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• Operationalisation

Last updated 22 Mar 2021

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This term describes when a variable is defined by the researcher and a way of measuring that variable is developed for the research.

This is not always easy and care must be taken to ensure that the method of measurement gives a valid measure for the variable.

The term operationalisation can be applied to independent variables (IV), dependent variables (DV) or co variables (in a correlational design)

Examples of operationalised variables are given in the table below:

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What is operationalization?

Last updated

5 February 2023

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Operationalization is the process of turning abstract concepts or ideas into observable and measurable phenomena. This process is often used in the social sciences to quantify vague or intangible concepts and study them more effectively. Examples are emotions and attitudes.

In this article, we will look at operationalization’s definition, benefits, and limitations. We will also provide a step-by-step guide on how to operationalize a concept, including examples and tips for choosing appropriate indicators.

• Defining operationalization

Operationalization is the process of defining abstract concepts in a way that makes them observable and measurable.

For example, suppose a researcher wants to study the concept of anxiety. They might operationalize it by measuring anxiety levels using a standardized questionnaire or by observing physiological changes, like increased heart rate.

Operationalization is mainly a social sciences tool that is applied in many other disciplines. It allows many unquantifiable concepts in these fields to be directly measured, enabling researchers to study and understand them with more accuracy.

• Why does operationalization matter?

As a qualitative researcher, accurately defining the types of variables you intend to study is vital. Transparent and specific operational definitions can help you measure relevant concepts and apply methods consistently.

Here are a few reasons why operationalization matters:

Improved reliability and validity. Researchers can ensure that their results are more reliable and valid when they clearly define and measure variables. This is especially important when comparing results from different studies, as it gives researchers confidence that they are measuring the same thing.

Enhanced objectivity: Operationalization helps reduce subjectivity in research by providing clear guidelines for measuring variables. This can help minimize bias and lead to more objective results.

Better decision-making. Operationalization allows researchers to collect and analyze quantifiable data . This can be useful for making informed decisions in various settings. For example, operationalization can be used to assess group or individual performance in the workplace, leading to improved productivity and execution.

Enhanced understanding of abstract concepts. Operationalizing abstract concepts helps researchers study and understand them more effectively. This can lead to new insights and a deeper understanding of complex phenomena.

Operationalization can reduce the possibility of research bias, minimize subjectivity, and enhance a study’s reliability.

• How to operationalize concepts

Researchers can operationalize abstract concepts in different ways. They will need to measure slightly varying aspects of a concept, so they must be specific about what they are measuring.

Testing a hypothesis using multiple operationalizations of an abstract concept allows you to analyze whether the results depend on the measure type you use. Your results will be labeled “robust” if there’s a lack of variance when using different measures.

The three main steps of operationalization are:

1. Identifying the main concepts you are interested in studying

Begin by defining your research topic and proposing an initial research question . For example, “What effects does daily social media use have on young teenagers’ attention spans?” Here, the main concepts are social media use and attention span.

2. Choosing variables to represent each concept

Each main concept will typically have several measurable properties or variables that can be used to represent it.

For example, the concept of social media use has the following variables:

Number of hours spent

Frequency of use

Preferred social media platform

The concept of attention span has the following variables:

Quality of attention

Amount of attention span

You can find additional variables to use in your study. Consider reviewing previous related studies and identifying underused or relevant variables to fill gaps in the existing literature.

3. Select indicators to measure your variables

Indicators are specific methods or tools used to numerically measure variables. There are two main types of indicators: objective and subjective.

Objective indicators are based on external, observable data, such as scores on a standardized test. You might use a standardized attention span test to measure the variable “amount of attention span.”

Subjective indicators are based on self-reported data, such as questionnaire responses. You might use a self-report questionnaire to measure the variable “quality of attention.”

Choose indicators that are appropriate for the variables you are studying that will provide accurate and reliable data.

Once you have operationalized your concepts, report your study variables and indicators in the methodology section. Evaluate how your operationalization choice may have impacted your results or interpretations under the discussion section.

• Strengths of operationalization

Operationalizing concepts in research allows you to measure variables across various contexts consistently. Below are the strengths of operationalization for your research purposes:

Objectivity

Data collection using a standardized approach reduces the chance and opportunity for biased or subjective observation interpretation. Operationalization provides clear guidelines for measuring variables, which allows you to interpret observations objectively.

Scientific research relies on observable and measurable findings. Operationalization breaks down abstract, unmeasurable concepts into observable and measurable elements.

Reliability

A good operationalization increases high replicability odds by other researchers. Clearly defining and measuring variables helps you ensure your results are reliable and valid. This is especially important when comparing results from different studies, as it gives you confidence that you’re measuring the same thing.

Better decision-making

Operationalization allows researchers to collect and analyze quantifiable data. It can aid informed decision-making in various settings. For example, operationalization can be used to assess group or individual performance in the workplace, leading to improved productivity and performance.

• Limitations of operationalization

Operationalization has many benefits, but it also has some limitations that researchers should be aware of:

Measurement error

Operationalization relies on the use of indicators to measure variables. These can be subject to measurement errors. For example, response bias can occur with self-reported questionnaires, and the concept being measured may not be accurately captured.

The Mars Climate Orbiter failure is an example of the effects of measurement errors. The expensive satellite disappeared somewhere above Mars, leading to a critical mission failure.

The failure occurred because of a massive error in the thrust force calculation. Engineering teams used different standardized measurements (metric and imperial) in their calculations. This non-standardization of units resulted in the loss of hundreds of millions of dollars and several wasted years of planning and construction.

Limited scope

Operationalization is limited to the specific variables and indicators chosen by the researcher. This issue is further compounded by the fact that concepts generally vary across different time periods and social settings. This means that certain aspects of a concept may be overlooked or captured inaccurately.

Reductiveness

It is relatively easy for operational definitions to miss valuable and subjective concept perceptions by attempting to simplify complex concepts to mere numbers.

Careful consideration is necessary

Researchers must carefully consider their operational definitions and choose appropriate indicators to measure their variables accurately. Failing to do so can lead to inaccurate or misleading results.

For instance, context-specific operationalization can validate real-life experiences. On the other hand, it becomes challenging to compare studies in case the measures vary greatly.

• Examples of operationalization

Operationalization is used to convert abstract concepts into observable and measurable traits.

For example, the concept of social anxiety is virtually impossible to measure directly, but you can operationalize it in different ways.

Using a social anxiety scale to self-rate scores is one such way. You can also measure the total incidents of recent behavioral occurrences related to avoiding crowded places. Observing and measuring the levels of physical anxiety symptoms in almost any social situation is another option.

The following are more examples of how researchers might operationalize different concepts:

Concept: happiness

Variables: life satisfaction, positive emotions, negative emotions

Indicators: self-report questionnaire, daily mood diary, facial expression analysis

Concept: intelligence

Variables: verbal ability, spatial ability, memory

Indicators: standardized intelligence test, reaction time tasks, memory tests

Concept: parenting styles

Variables: authoritative, authoritarian, permissive, neglectful

Indicators: parenting style questionnaire, observations of parent–child interactions, parent-reported child behavior

Operationalization can also be used to conduct research in a typical workplace setting.

• Applications of operationalization

Operationalization can be applied in a range of situations, including research studies, workplace performance assessments, and decision-making processes.

Here are a few examples of how operationalization might be used in different settings:

Research studies: It is commonly used in research studies to define and measure variables systematically and objectively. This allows researchers to collect and analyze quantifiable data that can be used to answer research questions and test hypotheses.

Workplace performance assessments: Operationalization can be used to assess group or individual performance in the workplace by defining and measuring relevant variables such as productivity, efficiency, and teamwork. This can help identify areas for improvement and increase overall workplace performance.

Decision-making processes: It can aid informed decision-making in various settings by defining and measuring relevant variables. For example, a business might use operationalization to compare the costs and benefits of different marketing strategies or to assess the effectiveness of employee training programs.

Business: Operationalization can be used in business settings to assess the performance of employees, departments, or entire organizations. It can also be used to measure the effectiveness of business processes or strategies, such as customer satisfaction or marketing campaigns.

Health: It can be used in the health field to define and measure variables such as disease prevalence, treatment effectiveness, and patient satisfaction. Personnel and organizational performance can also be measured through operationalization.

Education: Operationalization can be used in education settings to define and measure variables such as student achievement, teacher effectiveness, or school performance. It can also be used to assess the effectiveness of educational programs or interventions.

By defining and measuring variables in a systematic and objective way, operationalization can help researchers and professionals make more informed decisions, improve performance, and better understand complex concepts.

What is the process of operationalization in research?

Operationalization is the process of defining abstract concepts through measurable observations and quantifiable data. It involves identifying the main concepts you are interested in studying, choosing variables to represent each concept, and selecting indicators to measure those variables.

Operationalization helps researchers study abstract concepts in a more systematic and objective way, improving the reliability and validity of their research and reducing subjectivity and bias.

