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Descriptive Correlational: Descriptive vs Correlational Research

Descriptive research and Correlational research are two important types of research studies that help researchers make ambitious and measured decisions in their respective fields. Both descriptive research and correlational research are used in descriptive correlational research.

Descriptive research is defined as a research method that involves observing behavior to describe attributes objectively and systematically. A descriptive research project seeks to comprehend phenomena or groups in depth.

Correlational research , on the other hand, is a method that describes and predicts how variables are naturally related in the real world without the researcher attempting to alter them or assign causation between them.

The main objective of descriptive research is to create a snapshot of the current state of affairs, whereas correlational research helps in comparing two or more entities or variables.

What is descriptive correlational research?

Descriptive correlational research is a type of research design that tries to explain the relationship between two or more variables without making any claims about cause and effect. It includes collecting and analyzing data on at least two variables to see if there is a link between them.

In descriptive correlational research, researchers collect data to explain the variables of interest and figure out how they relate. The main goal is to give a full account of the variables and how they are related without changing them or assuming that one thing causes another.

In descriptive correlational research, researchers do not change any variables or try to find cause-and-effect connections. Instead, they just watch and measure the variables of interest and then look at the patterns and relationships that emerge from the data.

Experimental research involves the independent variable to see how it affects the dependent variable, while descriptive correlational research just describes the relationship between variables.

In descriptive correlational research, correlational research designs measure the magnitude and direction of the relationship between two or more variables, revealing their associations. At the outset creating initial equivalence between the groups or variables being compared is essential in descriptive correlational research

The independent variable occurs prior to the measurement of the measured dependent variable in descriptive correlational research. Its goal is to explain the traits or actions of a certain population or group and look at the connections between independent and dependent variables.

How are descriptive research and correlational research carried out?

Descriptive research is carried out using three methods, namely:

Case studies – Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they don’t have the capacity to make accurate predictions.
Surveys – A survey is a set of questions that is administered to a population, also known as respondents. Surveys are a popular market research tool that helps collect meaningful insights from the respondents. To gather good quality data, a survey should have good survey questions, which should be a balanced mix of open-ended and close-ended questions .
Naturalistic Observation – Naturalistic observations are carried out in the natural environment without disturbing the person/ object in observation. It is much like taking notes about people in a supermarket without letting them know. This leads to a greater validity of collected data because people are unaware they are being observed here. This tends to bring out their natural characteristics.

Correlational research also uses naturalistic observation to collect data. However, in addition, it uses archival data to gather information. Archival data is collected from previously conducted research of a similar nature. Archival data is collected through primary research.

In contrast to naturalistic observation, information collected through archived is straightforward. For example, counting the number of people named Jacinda in the United States using their social security number.

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

The key features of descriptive correlational research include the following:

01. Description

The main goal, just like with descriptive research, is to describe the variables of interest thoroughly. Researchers aim to explain a certain group or event’s traits, behaviors, or attitudes.

02. Relationships

Like correlational research, descriptive correlational research looks at how two or more factors are related. It looks at how variables are connected to each other, such as how they change over time or how they are linked.

03. Quantitative analysis

Most methods for analyzing quantitative analysis data are used in descriptive correlational research. Researchers use statistical methods to study and measure the size and direction of relationships between variables.

04. No manipulation

As with correlational research, the researcher does not change or control the variables. The data is taken in its natural environment without any changes or interference.

05. Cross-sectional or longitudinal

Cross-sectional or longitudinal designs can be used for descriptive correlational research. It collects data at one point in time, while longitudinal research collects data over a long period of time to look at changes and relationships over time.

Examples of descriptive correlational research

For example, descriptive correlational research could look at the link between a person’s age and how much money they make. The researcher would take a sample of people’s ages and incomes and then look at the data to see if there is a link between the two factors.

Example 1 : A research project is done to find out if there is a link between how long college students sleep and how well they do in school. They keep track of how many hours kids sleep each night and what their GPAs are. By studying the data, the researcher can describe how the students sleep and find out if there is a link between how long they sleep and how well they do in school.
Example 2 : A researcher wants to know how people’s exercise habits affect their physical health if they are between the ages of 40 and 60. They take notes on things like how often and how hard you work out, your body mass index (BMI), blood pressure, and cholesterol numbers. By analyzing the data, the researcher can describe the participants’ exercise habits and physical health and look for any links between these factors.
Example 3 : Let’s say a researcher wants to find out if college students who work out feel less stressed. Using a poll, the researcher finds out how many hours students spend exercising each week and how stressed they feel. By looking at the data, the researcher may find that there is a moderate negative correlation between exercise and stress levels. This means that as exercise grows, stress levels tend to go down.

Descriptive correlational research is a good way to learn about the characteristics of a population or group and the relationships between its different parts. It lets researchers describe variables in detail and look into their relationships without suggesting that one variable caused another.

Descriptive correlational research gives useful insights and can be used as a starting point for more research or to come up with hypotheses. It’s important to be aware of the problems with this type of study, such as the fact that it can’t show cause and effect and relies on cross-sectional data.

Still, descriptive correlational research helps us understand things and makes making decisions in many areas easier.

QuestionPro is a very useful tool for descriptive correlational research. Its many features and easy-to-use interface help researchers collect and study data quickly, giving them a better understanding of the characteristics and relationships between variables in a certain population or group.

The different kinds of questions, analytical research tools, and reporting features on the software improve the research process and help researchers come up with useful results. QuestionPro makes it easier to do descriptive correlational research, which makes it a useful tool for learning important things and making decisions in many fields.

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Correlational Research vs. Descriptive Research

What's the difference.

