\overline{r }\) = 0.32, k  = 1; N  = 118). A moderate, negative relationship existed between relational crafting and job dissatisfaction ( \(\overline{r }\) = -0.15, k  = 1; N  = 246). By contrast, only a weak relationship existed between task crafting and work-family balance ( \(\overline{r }\) = 0.08, k  = 1; N  = 1,411) and job dissatisfaction ( \(\overline{r }\) = 0.04, k  = 1; N  = 1,411). Task crafting had a moderate, positive association with creativity ( \(\overline{r }\) = 0.15, k  = 1; N  = 86).

Work engagement and job satisfaction had strong, positive associations with all of Tims et al.’s ( 2012 ) facets of job crafting, except for hindering demands crafting which had near-zero, non-significant associations with these outcomes (work engagement: \(\overline{\rho }\) = -0.03 [95% CI = -0.11, 0.05]; k  = 7; N  = 1,797; job satisfaction: \(\overline{\rho }\) = -0.04 [95% CI = -0.20, 0.12]; k  = 5; N  = 1,670). A similar pattern of results was observed for job performance (structural resources crafting: \(\overline{\rho }\) = 0.33 [95% CI = 0.25, 0.41]; k  = 5; N  = 1,719; social resources crafting: \(\overline{\rho }\) = 0.13 [95% CI = 0.06, 0.21]; k  = 5; N  = 1,313; challenging demands crafting: \(\overline{\rho }\) = 0.28 [95% CI = 0.13, 0.42]; k  = 6; N  = 1,893; hindering resources crafting: \(\overline{\rho }\) = 0.05 [95% CI = -0.07, 0.17]; k  = 6; N  = 2,059). Similarly but conversely, all of Tims et al.’s ( 2012 ) facets of job crafting had moderate to strong, negative associations with burnout, except hindering demands crafting which exhibited a positive, yet non-significant association with burnout ( \(\overline{\rho }\) = 0.19 [95% CI = -0.08, 0.46]; k  = 3; N  = 1,192). Generally, all of Tims et al.’s ( 2012 ) facets of job crafting, except decreasing hindering resources crafting, had a significant, moderate to strong correlation with other commonly measured job attitudes, including organisational citizenship behaviour (structural resources crafting: \(\overline{r }\) = 0.32, k  = 1; N  = 288; challening demands crafting: \(\overline{r }\) = 0.27, k  = 1; N  = 288) and psychological captial (structural resources crafting: \(\overline{r }\) = 0.54, k  = 1; N  = 940). The exception to this was the association between social resources crafting and psychological empowerment, which exhibited a near-zero relationship ( \(\overline{r }\) = 0.03, k  = 1; N  = 320). By contrast, decreasing hindering resource crafting had near-zero to small, negative correlations with job attitudes such as flourishing ( \(\overline{r }\) = -0.03, k  = 1; N  = 443) and adaptivity ( \(\overline{r }\) = -0.07, k  = 1; N  = 368).

3.3 Moderator Analysis: Time Lag

We used meta-regression to examine whether the aforementioned correlations were related to the time lag between measurements. To do this, we used z-transformed effect sizes and examined whether these changed as a function of time-lag. In line with Cochrane guidelines (Deeks et al., 2019 ), we only conducted the meta-regression when there were at least 10 effect sizes for each outcome variable, which limited our analyses to work engagement, job satisfaction and job performance, which were all outcomes of overall job crafting. To aid our interpretation of these analyses, we generated bubble plots to convey overall trends in the associations over time (see Fig.  2 below).

