Discriminant validity concerns whether measures that should be distinct are not highly related.

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Multiple Choice

Discriminant validity concerns whether measures that should be distinct are not highly related.

Explanation:
Discriminant validity is about making sure that measures of different constructs are truly distinct and not highly related. In practice, this means a measure that captures one concept shouldn’t correlate strongly with a measure of a different concept. When discriminant validity holds, you can be more confident that each instrument is tapping a unique aspect of the phenomenon rather than overlapping with others. By comparison, convergent validity looks at whether measures that should be related are indeed related, showing that different methods or instruments converge on the same construct. Face validity is about whether the measure appears to assess what it’s supposed to, based on judgment, and isn’t a rigorous test of how constructs relate. Criterion validity concerns how well a measure predicts or relates to an external standard or outcome, rather than whether constructs stay distinct from one another. Therefore, the statement that describes whether measures that should be distinct are not highly related correctly identifies discriminant validity.

Discriminant validity is about making sure that measures of different constructs are truly distinct and not highly related. In practice, this means a measure that captures one concept shouldn’t correlate strongly with a measure of a different concept. When discriminant validity holds, you can be more confident that each instrument is tapping a unique aspect of the phenomenon rather than overlapping with others.

By comparison, convergent validity looks at whether measures that should be related are indeed related, showing that different methods or instruments converge on the same construct. Face validity is about whether the measure appears to assess what it’s supposed to, based on judgment, and isn’t a rigorous test of how constructs relate. Criterion validity concerns how well a measure predicts or relates to an external standard or outcome, rather than whether constructs stay distinct from one another.

Therefore, the statement that describes whether measures that should be distinct are not highly related correctly identifies discriminant validity.

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