Correlational research and descriptive research are both methods used in scientific inquiry, but they differ in their objectives and approaches. Correlational research aims to examine the relationship between two or more variables and determine the strength and direction of their association. It seeks to establish whether a relationship exists, but it does not imply causation. On the other hand, descriptive research focuses on describing and documenting the characteristics or behaviors of a particular group or phenomenon. It aims to provide a detailed and accurate account of the subject under study, without attempting to establish relationships or causality. While correlational research explores connections between variables, descriptive research provides a comprehensive snapshot of a specific situation or group.

Further Detail

Introduction.

Research plays a crucial role in expanding our knowledge and understanding of various phenomena. Two common types of research methods used in social sciences are correlational research and descriptive research. While both approaches aim to gather information and provide insights, they differ in their objectives, designs, and data analysis techniques. In this article, we will explore the attributes of correlational research and descriptive research, highlighting their similarities and differences.

Correlational Research

Correlational research is a quantitative research method that aims to examine the relationship between two or more variables. It seeks to determine whether a relationship exists, the strength of the relationship, and the direction of the relationship. This type of research does not involve manipulating variables or establishing causality. Instead, it focuses on measuring and analyzing the degree of association between variables.

In correlational research, data is collected through surveys, questionnaires, observations, or existing datasets. Researchers use statistical techniques, such as correlation coefficients, to analyze the data and determine the strength and direction of the relationship. The results of correlational research can be presented in the form of scatter plots, correlation matrices, or regression analyses.

One of the key advantages of correlational research is its ability to explore relationships between variables that cannot be manipulated or controlled. For example, researchers can examine the relationship between smoking and lung cancer by collecting data from individuals without intervening in their behavior. Correlational research also allows for the examination of complex relationships involving multiple variables, providing a more comprehensive understanding of the phenomenon under investigation.

However, correlational research has limitations. It cannot establish causality, meaning that it cannot determine whether changes in one variable directly cause changes in another. Additionally, correlational research relies heavily on the quality and accuracy of the data collected. If the data is flawed or incomplete, the results may be misleading or inaccurate. Despite these limitations, correlational research remains a valuable tool for exploring relationships and generating hypotheses for further investigation.

Descriptive Research

Descriptive research, as the name suggests, aims to describe and document the characteristics, behaviors, or conditions of a particular population or phenomenon. It focuses on providing an accurate and detailed account of the subject under study without attempting to establish relationships or causality. Descriptive research is often used in the early stages of a research project to gain a better understanding of the topic or to generate hypotheses for further investigation.

Data in descriptive research is collected through various methods, including surveys, interviews, observations, or existing records. Researchers aim to collect comprehensive and representative data to ensure the accuracy and reliability of their findings. The collected data is then analyzed using descriptive statistics, such as frequencies, percentages, means, or standard deviations, to summarize and present the information in a meaningful way.

One of the main advantages of descriptive research is its ability to provide a detailed and comprehensive account of a particular phenomenon or population. It allows researchers to gather information about various aspects, such as demographics, behaviors, attitudes, or opinions, which can be useful for decision-making or policy development. Descriptive research also provides a foundation for further research by identifying gaps in knowledge or areas that require further investigation.

However, descriptive research also has limitations. It does not involve manipulation of variables or testing of hypotheses, which limits its ability to establish causality or determine the underlying mechanisms of a phenomenon. Descriptive research is also susceptible to biases and errors, such as social desirability bias or sampling errors, which can affect the accuracy and generalizability of the findings. Despite these limitations, descriptive research remains an essential tool for providing a detailed and accurate description of various phenomena.

Comparing Correlational Research and Descriptive Research

While correlational research and descriptive research have distinct objectives and designs, they also share some similarities. Both approaches are quantitative in nature, relying on the collection and analysis of numerical data. They also involve the use of statistical techniques to analyze the data and draw conclusions. Additionally, both types of research can be conducted using various data collection methods, such as surveys, questionnaires, or observations.

However, the main difference between correlational research and descriptive research lies in their objectives and focus. Correlational research aims to examine the relationship between variables, while descriptive research focuses on providing a detailed description of a particular phenomenon or population. Correlational research seeks to determine the strength and direction of the relationship, whereas descriptive research aims to document and summarize the characteristics or behaviors of the subject under study.

Another difference between the two approaches is their data analysis techniques. Correlational research involves the use of correlation coefficients or regression analyses to determine the relationship between variables. On the other hand, descriptive research relies on descriptive statistics, such as frequencies or means, to summarize and present the collected data. While both approaches use statistical techniques, the specific methods employed differ based on the research objectives.

Furthermore, correlational research and descriptive research differ in their ability to establish causality. Correlational research cannot determine causality, as it does not involve manipulation of variables or control over extraneous factors. It can only identify associations between variables. In contrast, descriptive research does not aim to establish causality and focuses solely on describing the subject under study.

Despite their differences, both correlational research and descriptive research have their own strengths and limitations. Correlational research allows for the exploration of relationships between variables that cannot be manipulated, providing valuable insights and generating hypotheses for further investigation. Descriptive research, on the other hand, provides a detailed and accurate description of a particular phenomenon or population, serving as a foundation for decision-making or further research.

Correlational research and descriptive research are two common research methods used in social sciences. While correlational research aims to examine the relationship between variables, descriptive research focuses on providing a detailed description of a particular phenomenon or population. Both approaches have their own strengths and limitations, and the choice between them depends on the research objectives and the nature of the subject under study. By understanding the attributes of correlational research and descriptive research, researchers can make informed decisions about the most appropriate method to use in their studies, ultimately contributing to the advancement of knowledge in their respective fields.

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7.2 Correlational Research

Learning objectives.

Define correlational research and give several examples.
Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.

Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.

