ABSTRACT
Statistical methods commonly used in organizational research usually either take a variable-centered approach or a person-centered approach (Wang & Hanges, 2011). Methods taking a variable-centered, or dimension-centered, approach are used in research to capture the interrelatedness (often in the form of covariance or latent factor) between or among different variables and using it to infer the underlying processes or causes. The typical variable-centered methods used in organizational research include ANOVA, regression analysis, and structural equation modeling, just to name a few. However, sometimes, organizational researchers are interested in classifying individuals into subpopulations that differ from each other in patterns of variables (e.g. Mumford et al., 2000). Statistical methods taking a person-centered approach are used in these studies, which include cluster analysis and latent class analysis. Recent development in quantitative methods has extended the latent class analysis to integrate variable-centered and person-centered analytical approaches (Langeheine & van de Pol, 2002; Muthén, 2004). Specifically, the newly developed latent class procedures (e.g., mixed-measurement item response models, growth mixture modeling, and latent mixture Markov modeling) classify indi viduals into subpopulations, conditional not only on their similarities in patterns of variables, but also on various types of interrelatedness among variables (e.g., item response patterns and longitudinal quantitative and qualitative changes). These recently developed methods have been used in research areas such as developmental psychology (e.g., Schaeffer, Petras, Ialongo, Poduska, & Kellam, 2003) and consumer behavior (e.g., Varki & Chintagunta, 2004), and have started to be applied to organizational research (e.g. Zickar, Gibby, & Robie, 2004; Wang, 2007).
