What Is Convergent Validity? | Definition & Examples
Convergent validity refers to how closely a test is related to other tests that measure the same (or similar) constructs. Here, a construct is a behavior, attitude, or concept, particularly one that is not directly observable.
Ideally, two tests measuring the same construct, such as stress, should have a moderate to high correlation. High correlation is evidence of convergent validity, which, in turn, is an indication of construct validity.
What is convergent validity?
Convergent validity is a subtype of construct validity. Construct validity is an indication of how well a test measures the concept it was designed to measure.
Convergent validity is a bit more nuanced, in that it measures whether constructs that theoretically should be related to each other are, in fact, related to each other.
For example, the scores of two tests, one measuring self-esteem and the other measuring extroversion, are likely to be correlated—individuals scoring high in self-esteem are more likely to score high in extroversion. These two tests would then have high convergent validity.
Convergent vs. discriminant validity
Together, convergent and discriminant validity help you establish construct validity. In research, they are evaluated together because both must be assessed in order to demonstrate construct validity. Neither alone is sufficient, but it’s important to remember that they are not the same thing.
In short, while convergent validity focuses on similarities, discriminant validity focuses on differences.
- Convergent validity shows you whether two tests that should be highly related to each other are indeed related.
- Discriminant validity shows you whether two tests that should not be highly related to each other are, indeed, unrelated.
The idea here is that not only should a test correlate with a similar test (i.e., measuring the same or a related construct), but it should also not correlate with dissimilar or unrelated tests (i.e., measuring different constructs).
For example, if there is no (or weak) correlation between the scores of a test measuring honesty and a test measuring the favorite color of participants, the test can be said to have high discriminant validity. This means that it only measures the construct it is supposed to measure, and not other constructs.
When both conditions (convergent and divergent validity) are met, you can conclude that a test shows evidence of construct validity.
Examples of convergent and discriminant validity
To establish the convergent validity of your test, you must do one of the following:
- Compare your test scores against the results of at least one more test measuring the same or a similar construct. For example, you can measure your self-report questionnaire on introversion against an existing one also measuring introversion.
- Compare the results of two different methods measuring the same construct. For example, you can compare the results of an observation and a questionnaire both measuring introversion.
You should first establish convergent validity before testing for discriminant validity. Together, these two tests allow you to assess construct validity. Investigating relationships between constructs helps you ensure a high correlation for convergent validity and a low (or nonexistent) correlation for discriminant validity.
How to measure convergent validity
To measure the convergent validity of your test, you must demonstrate that there is a positive correlation between measures of related constructs. In other words, if you have two related scales, people who score high on one scale should score high on the other as well.
Correlation is estimated by a correlation coefficient, such as Pearson’s r, which is a number that ranges between 1 and −1. This coefficient shows you the strength and direction of the relationship between variables.
Correlation coefficient values can be interpreted as follows:
- r = 1: There is perfect positive correlation
- r = 0: There is no correlation at all.
- r = −1: There is perfect negative correlation
You can automatically calculate Pearson’s r in Excel, R, SPSS, or other statistical software.
To find a similar construct, and thus a similar test to compare for convergent validity, you need to look into relevant academic literature. What do you already know about the relationships between constructs? What other constructs are related to the construct you are studying?
Although there’s no hard and fast rule, an r value of >0.50 is generally considered sufficient to suggest convergent validity. However, keep in mind that correlations with related constructs should be higher than those of unrelated constructs.
Frequently asked questions about convergent validity
- Why are convergent and discriminant validity often evaluated together?
Convergent validity and discriminant validity are both subtypes of construct validity. Together, they help you evaluate whether a test measures the concept it was designed to measure.
- Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
- Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
- What is the definition of construct validity?
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity, which includes construct validity, face validity, and criterion validity.
There are two subtypes of construct validity.
- Convergent validity: The extent to which your measure corresponds to measures of related constructs
- Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs
- How do I measure construct validity?
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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