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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12 Quantitative Descriptive and Correlational Research

Published: February 2023
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This chapter presents research designs for descriptive and correlational quantitative research. Descriptive research designs are used to address the question “What is x?” Correlational research designs are used to address the question “How are things related?” In contrast to some experimental research designs, in these design types the primary area of interest under investigation is not manipulated by the researcher. Researchers investigating descriptive or correlational research questions commonly use surveys or observational methods to gather data. Surveys are an efficient method for gathering large amounts of information about such things as individuals’ experiences, beliefs, and attitudes. When designing a survey, researchers must consider many things, such as how long it will be and what it will cover. Observation is an important means of gathering data, as when researchers observe video recordings of teachers or students in various situations. Another approach to observational research is the experience sampling method (ESM). In ESM, participants are interrupted at random times throughout the day and asked to respond to questions concerning their experiences in real time. In other words, researchers ask participants what they are doing at the moment they are contacted.

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2.2 Research Designs in Psychology

Learning objectives.

Differentiate the goals of descriptive, correlational, and experimental research designs, and explain the advantages and disadvantages of each.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. Researchers have a variety of research designs available to them in testing their predictions. A research design is the specific method a researcher uses to collect, analyze, and interpret data. Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is designed to provide a snapshot of the current state of affairs. Correlational research is designed to discover relationships among variables. Experimental research is designed to assess cause and effect. Each of the three research designs has specific strengths and limitations, and it is important to understand how each differs. See the table below for a summary.

Descriptive research: Assessing the current state of affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews four types of descriptive research: case studies, surveys and tests, naturalistic observation, and laboratory observation.

Sometimes the data in a descriptive research project are collected from only a small set of individuals, often only one person or a single small group. These research designs are known as case studies , which are descriptive records of one or more individual’s experiences and behaviour. Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics, this may include those who find themselves in particularly difficult or stressful situations. The assumption is that carefully studying individuals can give us results that tell us something about human nature. Of course, one individual cannot necessarily represent a larger group of people who were in the same circumstances.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses was interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is of Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Milton Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

Research using case studies has some unique challenges when it comes to interpreting the data. By definition, case studies are based on one or a very small number of individuals. While their situations may be unique, we cannot know how well they represent what would be found in other cases. Furthermore, the information obtained in a case study may be inaccurate or incomplete. While researchers do their best to objectively understand one case, making any generalizations to other people is problematic. Researchers can usually only speculate about cause and effect, and even then, they must do so with great caution. Case studies are particularly useful when researchers are starting out to study something about which there is not much research or as a source for generating hypotheses that can be tested using other research designs.

In other cases, the data from descriptive research projects come in the form of a survey , which is a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest. The people chosen to participate in the research, known as the sample , are selected to be representative of all the people that the researcher wishes to know about, known as the population . The representativeness of samples is enormously important. For example, a representative sample of Canadians must reflect Canada’s demographic make-up in terms of age, sex, gender orientation, socioeconomic status, ethnicity, and so on. Research based on unrepresentative samples is limited in generalizability , meaning it will not apply well to anyone who was not represented in the sample. Psychologists use surveys to measure a wide variety of behaviours, attitudes, opinions, and facts. Surveys could be used to measure the amount of exercise people get every week, eating or drinking habits, attitudes towards climate change, and so on. These days, many surveys are available online, and they tend to be aimed at a wide audience. Statistics Canada is a rich source of surveys of Canadians on a diverse array of topics. Their databases are searchable and downloadable, and many deal with topics of interest to psychologists, such as mental health, wellness, and so on. Their raw data may be used by psychologists who are able to take advantage of the fact that the data have already been collected. This is called archival research .

Related to surveys are psychological tests . These are measures developed by psychologists to assess one’s score on a psychological construct, such as extroversion, self-esteem, or aptitude for a particular career. The difference between surveys and tests is really down to what is being measured, with surveys more likely to be fact-gathering and tests more likely to provide a score on a psychological construct.

As you might imagine, respondents to surveys and psychological tests are not always accurate or truthful in their replies. Respondents may also skew their answers in the direction they think is more socially desirable or in line with what the researcher expects. Sometimes people do not have good insight into their own behaviour and are not accurate in judging themselves. Sometimes tests have built-in social desirability or lie scales that attempt to help researchers understand when someone’s scores might need to be discarded from the research because they are not accurate.

Tests and surveys are only useful if they are valid and reliable . Validity exists when an instrument actually measures what you think it measures (e.g., a test of intelligence that actually measures how many years of education you have lacks validity). Demonstrating the validity of a test or survey is the responsibility of any researcher who uses the instrument. Reliability is a related but different construct; it exists when a test or survey gives the same responses from time to time or in different situations. For example, if you took an intelligence test three times and every time it gave you a different score, that would not be a reliable test. Demonstrating the reliability of tests and surveys is another responsibility of researchers. There are different types of validity and reliability, and there is a branch of psychology devoted to understanding not only how to demonstrate that tests and surveys are valid and reliable, but also how to improve them.

An important criticism of psychological research is its reliance on so-called WEIRD samples (Henrich, Heine, & Norenzayan, 2010). WEIRD stands for Western, educated, industrialized, rich, and democratic. People fitting the WEIRD description have been over-represented in psychological research, while people from poorer, less-educated backgrounds, for example, have participated far less often. This criticism is important because in psychology we may be trying to understand something about people in general. For example, if we want to understand whether early enrichment programs can boost IQ scores later, we need to conduct this research using people from a variety of backgrounds and situations. Most of the world’s population is not WEIRD, so psychologists trying to conduct research that has broad generalizability need to expand their participant pool to include a more representative sample.

