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Identifying and Minimizing Measurement Invariance among Intersectional Groups

The Alignment Method Applied to Multi-category Items

Published online by Cambridge University Press:  23 June 2023

Rachel A. Gordon
Affiliation:
Northern Illinois University
Tianxiu Wang
Affiliation:
University of Pittsburgh
Hai Nguyen
Affiliation:
University of Illinois, Chicago
Ariel M. Aloe
Affiliation:
University of Iowa

Summary

This Element demonstrates how and why the alignment method can advance measurement fairness in developmental science. It explains its application to multi-category items in an accessible way, offering sample code and demonstrating an R package that facilitates interpretation of such items' multiple thresholds. It features the implications for group mean differences when differences in the thresholds between categories are ignored because items are treated as continuous, using an example of intersectional groups defined by assigned sex and race/ethnicity. It demonstrates the interpretation of item-level partial non-invariance results and their implications for group-level differences and encourages substantive theorizing regarding measurement fairness.
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Identifying and Minimizing Measurement Invariance among Intersectional Groups
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