Nominal Factor Analysis of Situational Judgment Tests: Evaluation of Latent Dimensionality and Factorial Invariance

Autor/inn/en	Revuelta, Javier; Franco-Martínez, Alicia; Ximénez, Carmen
Titel	Nominal Factor Analysis of Situational Judgment Tests: Evaluation of Latent Dimensionality and Factorial Invariance
Quelle	In: Educational and Psychological Measurement, 81 (2021) 6, S.1054-1088 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Revuelta, Javier) ORCID (Franco-Martínez, Alicia) ORCID (Ximénez, Carmen)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164421994321
Schlagwörter	Factor Analysis; Situational Tests; Statistical Analysis; Sex Stereotypes; Foreign Countries; Factor Structure; Spain + Suchen Sie Ihr Suchwort? Faktorenanalyse; Situationsansatz; Statistische Analyse; Ausland; Faktorenstruktur; Spanien
Abstract	Situational judgment tests have gained popularity in educational and psychological measurement and are widely used in personnel assessment. A situational judgment item presents a hypothetical scenario and a list of actions, and the individuals are asked to select their most likely action for that scenario. Because actions have no explicit order, the item generates nominal responses consisting of the actions selected by the individuals. This article shows how to factor-analyze the nominal responses originated from such a test, including the estimation of the number of latent factors and a factor invariance analysis in a multiple group design. The method consists of applying the MNCM, a multidimensional extension of the nominal categories model by Bock. The article includes the results of two studies: (1) a simulation study about Type-I error rate, statistical power, and recovery of the parameters in a multigroup factorial invariance design and (2) a real data example using responses to a situational judgment test measuring gender stereotypes to illustrate the approach. Results suggest the use of the Akaike information criterion, Bayesian information criterion, and corrected Bayesian information criterion indices to guide the selection of the number of factors with nominal responses. All the analyses are conducted using the computer program "Mplus." The code is included as Supplemental Material (available online) for the readers so that they can adapt it to their own purposes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01