hypothesis operational definition example

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.”

Operational Definition Psychology – Definition, Examples, and How to Write One

Elizabeth Research

Every good psychology study contains an operational definition for the variables in the research. An operational definition allows the researchers to describe in a specific way what they mean when they use a certain term. Generally, operational definitions are concrete and measurable. Defining variables in this way allows other people to see if the research has validity . Validity here refers to if the researchers are actually measuring what they intended to measure.

Definition: An operational definition is the statement of procedures the researcher is going to use in order to measure a specific variable.

We need operational definitions in psychology so that we know exactly what researchers are talking about when they refer to something. There might be different definitions of words depending on the context in which the word is used. Think about how words mean something different to people from different cultures. To avoid any confusion about definitions, in research we explain clearly what we mean when we use a certain term.

Operational Definition Examples

Example one:.

A researcher wants to measure if age is related to addiction. Perhaps their hypothesis is: the incidence of addiction will increase with age. Here we have two variables, age and addiction. In order to make the research as clear as possible, the researcher must define how they will measure these variables. Essentially, how do we measure someone’s age and how to we measure addiction?

Variable One: Age might seem straightforward. You might be wondering why we need to define age if we all know what age is. However, one researcher might decide to measure age in months in order to get someone’s precise age, while another researcher might just choose to measure age in years. In order to understand the results of the study, we will need to know how this researcher operationalized age. For the sake of this example lets say that age is defined as how old someone is in years.

Variable Two: The variable of addiction is slightly more complicated than age. In order to operationalize it the researcher has to decide exactly how they want to measure addiction. They might narrow down their definition and say that addiction is defined as going through withdrawal when the person stops using a substance. Or the researchers might decide that the definition of addiction is: if someone currently meets the DSM-5 diagnostic criteria for any substance use disorder. For the sake of this example, let’s say that the researcher chose the latter.

Final Definition: In this research study age is defined as participant’s age measured in years and the incidence of addiction is defined as whether or not the participant currently meets the DSM-5 diagnostic criteria for any substance use disorder.

Example Two

A researcher wants to measure if there is a correlation between hot weather and violent crime. Perhaps their guiding hypothesis is: as temperature increases so will violent crime. Here we have two variables, weather and violent crime. In order to make this research precise the researcher will have to operationalize the variables.

Variable One: The first variable is weather. The researcher needs to decide how to define weather. Researchers might chose to define weather as outside temperature in degrees Fahrenheit. But we need to get a little more specific because there is not one stable temperature throughout the day. So the researchers might say that weather is defined as the high recorded temperature for the day measured in degrees Fahrenheit.

Variable Two: The second variable is violent crime. Again, the researcher needs to define how violent crime is measured. Let’s say that for this study it they use the FBI’s definition of violent crime . This definition describes violent crime as “murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault”.

However, how do we actually know how many violent crimes were committed on a given day? Researchers might include in the definition something like: the number of people arrested that day for violent crimes as recorded by the local police.

Final Definition: For this study temperature was defined as high recorded temperature for the day measured in degrees Fahrenheit. Violent crime was defined as the number of people arrested in a given day for murder, forcible rape, robbery, and aggravated assault as recorded by the local police.

How to Write an Operational Definition

For the last example take the opportunity to see if you can write a clear operational definition for yourself. Imagine that you are creating a research study and you want to see if group therapy is helpful for treating social anxiety.

Variable One: How are you going to define group therapy? here are some things you might want to consider when creating your operational definition:

• What type of group therapy?
• Who is leading the therapy group?
• How long do people participate in the therapy group for?
• How can you “measure” group therapy?

There is no one way to write the operational definition for this variable. You could say something like group therapy was defined as a weekly cognitive behavioral therapy group led by a licensed MFT held over the course of ten weeks. Remember there are many ways to write an operational definition. You know you have written an effective one if another researcher could pick it up and create a very similar variable based on your definition.

Variable Two: The second variable you need to define is “effective treatment social anxiety”. Again, see if you can come up with an operational definition of this variable. This is a little tricky because you will need to be specific about what an effective treatment is as well as what social anxiety is. Here are some things to consider when writing your definition:

• How do you know a treatment is effective?
• How do you measure the effectiveness of treatment?
• Who provides a reliable definition of social anxiety?
• How can you measure social anxiety?

Again, there is no one right way to write this operational definition. If someone else could recreate the study using your definition it is probably an effective one. Here as one example of how you could operationalize the variable: social anxiety was defined as meeting the DSM-5 criteria for social anxiety and the effectiveness of treatment was defined as the reduction of social anxiety symptoms over the 10 week treatment period.

Final Definition: Take your definition for variable one and your definition for variable two and write them in a clear and succinct way. It is alright for your definition to be more than one sentence.

Why We Need Operational Definitions

There are a number of reasons why researchers need to have operational definitions including:

• Replicability
• Generalizability
• Dissemination

The first reason was mentioned earlier in the post when reading research others should be able to assess the validity of the research. That is, did the researchers measure what they intended to measure? If we don’t know how researchers measured something it is very hard to know if the study had validity.

The next reason it is important to have an operational definition is for the sake of replicability . Research should be designed so that if someone else wanted to replicate it they could. By replicating research and getting the same findings we validate the findings. It is impossible to recreate a study if we are unsure about how they defined or measured the variables.

Another reason we need operational definitions is so that we can understand how generalizable the findings are. In research, we want to know that the findings are true not just for a small sample of people. We hope to get findings that generalize to the whole population. If we do not have operational definitions it is hard to generalize the findings because we don’t know who they generalize to.

Finally, operational definitions are important for the dissemination of information. When a study is done it is generally published in a peer-reviewed journal and might be read by other psychologists, students, or journalists. Researchers want people to read their research and apply their findings. If the person reading the article doesn’t know what they are talking about because a variable is not clear it will be hard to them to actually apply this new knowledge.

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• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

Operationalisation means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalisation, you can systematically collect data on processes and phenomena that aren’t directly observable.

• Self-rating scores on a social anxiety scale
• Number of recent behavioural incidents of avoidance of crowded places
• Intensity of physical anxiety symptoms in social situations

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Table of contents

Why operationalisation matters, how to operationalise concepts, strengths of operationalisation, limitations of operationalisation, frequently asked questions about operationalisation.

