Fuzzy Clusterwise Generalized Structured Component Analysis

Autor/inn/en	Hwang, Heungsun; Desarbo, Wayne S.; Takane, Yoshio
Titel	Fuzzy Clusterwise Generalized Structured Component Analysis
Quelle	In: Psychometrika, 72 (2007) 2, S.181-198 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-005-1314-x
Schlagwörter	Urban Areas; Path Analysis; Monte Carlo Methods; Drinking; Item Response Theory; Adolescents; Least Squares Statistics; Componential Analysis; Psychometrics + Suchen Sie Ihr Suchwort? Urban area; Stadtregion; Pfadanalyse; Monte-Carlo-Methode; Trinken; Item-Response-Theorie; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Psychometry; Psychometrie
Abstract	Generalized Structured Component Analysis (GSCA) was recently introduced by Hwang and Takane (2004) as a component-based approach to path analysis with latent variables. The parameters of GSCA are estimated by pooling data across respondents under the implicit assumption that they all come from a single, homogenous group. However, as has been empirically demonstrated by various researchers across a number of areas of inquiry, such aggregate analyses can often mask the true structure in data when respondent heterogeneity is present. In this paper, GSCA is generalized to a fuzzy clustering framework so as to account for potential group-level respondent heterogeneity. An alternating least-squares procedure is developed and technically described for parameter estimation. A small-scale Monte Carlo study involving synthetic data is carried out to compare the performance between the proposed method and an extant approach. In addition, an empirical application concerning alcohol use among adolescents from US northwestern urban areas is presented to illustrate the usefulness of the proposed method. Finally, a number of directions for future research are provided. (Author).
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Update	2017/4/10