Another type of descriptive research is naturalistic observation , which refers to research based on the observation of everyday events. For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting naturalistic observation, as is a biopsychologist who observes animals in their natural habitats. Naturalistic observation is challenging because, in order for it to be accurate, the observer must be effectively invisible. Imagine walking onto a playground, armed with a clipboard and pencil to watch children a few feet away. The presence of an adult may change the way the children behave; if the children know they are being watched, they may not behave in the same ways as they would when no adult is present. Researchers conducting naturalistic observation studies have to find ways to recede into the background so that their presence does not cause the behaviour they are watching to change. They also must find ways to record their observations systematically and completely — not an easy task if you are watching children, for example. As such, it is common to have multiple observers working independently; their combined observations can provide a more accurate record of what occurred.

Sometimes, researchers conducting observational research move out of the natural world and into a laboratory. Laboratory observation allows much more control over the situation and setting in which the participants will be observed. The downside to moving into a laboratory is the potential artificiality of the setting; the participants may not behave the same way in the lab as they would in the natural world, so the behaviour that is observed may not be completely authentic. Consider the researcher who is interested in aggression in children. They might go to a school playground and record what occurs; however, this could be quite time-consuming if the frequency is low or if the children are playing some distance away and their behaviour is difficult to interpret. Instead, the researcher could construct a play setting in a laboratory and attempt to observe aggressive behaviours in this smaller and more controlled context; for instance, they could only provide one highly desirable toy instead of one for each child. What they gain in control, they lose in artificiality. In this example, the possibility for children to act differently in the lab than they would in the real world would create a challenge in interpreting results.

Correlational research: Seeking relationships among variables

In contrast to descriptive research — which is designed primarily to provide a snapshot of behaviour, attitudes, and so on — correlational research involves measuring the relationship between two variables. Variables can be behaviours, attitudes, and so on. Anything that can be measured is a potential variable. The key aspect of correlational research is that the researchers are not asking some of their participants to do one thing and others to do something else; all of the participants are providing scores on the same two variables. Correlational research is not about how an individual scores; rather, it seeks to understand the association between two things in a larger sample of people. The previous comments about the representativeness of the sample all apply in correlational research. Researchers try to find a sample that represents the population of interest.

An example of correlation research would be to measure the association between height and weight. We should expect that there is a relationship because taller people have more mass and therefore should weigh more than short people. We know from observation, however, that there are many tall, thin people just as there are many short, overweight people. In other words, we would expect that in a group of people, height and weight should be systematically related (i.e., correlated), but the degree of relatedness is not expected to be perfect. Imagine we repeated this study with samples representing different populations: elite athletes, women over 50, children under 5, and so on. We might make different predictions about the relationship between height and weight based on the characteristics of the sample. This highlights the importance of obtaining a representative sample.

Psychologists make frequent use of correlational research designs. Examples might be the association between shyness and number of Facebook friends, between age and conservatism, between time spent on social media and grades in school, and so on. Correlational research designs tend to be relatively less expensive because they are time-limited and can often be conducted without much equipment. Online survey platforms have made data collection easier than ever. Some correlational research does not even necessitate collecting data; researchers using archival data sets as described above simply download the raw data from another source. For example, suppose you were interested in whether or not height is related to the number of points scored in hockey players. You could extract data for both variables from nhl.com , the official National Hockey League website, and conduct archival research using the data that have already been collected.

Correlational research designs look for associations between variables. A statistic that measures that association is the correlation coefficient. Correlation coefficients can be either positive or negative, and they range in value from -1.0 through 0 to 1.0. The most common statistical measure is the Pearson correlation coefficient , which is symbolized by the letter r . Positive values of r (e.g., r = .54 or r = .67) indicate that the relationship is positive, whereas negative values of r (e.g., r = –.30 or r = –.72) indicate negative relationships. The closer the coefficient is to -1 or +1, and the further away from zero, the greater the size of the association between the two variables. For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Correlations of 0 indicate no relationship between the two variables.

Examples of positive correlation coefficients would include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher, or lower, on one of the variables also tend to score higher, or lower, on the other variable. Negative correlations occur when people score high on one variable and low on the other. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses and between time practising and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable. Note that the correlation coefficient does not tell you anything about one specific person’s score.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatterplot. A scatterplot is a visual image of the relationship between two variables (see Figure 2.3 ). A point is plotted for each individual at the intersection of his or her scores for the two variables. In this example, data extracted from the official National Hockey League (NHL) website of 30 randomly picked hockey players for the 2017/18 season. For each of these players, there is a dot representing player height and number of points (i.e., goals plus assists). The slope or angle of the dotted line through the middle of the scatter tells us something about the strength and direction of the correlation. In this case, the line slopes up slightly to the right, indicating a positive but small correlation. In these NHL players, there is not much of relationship between height and points. The Pearson correlation calculated for this sample is r = 0.14. It is possible that the correlation would be totally different in a different sample of players, such as a greater number, only those who played a full season, only rookies, only forwards, and so on.

For practise constructing and interpreting scatterplots, see the following:

Interactive Quiz: Positive and Negative Associations in Scatterplots (Khan Academy, 2018)

When the association between the variables on the scatterplot can be easily approximated with a straight line, the variables are said to have a linear relationship . We are only going to consider linear relationships here. Just be aware that some pairs of variables have non-linear relationships, such as the relationship between physiological arousal and performance. Both high and low arousal are associated with sub-optimal performance, shown by a U-shaped scatterplot curve.

The most important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables; in other words, we cannot know what causes what in correlational research. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. The researcher has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week as well as a measure of how aggressively each child plays on the school playground. From the data collected, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite of what has been hypothesized; perhaps children who have behaved aggressively at school are more likely to prefer violent television shows at home.

Still another possible explanation for the observed correlation is that it has been produced by a so-called third variable , one that is not part of the research hypothesis but that causes both of the observed variables and, thus, the correlation between them. In our example, a potential third variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may allow children to watch violent television and to behave aggressively in comparison to children whose parents use less different types of discipline.

