If a study uses more than one administration method for a self-reported instrument, what should researchers do?

If a study uses more than one administration method for a self-reported instrument, what should researchers do?

• A. Use only the mode with the highest reliability
• B. Reexamine measurement properties for each mode
• C. Ignore mode differences
• D. Rely on the original validation

Answer: (B)

When a self-report instrument is given through more than one administration method, responses can be affected by the mode itself, so you need to verify that the instrument works the same way across modes. This means reexamining its measurement properties for each mode—looking at reliability, validity, and how well the item responses relate to the underlying construct in each mode. You’d also assess measurement equivalence across modes, such as whether items operate differently for different formats (differential item functioning) and whether a unified factor structure holds (invariance testing). If you find differences, you shouldn’t pool data blindly; you might adjust scoring, use mode-specific norms, or analyze modes separately and report the limitations. Simply picking the mode with the highest reliability ignores potential validity issues across modes, ignoring mode differences risks biased conclusions or misinterpretation, and relying on the original validation assumes the same properties across modes, which may not be true.

When a self-report instrument is given through more than one administration method, responses can be affected by the mode itself, so you need to verify that the instrument works the same way across modes. This means reexamining its measurement properties for each mode—looking at reliability, validity, and how well the item responses relate to the underlying construct in each mode. You’d also assess measurement equivalence across modes, such as whether items operate differently for different formats (differential item functioning) and whether a unified factor structure holds (invariance testing). If you find differences, you shouldn’t pool data blindly; you might adjust scoring, use mode-specific norms, or analyze modes separately and report the limitations. Simply picking the mode with the highest reliability ignores potential validity issues across modes, ignoring mode differences risks biased conclusions or misinterpretation, and relying on the original validation assumes the same properties across modes, which may not be true.