What does it mean to operationalize a variable?

Operationalizing a variable involves clearly defining and measuring it in a way that allows researchers to collect and analyze quantifiable data.

It typically involves selecting indicators to measure the variable and determining how the data will be interpreted.

Operationalization helps researchers measure variables with more accuracy and consistency, improving the reliability and validity of their research.

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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Operationalization

Operationalization is the process of strictly defining variables into measurable factors. The process defines fuzzy concepts and allows them to be measured, empirically and quantitatively.

This article is a part of the guide:

• Null Hypothesis
• Research Hypothesis
• Defining a Research Problem
• Selecting Method

Browse Full Outline

• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

For experimental research , where interval or ratio measurements are used, the scales are usually well defined and strict.

Operationalization also sets down exact definitions of each variable, increasing the quality of the results, and improving the robustness of the design .

For many fields, such as social science, which often use ordinal measurements, operationalization is essential. It determines how the researchers are going to measure an emotion or concept, such as the level of distress or aggression.

Such measurements are arbitrary, but allow others to replicate the research, as well as perform statistical analysis of the results.

Fuzzy Concepts

Fuzzy concepts are vague ideas, concepts that lack clarity or are only partially true. These are often referred to as " conceptual variables ".

It is important to define the variables to facilitate accurate replication of the research process . For example, a scientist might propose the hypothesis :

“Children grow more quickly if they eat vegetables.”

What does the statement mean by 'children'? Are they from America or Africa. What age are they? Are the children boys or girls? There are billions of children in the world, so how do you define the sample groups?

How is 'growth' defined? Is it weight, height, mental growth or strength? The statement does not strictly define the measurable, dependent variable .

What does the term 'more quickly' mean? What units, and what timescale, will be used to measure this? A short-term experiment, lasting one month, may give wildly different results than a longer-term study.

The frequency of sampling is important for operationalization , too.

If you were conducting the experiment over one year, it would not be practical to test the weight every 5 minutes, or even every month. The first is impractical, and the latter will not generate enough analyzable data points.

What are 'vegetables'? There are hundreds of different types of vegetable, each containing different levels of vitamins and minerals. Are the children fed raw vegetables, or are they cooked? How does the researcher standardize diets, and ensure that the children eat their greens?

The above hypothesis is not a bad statement, but it needs clarifying and strengthening, a process called operationalization.

The researcher could narrow down the range of children, by specifying age, sex, nationality, or a combination of attributes. As long as the sample group is representative of the wider group, then the statement is more clearly defined.

Growth may be defined as height or weight. The researcher must select a definable and measurable variable, which will form part of the research problem and hypothesis.

Again, 'more quickly' would be redefined as a period of time, and stipulate the frequency of sampling. The initial research design could specify three months or one year, giving a reasonable time scale and taking into account time and budget restraints.

Each sample group could be fed the same diet, or different combinations of vegetables. The researcher might decide that the hypothesis could revolve around vitamin C intake, so the vegetables could be analyzed for the average vitamin content.

Alternatively, a researcher might decide to use an ordinal scale of measurement, asking subjects to fill in a questionnaire about their dietary habits.

Already, the fuzzy concept has undergone a period of operationalization, and the hypothesis takes on a testable format.

The Importance of Operationalization

Of course, strictly speaking, concepts such as seconds, kilograms and centigrade are artificial constructs, a way in which we define variables.

Pounds and Fahrenheit are no less accurate, but were jettisoned in favor of the metric system. A researcher must justify their scale of scientific measurement .

Operationalization defines the exact measuring method used, and allows other scientists to follow exactly the same methodology. One example of the dangers of non-operationalization is the failure of the Mars Climate Orbiter .

This expensive satellite was lost, somewhere above Mars, and the mission completely failed. Subsequent investigation found that the engineers at the sub-contractor, Lockheed, had used imperial units instead of metric units of force.

A failure in operationalization meant that the units used during the construction and simulations were not standardized. The US engineers used pound force, the other engineers and software designers, correctly, used metric Newtons.

This led to a huge error in the thrust calculations, and the spacecraft ended up in a lower orbit around Mars, burning up from atmospheric friction. This failure in operationalization cost hundreds of millions of dollars, and years of planning and construction were wasted.

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Martyn Shuttleworth (Jan 17, 2008). Operationalization. Retrieved May 05, 2024 from Explorable.com: https://explorable.com/operationalization

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Updated June 2020

Writing good hypotheses in IB Psychology IAs is something many students find challenging. After moderating another 175+ IA’s this year I could see some common errors students were making. This post hopes to give a clear explanation with examples to help with this tricky task. 

Null and Alternative Hypotheses

Null hypothesis (h0).

Our teacher support pack has everything students and teachers need to get top marks in the IA. Download a Free preview from https://store.themantic-education.com/

The term “null” means having no value, significance or effect. It also refers to something associated with zero. A null hypothesis in a student’s IA, therefore, should state that there is (or will be) no effect of the IV on the DV. This is what we assume to be true until we have the evidence to suggest otherwise.

A common misconception is that the hypothesis is based on the sample in the study. Our hypotheses should actually be about the population from which we’ve drawn the sample, not the sample itself. Therefore, when writing our hypotheses we can use present tense instead of future tense (e.g. There is instead of There will be… ).

Having said that, in the IB Psych’ IA, the IB is apparently assuming the hypotheses are based on the sample (because variables need to be operationalized) so writing your hypotheses as predictions of what might happen in the experiment is fine (see below for examples).

IB Psych IA Tip: It’s fine (and even recommended) to state in your null hypotheses that there will be no significant difference between the two conditions in your experiment or any differences are due to chance (see footnote 1)

The Alternative Hypothesis (H1)

This is also referred to as the research hypothesis or the experimental hypothesis. It’s an alternative hypothesis to the null because if the null is not true, there must be an alternative explanation.

Generally speaking it’s not a prediction of what will happen in the study, but it’s an assumption about what is true for the population being studied. But, similar to the null hypothesis in the IB Psych IA you can (and should) write this about a prediction of what you think will happen in your study (see examples below).

This must be operationalized: it must be evident how the variables will be quantified, and may be either one- or two-tailed (directional or non-directional).

Read more: 

Operational Definitions

• Key Studies for the IA
• Lesson Idea: Inferential Statistics

To avoid issues with copying and plagiarism, the following examples are from studies that students cannot do for the internal assessment. Some are taken from this post on how to operationalize definitions of variables .

A Fictional Drug Trial

• H1: Taking Paroxetine  will decrease symptoms of PTSD.
• Ho: Taking paroxetine will not decrease symptoms of PTSD.

Operationalized (as if for an IB Psych IA):

• H1: The experimental group who take 20mg of Paroxetine (as a pill) every morning for 7 days will have a larger decrease in symptoms (as measured by the CAPs scale) when compared to the control group who will take an identical placebo pill every morning for 7 days.

A Fictional Study on Body Image*

• H1: Viewing media that portrays the thin ideal increases feelings of body image dissatisfaction.
• Ho: Types of media viewed does not affect body image dissatisfaction.
• H1: Watching a video portraying the thin ideal in a  Baywatch  film trailer will result in higher scores on the Body Shape Questionnaire (BSQ-34) compared with watching media with “normal” body types in the Grownups film trailer.

*This entire IA exemplar is included in the IA Teacher Support Pack.  

A fictional study on weight training.

• H1: Listening to music affects training performance.
• Ho: Music has no effect on training performance.
• H1:  Listening to heavy metal rock music (AC/DC songs) causes a difference in the number of push-ups performed compared to listening to classical music (Mozart’s symphony #41).

One vs. Two Tailed

It is important to know if your hypothesis is one or two-tailed. This will influence the type of inferential statistics test you use later. If you have a one-tailed hypotheses, you should use a one-tailed test. And if you have a two-tailed hypothesis? You guessed it – a two-tailed test.

The one vs two tailed debate still continues in Psychology ( read more ). The IB ignores this and makes it simple: one tailed hypotheses = one tailed test. No ifs, ands, or buts!

If you are predicting that one of your conditions in your experiment will have a higher value than the other, it’s one-tailed (because you know the direction of the effect – the IV is increasing the DV). Similarly, your hypothesis is one-tailed if you are predicting that manipulating the IV will cause a decrease in the DV.

However, if you think your IV will have an effect, but you’re not sure if it will increase  or  decrease it, this is two-tailed.

Of the three examples above, can you tell which one is two-tailed and which one is one-tailed?

Read more about operationally defining your variables in your hypotheses in this blog post .

Points to Remember

• Hypotheses are based on the population, not the sample, so you can write in present tense. However, the norm for IB Psych IA’s is to write in the future tense as a prediction of what will happen in your experiment.
• In IB IA’s, we’re hypothesizing about a causal relationship of an IV on a DV in a population – the hypotheses should reflect that causal relationship.
• Inferential tests are test of the null hypothesis (hence it’s called null hypothesis testing). We are conducting the tests to see the chances of obtaining our results even if the null is true (i.e. there is no effect).