In quantitative research , it’s important to precisely define the variables that you want to study.

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalisation reduces subjectivity and increases the reliability  of your study.

Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioural avoidance of crowded places. This means that your results are context-specific and may not generalise to different real-life settings.

Generally, abstract concepts can be operationalised in many different ways. These differences mean that you may actually measure slightly different aspects of a concept, so it’s important to be specific about what you are measuring.

If you test a hypothesis using multiple operationalisations of a concept, you can check whether your results depend on the type of measure that you use. If your results don’t vary when you use different measures, then they are said to be ‘robust’.

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There are three main steps for operationalisation:

• Identify the main concepts you are interested in studying.
• Choose a variable to represent each of the concepts.
• Select indicators for each of your variables.

Step 1: Identify the main concepts you are interested in studying

Based on your research interests and goals, define your topic and come up with an initial research question .

There are two main concepts in your research question:

• Social media behaviour

Step 2: Choose a variable to represent each of the concepts

Your main concepts may each have many variables , or properties, that you can measure.

For instance, are you going to measure the  amount of sleep or the  quality of sleep? And are you going to measure  how often teenagers use social media,  which social media they use, or when they use it?

• Alternate hypothesis: Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis: There is no relation between quality of sleep and night-time social media use in teenagers.

Step 3: Select indicators for each of your variables

To measure your variables, decide on indicators that can represent them numerically.

Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure.

You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.

• To measure sleep quality, you give participants wristbands that track sleep phases.
• To measure night-time social media use, you create a questionnaire that asks participants to track how much time they spend using social media in bed.

After operationalising your concepts, it’s important to report your study variables and indicators when writing up your methodology section. You can evaluate how your choice of operationalisation may have affected your results or interpretations in the discussion section.

Operationalisation makes it possible to consistently measure variables across different contexts.

Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics.

Objectivity

A standardised approach for collecting data leaves little room for subjective or biased personal interpretations of observations.

Reliability

A good operationalisation can be used consistently by other researchers. If other people measure the same thing using your operational definition, they should all get the same results.

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

For example, poverty is a worldwide phenomenon, but the exact income level that determines poverty can differ significantly across countries.

Reductiveness

Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers.

For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.

Lack of universality

Context-specific operationalisations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalised in a wide range of ways (e.g., perceptions of corrupt business practices, or frequency of bribe requests from public officials), but the measures may not consistently reflect the same concept.

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.

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 .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

10.3 Operational definitions

Learning objectives.

Learners will be able to…

• Define and give an example of indicators and attributes for a variable
• Apply the three components of an operational definition to a variable
• Distinguish between levels of measurement for a variable and how those differences relate to measurement
• Describe the purpose of composite measures like scales and indices

Conceptual definitions are like dictionary definitions. They tell you what a concept means by defining it using other concepts. Operationalization occurs after conceptualization and is the process by which researchers spell out precisely how a concept will be measured in their study. It involves identifying the specific research procedures we will use to gather data about our concepts. It entails identifying indicators that can identify when your variable is present or not, the magnitude of the variable, and so forth.

Operationalization works by identifying specific  indicators that will be taken to represent the ideas we are interested in studying. Let’s look at an example. Each day, Gallup researchers poll 1,000 randomly selected Americans to ask them about their well-being. To measure well-being, Gallup asks these people to respond to questions covering six broad areas: physical health, emotional health, work environment, life evaluation, healthy behaviors, and access to basic necessities. Gallup uses these six factors as indicators of the concept that they are really interested in, which is well-being .

Identifying indicators can be even simpler than this example. Political party affiliation is another relatively easy concept for which to identify indicators. If you asked a person what party they voted for in the last national election (or gained access to their voting records), you would get a good indication of their party affiliation. Of course, some voters split tickets between multiple parties when they vote and others swing from party to party each election, so our indicator is not perfect. Indeed, if our study were about political identity as a key concept, operationalizing it solely in terms of who they voted for in the previous election leaves out a lot of information about identity that is relevant to that concept. Nevertheless, it’s a pretty good indicator of political party affiliation.

Choosing indicators is not an arbitrary process. Your conceptual definitions point you in the direction of relevant indicators and then you can identify appropriate indicators in a scholarly manner using theory and empirical evidence.  Specifically, empirical work will give you some examples of how the important concepts in an area have been measured in the past and what sorts of indicators have been used. Often, it makes sense to use the same indicators as previous researchers; however, you may find that some previous measures have potential weaknesses that your own study may improve upon.

So far in this section, all of the examples of indicators deal with questions you might ask a research participant on a questionnaire for survey research. If you plan to collect data from other sources, such as through direct observation or the analysis of available records, think practically about what the design of your study might look like and how you can collect data on various indicators feasibly. If your study asks about whether participants regularly change the oil in their car, you will likely not observe them directly doing so. Instead, you would rely on a survey question that asks them the frequency with which they change their oil or ask to see their car maintenance records.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

What indicators are commonly used to measure the variables in your research question?

• How can you feasibly collect data on these indicators?
• Are you planning to collect your own data using a questionnaire or interview? Or are you planning to analyze available data like client files or raw data shared from another researcher’s project?

Remember, you need raw data . Your research project cannot rely solely on the results reported by other researchers or the arguments you read in the literature. A literature review is only the first part of a research project, and your review of the literature should inform the indicators you end up choosing when you measure the variables in your research question.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS): 

You are interested in studying older adults’ social-emotional well-being. Specifically, you would like to research the impact on levels of older adult loneliness of an intervention that pairs older adults living in assisted living communities with university student volunteers for a weekly conversation.

• How could you feasibly collect data on these indicators?
• Would you collect your own data using a questionnaire or interview? Or would you analyze available data like client files or raw data shared from another researcher’s project?

Steps in the Operationalization Process

Unlike conceptual definitions which contain other concepts, operational definition consists of the following components: (1) the variable being measured and its attributes, (2) the measure you will use, and (3) how you plan to interpret the data collected from that measure to draw conclusions about the variable you are measuring.

Step 1 of Operationalization: Specify variables and attributes

The first component, the variable, should be the easiest part. At this point in quantitative research, you should have a research question with identifiable variables. When social scientists measure concepts, they often use the language of variables and attributes . A variable refers to a quality or quantity that varies across people or situations.  Attributes are the characteristics that make up a variable. For example, the variable hair color could contain attributes such as blonde, brown, black, red, gray, etc.