Bubble plots showing the moderation analyses between job crafting and ( a ) work engagement, ( b ) job satisfaction, and ( c ) job performance. Note : Size of the bubble represents sample size in each study, with larger bubbles representing larger sample sizes

As displayed in Fig.  2 , a clear downward trend in correlation effect sizes can be observed across all of these criteria, suggesting that effect sizes attenuate as a function of time lag. However, results did not reach significance. The moderation analysis with work engagement,while trending downwards, was not significant ( k  = 14, SE  = 0.0033, β =  − 0.006, CI = [− 0.0124, 0.0005]), as the confidence intervals encompass zero (see Fig.  2 a). Moderation also did not reach significance between job crafting and job satisfaction ( k  = 10, SE  = 0.0105, β =  − 0.0076, CI = [− 0.0283, 0.0130]), or job peformance ( k  = 15, SE  = 0.009, β =  − 0.0091, CI = [− 0.0269, 0.0086]) (see Fig.  2 b and c).

3.4 Publication Bias

Within our supplemental material (SM), SM Table 1  displays the sensitivity analysis results for all correlates of job crafting that had 10 or more effect sizes. Again, the correlates that fit this criterion were work engagement, job satisfaction and job performance, which were all outcomes of overall job crafting.

An assessment of SM Table 1  suggests that outliers did not threaten the observed meta-analytic findings. No outliers were found for job satisfaction, and one outlier was removed for both work engagement and job performance. However, an examination of the adjusted meta-analytic mean estimates shows a similar value for the corresponding original mean estimate before the outlier was removed. Specifically, the absolute differences between the original meta-analytic estimate and adjusted estimate did not exceed 20% of the original estimate (Field et al., 2021 ). Hence, results of our sensitivity analyses suggest that outliers did not threaten the observed meta-analytic results.

Similarly for publication bias, the sensitivity analyses suggest that the meta-analytic estimates between overall job crafting and work engagement, job satisfaction and job performance were not threatened by publication bias (see SM Table 1 ). In fact, results of the adjusted meta-analytic effect sizes suggest that, if anything, the original effect size was underestimated, as indicated by typically stronger, not weaker, effects after accounting for publication bias. Hence, the sensitivity analyses indicate that our original meta-analytic estimates are likely conservative estimates of the observed data and are not threatened by outliers or publication bias.

4 Discussion

Our aim in the present meta-analysis was to uncover key antecedents and outcomes in the longitudinal job crafting literature, and determine the strength of association between these variables. We also aimed to examine whether the lag between job crafting and its key correlates moderated the strength of the relationship between these variables. In the following sections, we summarize and interpret our findings, discuss relevant limitations, and suggest directions for future research.

4.1 Study Findings and Contributions

Our results contribute to the literature in several ways. First, we extend previous meta-analyses that only included a select few variables, by examining all the key variables within longitudinal job crafting research, and reveal that certain antecedents have a meaningful relationship with overall job crafting. Individual demographic characteristics (i.e., education levels, experience and working hours), psychological factors (i.e., motivation and psychological capital), and personality traits were all found to be positively associated with overall job crafting behaviour. Our findings are consistent with human capital theory, which suggests that older employees, as well as more experienced, educated, and longer-tenured employees possess greater accumulated knowledge about their job, and are more readily able to find opportunities where they can craft their job, and are thus in a better position to do so. Unsurprisingly, our findings also revealed that employees who have more traits associated with a proactive personality were also more likely to engage in job crafting. Proactive employees generally have higher levels of initiative, are able to overcome barriers, identify opportunities, and persevere to achieve their goals, thus making them more likely to engage in job crafting behaviour (Bakker et al., 2012 ; Bindl & Parker, 2011 ; Parker et al., 2010 ). However, a point of difference between findings from this study and previous meta-analyses, was that gender, age and tenure were found to be non-significantly associated to overall job crafting, whereas Rudolph et al. ( 2017 ) found a significant effect. However, the association between gender and relational and cognitive crafting was the exception to these results, with our findings indicating that women tended to engage in relational and cognitive crafting more than men.