Footnote 1: Saying “that there will be no significant difference between the two conditions in or any differences are due to chance” is technically an incorrect way to state a null hypothesis. That’s because when we conduct our inferential tests we’re seeing what the probability is of getting our results even if our null were true. So if we get a p value of say 0.10 (10%), according to the above null hypothesis we’re saying there is a 10% chance that there will be no significant difference between the two conditions, which isn’t actually accurate (don’t worry if I’ve lost you – it’s mind bending stuff). This is one of those instances where poor statistical practice has ingrained itself in IB assessment. But on the plus side it does make it easier for students (and not enough time is spent on this for the bad habits to be too ingrained anyway).

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

Frequently asked questions

What is operationalisation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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1.5: Conceptualizing and operationalizing (and sometimes hypothesizing)

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Research questions are an essential starting point, but they tend to be too abstract, especially in the beginning. If we’re ultimately about making observations, we need to know more specifically what to observe. Conceptualization is a step in that direction. In this stage of the research process, we specify what concepts and what relationships among those concepts we need to observe. My research question might be How does government funding affect nonprofit organizations? This is fine, but I need to identify what I want to observe much more specifically. Theory (like the crowding out theory I referred to before) and previous research help me identify a set of concepts that I need to consider: different types of government funding, the amount of funding, effects on fundraising, effects on operations management, managerial capacity, donor attitudes, policies of intermediary funding agencies, and so on. It’s helpful at this stage to write what are called nominal definitions of the concepts that are central to my study. These are definitions like what you’d find in a dictionary, but tailored to your study; a nominal definition of government subsidy would describe what I mean in this study when I use the term.

After identifying and defining concepts, we’re ready to operationalize them. To operationalize a concept is to describe how to measure it. (Some authors refer to this as the operational definition , which I find confuses students since it doesn’t necessarily look like a definition.) Operationalization is where we get quite concrete: To operationalize the concept revenue of a nonprofit organization , we might record the dollar amount entered in line 12 of their most recent Form 990 (an income statement nonprofit organizations must file with the IRS annually). This dollar amount will be my measure of nonprofit revenue.

Sometimes, the way we operationalize a concept is more indirect. Public support for nonprofit organizations, for example, is more of a challenge to operationalize. We might write a nominal definition for public support that describes it as having something to do with the sum of individuals’ active, tangible support of a nonprofit organization’s mission. We might operationalize this concept by recording the amount of direct charitable contributions, indirect charitable contributions, revenue from fundraising events, and the number of volunteer hours entered in the respective Form 990 lines.

Note that when we operationalized nonprofit revenue, the operationalization yielded a single measure. When we operationalized public support, however, the operationalization yielded multiple measures. Public support is probably a broader, more complex concept, and it’s hard to think of just one measure that would convincingly measure it. Also, when we’re using measures that measure the concept more indirectly, like our measures for public support, we’ll sometimes use the word indicator instead of measure . The term indicator can be more accurate; we know that measuring something as abstract as public support would be impossible; it is, after all, a social construct, not something concrete. Our measures, then, indicate the level of public support more than actually measure it.

I just slipped in that term, social construct , so we should go ahead and face an issue we’ve been sidestepping so far: Many concepts we’re interested in aren’t observable in the sense that they can’t be seen, felt, heard, tasted, or smelled. But aren’t we supposed to be building knowledge based on observations? Are unobservable concepts off limits for empirical social researchers? Let’s hope not! Lots of important concepts (maybe all the most important concepts) are social constructs, meaning that these terms don’t have meaning apart from the meaning that we, collectively, assign to them. Consider political literacy, racial prejudice, voter intent, employee motivation, issue saliency, self-esteem, managerial capacity, fundraising effectiveness, introversion, and Constitutional ideology. These terms are a shorthand for sets of characteristics that we all more or less agree “belong” to the concepts they name. Can we observe political ideology? Not directly, but we can pretty much agree on what observations serve as indicators for political ideology. We can observe behaviors, like putting bumper stickers on cars, we can see how people respond to survey items, and we can hear how people respond to interview questions. We know we’re not directly measuring political ideology (which is impossible, after all, since it’s a social construct), but we can persuade each other that our measures of political ideology make sense (which seems fitting, since, again, it’s a social construct).

Each indicator or measure—each observation we repeat over and over again—yields a variable . The term variable is one of these terms that’s easier to learn by example than by definition. The definition, though, is something like “a logical grouping of attributes.” (Not very helpful!) Think of the various attributes that could be used to describe you and your friends: brown hair, green eyes, 6’2” tall, brown eyes, black hair, 19 years old, 5’8” tall, blue eyes, and so on. Obviously, some of these attributes go together, like green eyes, brown eyes, and blue eyes. We can group these attributes together and give them a label: eye color. Eye color, then, is a variable. In this example, the variable eye color takes on the values green, brown, and blue. Our goal in making observations is to assign values to variables for cases. Cases are the things—here, you and your friends—that we’re observing and to which we’re assigning values. In social science research, cases are often individuals (like individual voters or individual respondents to a survey) or groups of people (like families or organizations), but cases can also be court rulings, elections, states, committee meetings, and an infinite number of other things that can be observed. The term unit of analysis is used to describe cases, too, but it’s usually a more general term; if your cases are firefighters, then your unit of analysis is the individual.

Getting this terminology—cases, variables, values—is essential. Here are some examples of cases, variables, and values . . .

• Cases: undergraduate college students; variable: classification; values: Freshmen, Sophomore, Junior, Senior;
• Cases: states; variable: whether or not citizen referenda are permitted; values: yes, no;
• Cases: counties; variable: type of voting equipment; values: manual mark, punch card, optical scan, electronic;
• Cases: clients; variable: length of time it took them to see a counselor; values: any number of minutes;
• Cases: Supreme Court dissenting opinions; variable: number of signatories; values: a number from 1 to 4;
• Cases: criminology majors; variable: GPA; values: any number from 0 to 4.0.

Researchers have a language for describing variables. A variable’s level of measurement describes the structure of the values it can take on, whether nominal, ordinal, interval, or ratio. Nominal and ordinal variables are the categorical variables; their values divide up cases into distinct categories. The values of nominal-level variables have no inherent order. The variable sex can take on the values male and female; eye color—brown, blue, and green eyes; major— political science, sociology, biology, etc. Placing these values in one order—brown, blue, green— makes just as much sense as any other—blue, green, brown. The values of ordinal-level variables, though, have an inherent order. Classification—freshmen, sophomore, junior, senior; love of research methods—low, medium, high; class rank—first, second, . . . , 998th. These values can be placed in an order that makes sense—first to last (or last to first), least to most, best to worst, and so on. A point of confusion to be avoided: When we collect and record data, sometimes we assign numbers to values of categorical variables (like brown hair equals 1), but that’s just for the sake of convenience. Those numbers are just placeholders for the actual values, which remain categorical.

When values take on actual numeric values, the variables they belong to are numeric variables. If a numeric variable takes on the value 28, it means there are actually 28 of something—28 degrees, 28 votes, 28 pounds, 28 percentage points. It makes sense to add and subtract these values. If one state has a 12% unemployment rate, that’s 3 more points than a state with a 9% unemployment rate. Numeric variables can be either interval-level variables or ratio-level variables. When ratio-level variables take on the value zero, zero means zero—it means nothing of whatever we’re measuring. Zero votes means no votes; zero senators means no senators. Most numeric variables we use in social research are ratio-level. (Note that many ratio-level variables, like height, age, states’ number of senators, would never actually take on the value zero, but if they did, zero would mean zero.) Occasionally, zero means something else besides nothing of something, and variables that take on these odd zeroes are interval-level variables. Zero degrees means—well, not “no degrees,” which doesn’t make sense. Year zero doesn’t mean the year that wasn’t. We can add and subtract the values of interval-level variables, but we cannot multiply and divide them. Someone born in 996 is not half the age of someone born in 1992, and 90 degrees is not twice as hot as 45.

We can sometimes choose the level of measurement when constructing a variable. We could measure age with a ratio-level variable (the number of times you’ve gone around the sun) or with an ordinal-level variable (check whether you’re 0-10, 11-20, 21-30, or over 30). We should make this choice intentionally because it will determine what kinds of statistical analysis we can do with our data later. If our data are ratio-level, we can do any statistical analysis we want, but our choices are more limited with interval-level data, still more limited with ordinal-level data, and most limited with nominal-level data.

