TY - JOUR
T1 - Fuzzy clusterwise generalized structured component analysis
AU - Hwang, Heungsun
AU - DeSarbo, Wayne S.
AU - Takane, Yoshio
N1 - Funding Information:
Requests for reprints should be sent to Heungsun Hwang, Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada. E-mail: [email protected] The work reported in this paper was supported by Grant 290439 and Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the first and third authors, respectively. We wish to thank Terry Duncan for generously providing us with his alcohol use data. We also wish to thank the Editor, Associate Editor, and two anonymous reviewers for their constructive comments which helped improve the overall quality and readability of this manuscript.
PY - 2007/6
Y1 - 2007/6
N2 - 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.
AB - 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.
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U2 - 10.1007/s11336-005-1314-x
DO - 10.1007/s11336-005-1314-x
M3 - Article
AN - SCOPUS:69149101154
SN - 0033-3123
VL - 72
SP - 181
EP - 198
JO - Psychometrika
JF - Psychometrika
IS - 2
ER -