Our findings revealed that, in addition to positive traits (e.g., proactive personality), negative traits were also found to be associated with job crafting behaviour. Specifically, workaholism was found to be a positive, weak to moderate, antecedent of all facets of job crafting, except decreasing hindering demands crafting. This may be because, workaholics have been found to self-impose demands on themselves and choose to take up new challenges and tasks at work, whilst seeking to expand their capabilities at work, as well as manage their job demands (Schaufeli et al., 2008 ). These characteristics of workaholism overlap with Tims et al.’s ( 2012 ) dimensions of job crafting, and our weak to moderate associations reflect this. However, workaholism was positively associated with decreasing hindering demands crafting, as workaholics have been found to do whatever is important at work, including avoiding demanding tasks or people that may be potential obstacles in achieving their work goals (Hakanen et al., 2018 ). Our findings are consistent with previous research which has found that other negative traits, such as obsessive passion examined in previous research, is also positively associated with job crafting, thereby further supporting existing theoretical propositions stating that job crafting does not yield only ‘good’ outcomes for both the employee and organizations (Slemp et al., 2021 ; Wrzesniewski & Dutton, 2001 ). Instead, job crafting contains both positive and negative qualities, and certain forms of job crafting, potentially through workaholism, could entice maladaptive outcomes such as burnout (Petrou et al., 2015 ; Tims et al., 2015 ). Thus, future job crafting interventions should consider the potentially negative effects that job crafting could have, and attempt to limit this by educating employees on ways in which they can reduce maladaptive forms of job crafting.

Our results are consistent with Wang et al.’s ( 2020 ) previous meta-analytic findings, which suggest that work context and social factors serve key antecedent functions for job crafting. Specifically, our findings reveal that working environments that are more flexible and supportive, in addition to the presence of positive leadership styles, may enhance an individual’s motivational state to encourage employees to craft their job (Parker et al., 2010 ; Zhang & Parker, 2019 ). Our findings reflect this notion as, positive leadership styles (e.g., empowering and charismatic leadership), HR flexibility, feelings of autonomy and social support, were all found to be strong, positive antecedents of overall job crafting behaviour. These findings have practical implications in terms of emphasising the need to incorporate the organisation within future job crafting interventions, as this may be more successful in encouraging and motivating employees to craft their job and make a proactive effort to improve their work wellbeing.

An interesting finding was that work engagement and burnout were both antecedents and outcomes of overall job crafting and for each facet of Tims et al.’s ( 2012 ) job crafting model. Specifically, lower burnout and higher work engagement was significantly associated with later job crafting behaviour, but were also possible outcomes of job crafting. Indeed, it is possible that a reciprocal relationship exists between job crafting and burnout/work engagement, such that more engaged/less burned out employees are more/less likely to engage in job crafting in the first place, thereby influencing later engagement and burnout. While we suggest that this is a possibility, the literature in its current form does not allow for such an analysis, and this should be an aim for future research to address. Nevertheless, our findings extend previous meta-analyses by highlighting that burnout and engagement serve as both antecedents and outcomes of job crafting, raising the possibility of a reciprocal relationship.

Although our findings align with previous meta-analyses that showed job crafting to be positively associated with work engagement and negatively with burnout at later time points (Frederick & VanderWeele, 2020 ; Lichtenthaler & Fischbach, 2019 ), there were some differences in effect sizes. The meta-analytic correlation between job crafting and later work engagement was significantly stronger than that found in Frederick and VanderWeele ( 2020 ). This may be because, Frederick and VanderWeele’s ( 2020 ) meta-analysis combined longitudinal and experimental research together when applying the meta-analysis. As interventions typically rely on successfully increasing employee job crafting beyond an existing level that they already display, typically unconsciously, intervention studies are likely to yield smaller effect sizes than observational studies insofar as effects are contingent on the difference between pre-post levels of job crafting, rather than capturing natural variance aggregating correlations (Bakker et al., 2019 ; Diener et al., 2021 ). Consequently, combining interventions with longitudinal research may be pulling the meta-analytic effect size downwards, thereby making them smaller than what would be found when observing job crafting in its natural form in organisations. As we only included longitudinal research in our meta-analysis, this may explain why we found much stronger correlational estimates for work engagement than Frederick and VanderWeele ( 2020 ).