Variables can also be described as being either continuous or discrete. Just like with the level of measurement, we look at the variable’s values to determine whether it’s a continuous or discrete variable. All categorical variables are discrete, meaning their variables can only take on specific, discrete values. This is in contrast to some (but not all!) numeric variables. Take temperature, for example. For any two values of the variable temperature , we can always imagine a case with a value in between them. If Monday’s high is 62.5 degrees and Tuesday’s high is 63.0 degrees, Wednesday’s high could be 62.75 degrees. Temperature, then, measured in degrees, is a continuous variable. Other numeric variables are discrete variables, though. Any variable that is just a count of things is discrete. For the variable number of siblings , Anna has two siblings and Henry has three siblings. We cannot imagine a person with any number of siblings between two and three—nobody could have 2.5 siblings. Number of siblings , then, is a discrete variable. (Note: Some textbooks and websites incorrectly state that all numeric variables are continuous. Do not be misled.)

If we’re engaging in causal research, we can also describe our variables in terms of their role in causal explanation. The “cause” variable is the independent variable . The “effect” variable is the dependent variable. If you’re interested in determining the effect of level of education on political party identification, level of education is the independent variable, and political party identification is the dependent variable.

I’m being a bit loose in using “cause” and “effect” here. Recall the concept of underlying causal mechanism. We may identify independent and dependent variables that really represent a much more complex underlying causal mechanism. Why, for example, do people make charitable contributions? At least four studies have asked whether people are more likely to make a contribution when the person asking for it is dressed nicely. (See the examples cited in Bekkers and Wiepking’s 2010 “A Literature Review of Empirical Studies of Philanthropy,” Nonprofit and Voluntary Sector Quarterly , volume 40, p. 924, which I also commend for its many examples of how social research explores questions of causality.) Do these researchers believe the quality of stitching affects altruism? Sort of, but not exactly. More likely, they believe potential donors’ perceptions of charitable solicitors will shape their attitudes toward the requests, which will make them more or less likely to respond positively. It’s a bit reductionist to say charitable solicitors’ clothing “causes” people to make charitable donations, but we still use the language of independent variables and dependent variables as labels for the quality of the solicitors’ clothing and the solicitees’ likelihood of making charitable donations, respectively. Think carefully about how this might apply anytime an independent variable—sometimes more helpfully called an explanatory variable —is a demographic characteristic. Women, on average, make lower salaries than men. Does sex “cause” salary? Not exactly, though we would rightly label sex as an independent variable and salary as a dependent variable. Underlying this simple dyad of variables is a set of complex, interacting, causal factors—gender socialization, discrimination, occupational preferences, economic systems’ valuing of different jobs, family leave policies, time in labor market—that more fully explain this causal relationship.

Identifying independent variables (IVs) and dependent variables (DVs) is often challenging for students at first. If you’re unsure which is which, try plugging your variables into the following phrases to see what makes sense:

• IV causes DV
• Change in IV causes change in DV
• IV affects DV
• DV is partially determined by IV
• A change in IV predicts a change in DV
• DV can be partially explained by IV
• DV depends on IV

In the later section on formal research designs, we’ll learn about control variables, another type of variable in causal studies often used in conjunction with independent and dependent variables.

Sometimes, especially if we’re collecting quantitative data and planning to conduct inferential statistical analysis, we’ll specify hypotheses at this point in the research process as well. A hypothesis is a statement of the expected relationship between two or more variables. Like operationalizing a concept, constructing a hypothesis requires getting specific. A good hypothesis will not just predict that two (or more) variables are related, but how. So, not Political science majors’ amount of volunteer experience will be related to their choice of courses, but Political science majors with more volunteer experience will be more likely to enroll in the public policy, public administration, and nonprofit management courses . Note that you may have to infer the actual variables; hypotheses often refer only to specific values of the variables. Here, public policy, public administration, and nonprofit management courses are values of the implied variable, types of courses .

psychologyrocks

Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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Theory, hypothesis, and operationalization

Approach, theory, model.

First, you have to determine the general state of knowledge (or state of the art) as regards a certain objective. Are there already relevant attempts of explanation (models, theories, approaches, debates)? Many times there are theories already existing that provide a basis for discussing or looking at a certain problem.

When choosing a certain approach to explain complex circumstances, specific aspects of your problem area will be highlighted more prominently. Deciding on an approach means considering which questions can then be answered best. After choosing an approach it is necessary to use its related methods consequently.

Examples for approaches: «Education is an important prerequisite for a society's economic development» or «Earnings from tourism support national economy.»

Hypotheses and presumptions

Hypotheses are assumptions that could explain reality or - in other words - that could be the answer to your question. Such an assumption is based on the current state of research; it therefore delivers an answer that is theoretically possible («proposed solution») and applies at least to some extent to the question posed. When dealing with complex topics it is sometimes easier to develop a number of subordinate working hypotheses from just a few main hypotheses.

Example for a hypothesis: «Tourism offers children the possibility to earn money instead of going to school» or «The more tourists the fewer the children are going to school.»

Not all research projects are conducted by means of methods to test hypotheses. In social research, for example, there are reconstructive or interpretive methods as well. Here you try to explain and understand people's actions based on their interpretation of certain issues ( Bohnsack 2000: 12–13). However, also with such an approach researchers use hypotheses or presumptions to structure their work. The point is not to finally acknowledge or reject those hypotheses. You rather search for explanations that are plausible and comprehensible.

Example for a presumption: «In developing countries parents are skeptical about their children working for the tourism industry.»

However, most of the time one again acts on theses or presumptions. The point is not to finally acknowledge or reject those assumptions. One rather searches for explanations that are plausible and comprehensible.

Example for an explanation: «Parents don't worry about their children not going to school; they are afraid of losing their status when earning less than their children.»

Operationalization

It is necessary to operationalize the terms used in scientific research (that means particularly the central terms of a hypothesis). In order to guarantee the viability of a research method you have to define first which data will be collected by means of which methods. Research operations have to be specified to comprehend a subject matter in the first place ( Bopp 2000: 21). In order to turn the operationalized term into something manageable you determine its exact meaning during a research process.

Example for an operationalization: «When compared to other areas, tourist destinations are areas where children are less likely to go to school.»

Online Guidelines for Academic Research and Writing : The academic research process : Theory, hypothesis, and operationalization

Update: 28.10.2021 ( eLML ) - Contact - Print (PDF) - © OLwA 2011 (Creative Commons)

Philosophia Scientiæ

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Accueil Numéros 15-2 Varia The operationalization of general...

The operationalization of general hypotheses versus the discovery of empirical laws in Psychology

L’enseignement de la méthodologie scientifique en Psychologie confère un rôle paradigmatique à l’opérationnalisation des « hypothèses générales » : une idée sans rapport précis à l’observation concrète se traduit par la tentative de rejeter une hypothèse statistique nulle au profit d’une hypothèse alternative, dite de recherche, qui opérationnalise l’idée générale. Cette démarche s’avère particulièrement inadaptée à la découverte de lois empiriques. Une loi empirique est définie comme un trou nomothétique émergeant d’un référentiel de la forme Ω x  M ( X ) x  M ( Y ), où Ω est un ensemble d’événements ou d’objets datés dont certains états dans l’ensemble M ( Y ) sont par hypothèse impossibles étant données certaines conditions initiales décrites dans l’ensemble M ( X ) . Cette approche permet de préciser le regard que l’historien des connaissances peut porter sur les avancées descriptives et nomothétiques de la Psychologie empirique contemporaine.

Psychology students learn to operationalise ’general hypotheses’ as a paradigm of scientific Psychology : relatively vague ideas result in an attempt to reject the null hypothesis in favour of an alternative hypothesis, a so-called research hypothesis, which operationalises the general idea. Such a practice turns out to be particularly at odds with the discovery of empirical laws. An empirical law is defined as a nomothetic gap emerging from a reference system of the form Ω x  M ( X ) x  M ( Y ), where Ω is a set of events or dated objects for which some states in the set M ( Y ) are hypothetically impossible given some initial conditions depicted in the set M ( X ). This approach allows the knowledge historian to carefully scrutinise descriptive and nomothetic advances in contemporary empirical Psychology.

Texte intégral

I wish to express my thanks to Nadine Matton and Éric Raufaste for their helpful comments on a previous version of this article. This work was funded in part by the ANR-07-JCJC-0065-01 programme.

1 This article is the result of the author’s need to elaborate on the persistent dissatisfaction he feels with the methodology of scientific research in Psychology, and more precisely with his perception of the way in which it is taught. It would indeed be presumptuous to present the following criticism as being a criticism of the methodology of scientific research in Psychology as a whole, since the latter is a notion which is too all-encompassing in its scope to serve as a precise description of the diversity of research practice in this vast field. The source of this dissatisfaction is to be found in what [Reuchlin 1992, 32] calls the ‘distance’ between ‘general theory’ and a ‘specific, falsifiable hypothesis’. A certain form of academism shapes the approach to scientific research in Psychology according to a three-stage process for the formulation of hypotheses e.g., [Charbonneau 1988]. When they write the report of an empirical study, researchers in Psychology must supply the grounds for their research by introducing a so-called general (or theoretical) hypothesis, then show how they have tested this hypothesis by restating it as a so-called operational (or research) hypothesis. In principle, this restatement should involve data analysis, finalised by testing at least one inferential statistical hypothesis, the so-called null hypothesis.

