Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion

Zhao, Jianhua; Shang, Changchun; Li, Shulan; Xin, Ling; Yu, Philip L. H.

doi:10.1007/s11634-024-00582-w

Adv Data Anal Classif

2024

DOI: 10.1007/s11634-024-00582-w

|View full text |Cite

Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion

Jianhua Zhao,

Changchun Shang,

Shulan Li

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

Robitzsch

2024

Analytics

View full text Add to dashboard Cite

The Bayesian information criterion (BIC) is a widely used statistical tool originally derived for fully observed data. The BIC formula includes the sample size and the number of estimated parameters in the penalty term. However, not all variables are available for every subject in planned missingness designs. This article demonstrates that a modified BIC, tailored for planned missingness designs, outperforms the original BIC. The modification adjusts the penalty term by using the average number of estimable parameters per subject rather than the total number of model parameters. This new criterion was successfully applied to item response theory models in two simulation studies. We recommend that future studies utilizing planned missingness designs adopt the modified BIC formula proposed here.

show abstract

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

Robitzsch

2024

Analytics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion

Cited by 1 publication

References 30 publications

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

Contact Info

Product

Resources

About