To review, whenever we have a correlation that is not zero, there are three potential pathways of cause and effect that must be acknowledged. The easiest way to practise understanding this challenge is to automatically designate the two variables X and Y. It does not matter which is which. Then, think through any ways in which X might cause Y. Then, flip the direction of cause and effect, and consider how Y might cause X. Finally, and possibly the most challenging, try to think of other variables — let’s call these C — that were not part of the original correlation, which cause both X and Y. Understanding these potential explanations for correlational research is an important aspect of scientific literacy. In the above example, we have shown how X (i.e., viewing violent TV) could cause Y (i.e., aggressive behaviour), how Y could cause X, and how C (i.e., parenting) could cause both X and Y.

Test your understanding with each example below. Find three different interpretations of cause and effect using the procedure outlined above. In each case, identify variables X, Y, and C:

A positive correlation between dark chocolate consumption and health
A negative correlation between sleep and smartphone use
A positive correlation between children’s aggressiveness and time spent playing video games
A negative association between time spent exercising and consumption of junk food

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible or when fewer resources are available. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. We can also use correlational designs to make predictions, such as predicting the success of job trainees based on their test scores during training. They are also excellent sources of suggested avenues for further research, but we cannot use such correlational information to understand cause and effect. For that, researchers rely on experiments.

Experimental research: Understanding the causes of behaviour

The goal of experimental research design is to provide definitive conclusions about the causal relationships among the variables in the research hypothesis. In an experimental research design, there are independent variables and dependent variables. The independent variable is the one manipulated by the researchers so that there is more than one condition. The dependent variable is the outcome or score on the measure of interest that is dependent on the actions of the independent variable. Let’s consider a classic drug study to illustrate the relationship between independent and dependent variables. To begin, a sample of people with a medical condition are randomly assigned to one of two conditions. In one condition, they are given a drug over a period of time. In the other condition, a placebo is given for the same period of time. To be clear, a placebo is a type of medication that looks like the real thing but is actually chemically inert, sometimes referred to as a”sugar pill.” After the testing period, the groups are compared to see if the drug condition shows better improvement in health than the placebo condition.

While the basic design of experiments is quite simple, the success of experimental research rests on meeting a number of criteria. Some important criteria are:

Participants must be randomly assigned to the conditions so that there are no differences between the groups. In the drug study example, you could not assign the males to the drug condition and the females to the placebo condition. The groups must be demographically equivalent.
There must be a control condition. Having a condition that does not receive treatment allows experimenters to compare the results of the drug to the results of placebo.
The only thing that can change between the conditions is the independent variable. For example, the participants in the drug study should receive the medication at the same place, from the same person, at the same time, and so on, for both conditions. Experiments often employ double-blind procedures in which neither the experimenter nor the participants know which condition any participant is in during the experiment. In a single-blind procedure, the participants do not know which condition they are in.
The sample size has to be large and diverse enough to represent the population of interest. For example, a pharmaceutical company should not use only men in their drug study if the drug will eventually be prescribed to women as well.
Experimenter effects should be minimized. This means that if there is a difference in scores on the dependent variable, they should not be attributable to something the experimenter did or did not do. For example, if an experiment involved comparing a yoga condition with an exercise condition, experimenters would need to make sure that they treated the participants exactly the same in each condition. They would need to control the amount of time they spent with the participants, how much they interacted verbally, smiled at the participants, and so on. Experimenters often employ research assistants who are blind to the participants’ condition to interact with the participants.

As you can probably see, much of experimental design is about control. The experimenters have a high degree of control over who does what. All of this tight control is to try to ensure that if there is a difference between the different levels of the independent variable, it is detectable. In other words, if there is even a small difference between a drug and placebo, it is detected. Furthermore, this level of control is aimed at ensuring that the only difference between conditions is the one the experimenters are testing while making correct and accurate determinations about cause and effect.

Research Focus

Video games and aggression

Consider an experiment conducted by Craig Anderson and Karen Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (e.g., Wolfenstein 3D) or a nonviolent video game (e.g., Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (i.e., aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown below (see Figure 2.4 ).

There are two strong advantages of the experimental research design. First, there is assurance that the independent variable, also known as the experimental manipulation , occurs prior to the measured dependent variable; second, there is creation of initial equivalence between the conditions of the experiment, which is made possible by using random assignment to conditions.

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table. Anderson and Dill first randomly assigned about 100 participants to each of their two groups: Group A and Group B. Since they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation; they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then, they compared the dependent variable (i.e., the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable, and not some other variable, that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Sometimes, experimental research has a confound. A confound is a variable that has slipped unwanted into the research and potentially caused the results because it has created a systematic difference between the levels of the independent variable. In other words, the confound caused the results, not the independent variable. For example, suppose you were a researcher who wanted to know if eating sugar just before an exam was beneficial. You obtain a large sample of students, divide them randomly into two groups, give everyone the same material to study, and then give half of the sample a chocolate bar containing high levels of sugar and the other half a glass of water before they write their test. Lo and behold, you find the chocolate bar group does better. However, the chocolate bar also contains caffeine, fat and other ingredients. These other substances besides sugar are potential confounds; for example, perhaps caffeine rather than sugar caused the group to perform better. Confounds introduce a systematic difference between levels of the independent variable such that it is impossible to distinguish between effects due to the independent variable and effects due to the confound.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Do people act the same in a laboratory as they do in real life? Often researchers are forced to balance the need for experimental control with the use of laboratory conditions that can only approximate real life.