2 As a socially regulated procedure, the sequencing of theoretical, operational and null hypotheses—which we refer to here as operationaliza-tion —may not pose scientific problems to researchers who are mainly concerned with adhering to a socio-technical norm. The sense of dissatisfaction arises when this desire for socio-technical compliance is considered in the light of the hope (albeit an admittedly pretentious or naïve hope) of discovering one or more empirical laws, i.e. demonstrating at least one, corroborated general empirical statement, [Vautier 2011].

3 With respect to the discovery of empirical laws, operationalization may be characterised as a paradigm, based on a ‘sandwich’ system, whose workings prove to be strikingly ineffective. The ‘general hypothesis’ (the uppermost layer of the ‘sandwich’ system) is not the statement of an empirical law, but a pre-referential statement, i.e. a statement whose empirical significance has not (yet) been determined. The null hypothesis test (the lower layer of the ‘sandwich’) binds the research procedure to a narrow, pragmatic decision-making approach amid uncertainty— rejection or acceptance of the null hypothesis—which is not germane to the search for empirical laws if the null hypothesis is not a general statement in the strict sense of the term, i.e. held to be true for all the elements in a given set. Between the external layers of the ’sandwich’ system lies the psychotechnical and statistical core of the operationalization paradigm, i.e. the production of psychological measurements to which the variables required for the formulation of the operational hypothesis are linked. Again, the claim here is not that this characterization of research procedure in Psychology applies absolutely universally ; however, operationalization as outlined above does appear to be sufficiently typical of a certain orthodoxy to warrant a thorough critical analysis.

4 This paradigm governs an approach which is destined to establish a favourable view of ‘general hypotheses’ inasmuch as they have psy-chotechnical and inferential support. However, the ideological interest of these statements does not automatically confer them with nomothetic import. Consequently, one cannot help wondering whether the rule of operationalization does not in fact serve to prevent those who practise it from ever discerning a possible historical failure of orthodox Psychology to discover its own empirical laws, by training the honest researcher not to hope for the impossible. After all, we are unlikely to worry about failing to obtain something which we were not looking for in the first place. We shall see that an empirical law consists precisely of stating an empirical impossibility, i.e. a partially deterministic falsifiable statement. As a result, we have inevitably come to question psychological thought as regards the reasons and consequences of an apodictic approach to probabilistic treatment of the empirical phenomena which it is investigating.

5 This article comprises four major parts. First of all, we shall illustrate operationalization on the basis of an example put forward by [Fernandez & Catteeuw 2001]. Next, we shall identify two logical and empirical difficulties which arise from this paradigm and demonstrate that they render it unsuitable for the discovery of empirical laws, then detail the logical structure of these laws. Lastly, we shall identify some methodological guidelines which are compatible with an inductive search for partial determinisms.

1 An example of operationalization : smoking cessation and anxiety

6 [Fernandez & Catteeuw 2001, 125] put forward the following sequence :

General hypothesis : undergoing smoking cessation tends to increase anxiety in smokers rather than reduce it.

Operational hypothesis : smokers undergoing smoking cessation are more prone to anxiety than non-cessation smokers.

Null hypothesis : there is no difference between anxiety scores for smokers undergoing smoking cessation and non-cessation smokers.

7 This example can be expanded so as to offer more opportunities to engage with the critical exercise. There is no difficulty in taking [Fernandez & Catteeuw 2001] operational hypothesis as a ‘general hypothesis’. Their formulation specifies neither the empirical (nominal) meaning of the notion of smoking cessation, nor the empirical (ordinal or quantitative) significance of the notion of anxiety, even though it makes reference to the ordinal operator more prone to anxiety than  ; lastly, the noun smokers signifies only an indefinite number of people who smoke.

8 The researcher may have given themselves a set of criteria which is sufficient to decide whether, at the moment when they examine an individual, the person is a smoker or not, and if they are a smoker, another set of criteria sufficient to decide whether or not they are undergoing smoking cessation. These sets of criteria allow the values for two nominal variables to be defined, the first attributing the value of smoker or non-smoker, and the second, which is conditional on the status of ‘smoker’, attributing the value of undergoing cessation or non-cessation. However, the statistical definition of the ’undergoing cessation’ variable requires a domain, i.e. elements assigned a value according to its codomain, the (descriptive) reference system of the variable : {undergoing cessation, non-cessation}. The researcher may circumscribe the domain to pairs (smoker, examination date) which they already have obtain or will obtain during the course of their study, and thus define a so-called independent nominal variable.

9 They then need to specify the function which assigns an anxiety score for each (smoker, examination date) pair, in order to define the ’anxiety score’ statistical variable, taken as the dependent variable. The usual solution for specifying such a function consists in using the answers to an anxiety questionnaire to determine this score, according to a numerical coding rule for the responses to the items on the questionnaire. Such procedures, in which standardised observation of a verbal behaviour is associated with the numerical coding of responses, constitute one of the fundamental contributions of psychotechnics (or psychological testing) to Psychology ; it enables anxiety means conditional on the values of the independent variable to be calculated, whence the operational hypothesis : smokers undergoing smoking cessation are more anxious than non-cessation smokers.

10 The operational hypothesis constitutes a descriptive proposition whose validity can easily be examined. However, to the extent that they consider their sample of observations to be a means of testing a general hypothesis, the researcher must also demonstrate that the mean difference observed is significant, i.e. rejects the null hypothesis of the equality of the means for the statistical populations composed of the two types of smokers, using a probabilistic procedure selected from the available range of inferential techniques, for instance Student’s t -test for independent samples. Only then can the operational hypothesis, considered in the light of the two statistical populations, acquire the status of an alternative hypothesis with respect to the null hypothesis.

11 Now, let us restate the sequence of hypotheses put forward by [Fernandez & Catteeuw 2001] thus :

General hypothesis : smokers undergoing smoking cessation are more anxious than non-cessation smokers

Operational hypothesis : given a pair of variables (‘undergoing cessation’, ‘anxiety score’), mean anxiety conditional on the undergoing cessation value is greater than mean anxiety conditional on the non-cessation value.

Null hypothesis : the two conditional means are equal.

2 Operationalization criticised

12 The example which we have just developed is typical of operational-ization in Psychology, irrespective of the experimental or correlational nature [Cronbach 1957, 1975] of the study. In this section, we make two assertions by dealing with the operationalization approach in reverse : (i) the empirical relevance of the test of the null hypothesis is indeterminate (ii) the statistical fact of a mean difference has no general empirical import.

2.1 The myth of the statistical population

13 To simplify the discussion, let us suppose that the researcher tests the null hypothesis of the equality of two means using Student’s t procedure. The issue at stake in the test from a socio-technical point of view is that by qualifying the difference observed as a significant difference, the cherished notation “p < .05” or “p < .01” may be included in a research paper. The null hypothesis test has been the subject of purely statistical criticisms e.g., [Krueger 2001], [Nickerson 2000] and it is not within the scope of this paper to draw up an inventory of these criticisms. In the empirical perspective under examination here, the problem is that this type of procedure is nothing more than a rhetorical device, insofar as the populations to which the test procedure is applied remain virtual in nature.

14 In practice, the researcher knows how to define their conditional variables on the basis of pairs : (smoker undergoing cessation, examination date) and (non-cessation smoker, examination date), assembled by them through observation. But what is the significance of the statistical population to which the inferential exercise makes reference ? If we consider the undergoing cessation value, for example, how should the statistical population of the (smoker undergoing cessation, examination date) pairs be defined ? Let us imagine a survey which would enable the anxiety score for all the human beings on the planet with the status of ‘smoker undergoing smoking cessation’ to be known on a certain date each month in the interval of time under consideration. We would then have as many populations as we have monthly surveys ; we could then consider grouping together all of these monthly populations to define the population of observations relating to the ‘cessation’ status. There is not one single population, but rather a number of virtual populations. The null hypothesis is therefore based on a mental construct. As soon as this is defined more precisely, questions arise as to its plausibility and the interest of the test. Indeed, why should a survey supply an anxiety variable whose conditional means, subject to change, are identical ?

15 Ultimately, it appears that the null hypothesis test constitutes a decision-making procedure with respect to the plausibility of a hypothesis devoid of any determined empirical meaning. The statistical inference used in the operationalization system is an odd way of settling the issue of generality : it involves deciding whether the difference between observed means may be generalised, even if the empirical meaning of this generality has not been established.

