Identifying and Minimizing Measurement Invariance among Intersectional Groups: The Alignment Method Applied...

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Rachel A. Gordon (Northern Illinois University) Tianxiu Wang (University of Pittsburgh)

Identifying and Minimizing Measurement Invariance among Intersectional Groups

The Alignment Method Applied to Multi-category Items

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

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English

Cambridge University Press
06 July 2023

Psychological testing & measurement; Child & developmental psychology

Series: Elements in Research Methods for Developmental Science

Summary
Details

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.

By: Rachel A. Gordon (Northern Illinois University), Tianxiu Wang (University of Pittsburgh), Hai Nguyen (University of Illinois, Chicago), Ariel M. Aloe (University of Iowa)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 229mm, Width: 152mm, Spine: 4mm
Weight: 137g
ISBN: 9781009357746
ISBN 10: 1009357743
Series: Elements in Research Methods for Developmental Science
Pages: 75
Publication Date: 06 July 2023
Audience: College/higher education , Further / Higher Education
Format: Paperback
Publisher's Status: Active