Levels of measurement

A variable’s attributes determine its level of measurement. There are four possible levels of measurement: nominal, ordinal, interval, and ratio. The first two levels of measurement are  categorical , meaning their attributes are categories rather than numbers. The latter two levels of measurement are  continuous , meaning their attributes are numbers within a range.

Nominal level of measurement

Hair color is an example of a nominal level of measurement. At the nominal level of measurement , attributes are categorical, and those categories cannot be mathematically ranked. In all nominal levels of measurement, there is no ranking order; the attributes are simply different. Gender and race are two additional variables measured at the nominal level. A variable that has only two possible attributes is called binary or dichotomous . If you are measuring whether an individual has received a specific service, this is a dichotomous variable, as the only two options are received or not received.

What attributes are contained in the variable  hair color ?  Brown, black, blonde, and red are common colors, but if we only list these attributes, many people may not fit into those categories. This means that our attributes were not exhaustive. Exhaustiveness means that every participant can find a choice for their attribute in the response options. It is up to the researcher to include the most comprehensive attribute choices relevant to their research questions. We may have to list a lot of colors before we can meet the criteria of exhaustiveness. Clearly, there is a point at which exhaustiveness has been reasonably met. If a person insists that their hair color is light burnt sienna , it is not your responsibility to list that as an option. Rather, that person would reasonably be described as brown-haired. Perhaps listing a category for  other color  would suffice to make our list of colors exhaustive.

What about a person who has multiple hair colors at the same time, such as red and black? They would fall into multiple attributes. This violates the rule of  mutual exclusivity , in which a person cannot fall into two different attributes. Instead of listing all of the possible combinations of colors, perhaps you might include a  multi-color  attribute to describe people with more than one hair color.

Making sure researchers provide mutually exclusive and exhaustive attribute options is about making sure all people are represented in the data record. For many years, the attributes for gender were only male or female. Now, our understanding of gender has evolved to encompass more attributes that better reflect the diversity in the world. Children of parents from different races were often classified as one race or another, even if they identified with both. The option for bi-racial or multi-racial on a survey not only more accurately reflects the racial diversity in the real world but also validates and acknowledges people who identify in that manner. If we did not measure race in this way, we would leave empty the data record for people who identify as biracial or multiracial, impairing our search for truth.

Ordinal level of measurement

Unlike nominal-level measures, attributes at the  ordinal level of measurement can be rank-ordered. For example, someone’s degree of satisfaction in their romantic relationship can be ordered by magnitude of satisfaction. That is, you could say you are not at all satisfied, a little satisfied, moderately satisfied, or highly satisfied. Even though these have a rank order to them (not at all satisfied is certainly worse than highly satisfied), we cannot calculate a mathematical distance between those attributes. We can simply say that one attribute of an ordinal-level variable is more or less than another attribute.  A variable that is commonly measured at the ordinal level of measurement in social work is education (e.g., less than high school education, high school education or equivalent, some college, associate’s degree, college degree, graduate  degree or higher). Just as with nominal level of measurement, ordinal-level attributes should also be exhaustive and mutually exclusive.

Rating scales for ordinal-level measurement

The fact that we cannot specify exactly how far apart the responses for different individuals in ordinal level of measurement can become clear when using rating scales . If you have ever taken a customer satisfaction survey or completed a course evaluation for school, you are familiar with rating scales such as, “On a scale of 1-5, with 1 being the lowest and 5 being the highest, how likely are you to recommend our company to other people?” Rating scales use numbers, but only as a shorthand, to indicate what attribute (highly likely, somewhat likely, etc.) the person feels describes them best. You wouldn’t say you are “2” likely to recommend the company, but you would say you are “not very likely” to recommend the company. In rating scales the difference between 2 = “ not very likely” and 3 = “ somewhat likely” is not quantifiable as a difference of 1. Likewise, we couldn’t say that it is the same as the difference between 3 = “ somewhat likely ” and 4 = “ very likely .”

Rating scales can be unipolar rating scales where only one dimension is tested, such as frequency (e.g., Never, Rarely, Sometimes, Often, Always) or strength of satisfaction (e.g., Not at all, Somewhat, Very). The attributes on a unipolar rating scale are different magnitudes of the same concept.

There are also bipolar rating scales where there is a dichotomous spectrum, such as liking or disliking (Like very much, Like somewhat, Like slightly, Neither like nor dislike, Dislike slightly, Dislike somewhat, Dislike very much). The attributes on the ends of a bipolar scale are opposites of one another. Figure 10.1 shows several examples of bipolar rating scales.

Interval level of measurement

Interval measures are continuous, meaning the meaning and interpretation of their attributes are numbers, rather than categories. Temperatures in Fahrenheit and Celsius are interval level, as are IQ scores and credit scores. Just like variables measured at the ordinal level, the attributes for variables measured at the interval level should be mutually exclusive and exhaustive, and are rank-ordered. In addition, they also have an equal distance between the attribute values.

The interval level of measurement allows us to examine “how much more” is one attribute when compared to another, which is not possible with nominal or ordinal measures. In other words, the unit of measurement allows us to compare the distance between attributes. The value of one unit of measurement (e.g., one degree Celsius, one IQ point) is always the same regardless of where in the range of values you look. The difference of 10 degrees between a temperature of 50 and 60 degrees Fahrenheit is the same as the difference between 60 and 70 degrees Fahrenheit.

We cannot, however, say with certainty what the ratio of one attribute is in comparison to another. For example, it would not make sense to say that a person with an IQ score of 140 has twice the IQ of a person with a score of 70. However, the difference between IQ scores of 80 and 100 is the same as the difference between IQ scores of 120 and 140.

You may find research in which ordinal-level variables are treated as if they are interval measures for analysis. This can be a problem because as we’ve noted, there is no way to know whether the difference between a 3 and a 4 on a rating scale is the same as the difference between a 2 and a 3. Those numbers are just placeholders for categories.

Ratio level of measurement

The final level of measurement is the ratio level of measurement .  Variables measured at the ratio level of measurement are continuous variables, just like with interval scale. They, too, have equal intervals between each point. However, the ratio level of measurement has a true zero, which means that  a value of zero on a ratio scale means that the variable you’re measuring is absent. For example, if you have no siblings, the a value of 0 indicates this (unlike a temperature of 0 which does not mean there is no temperature). What is the advantage of having a “true zero?” It allows you to calculate ratios. For example, if you have a three siblings, you can say that this is half the number of siblings as a person with six.