Additionally, it is very important to understand that many of the variables that psychologists are interested in are not things that can be manipulated experimentally. For example, psychologists interested in sex differences cannot randomly assign participants to be men or women. If a researcher wants to know if early attachments to parents are important for the development of empathy, or in the formation of adult romantic relationships, the participants cannot be randomly assigned to childhood attachments. Thus, a large number of human characteristics cannot be manipulated or assigned. This means that research may look experimental because it has different conditions (e.g., men or women, rich or poor, highly intelligent or not so intelligent, etc.); however, it is quasi-experimental . The challenge in interpreting quasi-experimental research is that the inability to randomly assign the participants to condition results in uncertainty about cause and effect. For example, if you find that men and women differ in some ability, it could be biology that is the cause, but it is equally likely it could be the societal experience of being male or female that is responsible.

Of particular note, while experiments are the gold standard for understanding cause and effect, a large proportion of psychology research is not experimental for a variety of practical and ethical reasons.

Key Takeaways

Descriptive, correlational, and experimental research designs are used to collect and analyze data.
Descriptive designs include case studies, surveys, psychological tests, naturalistic observation, and laboratory observation. The goal of these designs is to get a picture of the participants’ current thoughts, feelings, or behaviours.
Correlational research designs measure the relationship between two or more variables. The variables may be presented on a scatterplot to visually show the relationships. The Pearson correlation coefficient is a measure of the strength of linear relationship between two variables. Correlations have three potential pathways for interpreting cause and effect.
Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Done correctly, experiments allow researchers to make conclusions about cause and effect. There are a number of criteria that must be met in experimental design. Not everything can be studied experimentally, and laboratory experiments may not replicate real-life conditions well.

Exercises and Critical Thinking

There is a negative correlation between how close students sit to the front of the classroom and their final grade in the class. Explain some possible reasons for this.
Imagine you are tasked with creating a survey of online habits of Canadian teenagers. What questions would you ask and why? How valid and reliable would your test be?
Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 2.2. This Might Be Me in a Few Years by Frank Kovalchek is used under a CC BY 2.0 license.

Figure 2.3. Used under a CC BY-NC-SA 4.0 license.

Figure 2.4. Used under a CC BY-NC-SA 4.0 license.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Henrich, J., Heine, S. J., & Norenzaya, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 , 61–83.

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.) . Mountain View, CA: Cengage.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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Muhammad Hassan

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v.12(1); 2020 Jan

Observational Study Designs: Synopsis for Selecting an Appropriate Study Design

Assad a rezigalla.

1 Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha, SAU

The selection of a study design is the most critical step in the research methodology. Crucial factors should be considered during the selection of the study design, which is the formulated research question, as well as the method of participant selection. Different study designs can be applied to the same research question(s). Research designs are classified as qualitative, quantitative, and mixed design. Observational design occupies the middle and lower parts of the hierarchy of evidence-based pyramid. The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the selection of descriptive study design.

Introduction and background

A research design is defined as the “set up to decide on, among other issues, how to collect further data, analyze and interpret them, and finally, to provide an answer to the question” [ 1 ]. The primary objective of a research design is to guarantee that the collected evidence allows the answering of the initial question(s) as clearly as possible [ 2 ]. Various study designs have been described in the literature [ 1 - 3 ]. Each of them deals with the specific type of research or research questions and has points of strength and weakness. Broadly, research designs are classified into qualitative and quantitative research and mixed methods [ 3 ]. The quantitative study design is subdivided into descriptive versus analytical study designs or as observational versus interventional (Figure (Figure1). 1 ). Descriptive designs occupy the middle and lower parts of the hierarchy of evidence-based medicine pyramid. Study designs are organized in a hierarchy beginning from the basic "case report" to the highly valued "randomised clinical trial" [ 4 - 5 ].

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Case report

The case report describes an individual case or cases in their natural settings. Also, it describes unrecognized syndromes or variants, abnormal findings or outcomes, or association between risk factors and disease. It is the lowest level and the first line of evidence and usually deals with the newly emerging issues and ideas (Table (Table1) 1 ) [ 4 , 6 - 10 ].

Case series

A case series is a report on data from a subject group (multiple patients) without control [ 6 , 11 - 12 ]. Commonly, this design is used for the illustration of novel, unusual, or atypical features identified in medical practice [ 6 ]. The investigator is governed by the availability and accuracy of the records, which can cause biases [ 13 - 14 ]. Bias in a case series can be decreased through consecutive patient enrollment and predefined inclusion and exclusion criteria, explicit specification of study duration, and enrollment of participants (Table 2 ) [ 11 - 12 ].

Correlational study design

Correlational studies (ecologic studies) explore the statistical relationships between the outcome of interest in population and estimate the exposures. It deals with the community rather than in individual cases. The correlational study design can compare two or more relevant variables and reports the association between them without controlling the variables. The aim of correlational study design or research is to uncover any types of systematic relationships between the studied variables. Ecological studies are often used to measure the prevalence and incidence of disease, mainly when the disease is rare. The populations compared can be defined in several ways, such as geographical, time trends, migrants, longitudinal, occupation, and social class. It should be considered that in ecological studies, the results are presented at the population (group) level rather than individuals. Ecological studies do not provide information about the degree or extent of exposure or outcome of interest for particular individuals within the study group (Table 3 ) [ 7 , 15 - 16 ]. For example, we do not know whether those individuals who died in the study group under observation had higher exposure than those remained alive.

Cross-sectional study design

The cross-sectional study examines the association between exposures and outcomes on a snap of time. The assessed associations are guided by sound hypotheses and seen as hypothesis-generating [ 17 ]. This design can be descriptive (when dealing with prevalence or survey) or analytic (when comparing groups) [ 17 - 18 ]. The selection of participants in a cross-sectional study design depends on the predefined inclusion and exclusion criteria [ 18 - 19 ]. This method of selection limits randomization (Table 4 ).

