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Empirical Underidentification with the Bifactor Model: A Case Study
- Source :
- Educ Psychol Meas
- Publication Year :
- 2017
- Publisher :
- SAGE Publications, 2017.
-
Abstract
- Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.
- Subjects :
- Computer science
business.industry
Applied Mathematics
05 social sciences
050401 social sciences methods
050109 social psychology
Articles
Machine learning
computer.software_genre
Data type
Structural equation modeling
Education
Set (abstract data type)
Identification (information)
0504 sociology
Developmental and Educational Psychology
0501 psychology and cognitive sciences
Artificial intelligence
business
computer
Applied Psychology
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Educ Psychol Meas
- Accession number :
- edsair.doi.dedup.....68dc2395900da54ba4f0210fd177294f