Stochastic approximation EM for large-scale exploratory IRT factor analysis.
A stochastic approximation EM algorithm (SAEM) is described for exploratory factor analysis of dichotomous or ordinal variables. The factor structure is obtained from sufficient statistics that are updated during iterations with the Robbins-Monro procedure. Two large-scale simulations are reported t...
| Publicado en: | Statistics in Medicine Vol. 38; no. 21; pp. 3997 - 4013 |
|---|---|
| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
9/20/2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137988078&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137988078 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 9/20/2019 vid: 38 iid: 21 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 137988078 137988078 NLM31267550 137988078 10.1002/sim.8217 NLM31267550 137988078 ppf: 3997 ppct: 16 formats: tig: atl: Stochastic approximation EM for large-scale exploratory IRT factor analysis. aug: au: Camilli, Gregory Geis, Eugene affil: Graduate School of Education, Rutgers University, New Brunswick New Jersey sug: subj: Algorithms Factor Analysis Statistics Computer Simulation Probability Regression Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: A stochastic approximation EM algorithm (SAEM) is described for exploratory factor analysis of dichotomous or ordinal variables. The factor structure is obtained from sufficient statistics that are updated during iterations with the Robbins-Monro procedure. Two large-scale simulations are reported that compare accuracy and CPU time of the proposed SAEM algorithm to the Metropolis-Hasting Robbins-Monro procedure and to a generalized least squares analysis of the polychoric correlation matrix. A smaller-scale application to real data is also reported, including a method for obtaining standard errors of rotated factor loadings. A simulation study based on the real data analysis is conducted to study bias and error estimates. The SAEM factor algorithm requires minimal lines of code, no derivatives, and no large-matrix inversion. It is programmed entirely in R. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|