Assessing Essential Unidimensionality of Scales and Structural Coefficient Bias

Autor/inn/en	Liu, Xiaoling; Cao, Pei; Lai, Xinzhen; Wen, Jianbing; Yang, Yanyun
Titel	Assessing Essential Unidimensionality of Scales and Structural Coefficient Bias
Quelle	In: Educational and Psychological Measurement, 83 (2023) 1, S.28-47 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liu, Xiaoling) ORCID (Yang, Yanyun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221075580
Schlagwörter	Correlation; Measurement Techniques; Prediction; Regression (Statistics); Models; Factor Analysis; Factor Structure + Suchen Sie Ihr Suchwort? Korrelation; Messtechnik; Vorhersage; Regression; Regressionsanalyse; Analogiemodell; Faktorenanalyse; Faktorenstruktur
Abstract	Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical ([omega]H) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices has been investigated in the context of bifactor models with balanced structures. This study extends the examination by focusing on bifactor models with unbalanced structures. The maximum and minimum PUC values given the total number of items and factors were derived. The usefulness of PUC, ECV, and [omega]H in predicting structural coefficient bias was examined under a variety of structural regression models with bifactor measurement components. Results indicated that the performance of these indices in predicting structural coefficient bias depended on whether the bifactor measurement model had a balanced or unbalanced structure. PUC failed to predict structural coefficient bias when the bifactor model had an unbalanced structure. ECV performed reasonably well, but worse than [omega]H. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01