Although Lichtenthaler and Fischbach ( 2019 ) did not correct for measurement error in their meta-analysis, we found similar effect sizes for both work engagement and burnout. This suggests that while Lichtenthaler and Fischbach’s ( 2019 ) meta-analytic findings were fairly accurate in their estimates, it was necessary to conduct our meta-analysis to show that their findings are not simply an artifact of CMV or measurement error. This is because, in our meta-analysis, we limited both upward bias which is created through CMV by including only longitudinal research in our analysis. At the same time, we also control for downward bias by imposing corrections for measurement error, thereby resulting in the most accurate effect sizes. Thus, these consistent findings allude to the robustness of the relationship that job crafting has with work engagement and burnout, and further suggests that the measures used in job crafting research are consistent with the way they associate with other variables.

Decreasing hindering job demands crafting was the only dimension of job crafting that had little to no association with any of the correlates included in our findings. Although it is expected that reducing problematic job demands through decreasing hindering demands crafting is thought to have a positive influence on job outcomes, these findings support previous speculations which suggest that decreasing hindering demands may signal a withdrawal from work (Demerouti, 2014 ). For example, previous research has found that decreasing hindering demands is positively related with exhaustion (Petrou, 2013 ) and has detrimental effects on motivation (Petrou et al., 2012 ). Thus, it is possible that in instances where employees are withdrawing from work, decreasing demands may potentially be an ineffective strategy to improve job attitudes and performance at work (Demerouti, 2014 ).

While prior intervention research suggests that job crafting effects likely wane over time (Sakuraya et al., 2016 ; van Wingerden et al., 2017 ), our non-significant findings from the moderation analysis indicate that the effect of time on correlation magnitudes is likely to be modest. These findings contribute to the limited literature that examines the long-term effects of job crafting. Longitudinal research allows for the examination of the dynamic nature of job crafting over time that would not otherwise be possible with cross-sectional research. In doing this, more precise estimates between job crafting and other variables can be found, leading to more accurate conclusions about the effect of job crafting. It should be noted that while we were interested in the moderation effect of time lag on both the antecedents of job crafting as well as how job crafting relates to its outcomes, there were only sufficiently available studies for the outcomes, which we tested. However, it is possible that the association between certain antecedents and later job crafting behaviour may also change over time, and thus should be explored in future research.

4.2 Limitations and Future Directions

The current study includes a number of limitations that should be considered. First, one limitation of meta-analyses is that it is dependent on the quality, scope and number of studies present in the existing literature. Also, the inclusion of questionnaire studies in the current meta-analysis may potentially make it more susceptible to the traditional limitations of self-report questionnaires such as response bias, monomethod bias, and method variance (Razavi, 2001 ). Although interest and research on job crafting continues to grow, some of our meta-analytic findings were based on a small number of studies, which was further limited due to the focus of time-lagged correlations within the job crafting literature. Although only two studies are needed to conduct a meta-analysis (Valentine et al., 2010 ), this may have increased the possibility that our results for such analyses are influenced by second-order sampling error (Schmidt & Oh, 2013 ), or by extreme or inflated effect sizes (Turner et al., 2013 ). Thus, having a larger sample size would allow more power to estimate robust effect sizes. Second, there is not sufficient research that examines Wrzesniewski and Dutton’s ( 2001 ) cognitive crafting dimension, and, as such, we were unable to further examine the antecedents and outcomes associated with these dimensions of job crafting. Cognitive crafting is a valuable component of job crafting and is viewed as the facet of job crafting that is most closely aligned with work identity and meaningfulness (Slemp & Vella-Brodrick, 2013 ; Zhang & Parker, 2019 ). Employees can achieve greater fit with their work environment by reframing and redefining the way they perceive their work (Wrzesniewski & Dutton, 2001 ; Zhang & Parker, 2019 ), even without any physical behaviour change. Thus, it is possible that cognitive crafting may have a significant positive effect on perceived job characteristics, as well as other desirable outcomes including work identity, meaningfulness, and emotions (Berg et al., 2013 ; Wrzesniewski & Dutton, 2001 ; Zhang & Parker, 2019 ), which we were not able to directly test. Research exploring the effect of cognitive crafting on work outcomes, as well as the interaction between behavioural- and cognitive-crafting, would be a valuable avenue for future research.

