Identifying and minimizing measurement invariance among intersectional groups: the alignment method applied to multi-category items
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' mult...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2023
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| Schriftenreihe: | Cambridge elements
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| Schlagworte: | |
| Online-Zugang: | BSB01 UBG01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Also issued in print: 2023. - Includes bibliographical references |
| Beschreibung: | 1 Online-Ressource (67 Seiten) Illustrationen |
| ISBN: | 9781009357784 |
| DOI: | 10.1017/9781009357784 |
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| index_date | 2024-07-03T22:46:42Z |
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| isbn | 9781009357784 |
| language | English |
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| spelling | Gordon, Rachel A. aut 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 Cambridge Cambridge University Press 2023 1 Online-Ressource (67 Seiten) Illustrationen txt rdacontent sti rdacontent c rdamedia cr rdacarrier Cambridge elements Also issued in print: 2023. - Includes bibliographical references 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 Psychometrics / Data processing Developmental psychology / Research / Methodology Wang, Tianxiu aut Nguyen, Hai aut Aloe, Ariel M. 1975- aut Erscheint auch als Druck-Ausgabe 978-1-009-35774-6 https://doi.org/10.1017/9781009357784 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Gordon, Rachel A. Wang, Tianxiu Nguyen, Hai Aloe, Ariel M. 1975- Identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi-category items Psychometrics / Data processing Developmental psychology / Research / Methodology |
| title | Identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi-category items |
| title_auth | Identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi-category items |
| title_exact_search | Identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi-category items |
| title_exact_search_txtP | Identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi-category items |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | Identifying and minimizing measurement invariance among intersectional groups |
| title_sort | identifying and minimizing measurement invariance among intersectional groups the alignment method applied to multi category items |
| title_sub | the alignment method applied to multi-category items |
| topic | Psychometrics / Data processing Developmental psychology / Research / Methodology |
| topic_facet | Psychometrics / Data processing Developmental psychology / Research / Methodology |
| url | https://doi.org/10.1017/9781009357784 |
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