Properties of and Algorithms for Fitting Three-Way Component Models with Offset Terms

Autor/in	Kiers, Henk A. L.
Titel	Properties of and Algorithms for Fitting Three-Way Component Models with Offset Terms
Quelle	In: Psychometrika, 71 (2006) 2, S.231-256 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-001-0953-x
Schlagwörter	Measures (Individuals); Computation; Item Response Theory; Factor Analysis; Interaction; Models; Multidimensional Scaling + Suchen Sie Ihr Suchwort? Messdaten; Item-Response-Theorie; Faktorenanalyse; Interaktion; Analogiemodell; Mehrdimensionale Analysis
Abstract	Prior to a three-way component analysis of a three-way data set, it is customary to preprocess the data by centering and/or rescaling them. Harshman and Lundy (1984) considered that three-way data actually consist of a three-way model part, which in fact pertains to ratio scale measurements, as well as additive "offset" terms that turn the ratio scale measurements into interval scale measurements. They mentioned that such offset terms might be estimated by incorporating additional components in the model, but discarded this idea in favor of an approach to remove such terms from the model by means of centering. Then estimates for the three-way component model parameters are obtained by analyzing the centered data. In the present paper, the possibility of actually estimating the offset terms is taken up again. First, it is mentioned in which cases such offset terms can be estimated uniquely. Next, procedures are offered for estimating model parameters and offset parameters simultaneously, as well as successively (i.e., providing offset term estimates "after" the three-way model parameters have been estimated in the traditional way on the basis of the centered data). These procedures are provided for both the CANDECOMP/PARAFAC model and the Tucker3 model extended with offset terms. The successive and the simultaneous approaches for estimating model and offset parameters have been compared on the basis of simulated data. It was found that both procedures perform well when the fitted model captures "at least" all offset terms actually underlying the data. The simultaneous procedures performed slightly better than the successive procedures. If "fewer" offset terms are fitted than there are underlying the model, the results are considerably poorer, but in these cases the successive procedures performed better than the simultaneous ones. All in all, it can be concluded that the traditional approach for estimating model parameters can hardly be improved upon, and that offset terms can sufficiently well be estimated by the proposed successive approach, which is a simple extension of the traditional approach. (As Provided).
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Update	2017/4/10