At the ratio level, the attribute values are mutually exclusive and exhaustive, can be rank-ordered, the distance between attributes is equal, and attributes have a true zero point. Thus, with these variables, we can  say what the ratio of one attribute is in comparison to another. Examples of ratio-level variables include age and years of education. We know that a person who is 12 years old is twice as old as someone who is 6 years old. Height measured in meters and weight measured in kilograms are good examples. So are counts of discrete objects or events such as the number of siblings one has or the number of questions a student answers correctly on an exam. Measuring interval and ratio data is relatively easy, as people either select or input a number for their answer. If you ask a person how many eggs they purchased last week, they can simply tell you they purchased `a dozen eggs at the store, two at breakfast on Wednesday, or none at all.

The differences between each level of measurement are visualized in Table 10.2.

Levels of measurement=levels of specificity

We have spent time learning how to determine a variable’s level of measurement. Now what? How could we use this information to help us as we measure concepts and develop measurement tools? First, the types of statistical tests that we are able to use depend on level of measurement. With nominal-level measurement, for example, the only available measure of central tendency is the mode. With ordinal-level measurement, the median or mode can be used. Interval- and ratio-level measurement are typically considered the most desirable because they permit any indicators of central tendency to be computed (i.e., mean, median, or mode). Also, ratio-level measurement is the only level that allows meaningful statements about ratios of scores. The higher the level of measurement, the more options we have for the statistical tests we are able to conduct. This knowledge may help us decide what kind of data we need to gather, and how.

That said, we have to balance this knowledge with the understanding that sometimes, collecting data at a higher level of measurement could negatively impact our studies. For instance, sometimes providing answers in ranges may make prospective participants feel more comfortable responding to sensitive items. Imagine that you were interested in collecting information on topics such as income, number of sexual partners, number of times someone used illicit drugs, etc. You would have to think about the sensitivity of these items and determine if it would make more sense to collect some data at a lower level of measurement (e.g., nominal: asking if they are sexually active or not) versus a higher level such as ratio (e.g., their total number of sexual partners).

Finally, sometimes when analyzing data, researchers find a need to change a variable’s level of measurement. For example, a few years ago, a student was interested in studying the association between mental health and life satisfaction. This student used a variety of measures. One item asked about the number of mental health symptoms, reported as the actual number. When analyzing data, the student examined the mental health symptom variable and noticed that she had two groups, those with none or one symptoms and those with many symptoms. Instead of using the ratio level data (actual number of mental health symptoms), she collapsed her cases into two categories, few and many. She decided to use this variable in her analyses. It is important to note that you can move a higher level of data to a lower level of data; however, you are unable to move a lower level to a higher level.

• Check that the variables in your research question can vary…and that they are not constants or one of many potential attributes of a variable.
• Think about the attributes your variables have. Are they categorical or continuous? What level of measurement seems most appropriate?

Step 2 of Operationalization: Specify measures for each variable

Let’s pick a social work research question and walk through the process of operationalizing variables to see how specific we need to get. Suppose we hypothesize that residents of a psychiatric unit who are more depressed are less likely to be satisfied with care. Remember, this would be an inverse relationship—as levels of depression increase, satisfaction decreases. In this hypothesis, level of depression is the independent (or predictor) variable and satisfaction with care is the dependent (or outcome) variable.

How would you measure these key variables? What indicators would you look for? Some might say that levels of depression could be measured by observing a participant’s body language. They may also say that a depressed person will often express feelings of sadness or hopelessness. In addition, a satisfied person might be happy around service providers and often express gratitude. While these factors may indicate that the variables are present, they lack coherence. Unfortunately, what this “measure” is actually saying is that “I know depression and satisfaction when I see them.” In a research study, you need more precision for how you plan to measure your variables. Individual judgments are subjective, based on idiosyncratic experiences with depression and satisfaction. They couldn’t be replicated by another researcher. They also can’t be done consistently for a large group of people. Operationalization requires that you come up with a specific and rigorous measure for seeing who is depressed or satisfied.

Finding a good measure for your variable depends on the kind of variable it is. Variables that are directly observable might include things like taking someone’s blood pressure, marking attendance or participation in a group, and so forth. To measure an indirectly observable variable like age, you would probably put a question on a survey that asked, “How old are you?” Measuring a variable like income might first require some more conceptualization, though. Are you interested in this person’s individual income or the income of their family unit? This might matter if your participant does not work or is dependent on other family members for income. Do you count income from social welfare programs? Are you interested in their income per month or per year? Even though indirect observables are relatively easy to measure, the measures you use must be clear in what they are asking, and operationalization is all about figuring out the specifics about how to measure what you want to know. For more complicated variables such as constructs, you will need compound measures that use multiple indicators to measure a single variable.

How you plan to collect your data also influences how you will measure your variables. For social work researchers using secondary data like client records as a data source, you are limited by what information is in the data sources you can access. If a partnering organization uses a given measurement for a mental health outcome, that is the one you will use in your study. Similarly, if you plan to study how long a client was housed after an intervention using client visit records, you are limited by how their caseworker recorded their housing status in the chart. One of the benefits of collecting your own data is being able to select the measures you feel best exemplify your understanding of the topic.

Composite measures

Depending on your research design, your measure may be something you put on a survey or pre/post-test that you give to your participants. For a variable like age or income, one well-worded item may suffice. Unfortunately, most variables in the social world are not so simple. Depression and satisfaction are multidimensional concepts. Relying on a indicator that is a single item on a questionnaire like a question that asks “Yes or no, are you depressed?” does not encompass the complexity of constructs.

For more complex variables, researchers use scales and indices (sometimes called indexes) because they use multiple items to develop a composite (or total) score as a measure for a variable. As such, they are called composite measures . Composite measures provide a much greater understanding of concepts than a single item could.

It can be complex to delineate between multidimensional and unidimensional concepts. If satisfaction were a key variable in our study, we would need a theoretical framework and conceptual definition for it. Perhaps we come to view satisfaction has having two dimensions: a mental one and an emotional one. That means we would need to include indicators that measured both mental and emotional satisfaction as separate dimensions of satisfaction. However, if satisfaction is not a key variable in your theoretical framework, it may make sense to operationalize it as a unidimensional concept.