Case-control study

A case-control study is an observational analytic retrospective study design [ 12 ]. It starts with the outcome of interest (referred to as cases) and looks back in time for exposures that likely caused the outcome of interest [ 13 , 20 ]. This design compares two groups of participants - those with the outcome of interest and the matched control [ 12 ]. The controls should match the group of interest in most of the aspects, except for the outcome of interest [ 18 ]. The controls should be selected from the same localization or setting of the cases [ 13 , 21 - 22 ]. Case-control studies can determine the relative importance of a predictor variable about the presence or absence of the disease (Table (Table5 5 ).

Cohort study design

The cohort study design is classified as an observational analytic study design. This design compares two groups, with exposure of interest and control one [ 12 , 18 , 22 - 24 ].

Cohort design starts with exposure of interest comparing them to non-exposed participants at the time of study initiation [ 18 , 22 , 24 ]. The non-exposed serve as external control. A cohort design can be either prospective [ 18 ] or retrospective [ 12 , 20 , 24 - 25 ]. In prospective cohort studies, the investigator measures a variety of variables that might be a risk factor or relevant to the development of the outcome of interest. Over time, the participants are observed to detect whether they develop the outcome of interest or not. In this case, the participants who do not develop the outcome of interest can act as internal controls. Retrospective cohort studies use data records that were documented for other purposes. The study duration may vary according to the commencement of data recording. Completion of the study is limited to the analysis of the data [ 18 , 22 , 24 ]. In 2016, Setia reported that, in some instances, cohort design could not be well-defined as prospective or retrospective; this happened when retrospective and prospective data were collected from the same participants (Table (Table6) 6 ) [ 24 ].

The selection of the study design is the most critical step in research methodology [ 4 , 26 ]. An appropriate study design guarantees the achievement of the research objectives. The crucial factors that should be considered in the selection of the study design are the formulated research question, as well as the method of sampling [ 4 , 27 ]. The study design determines the way of sampling and data analysis [ 4 ]. The selection of a research study design depends on many factors. Two crucial points that should be noted during the process selection include different study designs that may be applicable for the same research question(s) and researches may have grey areas in which they have different views about the type of study design [ 4 ].

Conclusions

The selection of appropriate study designs for research is critical. Many research designs can apply to the same research. Appropriate selection guarantees that the author will achieve the research objectives and address the research questions.

Acknowledgments

The author would like to acknowledge Dr. M. Abass, Dr. I. Eljack, Dr. K. Salih, Dr. I. Jack, and my colleagues. Special thanks and appreciation to the college dean and administration of the College of Medicine, University of Bisha (Bisha, Saudi Arabia) for help and allowing the use of facilities.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how questions , but not why questions.

A descriptive research design can use a wide variety of research methods to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

How has the London housing market changed over the past 20 years?
Do customers of company X prefer product Y or product Z?
What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
What are the most popular online news sources among under-18s?
How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

Describing the demographics of a country or region
Gauging public opinion on political and social topics
Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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Module 1: Introduction to Psychology & Psychology Research

Descriptive research, what you’ll learn to do: describe the strengths and weaknesses of descriptive, experimental, and correlational research.

Three researchers review data while talking around a microscope.

If you think about the vast array of fields and topics covered in psychology, you understand that in order to do psychological research, there must be a diverse set of ways to gather data and perform experiments. For example, a biological psychologist might work predominately in a lab setting or alongside a neurologist. A social scientist may set up situational experiments, a health psychologist may administer surveys, and a developmental psychologist may make observations in a classroom. In this section, you’ll learn about the various types of research methods that psychologists employ to learn about human behavior.

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.

More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the link HERE (or on the link below) 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.

Learning Objectives

Differentiate between descriptive, experimental, and correlational 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

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 case studies, naturalistic observation, and surveys.

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.

Link to Learning

To learn more about Krista and Tatiana, watch this New York Times video about their lives.

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.

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 chapter: 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).

A photograph shows two police cars driving, one with its lights flashing.

Figure 1. Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

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 1).

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 2). 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).

(a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.

Figure 2. (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

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 chapter 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.

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 3). 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.

A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”

Figure 3. Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)

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

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students (Figure 1).

(a) A photograph shows stacks of paper files on shelves. (b) A photograph shows a computer.

Figure 1. A researcher doing archival research examines records, whether archived as a (a) hardcopy or (b) electronically. (credit “paper files”: modification of work by “Newtown graffiti”/Flickr; “computer”: modification of work by INPIVIC Family/Flickr)

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 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 2).

A photograph shows pack of cigarettes and cigarettes in an ashtray. The pack of cigarettes reads, “Surgeon general’s warning: smoking causes lung cancer, heart disease, emphysema, and may complicate pregnancy.”

Figure 2. Longitudinal research like the CPS-3 help us to better understand how smoking is associated with cancer and other diseases. (credit: CDC/Debora Cartagena)

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.

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Descriptive Research and Qualitative Research

Cite this chapter.

Eunsook T. Koh 2 &
Willis L. Owen 2

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Descriptive research is a study of status and is widely used in education, nutrition, epidemiology, and the behavioral sciences. Its value is based on the premise that problems can be solved and practices improved through observation, analysis, and description. The most common descriptive research method is the survey, which includes questionnaires, personal interviews, phone surveys, and normative surveys. Developmental research is also descriptive. Through cross-sectional and longitudinal studies, researchers investigate the interaction of diet (e.g., fat and its sources, fiber and its sources, etc.) and life styles (e.g., smoking, alcohol drinking, etc.) and of disease (e.g., cancer, coronary heart disease) development. Observational research and correlational studies constitute other forms of descriptive research. Correlational studies determine and analyze relationships between variables as well as generate predictions. Descriptive research generates data, both qualitative and quantitative, that define the state of nature at a point in time. This chapter discusses some characteristics and basic procedures of the various types of descriptive research.

