Construct Validity | Definition, Types, & Examples
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s crucial to establishing the overall validity of a method.
Assessing construct validity is especially important when you’re researching something that can’t be measured or observed directly, such as intelligence, self-confidence, or happiness. You need multiple observable or measurable indicators to measure those constructs or run the risk of introducing research bias into your work.
- Content validity: Is the test fully representative of what it aims to measure?
- Face validity: Does the content of the test appear to be suitable to its aims?
- Criterion validity: Do the results accurately measure the concrete outcome they are designed to measure?
What is a construct?
A construct is a theoretical concept, theme, or idea based on empirical observations. It’s a variable that’s usually not directly measurable.
Some common constructs include:
- Self-esteem
- Logical reasoning
- Academic motivation
- Social anxiety
You can’t directly observe or measure these constructs. You need to investigate a collection of indicators to test hypotheses about the constructs.
Constructs can range from simple to complex. For example, a concept like hand preference is easily assessed:
- A simple survey question: Ask participants which hand is their dominant hand.
- Observations: Ask participants to perform simple tasks, such as picking up an object or drawing a cat, and observe which hand they use to execute the tasks.
A more complex concept, like social anxiety, requires more nuanced measurements, such as psychometric questionnaires and clinical interviews.
Simple constructs tend to be narrowly defined, while complex constructs are broader and made up of dimensions. Dimensions are different parts of a construct that are coherently linked to make it up as a whole.
As a construct, social anxiety is made up of several dimensions.
- Psychological dimension: Intense fear and anxiety
- Physiological dimension: Physical stress indicators
- Behavioral dimension: Avoidance of social settings
What is construct validity?
Construct validity concerns the extent to which your test or measure accurately assesses what it’s supposed to.
In research, it’s important to operationalize constructs into concrete and measurable characteristics based on your idea of the construct and its dimensions.
Be clear on how you define your construct and how the dimensions relate to each other before you collect or analyze data. This helps you ensure that any measurement method you use accurately assesses the specific construct you’re investigating as a whole and helps avoid biases and mistakes like omitted variable bias or information bias.
- How often do you avoid entering a room when everyone else is already seated?
- Do other people tend to describe you as quiet?
- When talking to new acquaintances, how often do you worry about saying something foolish?
- To what extent do you fear giving a talk in front of an audience?
- How often do you avoid making eye contact with other people?
- Do you prefer to have a small number of close friends over a big group of friends?
When designing or evaluating a measure, it’s important to consider whether it really targets the construct of interest or whether it assesses separate but related constructs.
It’s crucial to differentiate your construct from related constructs and make sure that every part of your measurement technique is solely focused on your specific construct.
- Does your questionnaire solely measure social anxiety?
- Are all aspects of social anxiety covered by the questions?
- Do your questions avoid measuring other relevant constructs like shyness or introversion?
Some of your questions target shyness and introversion as well as social anxiety. This means your questionnaire is overly broad and needs to be narrowed down further to focus solely on social anxiety.
Types of construct validity
There are two main types of construct validity.
- Convergent validity: The extent to which your measure corresponds to measures of related constructs
- Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs
Convergent validity
Convergent validity is the extent to which measures of the same or similar constructs actually correspond to each other.
In research studies, you expect measures of related constructs to correlate with one another. If you have two related scales, people who score highly on one scale tend to score highly on the other as well.
You check that your new questionnaire has convergent validity by testing whether the responses to it correlate with those for the existing scale.
Discriminant validity
Conversely, discriminant validity means that two measures of unrelated constructs that should be unrelated, very weakly related, or negatively related actually are in practice.
You check for discriminant validity the same way as convergent validity: by comparing results for different measures and assessing whether or how they correlate.
How do you select unrelated constructs? It’s good to pick constructs that are theoretically distinct or opposing concepts within the same category.
For example, if your construct of interest is a personality trait (e.g., introversion), it’s appropriate to pick a completely opposing personality trait (e.g., extroversion). You can expect results for your introversion test to be negatively correlated with results for a measure of extroversion.
Alternatively, you can pick non-opposing unrelated concepts and check there are no correlations (or weak correlations) between measures.
You distribute both questionnaires to a large sample and assess validity. Based on a very weak correlation between the results, you can confirm that your questionnaire has discriminant validity.
How do you measure construct validity?
You often focus on assessing construct validity after developing a new measure. It’s best to test out a new measure with a pilot study, but there are other options.
- A pilot study is a trial run of your study. You test out your measure with a small sample to check its feasibility, reliability, and validity. This helps you figure out whether you need to tweak or revise your measure to make sure you’re accurately testing your construct.
- Statistical analyses are often applied to test validity with data from your measures. You test convergent and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
- You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity.
Threats to construct validity
It’s important to recognize and counter threats to construct validity for a robust research design. The most common threats are:
- Poor operationalization
- Experimenter expectancies
- Subject bias
Poor operationalization
A big threat to construct validity is poor operationalization of the construct.
A good operational definition of a construct helps you measure it accurately and precisely every time. Your measurement protocol is clear and specific, and it can be used under different conditions by other people.
Without a good operational definition, you may have random or systematic error, which compromises your results and can lead to information bias. Your measure may not be able to accurately assess your construct.
Experimenter expectancies
Experimenter expectancies about a study can bias your results. It’s best to be aware of this research bias and take steps to avoid it.
To combat this threat, use researcher triangulation and involve people who don’t know the hypothesis in taking measurements in your study. Since they don’t have strong expectations, they are unlikely to bias the results.
Subject bias
When participants hold expectations about the study, their behaviors and responses are sometimes influenced by their own biases. This can threaten your construct validity because you may not be able to accurately measure what you’re interested in.
You can mitigate subject bias by using masking (blinding) to hide the true purpose of the study from participants. By giving them a cover story for your study, you can lower the effect of subject bias on your results, as well as prevent them guessing the point of your research, which can lead to demand characteristics, social desirability bias, and a Hawthorne effect.
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Frequently asked questions about construct validity
- What is the definition of construct validity?
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Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity, which includes construct validity, face validity, and criterion validity.
There are two subtypes of construct validity.
- Convergent validity: The extent to which your measure corresponds to measures of related constructs
- Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs
- Why does construct validity matter?
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When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity, because it covers all of the other types. You need to have face validity, content validity, and criterion validity to achieve construct validity.
- How do I measure construct validity?
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Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity.
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Bhandari, P. (2023, June 22). Construct Validity | Definition, Types, & Examples. Scribbr. Retrieved January 8, 2024, from https://www.scribbr.com/methodology/construct-validity/