2.2 The myth of the average smoker

16 The difference between the two anxiety means may be interpreted as the difference between the degree of anxiety of the average smoker undergoing cessation and the degree of anxiety of the average non-cessation smoker, which poses two problems. Firstly, the discrete nature of the anxiety score contains a logical dead-end, i.e. the use of an impossibility to describe something which is possible. Let us assume an anxiety questionnaire comprising five items with answers scored 0, 1, 2 or 3, such that the score attributed to any group of 5 responses will fall within the sequence of natural numbers (0, 1, 15). A mean score of 8.2 may indeed ‘summarise’ a set of scores, but cannot exist as an individual score. Consequently, should we wish to use a mean score to describe a typical smoker, it must be recognised that such a smoker is not possible and therefore not plausible. As a result, the difference between the two means cannot be used to describe the difference in degrees of anxiety of the typical smokers, unless it is admitted that a typical smoker is in fact a myth.

17 Let us now assume that the numerical coding technique enables a continuous variable to be defined by the use of so-called analogue response scales. The score of any smoker is by definition composed of the sum of two quantities, the mean score plus the deviation from the mean, the latter expressing the fact that the typical smoker is replaced in practice by a particular specimen of the statistical population, whose variable nature is assumed to be random—without it appearing necessary to have empirical grounds for the probability space on which this notion is based. In these conditions, the mean score constitutes a parameter, whose specification is an empirical matter inasmuch as the statistical population is actually defined. An empirical parameter is not, however, the same thing as an empirical law.

3 Formalization of an empirical law

• 2  This is a more general and radical restatement of the definition given by [Piaget 1970, 17] of the (...)

18 According to the nomothetic perspective, scientific ambition consists in discovering laws, i.e. general implications 2 A general implication is a statement in the following form :

which reads thus “for any x of A , if p ( x ) then q ( x )”, where x is any component of a given set A , and p (•) and q (•) are singular statements. This formalization applies without any difficulty to any situation in which the researcher has a pair of variables ( X , Y ), from a domain Ω n  = { ω i , i  =   1, …, n }, whose elements w are pairs (person, observation date). The codomain of the independent variable X is a descriptive reference system of initial conditions M ( X ) = ( x i , i  = 1, …, k }, whilst the dependent variable, Y , specifies a value reference system, M ( Y ) = ( y i , i  = 1, …, l }, the effective observation of which depends, by hypothesis, on the independent conditions. Thus, the onto-logical substrate of an empirical law is the observation reference system Ω x  M ( X ) x  M ( Y ), where Ω ⊃ Ω is an extrapolation of Ω n  : any element of Ω is, as a matter of principle, assigned a unique value in M ( X ) x  M ( Y ) by means of the function ( X , Y ).

19 Two comments arise from this definition. Firstly, as noted by [Popper 1959, 48], “[natural laws] do not assert that something exists or is the case ; they deny it”. In other words, they state a general ontological impossibility in terms of Ω x  M ( X ) x  M ( Y ) : a law may indeed by formulated by identifying the initial conditions α ( X ) ⊂  M ( X ) for which a non-empty subset β ( Y ) ⊂  M ( Y ) exists such that,

This formulation excludes the possibility of X ( ω ) ∈  α ( Y ) and Y ( ω ) ∈ ∁ β ( Y ) being observed, where ∁ β ( Y ) designates the complementary set β ( Y ) with respect to M ( Y ). Making a statement in the form of (2) amounts to stating a general empirical fact in terms of Ω n , and an empirical law in terms of Ω, by inductive generalisation. This law can be falsified, simply by exhibiting an example of what is said to be impossible in order to falsify it. The general nature of the statement stems from the quantifier ∀ and its empirical limit is found in the extension of Ω. The law may then be corroborated or falsified. If it is corroborated, it is possible to measure its degree of corroboration by the number of observations applying to it, i.e. by the cardinality of the equivalence class formed by the antecedents of α ( X )—the class is noted Cl Ω n/X [ α ( X )].

20 The second comment relates to the notion of partial determinism. The mathematical culture passed on through secondary school teaching familiarises honest researchers with the notion of numerical functions y  =  f ( x ), which express a deterministic law, i.e. that x being given, y necessarily has a point value. If the informative nature of the law is envisaged in negative terms [Dubois & Prade 2003], the necessity of the point is defined as the impossibility of its complement. In the field of humanities [Granger 1995], seeking total determinisms appears futile, but this does not imply that there is no general impossibility in Ω x  M ( X ) x  M ( Y ) and therefore no partial determinism. The fact that partial determinism may not have a utility value from the point of view of social or medical decision-making engineering has nothing to do with its fundamental scientific value. The subject of nomothetic research therefore appears in the form of a ‘gap’ in a descriptive reference system, this gap being theoretically interpreted as the effect of a general ontological impossibility. This is why in teaching, a methodology to support the nomothetic goal of training student researchers to ’search for the impossible’ is called for.

4 How to seek the impossible

21 Discovery of a gap in the descriptive reference system involves the discovery of a general empirical fact, from which an empirical law is inferred by extending the set of observations Ω n to an unknown phe-nomenological field Ω ⊃ Ω n (e.g. future events). A general empirical fact makes sense only with reference to the descriptive reference system M ( X ) x  M ( Y ). Practically speaking, dependent and independent variables are multivariate. Let X  = ( X 1 , X 2 , ..., X p ) be a series of p independent variables and M ( X ) the reference system of X  ; M ( X ) is the Cartesian product of the p  reference systems M ( X i ), i  = 1, …, p . Similarly, let Y  = ( Y 1 , ..., Y q ) be a series of q  dependent variables and M ( Y ) the reference system of Y . The descriptive reference system of the study is therefore :

Thus the contingency table (the rows of which represent the multivari-ate values of X , and the columns the multivariate values of Y ) can be defined. Observation readings are then carried out so that the cells in the contingency table are gradually filled in... or remain empty.

22 Two cases must be distinguished here. The first corresponds to the situation in which the researcher is completely ignorant of what is happening in their observation reference system, in other words, they do not have any prior observations. They therefore have to carry out some kind of survey in order to learn more. Knowing what is happening in the reference system means knowing the frequency of each possible state. It does not involve calling on the notion of probability (the latter being firmly in the realm of mathematical mythology) since it would involve knowing the limit of the frequency of each cell in the contingency table as the number of observations ( n ) tends towards infinity.

• 3  “But in terms of truth, scientific psychology does not deal with natural objects. It deals with te (...)

23 A nomothetic gap arises when there is at least one empty cell in at least one row of the contingency table, when the margin of the row (or rows) is well above the cardinality of M (Y ). It is possible to identify all the gaps in the reference system only if its cardinality is well below the cardinality of lln, n. This empirical consideration sheds light on a specific epistemological drawback in Psychology : not only are its descriptive reference systems not given naturally, as emphasised by [Danziger 1990, 2], 3 but in addition the depth of constructible reality is such that its cardinality may be gigantic—so much so that discussing what is happening in an observation reference system cannot be achieved in terms of sensible intuition. The fact is that the socio-technical norms which shape the presentation of the observation techniques used in empirical studies do not refer either to the notion of descriptive reference system or the necessity of plotting the cardinality card[ M ( X ) x  M ( Y )] against the cardinality of the set of observations, card(Ω n ) =  n . If the quotient card[ M ( X ) x  M ( Y )]/ n is not much lower than 1, planning to carry out an exhaustive examination of the nomothetic gaps in the descriptive reference system is unfeasible. This does not prevent the researcher from working on certain initial conditions α ( X ), but in such cases it must nonetheless be established that dividing the number of values of M ( Y ) by the cardinality of the class Cl Ω n/ X [ α ( X )] of antecedents of α ( X ) in Ω n gives a result which is far less than 1.

24 Let us now present the second case, for which it is assumed that the researcher has been lucky enough to observe the phenomenon of a gap, whose ’coordinates’ in the descriptive reference system of the study are [ α ( X ), ∁ β ( Y )]. The permanent nature of this gap constitutes a proper general hypothesis. This hypothesis should be tested using a targeted observation strategy. Indeed, accumulating observations in l is of interest from the point of view of the hypothesis if these observations are such that : —  X ( ω ) ∈  α ( X ), in which case we seek to verify that Y ( ω ) ∈  β ( Y ), —  Y ( ω ) ∈ ∁ β ( Y ), in which case we seek to verify that X ( ω ) ∈ ∁ α ( X ).

This approach to observation is targeted, and indeed makes sense, in that it focuses on a limited number of states : the researcher knows exactly what they are looking for. It is the very opposite of blindly reproducing an experimental plan or survey plan.

25 When a counterexample is discovered, i.e. ω e exists such that X ( ω e ) ∈  α ( X ) and Y ( ω e ) ∈ ∁ β ( Y ), this observation falsifies the general hypothesis. The researcher can then decide either to reject the hypothesis or to defend it. If they decide to defend it, they may restrict the set of conditions α ( X ), or try to find a variable X p +1 which modulates verification of the rule. Formally speaking, this modulating variable is such that there is a strict non-empty subset of M ( X p +1 )—let this be γ ( X p +1 )—such that :

Irrespective of how they revise the original hypothesis, they will have to restrict its domain of validity with respect to the—implicit—set of possible descriptive reference systems. A major consequence of revising the law by expanding the descriptive reference system of initial conditions is resetting the corroboration counter, since the world being explored has been given an additional descriptive dimension : this is the reference system Ω x  M ( X 1 ) x  M ( Y ), where X 1  = ( X , X p +1 ).

