Lessons on measuring construct validity: A commentary on Delis, Jacobson, Bondi, Hamilton, and Salmon
Glenn J. Larrabee
Abstract
Glenn J. Larrabee
Abstract
This commentary expands on issues raised by Delis, Jacobson, Bondi, Hamilton, and Salmon, in their paper on the use of shared variance techniques to establish construct validity. Significant discussion is focused on method variance, and how this can distort the results of factor analysis. Solutions are offered for the appropriate use of factor analysis in construct validation. Examples are also provided of construct validation procedures that do not rely on correlational or shared variance techniques.
OpenAlex reports 31 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This commentary expands on issues raised by Delis, Jacobson, Bondi, Hamilton, and Salmon, in their paper on the use of shared variance techniques to establish construct validity. Significant discussion is focused on method variance, and how this can distort the results of factor analysis. Solutions are offered for the appropriate use of factor analysis in construct validation. Examples are also provided of construct validation procedures that do not rely on correlational or shared variance techniques.
Key concepts: Construct (python library), Variance (accounting), Construct validity, Variance components, Psychology, Computer science, Statistics, Mathematics