Although we won’t delve too deeply into the process of scale development, we will cover some important topics for you to understand how scales and indices developed by other researchers can be used in your project.

Need to make better sense of the following content:

Measuring abstract concepts in concrete terms remains one of the most difficult tasks in empirical social science research.

A scale , XXXXXXXXXXXX .

The scales we discuss in this section are a  different from “rating scales” discussed in the previous section. A rating scale is used to capture the respondents’ reactions to a given item on a questionnaire. For example, an ordinally scaled item captures a value between “strongly disagree” to “strongly agree.” Attaching a rating scale to a statement or instrument is not scaling. Rather, scaling is the formal process of developing scale items, before rating scales can be attached to those items.

If creating your own scale sounds painful, don’t worry! For most constructs, you would likely be duplicating work that has already been done by other researchers. Specifically, this is a branch of science called psychometrics. You do not need to create a scale for depression because scales such as the Patient Health Questionnaire (PHQ-9) [1] , the Center for Epidemiologic Studies Depression Scale (CES-D) [2] , and Beck’s Depression Inventory [3] (BDI) have been developed and refined over dozens of years to measure variables like depression. Similarly, scales such as the Patient Satisfaction Questionnaire (PSQ-18) have been developed to measure satisfaction with medical care. As we will discuss in the next section, these scales have been shown to be reliable and valid. While you could create a new scale to measure depression or satisfaction, a study with rigor would pilot test and refine that new scale over time to make sure it measures the concept accurately and consistently before using it in other research. This high level of rigor is often unachievable in smaller research projects because of the cost and time involved in pilot testing and validating, so using existing scales is recommended.

Unfortunately, there is no good one-stop-shop for psychometric scales. The Mental Measurements Yearbook provides a list of measures for social science variables, though it is incomplete and may not contain the full documentation for instruments in its database. It is available as a searchable database by many university libraries.

Perhaps an even better option could be looking at the methods section of the articles in your literature review. The methods section of each article will detail how the researchers measured their variables, and often the results section is instructive for understanding more about measures. In a quantitative study, researchers may have used a scale to measure key variables and will provide a brief description of that scale, its names, and maybe a few example questions. If you need more information, look at the results section and tables discussing the scale to get a better idea of how the measure works.

Looking beyond the articles in your literature review, searching Google Scholar or other databases using queries like “depression scale” or “satisfaction scale” should also provide some relevant results. For example, searching for documentation for the Rosenberg Self-Esteem Scale, I found this report about useful measures for acceptance and commitment therapy which details measurements for mental health outcomes. If you find the name of the scale somewhere but cannot find the documentation (i.e., all items, response choices, and how to interpret the scale), a general web search with the name of the scale and “.pdf” may bring you to what you need. Or, to get professional help with finding information, ask a librarian!

Unfortunately, these approaches do not guarantee that you will be able to view the scale itself or get information on how it is interpreted. Many scales cost money to use and may require training to properly administer. You may also find scales that are related to your variable but would need to be slightly modified to match your study’s needs. You could adapt a scale to fit your study, however changing even small parts of a scale can influence its accuracy and consistency. Pilot testing is always recommended for adapted scales, and researchers seeking to draw valid conclusions and publish their results should take this additional step.

Types of scales

Likert scales.

Although Likert scale is a term colloquially used to refer to almost any rating scale (e.g., a 0-to-10 life satisfaction scale), it has a much more precise meaning. In the 1930s, researcher Rensis Likert (pronounced LICK-ert) created a new approach for measuring people’s attitudes (Likert, 1932) . [4] It involves presenting people with several statements—including both favorable and unfavorable statements—about some person, group, or idea. Respondents then express their approval or disapproval with each statement on a 5-point rating scale: Strongly Approve ,  Approve , Undecided ,  Disapprove,  Strongly Disapprove . Numbers are assigned to each response a nd then summed across all items to produce a score representing the attitude toward the person, group, or idea. For items that are phrased in an opposite direction (e.g., negatively worded statements instead of positively worded statements), reverse coding is used so that the numerical scoring of statements also runs in the opposite direction.  The scores for the entire set of items are totaled for a score for the attitude of interest. This type of scale came to be called a Likert scale, as indicated in Table 10.3 below. Scales that use similar logic but do not have these exact characteristics are referred to as “Likert-type scales.” 

Semantic Differential Scales

Semantic differential scales are composite scales in which respondents are asked to indicate their opinions or feelings toward a single statement using different pairs of adjectives framed as polar opposites. Whereas in a Likert scale, a participant is asked how much they approve or disapprove of a statement, in a semantic differential scale the participant is asked to indicate how they about a specific item using several pairs of opposites. This makes the semantic differential scale an excellent technique for measuring people’s feelings toward objects, events, or behaviors. Table 10.4 provides an example of a semantic differential scale that was created to assess participants’ feelings about this textbook.

Guttman Scales

A specialized scale for measuring unidimensional concepts was designed by Louis Guttman. A Guttman scale (also called cumulative scale ) uses a series of items arranged in increasing order of intensity (least intense to most intense) of the concept. This type of scale allows us to understand the intensity of beliefs or feelings. Each item in the Guttman scale below has a weight (this is not indicated on the tool) which varies with the intensity of that item, and the weighted combination of each response is used as an aggregate measure of an observation.

Table XX presents an example of a Guttman Scale. Notice how the items move from lower intensity to higher intensity. A researcher reviews the yes answers and creates a score for each participant.

Example Guttman Scale Items

• I often felt the material was not engaging                               Yes/No
• I was often thinking about other things in class                     Yes/No
• I was often working on other tasks during class                     Yes/No
• I will work to abolish research from the curriculum              Yes/No

An index is a composite score derived from aggregating measures of multiple indicators. At its most basic, an index sums up indicators. A well-known example of an index is the consumer price index (CPI), which is computed every month by the Bureau of Labor Statistics of the U.S. Department of Labor. The CPI is a measure of how much consumers have to pay for goods and services (in general) and is divided into eight major categories (food and beverages, housing, apparel, transportation, healthcare, recreation, education and communication, and “other goods and services”), which are further subdivided into more than 200 smaller items. Each month, government employees call all over the country to get the current prices of more than 80,000 items. Using a complicated weighting scheme that takes into account the location and probability of purchase for each item, analysts then combine these prices into an overall index score using a series of formulas and rules.

