uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
H-Likelihood Approach to Factor Analysis for Ordinal Data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)In: Structural Equation Modeling, ISSN 1070-5511, E-ISSN 1532-8007, Vol. 25, no 4, p. 530-540Article in journal (Refereed) Published
Abstract [en]

Marginal likelihood-based methods are commonly used in factor analysis for ordinal data. To obtain the maximum marginal likelihood estimator, the full information maximum likelihood (FIML) estimator uses the (adaptive) Gauss-Hermite quadrature or stochastic approximation. However, the computational burden increases rapidly as the number of factors increases, which renders FIML impractical for large factor models. Another limitation of the marginal likelihood-based approach is that it does not allow inference on the factors. In this study, we propose a hierarchical likelihood approach using the Laplace approximation that remains computationally efficient in large models. We also proposed confidence intervals for factors, which maintains the level of confidence as the sample size increases. The simulation study shows that the proposed approach generally works well.

Place, publisher, year, edition, pages
2018. Vol. 25, no 4, p. 530-540
Keywords [en]
binary data, confidence interval of factors, second-order Laplace approximation, underlying distribution approach
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-349074DOI: 10.1080/10705511.2017.1403287ISI: 000430488600003OAI: oai:DiVA.org:uu-349074DiVA, id: diva2:1199364
Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-06-29Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Jin, Shaobo

Search in DiVA

By author/editor
Jin, Shaobo
By organisation
Department of Statistics
In the same journal
Structural Equation Modeling
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 53 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf