What is construct validity?

There are several ways to measure the validity of tests, one of which is construct validity. Construct validity explains how well a test measures the idea or concept it evaluates. This form of validation is particularly important when an idea or concept is hard to measure directly. 

When used in research, construct validity is essential to operationalizing constructs into measurable characteristics relevant to your idea of the construct and its dimensions. In simpler terms, construct validity defines how well a test measures up to its claims.

Construct validity assesses whether the variables that you are testing for behave in a way to support your theory. This form of testing usually is verified by comparing the test to other similar test qualities to see how the two measures correlate. 

Construct validity is a valuable tool used primarily in social sciences, psychology, and education where there is a lot of subjectivity to concepts. These areas of study work with intangible attributes such as emotional states, abilities, characteristics, traits, or intelligence levels, properties not easily measurable or observable.

To fully understand construct validity, you must first understand constructs. Constructs are complex ideas formed by combining simpler ideas and often have a subjective element. 

Generally, constructs can be considered the essential ingredients that make up theories. They are how we think about ideas, events, people, and things we are interested in. Constructs help us understand and explain how and why things behave and react the way they do and bring our theories to a place that we can understand and ultimately measure.

When used in a study, constructs provide broad topics and concepts that can be defined in theoretical terms. We often speak of constructs as being mental abstractions because seldom are constructs directly observed. When construct validity is referenced, it refers to how good of a job it does in measuring what it is supposed to be measuring.

Imagine the difficulty of getting acceptance of a theory without a way to validate your experiments. Without the proper, accepted validity, your theories would go unpublished and unaccepted. In the 1940s, scientists were faced with that problem. They began testing and validating theories with different systematic approaches which led to confusion and lack of acceptance. No one had figured out a way to validate qualities universally. 

During the mid-1950s, Paul Meehl and Lee Cronbach developed the term construct validity in their article "Construct Validity in Psychological Tests." In this article, they proposed using a set of three steps to evaluate construct validity:

1. Articulate a set of theoretical concepts

2. Develop ways to measure the constructs proposed by the original theory

3. Test the theory empirically

For two decades, scientists have continued to debate the construct validity method as opposed to the frameworks they were using to validate their theories.

More and more theorists began to see construct validity as more scientific than other approaches. In 1989, Samuel Messick presented a more widely accepted approach to construct validity, noting that a unified theory was not just his idea, but was the culmination of decades of debate, discussion, and research among those in the scientific community. 

His theory of construct validity listed six aspects:

1. What are the consequences of the test?

2. Are the test items measuring the construct of interest?

3. Is the theoretical premise of the construct sound? 

4. Will the structure of the testing correlate the construct appropriately with the test?

5. Does the test have predictive or convergent qualities?

6. Can a generalized conclusion be drawn and does it cover all the tasks, settings, groups, etc.?

The multitrait-multimethod matrix (MTMM) is often used to examine construct validity. This tool requires that the correlation between the measurements be examined regarding the variables that exist in the construct.

 Some other less-used methods involve structural equation modeling, factor analysis, and other statistical-based evaluations.  

Generally, construct validity is not based on a single study but on the correlations of multiple, ongoing studies using the construct being evaluated. 

Construct validity will continually be evaluated and refined with the repeated collection of these correlations, and these correlations that fit the expected pattern contribute to the construct validity.  

 Construct validity may be used to assess a new measure or theory. A pilot study can be used to test your ideas on a small sample, allowing you to discover an area of focus or tweak your data collection methods in a larger study. 

Great care must be taken to effectively measure only those responses that directly pertain to the construct in question.  

 Once the collection of information is complete, you can use statistical analyses to test the validity of data from your measurements. 

You can test both convergent and discriminant validity with correlations to determine if your test is positively or negatively related to your test. 

Regression analyses can also be used to assess whether your measure is predictive of the outcomes that you are expecting. If your claims are supported, your construct validity is strengthened.

As mentioned in the above paragraph, there are two types of construct validity: convergent validity and discriminant validity. These two types, or sub-types, refer to the relation between the two measures of constructs that are either related or unrelated.  

Convergent validity

Convergent validity refers to the degree to which two measures of constructs you theorize are related, are, indeed related. You can analyze convergent validity by taking the results of a test and comparing them with the results of another test that is designed to measure the same construct. Your test would have high convergent validity if the results had a strong positive correlation.

Discriminant validity

On the other hand, discriminant validity is when the two measures of unrelated constructs that should be unrelated are, in reality, unrelated. You get the results for discriminant validity in the same way as you do for convergent validity. With both sub-types, you compare the results for different measures and assess whether they correlate.  

When we discuss a threat to construct validity, we refer to the criticism and questions of your construct validity by your critics and opponents. That's why it is essential to manage issues around the soundness of your data before the nay-sayers can criticize or refute your findings.   

Several threats to construct validity should be recognized and countered for the research design to be effective and accurate. Though there are other threats, such as defining predicted outcomes too narrowly and omitting variables that should be included, the most common challenges are poor operationalization, experimenter expectancies, and subject bias.  

Poor operationalization of the construct

If your definition of a construct is handled correctly, you will be able to measure it accurately each time. 

You must be sure that your testing process is straightforward, standardized, can be used by a variety of people in different conditions, and provide precise answers. Be sure to spend time defining your construct and expectations clearly.  

Experimenter expectancies

 If researchers or experimenters already know the hypothesis in taking the measurements in your study, your results may be biased, sometimes unintentionally. 

One way to mitigate and minimize bias is to involve people who have no prior knowledge or stake in the theories. This should provide a more valid construct.

Subject bias

Participants in your study who have certain expectations may sometimes change their responses to give the answers they think they should provide. 

A participant may have a personal bias about your subject, so their response could reflect their bias. By masking the purpose of the study, so that they can’t guess what the expected response might be, a less biased, more honest reaction can be expected.

Constructs are often intangible reactions and experiences and can be highly subjective. Most have no defined unit of measurement. 

To evaluate construct validity, first assess the definition of your construct and determine the measurement instrument. Then you must judge the measures collected and determine how they correlate and the level of validity. 

This can be challenging - any efforts have been made to apply statistics to construct validity with limited success. 

Many times the solutions are overly complex and are hard to apply to the theory. Some testing such as clinical trials can statistically measure differences in some constructs, but for the most part, the experience and judgment of the researcher and past knowledge and testing of the same constructs can be acceptable norms for supporting construct validity.

Statistics can also be used to test the validity of your measures if there is a sufficient amount of data and the right statistical approach is used.

If the researcher wants to evaluate respondents' happiness levels, the instrument’s construct validity would be the extent to which it assesses the respondents' levels of energy, positivity, and smiling as opposed to fretfulness, anger, or negativity.

Tests like student's t-test can sometimes be used, but the judgment and experience of the researcher are acceptable for testing construct validity. Statistical analysis can be used to test the validity of your data.

There are several types of validity, but construct validity is used to ensure that the measurement method aligns well with the construct you are measuring.