Another example of an index is the Duncan Socioeconomic Index (SEI). This index is used to quantify a person’s socioeconomic status (SES) and is a combination of three concepts: income, education, and occupation. Income is measured in dollars, education in years or degrees achieved, and occupation is classified into categories or levels by status. These very different measures are combined to create an overall SES index score. However, SES index measurement has generated a lot of controversy and disagreement among researchers.

The process of creating an index is similar to that of a scale. First, conceptualize the index and its constituent components. Though this appears simple, there may be a lot of disagreement on what components (concepts/constructs) should be included or excluded from an index. For instance, in the SES index, isn’t income correlated with education and occupation? And if so, should we include one component only or all three components? Reviewing the literature, using theories, and/or interviewing experts or key stakeholders may help resolve this issue. Second, operationalize and measure each component. For instance, how will you categorize occupations, particularly since some occupations may have changed with time (e.g., there were no Web developers before the Internet)? As we will see in step three below, researchers must create a rule or formula for calculating the index score. Again, this process may involve a lot of subjectivity, so validating the index score using existing or new data is important.

Differences between scales and indices

Though indices and scales yield a single numerical score or value representing a concept of interest, they are different in many ways. First, indices often comprise components that are very different from each other (e.g., income, education, and occupation in the SES index) and are measured in different ways. Conversely, scales typically involve a set of similar items that use the same rating scale (such as a five-point Likert scale about customer satisfaction).

Second, indices often combine objectively measurable values such as prices or income, while scales are designed to assess subjective or judgmental constructs such as attitude, prejudice, or self-esteem. Some argue that the sophistication of the scaling methodology makes scales different from indexes, while others suggest that indexing methodology can be equally sophisticated. Nevertheless, indexes and scales are both essential tools in social science research.

Scales and indices seem like clean, convenient ways to measure different phenomena in social science, but just like with a lot of research, we have to be mindful of the assumptions and biases underneath. What if the developers of scale or an index were influenced by unconscious biases? Or what if it was validated using only White women as research participants? Is it going to be useful for other groups? It very well might be, but when using a scale or index on a group for whom it hasn’t been tested, it will be very important to evaluate the validity and reliability of the instrument, which we address in the rest of the chapter.

Finally, it’s important to note that while scales and indices are often made up of items measured at the nominal or ordinal level, the scores on the composite measurement are continuous variables.

Looking back to your work from the previous section, are your variables unidimensional or multidimensional?

• Describe the specific measures you will use (actual questions and response options you will use with participants) for each variable in your research question.
• If you are using a measure developed by another researcher but do not have all of the questions, response options, and instructions needed to implement it, put it on your to-do list to get them.
• Describe at least one specific measure you would use (actual questions and response options you would use with participants) for the dependent variable in your research question.

Step 3 in Operationalization: Determine how to interpret measures

The final stage of operationalization involves setting the rules for how the measure works and how the researcher should interpret the results. Sometimes, interpreting a measure can be incredibly easy. If you ask someone their age, you’ll probably interpret the results by noting the raw number (e.g., 22) someone provides and that it is lower or higher than other people’s ages. However, you could also recode that person into age categories (e.g., under 25, 20-29-years-old, generation Z, etc.). Even scales or indices may be simple to interpret. If there is an index of problem behaviors, one might simply add up the number of behaviors checked off–with a range from 1-5 indicating low risk of delinquent behavior, 6-10 indicating the student is moderate risk, etc. How you choose to interpret your measures should be guided by how they were designed, how you conceptualize your variables, the data sources you used, and your plan for analyzing your data statistically. Whatever measure you use, you need a set of rules for how to take any valid answer a respondent provides to your measure and interpret it in terms of the variable being measured.

For more complicated measures like scales, refer to the information provided by the author for how to interpret the scale. If you can’t find enough information from the scale’s creator, look at how the results of that scale are reported in the results section of research articles. For example, Beck’s Depression Inventory (BDI-II) uses 21 statements to measure depression and respondents rate their level of agreement on a scale of 0-3. The results for each question are added up, and the respondent is put into one of three categories: low levels of depression (1-16), moderate levels of depression (17-30), or severe levels of depression (31 and over) ( NEEDS CITATION) .

Operationalization is a tricky component of basic research methods, so don’t get frustrated if it takes a few drafts and a lot of feedback to get to a workable operational definition.

Key Takeaways

• Operationalization involves spelling out precisely how a concept will be measured.
• Operational definitions must include the variable, the measure, and how you plan to interpret the measure.
• There are four different levels of measurement: nominal, ordinal, interval, and ratio (in increasing order of specificity).
• Scales and indices are common ways to collect information and involve using multiple indicators in measurement.
• A key difference between a scale and an index is that a scale contains multiple indicators for one concept, whereas an indicator examines multiple concepts (components).
• Using scales developed and refined by other researchers can improve the rigor of a quantitative study.

Use the research question that you developed in the previous chapters and find a related scale or index that researchers have used. If you have trouble finding the exact phenomenon you want to study, get as close as you can.

• What is the level of measurement for each item on each tool? Take a second and think about why the tool’s creator decided to include these levels of measurement. Identify any levels of measurement you would change and why.
• If these tools don’t exist for what you are interested in studying, why do you think that is?

Using your working research question, find a related scale or index that researchers have used to measure the dependent variable. If you have trouble finding the exact phenomenon you want to study, get as close as you can.

• What is the level of measurement for each item on the tool? Take a second and think about why the tool’s creator decided to include these levels of measurement. Identify any levels of measurement you would change and why.
• Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of general internal medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x ↵
• Radloff, L. S. (1977). The CES-D scale: A self report depression scale for research in the general population. Applied Psychological Measurements, 1, 385-401. ↵
• Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of general psychiatry, 4, 561–571. https://doi.org/10.1001/archpsyc.1961.01710120031004 ↵
• Likert, R. (1932). A technique for the measurement of attitudes.  Archives of Psychology, 140 , 1–55. ↵

process by which researchers spell out precisely how a concept will be measured in their study

Clues that demonstrate the presence, intensity, or other aspects of a concept in the real world

unprocessed data that researchers can analyze using quantitative and qualitative methods (e.g., responses to a survey or interview transcripts)

“a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population” (Gillespie & Wagner, 2018, p. 9)

The characteristics that make up a variable

variables whose values are organized into mutually exclusive groups but whose numerical values cannot be used in mathematical operations.

variables whose values are mutually exclusive and can be used in mathematical operations

The lowest level of measurement; categories cannot be mathematically ranked, though they are exhaustive and mutually exclusive

Exhaustive categories are options for closed ended questions that allow for every possible response (no one should feel like they can't find the answer for them).