Third, there is limited research on prevention-oriented job crafting in the current literature. In this study, we defined prevention-oriented job crafting as behavioural changes that employees deploy in their job to reduce negative job demands. However, while Tims et al.’s ( 2012 ) measure of decreasing hindering job demands offers a reliable facet of prevention-oriented job crafting, it is only one form of prevention-oriented job crafting and does not include broader job crafting strategies (Bindl et al., 2019 ). Newer measures are emerging that integrate regulatory focused promotion- and prevention-oriented job crafting with Wrzesniewski and Dutton’s ( 2001 ) model of job crafting (Bindl et al., 2019 ), and offer a way forward in this regard.

Moreover, as findings from this meta-analysis revealed that decreasing hindering demands has potentially maladaptive effects on certain work outcomes, such as burnout, there is limited research exploring when and why these negative outcomes occur. Hu et al. ( 2020 ) suggests that employees reduce stress by alleviating or actively removing harmful stimuli, known as active coping, as well as by distancing themselves from the problem, known as withdrawal, which could lead to different outcomes. Active coping behaviour is a more adaptive form of job crafting that results in increased proactive behaviour, whereas employees who withdraw from stressful demands are less likely to engage in adaptive behaviour, which negatively impacts goal-related behaviours and hence leads to negative work-related wellbeing (Hu et al., 2020 ). Thus, future research should seek to create measures that differentiate between the coping mechanism employees use when engaging in prevention-oriented job crafting behaviour in order to identify the true effect that prevention-oriented job crafting has on individual and organisational outcomes.

While employee characteristics such as tenure and experience were included within this analysis, it did not include other potentially important descriptive variables such as size of the organisation, employee status (i.e., the employee’s position and seniority in an organisation) and specific job type. This is potentially a limitation within the current analysis as previous research has shown that variables such as employee status (Sekiguchi et al., 2017 ) and organisational rank (Roczniewska & Puchalska-Kaminska, 2017 ) affect employee job crafting behaviour such that those in higher positions typically craft their job more frequently than those in lower positions within the organisation. Thus, this is an avenue for future research to further understand.

Last, the current meta-analysis examined how job crafting changed over time. However, the nature of work also tends to change and evolve over time, and this change may not be well captured in longitudinal research which generally does not take potential moderators or covariates into account. Thus, future research could examine why changes in job crafting may occur in relation to changes in the work environment.

5 Conclusions

The present study provides a meta-analytical review of the antecedents and outcomes in the longitudinal job crafting literature. From a theoretical standpoint, we advanced on the knowledge of job crafting by remedying methodological shortcomings that were present in several previous meta-analyses. Results from our study revealed that promotion-oriented job crafting has a moderate to strong, positive association with all antecedents and outcomes examined in this analysis, except for burnout where a weak, negative effect was found. However, in most instances, prevention-oriented job crafting was found to be very weakly associated with most of the correlates examined in longitudinal literature, although such a result could be attributed to the limited literature available on this form of job crafting. Furthermore, moderation analyses suggest that effect sizes between job crafting and its key outcomes may attenuate over time, but are unlikely to completely diminish. From a practical standpoint, practitioners may be able to utilise findings from this analysis to highlight the effectiveness of job crafting on positive work outcomes and suggest that there is promise in job crafting as a way to target employee engagement and productivity.

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Silapurem, L., Slemp, G.R. & Jarden, A. Longitudinal Job Crafting Research: A Meta-Analysis. Int J Appl Posit Psychol (2024). https://doi.org/10.1007/s41042-024-00159-0

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