Identifying and Minimizing Measurement Invariance among Intersectional Groups

The Alignment Method Applied to Multi-category Items

Gordon, Rachel A. (Northern Illinois University), Wang, Tianxiu (University of Pittsburgh), Nguyen, Hai (University of Illinois, Aloe, Ariel M. (University of Iowa) - Identifying and Minimizing Measurement Invariance among Intersectional Groups

Description

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.

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Writer: Gordon, Rachel A. (Northern Illinois University), Wang, Tianxiu (University of Pittsburgh), Nguyen, Hai (University of Illinois, Aloe, Ariel M. (University of Iowa)
Title: Identifying and Minimizing Measurement Invariance among Intersectional Groups
Publisher: Cambridge University Press

Year: 2023
Language: English
Pages: 76
Weight: 138 gr

EAN: 9781009357746
Dimensions: 152 x 229 x 9 mm
Binding format: Paperback / softback

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Identifying and Minimizing Measurement Invariance among Intersectional Groups

The Alignment Method Applied to Multi-category Items

Description

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