Mutually exclusive categories are options for closed ended questions that do not overlap, so people only fit into one category or another, not both.

Level of measurement that follows nominal level. Has mutually exclusive categories and a hierarchy (rank order), but we cannot calculate a mathematical distance between attributes.

An ordered set of responses that participants must choose from.

A rating scale where the magnitude of a single trait is being tested

A rating scale in which a respondent selects their alignment of choices between two opposite poles such as disagreement and agreement (e.g., strongly disagree, disagree, agree, strongly agree).

A level of measurement that is continuous, can be rank ordered, is exhaustive and mutually exclusive, and for which the distance between attributes is known to be equal. But for which there is no zero point.

The highest level of measurement. Denoted by mutually exclusive categories, a hierarchy (order), values can be added, subtracted, multiplied, and divided, and the presence of an absolute zero.

measurements of variables based on more than one one indicator

An empirical structure for measuring items or indicators of the multiple dimensions of a concept.

measuring people’s attitude toward something by assessing their level of agreement with several statements about it

Composite (multi-item) scales in which respondents are asked to indicate their opinions or feelings toward a single statement using different pairs of adjectives framed as polar opposites.

A composite scale using a series of items arranged in increasing order of intensity of the construct of interest, from least intense to most intense.

a composite score derived from aggregating measures of multiple concepts (called components) using a set of rules and formulas

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• cognitive sophistication
• tolerance of diversity
• exposure to higher levels of math or science
• age (which is currently related to educational level in many countries)
• social class and other variables.
• For example, suppose you designed a treatment to help people stop smoking. Because you are really dedicated, you assigned the same individuals simultaneously to (1) a "stop smoking" nicotine patch; (2) a "quit buddy"; and (3) a discussion support group. Compared with a group in which no intervention at all occurred, your experimental group now smokes 10 fewer cigarettes per day.
• There is no relationship among two or more variables (EXAMPLE: the correlation between educational level and income is zero)
• Or that two or more populations or subpopulations are essentially the same (EXAMPLE: women and men have the same average science knowledge scores.)
• the difference between two and three children = one child.
• the difference between eight and nine children also = one child.
• the difference between completing ninth grade and tenth grade is  one year of school
• the difference between completing junior and senior year of college is one year of school
• In addition to all the properties of nominal, ordinal, and interval variables, ratio variables also have a fixed/non-arbitrary zero point. Non arbitrary means that it is impossible to go below a score of zero for that variable. For example, any bottom score on IQ or aptitude tests is created by human beings and not nature. On the other hand, scientists believe they have isolated an "absolute zero." You can't get colder than that.

2.5 Designing a Research Study

Learning objectives.

• Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
• Explain the difference between a population and a sample.
• Distinguish between experimental and non-experimental research.
• Distinguish between lab studies, field studies, and field experiments.

Identifying and Defining the Variables and Population

Variables and operational definitions.

Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A  variable  is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. Almost everything in our world varies and as such thinking of examples of constants (things that don’t vary) is far more difficult. A rare example of a constant is the speed of light. Variables can be either quantitative or categorical. A  quantitative variable  is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable  is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

After the researcher generates his or her hypothesis and selects the variables he or she wants to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an  operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.

Sampling and Measurement

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

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

Experimental vs. Non-Experimental Research

The next step a researcher must take is to decide which type of approach he or she will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.

Experimental Research

Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous factors, and then they measure how the manipulated variables affect participants’ responses.

The terms independent variable and dependent variable are used in the context of experimental research. The independent variable is the variable the experimenter manipulates (it is the presumed cause) and the dependent variable is the variable the experimenter measures (it is the presumed effect).

Confounds are also a term that is rather specific to experimental research. A confound is an extraneous variable (so a variable other than the independent variable and dependent variable) that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.

As an example, if we manipulate the lighting in the room and examine the effects of that manipulation on workers’ productivity, then the lighting conditions (bright lights vs. dim lights) would be considered the independent variable and the workers’ productivity would be considered the dependent variable. If the bright lights are noisy then that noise would be a confound since the noise would be present whenever the lights are bright and the noise would be absent when the lights are dim. If noise is varying systematically with light then we wouldn’t know if a difference in worker productivity across the two lighting conditions is due to noise or light. So confounds are bad, they disrupt our ability to make causal conclusions about the nature of the relationship between variables. However, if there is noise in the room both when the lights are on and when the lights are off then noise is merely an extraneous variable (it is a variable other than the independent or dependent variable) and we don’t worry much about extraneous variables. This is because unless a variable varies systematically with the manipulated independent variable it cannot be a competing explanation for the results.

Non-Experimental Research

Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental or descriptive research. Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is scientific in nature. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.

Laboratory vs. Field Research

The next major distinction between research methods is between laboratory and field studies. A laboratory study is a study that is conducted in the laboratory environment. In contrast, a field study is a study that is conducted in the real-world, in a natural environment.

Laboratory experiments typically have high  internal validity . Internal validity refers to the degree to which we can confidently infer a causal relationship between variables. When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables. When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable. In contrast, because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.

But there is typically a trade-off between internal and external validity . When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. External validity simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.

Finally, there are field studies which are nonexperimental in nature because nothing is manipulated. But there are also field experiments where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.

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Operational Definition

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Kantowitz BH, Roediger HL, Elmes DG (2015) Experimental psychology, 10th edn. Cengage Learning, Boston

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5.1 Understanding Psychological Measurement

Learning objectives.

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

What Is Measurement?

