skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Identification of Confirmatory Factor Analysis Models of Different Levels of Invariance for Ordered Categorical Outcomes

Psychometrika, 2016-12, Vol.81 (4), p.1014-1045 [Peer Reviewed Journal]

The Psychometric Society 2016 ;Psychometrika is a copyright of Springer, 2016. ;ISSN: 0033-3123 ;EISSN: 1860-0980 ;DOI: 10.1007/s11336-016-9506-0 ;PMID: 27402166

Full text available

Citations Cited by
  • Title:
    Identification of Confirmatory Factor Analysis Models of Different Levels of Invariance for Ordered Categorical Outcomes
  • Author: Wu, Hao ; Estabrook, Ryne
  • Subjects: Algorithms ; Assessment ; Behavioral Science and Psychology ; Computer Simulation ; Data Interpretation, Statistical ; Factor Analysis, Statistical ; Humanities ; Law ; Psychology ; Psychometrics ; Random variables ; Software ; Statistical Theory and Methods ; Statistics for Social Sciences ; Testing and Evaluation
  • Is Part Of: Psychometrika, 2016-12, Vol.81 (4), p.1014-1045
  • Description: This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on this identified baseline model. This approach is not optimal because different identification conditions on this baseline model identify the scales of latent continuous responses in different ways. Once an invariance condition is imposed on a parameter, these identification conditions may become restrictions and define statistically non-equivalent models, leading to different conclusions. By analyzing the transformation that leaves the model-implied probabilities of response patterns unchanged, we give identification conditions for models with invariance of different types of parameters without referring to a specific parametrization of the baseline model. Tests based on this approach have the advantage that they do not depend on the specific identification condition chosen for the baseline model.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0033-3123
    EISSN: 1860-0980
    DOI: 10.1007/s11336-016-9506-0
    PMID: 27402166
  • Source: ProQuest One Psychology
    MEDLINE
    ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait