Developing Multidimensional Likert Scales Using Item Factor Analysis: The Case of Four-Point Items

Autor/inn/en	Asún, Rodrigo A.; Rdz-Navarro, Karina; Alvarado, Jesús M.
Titel	Developing Multidimensional Likert Scales Using Item Factor Analysis: The Case of Four-Point Items
Quelle	In: Sociological Methods & Research, 45 (2016) 1, S.109-133 (25 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124114566716
Schlagwörter	Likert Scales; Item Analysis; Factor Analysis; Comparative Analysis; Maximum Likelihood Statistics; Least Squares Statistics; Correlation; Computation; Monte Carlo Methods; Factor Structure; Sample Size + Suchen Sie Ihr Suchwort? Likert-Skala; Itemanalyse; Faktorenanalyse; Korrelation; Monte-Carlo-Methode; Faktorenstruktur
Abstract	This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted least squares and unweighted least squares estimations using items polychoric correlation matrices are compared. Two hundred and ten conditions were simulated in a Monte Carlo study considering: one to three factor structures (either, independent and correlated in two levels), medium or low quality of items, three different levels of item asymmetry and five sample sizes. Results showed that IFA procedures achieve equivalent and accurate parameter estimates; in contrast, FA procedures yielded biased parameter estimates. Therefore, we do not recommend classical FA under the conditions considered. Minimum requirements for achieving accurate results using IFA procedures are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01