4.1 Example

26 Without it being necessary to develop the procedure presented here in its entirety, we can illustrate it using the example of smokers’ anxiety. The problem consists of restating the ’general hypothesis’ as a statement which is (i) general, properly speaking, as understood in (1) –, and (ii) falsifiable. We may proceed in two stages. Firstly, it is not necessary to talk in terms of reference systems to produce a general statement. Expressing the problem in terms of the difference between two means is not relevant to what is being sought ; however, the idea according to which any smoker undergoing cessation becomes more anxious may be examined, along the lines of the ’general hypothesis’ described by [Fernandez & Catteeuw 2001]. This idea is pre-referential inasmuch as we are unable to define a smoker, a smoker undergoing cessation, or a person who is becoming more anxious.

27 Since we cannot claim to be able to actually settle these issues of definition, we shall use certain definitions for the purposes of convenience. Let U be a population of people and T a population of dates on which they were observed. Let Ω n be a subset of U  x  T  x  T such that, for any triplet ω  = ( u , t 1 , t 2 ), u is known on dates t 1 and t 2 in terms of their status as : — a non-smoker, a smoker undergoing cessation or a non-cessation smoker — and their state of anxiety, for instance with reference to a set of clinical signs, of which the person is asked to evaluate the intensity on date t , using a standard ‘state-anxiety’ questionnaire.

28 It can be noted that the set Ω n is a finite, non-virtual set, in that a person u whose smoker status is not known on date t 1 or t 2 for example, constitutes a triplet which does not belong to this set. According to our approach to the statistical population, it is not necessary for the observations to be the result of applying a specific random sampling technique. Since Ω n constitutes a set of known observations from the point of view of the descriptive reference system, it is a numbered set, to which new observations can be added over time ; whence the notation Ω nj (read “j-mat”), where n j stands for the cardinality of the most recent update to the set of observations.

• 4  It may be noted that an observation p such that X j ( p ) = ( n f, f 2 ) is not plausible ; this relates t (...)

29 We can then define the following variables X j and Y j , from the subset P j of Ω nj , which includes the triplets ( u , t 1 , t 2 ) such that t 2  –  t 1  =  d , where d is a transition time (e.g. 2 days). The variable X j matches any component of P j with an image in M ( X j ) = { n f, f 1 , f 2 } x { n f, f 1 , f 2 } where n f, f 1 and f 2 signify ‘non-smoker’, ‘non-cessation smoker’ and ‘smoker undergoing cessation’ respectively. Let us call α ( X j ) the subset of M ( X j ) including all the pairs of values ending in f 2 which do not begin with f 2 and take an element p  ∈  P j , : the proposition ‘ X j ( p ) ∈  α ( X j )’ means that in the period during which they were observed, person u had been undergoing smoking cessation for two days whereas they have not been before. 4

30 The dependent variable Y j must now be defined. Let us assume that for any sign of anxiety, we have a description on an ordinal scale (i.e., a Likert scale). Anxiety can then be described as a multivariate state varying within a descriptive reference system A . Consider A  x  A  ; in this set a subset β ( Y j ) can be defined which includes changes in states defined as a worsening of the state of anxiety. The variable Y j can then be defined, which, for each p  ∈  P j , corresponds to a state in M ( Y j ). The proposition ‘ Y j ( p ) ∈  β ( Y j )’ signifies that in the period during which they were observed, person u became more anxious. Lastly, the general hypothesis can be formulated in terms which ensure that it may be falsified :

31 We have just illustrated an apparently hypothetical-deductive approach ; but in fact it is an exploratory procedure if the community is not aware of any database enabling a nomothetic gap to be identified. Let us assume that the work of the researcher leads to the provision of a database Ω 236 for the community and that sets α ( X j ) and β ( Y j ) are defined after the fact, such that at least one general fact may be stated. The community with an interest in the general fact revealed by this data may seek new supporting or falsifying observations in order to help update the database.

32 If a researcher finds an individual v , with q  = ( v , t v 1 , t v 2 ) and t v 2  –  t v 1  =  d , such that X j ( q ) ∈  α ( X j ) and Y j ( q ) ∈ ∁ β ( Y j ), this means that there is a smoker who has been undergoing cessation for two days, whose anxiety has not worsened. Let us assume that the researcher investigates whether the person was already ‘very anxious’ ; they may suggest that rule (5) should be revised so as to exclude people whose initial clinical state corresponds to certain values in the reference system A . This procedure usually consists in restricting the scope of validity of the general hypotheses.

5 Discussion

• 5  [Meehl 1967] noted several decades ago that the greater the ‘experimental precision’, i.e. sample (...)

33 Operationalization in Psychology consists in restating a pre-referential proposition in order to enable the researcher to test a statistical null hypothesis, the rejection of which enables the ‘general hypothesis’ to be credited with a certain degree of acceptability. 5 Using an example taken from [Fernandez & Catteeuw 2001], we have shown that the aim of such a procedure is not the discovery of empirical laws, i.e. the discovery of nomothetic gaps in a reference system. We shall discuss two consequences of our radical approach to seeking empirical laws in an observation reference system Ω x  M ( X ) x  M ( Y ). The first relates to the methodology for updating the state of knowledge in a field of research, the second to the probabilistic interpretation of accumulated observations.

34 The state of knowledge in a given field of research can be apprehended in practical terms by means of a list of m so-called scientific publications. Let us call this set composed of specialist literature Lm and let Zj be an element in this list. The knowledge historian can then ask the following question : does text Zj allow an observation reference system of the type Ω n  x  M ( X ) x  M ( Y ) to be defined ? Such a question can only be answered in the affirmative if it is possible to specify the following :

n   >  0 pairs ( u , t ),

p  > 0 reference systems enabling the description of the initial conditions affecting the n pairs ( u , t ),

q   >  0 reference systems enabling the description of states affecting the n pairs ( u , t ) according to the initial conditions in which they are found.

35 Specifying a descriptive reference system consists in identifying a finite set of mutually exclusive values. Not all the description methods used in Psychology allow such a set to be defined ; for example, a close examination of the so-called Exner scoring system [Exner 1995] for verbatims which may be collected for any [Rorschach 1921] test card did not enable us to determine the Cartesian product of the possible values. And yet, to find a gap in a reference system, this reference system must be constituted, so as to form a stabilised and objective descriptive framework. Faced with such a situation, a knowledge historian would be justified in describing a scientific era in which research is based on such a form of descriptive methodology as being a pre-referential age.

• 6  We cannot simply classify the sources of score-subjectivity as measurement errors in the quantitat (...)

36 With regard to the matter of the objectivity of a descriptive reference system, we shall confine ourselves to introducing the notion of score-objectivity. Let P   =  ( p i , i   =  1, …, z } be a set of Psychologists and ω j  ∈ Ω. ( X , Y ) i ( ω j ) is the value of ( ω j ) in M ( X ) x  M ( Y ) as determined by the Psychologist p i . We may say that M ( X ) x  M ( Y ) is score-objective relative to P if ( X , Y ) i ( ω j ) depends only on j for all values of j . If a descriptive reference system is not score-objective, an event in Ω x  M ( X ) x  M ( Y ) which occurs in a gap cannot categorically be interpreted as a falsifying observation, since it may depend on a particular feature of the way the reporting Psychologist views it. Unless and until the descriptive definition of an event is regulated in a score-objective manner, the nomothetic aspiration appears to be premature, since it requires the objective world to be singular in nature. 6 Only once a descriptive reference system has been identified may the knowledge historian test its score-objectivity experimentally.

• 7  This type of database, established by merging several databases, has nothing to do with the aggreg (...)

37 The historian might well discover that a field of research is in fact associated with the use of divergent description reference systems. Their task would then be to connect these different fields of reality by attempting to define the problem of the correspondence between the impossibilities identified in the field R a and the impossibilities identified in the field R b —which assumes such identification is possible. Given a certain descriptive reference system of cardinality c, the historian may evaluate its explorability and perhaps note that certain description reference systems are inexplorable. Concerning explorable reference systems, they could perhaps try to retrieve data collected during the course of empirical studies, constitute an updated database, and seek nomothetic gaps in it. 7

38 Let us now move on to the second point of this discussion. If the reference system is explorable and assumed to be score-objective, it may be that each of its possible states has been observed at least once. In this case, the descriptive reference system is sterile from the nomothetic point of view and this constitutes a singular observation fact : everything is possible therein. In other words, given an object in a certain initial state, nothing can be asserted regarding its Y -state. This does not prevent the decision-making engineer from wagering on the object’s Y -state based on the distribution of Y -states, conditioned by the initial conditions in which the object is found. These frequencies may be used to measure ’expectancies’, but they do not form a basis on which to deduce the existence of a probability function for these states. Indeed, defining a random variable Y or Y | X requires the definition of a probability space on the basis of the possible states M ( X ) x  M ( Y ). In order to be probabilistic, such a space requires a probability space established on the basis of Ω e.g. [Renyi 1966]. Since Ω is a virtual set, adding objective probabilities to it is wishful thinking : seeing ( X , Y ) as a pair of random variables constitutes an unfalsifiable interpretation. Since such an interpretation is nonetheless of interest for making decisions, the existence of a related law of probability being postulated, the probability of a given state may be estimated on the basis of its frequency. The higher the total number of observations, the more accurate this estimation will be, which is why a database established by bringing together the existing databases is of interest. With the advent of the internet, recourse to probabilistic mythology no longer requires the inferential machinery of null-hypotheses testers to be deployed ; it rather requires the empirical stabilization of the parameters of the mythical law.

