Identifying and Minimizing Measurement Invariance among Intersectional Groups: The Alignment Method Applied to Multi-category Items

Rachel A. Gordon , Tianxiu Wang , Hai Nguyen , Ariel M. Aloe

ISBN 10: 1009357743 / ISBN 13: 9781009357746

Published by Cambridge University Press, 2023

About this Item

75 pages. 8.90x5.98x0.08 inches. In Stock.

Seller Inventory # __1009357743

Bibliographic Details

• Title: Identifying and Minimizing Measurement Invariance among Intersectional Groups: The Alignment Method Applied to Multi-category Items
• Publisher: Cambridge University Press
• Publication Date: 2023
• Language: English
• ISBN 10: 1009357743
• ISBN 13: 9781009357746
• Binding: Paperback
• Condition: Brand New
• Weight: 0.51 kilograms

About this title

Synopsis

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.