A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis

Autor/in	Edwards, Michael C.
Titel	A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis
Quelle	In: Psychometrika, 75 (2010) 3, S.474-497 (24 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-010-9161-9
Schlagwörter	Structural Equation Models; Markov Processes; Factor Analysis; Item Response Theory; Psychometrics; Statistical Analysis; Measurement; Measurement Techniques; Monte Carlo Methods; Computation + Suchen Sie Ihr Suchwort? Markowscher Prozess; Faktorenanalyse; Item-Response-Theorie; Psychometry; Psychometrie; Statistische Analyse; Messverfahren; Messtechnik; Monte-Carlo-Methode
Abstract	Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show that these methods can be implemented in a flexible way which requires minimal technical sophistication on the part of the end user. After providing an overview of item factor analysis and MCMC, results from several examples (simulated and real) will be discussed. The bulk of these examples focus on models that are problematic for current "gold-standard" estimators. The results demonstrate that it is possible to obtain accurate parameter estimates using MCMC in a relatively user-friendly package. (Contains 6 tables, 5 figures and 7 footnotes.) (As Provided).
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Update	2017/4/10