39 We conclude this critical analysis with a reminder that scientific research in Psychology is also aimed at the discovery of empirical laws. This requires two types of objectives to be distinguished with care : practical objectives, which focus on decision amid uncertainty, and nomoth-etic objectives, which focus on the detection of empirical impossibilities. Has so-called scientific Psychology been able to discover any empirical laws, and if so, what are they ? From our contemporary standpoint, this question is easy to answer in principle—if not in practice.

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2  This is a more general and radical restatement of the definition given by [Piaget 1970, 17] of the notion of laws. For him laws designate “relatively constant quantitative relations which may be expressed in the form of mathematical functions”, “general fact” or “ordinal relationships, [...] structural analyses, etc. which are expressed in ordinary language or in more or less formalized language (logic, etc.)”.

3  “But in terms of truth, scientific psychology does not deal with natural objects. It deals with test scores, evaluation scales, response distributions, series lists, and countless other items which the researcher does not discover but rather constructs with great care. Conjectures about the world, whatever they may be, cannot escape from this universe of artefacts.”

4  It may be noted that an observation p such that X j ( p ) = ( n f, f 2 ) is not plausible ; this relates to the question of the definition of the state of cessation and does not affect the structure of the logic.

5  [Meehl 1967] noted several decades ago that the greater the ‘experimental precision’, i.e. sample size, the easier it is to corroborate the alternative hypothesis.

6  We cannot simply classify the sources of score-subjectivity as measurement errors in the quantitative domain [Stigler 1986], since most descriptive reference systems in Psychology are qualitative ; diverging viewpoints for the same event described in a certain descriptive reference system represent an error, not of measurement, but of definition.

7  This type of database, established by merging several databases, has nothing to do with the aggregation methodology of ‘meta-analyses’ based on the use of statistical summaries e.g., [Rosenthal & DiMatteo 2001].

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Stéphane Vautier , «  The operationalization of general hypotheses versus the discovery of empirical laws in Psychology  » ,  Philosophia Scientiæ [En ligne], 15-2 | 2011, mis en ligne le 01 septembre 2014 , consulté le 05 mai 2024 . URL  : http://journals.openedition.org/philosophiascientiae/656 ; DOI  : https://doi.org/10.4000/philosophiascientiae.656

Stéphane Vautier

OCTOGONE-CERPP, Université de Toulouse (France)

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Operationalization – how to do it right!

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Operationalization is essentially the measurement of a phenomenon that is not directly measurable. Operationalization defines a concept to make it measurable, stand out and be understandable. This article talks about operationalization to a broader extent by explaining what is and isn’t an example of the concept.

Inhaltsverzeichnis

• 1 Operationalization - FAQ
• 2 Operationalization: Definition
• 3 How to operationalize: Step by step
• 4 Advantages and disadvantages
• 5 In a Nutshell

Operationalization - FAQ

What is operationalization in qualitative research.

Operationalization is a process by which researchers set up indicators to measure concepts. Moreover, evaluators set indicators that help in measuring any changes in concepts. Qualitative researchers use this process in the definition of key concepts used in their research.

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What is an example of operationalization?

Social anxiety is a concept that can’t be measured directly. Instead, you can operationalize it in several different ways. For instance, using a social anxiety scale to self-rate scores. Total incidents of recent behavioral incidents on avoiding crowded places and level of physical anxiety symptoms while in any social situations.

What is an operationalized variable?

When it comes to psychological research, there are two main variables; the independent and dependent variables. The independent variable is a variable that is changed or manipulated, while the dependent variable is measured. It measures to see if the independent variable has an impact on human behavior. Therefore, operationalized variable implies defining how the independent variable and dependent variable are measured.

What is the difference between indicators, variables and concepts?

Concepts can be defined as the phenomena or abstract ideas which are being studied. Indicators are ways of quantifying or measuring variables, while variables are characteristics and properties of the concept.

What's the difference between validity and reliability?

The difference between reliability and validity is that reliability is the consistency of a measure. Meaning whether you can reproduce the results under the same conditions. While validity means the accuracy of a measure. If the results are an actual representation of what they intended to measure.

Operationalization: Definition

Operationalization refers to converting abstract concepts into measurable observations. However, you can easily measure concepts like age or height while others like anxiety and spirituality can’t be measured. Through operationalization, one can systematically collect data regarding phenomena or processes that can’t be observed directly.

How to operationalize: Step by step

There are three main steps involved in operationalization:

Coming up with a program logic and intervention protocol

For a start, there should be a development of a program logic that describes what the program is for, change process, objectives, outcome and expected impact of the intervention. While coming up with program logic, it needs to have the support of an operationalized program logic. An intervention protocol for intervention has to specify:

• Which components are effective
• What is the importance of fidelity concerning adaptation? Is there room for adaptation of the content to the desired target group, does the protocol have to follow strictly?

In-depth description of a complete and an acceptable delivery for the intervention

Elements that you will use in the intervention that will have to be delivered during the study to preserve treatment integrity need to be defined in terms of pre-specified success. The elements will have to be put in criteria for each intervention component for all the sessions and written down in the intervention protocol. Once there is a clear definition of the successful procedure, there must be a clarification of opportunities for adaptation to the intervention content to receivers. Lastly, success criteria must be measurable.

Description of factors that determine receipt of intervention

For the intervention receipt, it is up to the program developers to define the crucial components that determine when a person gets an intervention. For instance, an intervention may occur at a higher organizational level. What are the determinants of an individual’s exposure to an intervention? A simple receipt measurement of the intervention is attendance or participation. However, researchers need to pre-define the level of participation that is needed.

Moreover, during an intervention, a participant’s level will determine the amount of intervention that an individual takes up. Some factors play a huge role in an individual’s responsiveness, such as knowledge, satisfaction, engagement and pre-intervention expectations. However, in some situations, these factors don’t play a big role, while they may play a huge impact in some cases.

Advantages and disadvantages

Advantages of operationalization

Operationalization brings about the possibility of consistently measuring variables over different contexts. Some of the advantages of operationalization include;

• Objectivity : Operationalization brings about a standard approach that organizations such as colleges and universities can use to collect data that does not provide room for biased or subjective personal interpretations regarding observations.
• Empiricism : While carrying out scientific research, it is done based on observing and measuring findings. Moreover, operational definitions are used in breaking down intangible concepts into characteristics that are recordable.
• Reliability: A desirable operationalization can be used for long by other researchers. If other people use operationalization procedures to measure the same things, then the results need to be the same as those your organization got.

Disadvantages of operationalization

Operational definitions of concepts can have challenges at times. Some of the disadvantages include:

• Reductiveness : This procedure can easily miss out on subjective and meaningful perceptions of concepts through trying to eliminate complex concepts to numbers. For instance, asking students to rate their satisfaction with certain services offered in the university on a 10-point scale will not tell you the reasons why they were not satisfied.
• Underdetermination: Several concepts vary in different social settings and time periods. For instance, poverty is a global problem but there is no exact income level used to determine poverty across different countries.
• Lack of university : operationalization that is context-specific only help in preserving real-life experiences. However, they make it hard to relate studies, especially if there is a huge difference in the measures.

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In a Nutshell

• Operationalization refers to a process that defines measuring a phenomenon that can’t be measured directly if its existence is directly affected by the phenomena. Operationalization, therefore, defines a concept that can be said to be fuzzy to make it measurable, understandable and distinguishable by empirical observation.
• On the other hand, it can be said to define the extension to which a concept such as medicine which is a health phenomenon, can be defined by several indicators like tobacco smoking or body mass index. Another example can be visually processing certain objects’ availability in an environment that can be inferred by taking note of specific aspects of the reflected light.
• In an example like health, it is hard to directly observe or measure the phenomena. Operationalization will help in measuring the existence and certain elements of the extension through measurable and observable effects that they contain.
• At times, there are competing or multiple definitions for the same phenomenon. Analyzing the same phenomenon with different definitions can help check if the results will be affected by different definitions. This is known as checking robustness.

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How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.  

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

Example: There is a positive association between physical activity levels and overall health.  

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

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