Using the Graded Response Model to Control Spurious Interactions in Moderated Multiple Regression

Autor/inn/en	Morse, Brendan J.; Johanson, George A.; Griffeth, Rodger W.
Titel	Using the Graded Response Model to Control Spurious Interactions in Moderated Multiple Regression
Quelle	In: Applied Psychological Measurement, 36 (2012) 2, S.122-146 (25 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621612438725
Schlagwörter	Item Response Theory; Multiple Regression Analysis; Error of Measurement; Models; Scaling; Simulation + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Messfehler; Analogiemodell; Scale construction; Skalenkonstruktion; Simulation program; Simulationsprogramm
Abstract	Recent simulation research has demonstrated that using simple raw score to operationalize a latent construct can result in inflated Type I error rates for the interaction term of a moderated statistical model when the interaction (or lack thereof) is proposed at the latent variable level. Rescaling the scores using an appropriate item response theory (IRT) model can mitigate this effect under similar conditions. However, this work has thus far been limited to dichotomous data. The purpose of this study was to extend this investigation to multicategory (polytomous) data using the graded response model (GRM). Consistent with previous studies, inflated Type I error rates were observed under some conditions when polytomous number-correct scores were used, and were mitigated when the data were rescaled with the GRM. These results support the proposition that IRT-derived scores are more robust to spurious interaction effects in moderated statistical models than simple raw scores under certain conditions. (Contains 5 figures, 5 tables and 3 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10