Measurement is the assignment of scores to individuals so that the scores represent some characteristic of the individuals. This very general definition is consistent with the kinds of measurement that everyone is familiar with—for example, weighing oneself by stepping onto a bathroom scale, or checking the internal temperature of a roasting turkey by inserting a meat thermometer. It is also consistent with measurement throughout the sciences. In physics, for example, one might measure the potential energy of an object in Earth’s gravitational field by finding its mass and height (which of course requires measuring those variables) and then multiplying them together along with the gravitational acceleration of Earth (9.8 m/s 2 ). The result of this procedure is a score that represents the object’s potential energy.

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

The important point here is that measurement does not require any particular instruments or procedures. It does not require placing individuals or objects on bathroom scales, holding rulers up to them, or inserting thermometers into them. What it does require is some systematic procedure for assigning scores to individuals or objects so that those scores represent the characteristic of interest.

Psychological Constructs

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

Psychological constructs cannot be observed directly. One reason is that they often represent tendencies to think, feel, or act in certain ways. For example, to say that a particular college student is highly extroverted (see Note 5.6 “The Big Five” ) does not necessarily mean that she is behaving in an extroverted way right now. In fact, she might be sitting quietly by herself, reading a book. Instead, it means that she has a general tendency to behave in extroverted ways (talking, laughing, etc.) across a variety of situations. Another reason psychological constructs cannot be observed directly is that they often involve internal processes. Fear, for example, involves the activation of certain central and peripheral nervous system structures, along with certain kinds of thoughts, feelings, and behaviors—none of which is necessarily obvious to an outside observer. Notice also that neither extroversion nor fear “reduces to” any particular thought, feeling, act, or physiological structure or process. Instead, each is a kind of summary of a complex set of behaviors and internal processes.

The Big Five

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

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

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

Operational Definitions

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

In addition to self-report and behavioral measures, researchers in psychology use physiological measures. An electroencephalograph (EEG) records electrical activity from the brain.

Wikimedia Commons – public domain.

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

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

Levels of Measurement

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

The nominal level of measurement is used for categorical variables and involves assigning scores that are category labels. Category labels communicate whether any two individuals are the same or different in terms of the variable being measured. For example, if you look at your research participants as they enter the room, decide whether each one is male or female, and type this information into a spreadsheet, you are engaged in nominal-level measurement. Or if you ask your participants to indicate which of several ethnicities they identify themselves with, you are again engaged in nominal-level measurement.

The remaining three levels of measurement are used for quantitative variables. The ordinal level of measurement involves assigning scores so that they represent the rank order of the individuals. Ranks communicate not only whether any two individuals are the same or different in terms of the variable being measured but also whether one individual is higher or lower on that variable. The interval level of measurement involves assigning scores so that they represent the precise magnitude of the difference between individuals, but a score of zero does not actually represent the complete absence of the characteristic. A classic example is the measurement of heat using the Celsius or Fahrenheit scale. The difference between temperatures of 20°C and 25°C is precisely 5°, but a temperature of 0°C does not mean that there is a complete absence of heat. In psychology, the intelligence quotient (IQ) is often considered to be measured at the interval level. Finally, the ratio level of measurement involves assigning scores in such a way that there is a true zero point that represents the complete absence of the quantity. Height measured in meters and weight measured in kilograms are good examples. So are counts of discrete objects or events such as the number of siblings one has or the number of questions a student answers correctly on an exam.

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

Key Takeaways

• Measurement is the assignment of scores to individuals so that the scores represent some characteristic of the individuals. Psychological measurement can be achieved in a wide variety of ways, including self-report, behavioral, and physiological measures.
• Psychological constructs such as intelligence, self-esteem, and depression are variables that are not directly observable because they represent behavioral tendencies or complex patterns of behavior and internal processes. An important goal of scientific research is to conceptually define psychological constructs in ways that accurately describe them.
• For any conceptual definition of a construct, there will be many different operational definitions or ways of measuring it. The use of multiple operational definitions, or converging operations, is a common strategy in psychological research.
• Variables can be measured at four different levels—nominal, ordinal, interval, and ratio—that communicate increasing amounts of quantitative information. The level of measurement affects the kinds of statistics you can use and conclusions you can draw from your data.
• Practice: Complete the Rosenberg Self-Esteem Scale and compute your overall score.
• Practice: Think of three operational definitions for sexual jealousy, decisiveness, and social anxiety. Consider the possibility of self-report, behavioral, and physiological measures. Be as precise as you can.

Practice: For each of the following variables, decide which level of measurement is being used.

• A college instructor measures the time it takes his students to finish an exam by looking through the stack of exams at the end. He assigns the one on the bottom a score of 1, the one on top of that a 2, and so on.
• A researcher accesses her participants’ medical records and counts the number of times they have seen a doctor in the past year.
• Participants in a research study are asked whether they are right-handed or left-handed.

Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. Journal of Abnormal and Social Psychology, 63 , 575–582.

Costa, P. T., Jr., & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological Assessment, 4 , 5–13.

Segerstrom, S. E., & Miller, G. E. (2004). Psychological stress and the human immune system: A meta-analytic study of 30 years of inquiry. Psychological Bulletin, 130 , 601–630.

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

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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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

STM1001 Topic 2B (Science and Health)

1.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester, a conceptual definition would describe what is meant by 'stress'. An operational definition would describe how the 'stress' would be measured.

Sometimes the definitions themselves aren't important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an 'adult' in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 1.2 (Operational and conceptual definitions) A student project at my university used this RQ:

Amongst students[...], on average do student who participate in competitive swimming have greater shoulder flexibility than the remainder of the able-bodied USC student population?

Example 1.3 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an "impulsive" force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful... it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

... a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)... [ Note : This table includes symptoms such as 'convulsion', 'clearly dazed', etc.]; An abnormal post game, same day assessment...; An abnormal 36--48 h assessment...; The presence of clinical suspicion by the treating doctor at any time...

Example 1.4 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 1.5 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen, Kamarck, and Mermelstein 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of 'snacking':

...an eating occasion that occurred between meals based on time of day. --- Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the 'pace of life' and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for 'pace of life' (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave 'change for two $20 bills or [gave] two $20 bills for change'.
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure 'pace of life', of course. Nonetheless, the researchers found that, compared to people on the West Coast,

... people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch... --- Levine ( 1990 ) (p. 455)

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2.2: Concepts, Constructs, and Variables

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

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

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

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

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

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

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

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

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)

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

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).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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.

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