Contents
What is discriminant validity in research?
Definition. Discriminant validity is demonstrated by evidence that measures of constructs that theoretically should not be highly related to each other are, in fact, not found to be highly correlated to each other.
What type of validity is discriminant validity?
Convergent validity takes two measures that are supposed to be measuring the same construct and shows that they are related. Conversely, discriminant validity shows that two measures that are not supposed to be related are in fact, unrelated. Both types of validity are a requirement for excellent construct validity.
How do you show discriminant validity?
To establish discriminant validity, you need to show that measures that should not be related are in reality not related. In the figure below, we again see four measures (each is an item on a scale).
What is the importance of discriminant validity?
To establish validity, it is important to show not only that the instrument is associated with measures of the same concept but also that it is not associated with measures of concepts that are different.
What is an example of criterion validity?
Also called concrete validity, criterion validity refers to a test’s correlation with a concrete outcome. For example, a company could administer a sales personality test to its sales staff to see if there is an overall correlation between their test scores and a measure of their productivity.
What is the difference between divergent and discriminant validity?
Convergent validity tests that constructs that are expected to be related are, in fact, related. Discriminant validity (or divergent validity) tests that constructs that should have no relationship do, in fact, not have any relationship.
What are the two types of criterion validity?
Criterion validity is made up two subcategories: predictive and concurrent. Predictive validity refers to the extent to which a survey measure forecasts future performance. A graduate school entry examination that predicts who will do well in graduate school has predictive validity.
How do you show criterion validity?
One of the simplest ways to assess criterion related validity is to compare it to a known standard. A new intelligence test, for example, could be statistically analyzed against a standard IQ test; if there is a high correlation between the two data sets, then the criterion validity is high.
What is an example of divergent validity?
For example, if a test is supposed to measure suitability of applicants to a particular job, then it should not exhibit too strong correlation with, say, IQ-scores. Otherwise, the instrument is just another IQ-test.
When to use a measure of discriminant validity?
Essentially, measures of discriminant validity help us determine if two measures that should not be correlated/related are ACTUALLY not related. For example, if producing a scale that measures motivation, we might want to show that our scale measures motivation and not some other construct (e.g. self-belief).
How is discriminant validity related to negative coping?
Discriminant validity is suggested by a very low correlation with negative religious coping (0.06), although to our knowledge, there is no information available on relationships with non-religious psychosocial variables. From: Measures of Personality and Social Psychological Constructs, 2015.
When do you have convergent and discriminant evidence?
The important thing to recognize is that they work together – if you can demonstrate that you have evidence for both convergent and discriminant validity, then you’ve by definition demonstrated that you have evidence for construct validity. But, neither one alone is sufficient for establishing construct validity.
Does the pattern support convergent and discriminant validity?
But while the pattern supports discriminant and convergent validity, does it show that the three self esteem measures actually measure self esteem or that the three locus of control measures actually measure locus of control. Of course not. That would be much too easy.