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Koh, E.T., Owen, W.L. (2000). Descriptive Research and Qualitative Research. In: Introduction to Nutrition and Health Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1401-5_12

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Differences Between Descriptive Research and Correlational Research
Descriptive Correlational: Descriptive vs correlational research
Descriptive Correlational: Descriptive vs correlational research
Descriptive Correlational: Descriptive vs correlational research
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What Is a Correlational Study And Examples of correlational research

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Correlational Research Notes
REVISION LESSON 5 ON CORRELATION: || DOES CORRELATION DETERMINE CAUSALITY? WHAT IS THE DIFFERENCE?
The three types of research methods #reseach #study
Correlation vs Regression: What's the difference?
Descriptive, Correlational, Explanatory and Exploratory Research/ Types of Research-3/ NPA Teaching
Correlational Research Titles (Quantitative Research)

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Descriptive Correlational: Descriptive vs Correlational Research
Purpose. Descriptive research is used to uncover new facts and the meaning of research. Correlational research is carried out to measure two variables. Nature. Descriptive research is analytical, where in-depth studies help collect information during research. Correlational nature is mathematical in nature.
Correlational Research vs. Descriptive Research
Correlational research aims to examine the relationship between two or more variables and determine the strength and direction of their association. It seeks to establish whether a relationship exists, but it does not imply causation. On the other hand, descriptive research focuses on describing and documenting the characteristics or behaviors ...
2.2 Psychologists Use Descriptive, Correlational, and Experimental
Descriptive, correlational, and experimental research designs are used to collect and analyze data. Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people.
Descriptive Research
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables. Unlike in experimental research, the researcher does ...
3.2 Psychologists Use Descriptive, Correlational, and Experimental
Correlational Research: Seeking Relationships among Variables. In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables.
Correlational Research
Revised on June 22, 2023. A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them. A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.
Types of Research Designs Compared
You can also create a mixed methods research design that has elements of both. Descriptive research vs experimental research. Descriptive research gathers data without controlling any variables, while experimental research manipulates and controls variables to determine cause and effect.
Study designs: Part 2
INTRODUCTION. In our previous article in this series, [ 1] we introduced the concept of "study designs"- as "the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.". Study designs are primarily of two types - observational and interventional, with the former being ...
7.2 Correlational Research
Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between ...
12 Quantitative Descriptive and Correlational Research
Researchers investigating descriptive or correlational research questions commonly use surveys or observational methods to gather data. Surveys are an efficient method for gathering large amounts of information about such things as individuals' experiences, beliefs, and attitudes. When designing a survey, researchers must consider many things ...
Correlational Research
A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them. A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative. Positive correlation.
2.2 Research Designs in Psychology
Correlational research is designed to discover relationships among variables. Experimental research is designed to assess cause and effect. Each of the three research designs has specific strengths and limitations, and it is important to understand how each differs. See the table below for a summary. Table 2.2.
Chapter 12 Methods for Correlational Studies
Correlational studies aim to find out if there are differences in the characteristics of a population depending on whether or not its subjects have been exposed to an event of interest in the naturalistic setting. In eHealth, correlational studies are often used to determine whether the use of an eHealth system is associated with a particular set of user characteristics and/or quality of care ...
Descriptive/Correlational Research
Correlation, a statistical measure of a relationship between two or more variables, gives an indication of how one variable may predict another. The descriptive techniques discussed above permit a statement, in the form of correlations, about that relationship. However, correlation does not imply causation; that is, simply because two events ...
Descriptive Research Design
Correlational Research. This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables. ... Health research: Descriptive research design is ...
Observational Study Designs: Synopsis for Selecting an Appropriate
The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the ...
Descriptive Research Design
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when, and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables. Unlike in experimental research, the researcher does ...
The 3 Descriptive Research Methods of Psychology
Descriptive research may be an initial step before the other two types of psychological research are conducted: Correlational ... et al. (2015). Qualitative and descriptive research: Data type vs ...
Descriptive Research
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 ...
Descriptive Research and Qualitative Research
Correlational studies determine and analyze relationships between variables as well as generate predictions. Descriptive research generates data, both qualitative and quantitative, that define the state of nature at a point in time. This chapter discusses some characteristics and basic procedures of the various types of descriptive research.
Types of Research Questions: Descriptive, Predictive, or Causal
A previous Evidence in Practice article explained why a specific and answerable research question is important for clinicians and researchers. Determining whether a study aims to answer a descriptive, predictive, or causal question should be one of the first things a reader does when reading an article. Any type of question can be relevant and useful to support evidence-based practice, but ...
Correlation vs. Causation
Revised on June 22, 2023. Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable. In research, you might have come across the phrase "correlation doesn't imply causation.". Correlation and causation are two related ideas, but understanding ...
Differences Between Descriptive Research and Correlational ...
This video is about differences between descriptive research and correlational research. Watch till end. @Learneveryone @ThesisHelper01 @TheKnowledge @k...

Descriptive Correlational: Descriptive vs Correlational Research

What is descriptive correlational research?

How are descriptive research and correlational research carried out?

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

01. Description

02. Relationships

03. Quantitative analysis

04. No manipulation

05. Cross-sectional or longitudinal

Examples of descriptive correlational research

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Correlational Research

Descriptive Research

Comparing Correlational Research and Descriptive Research

7.2 Correlational Research

What Is Correlational Research?

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12 Quantitative Descriptive and Correlational Research

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2.2 Research Designs in Psychology

Descriptive research: Assessing the current state of affairs

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Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design

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