Latent Class Analysis: A Method for Capturing Heterogeneity

Social work researchers often use variable-centered approaches such as regression and factor analysis. However, these methods do not capture important aspects of relationships that are often imbedded in the heterogeneity of samples. Latent class analysis (LCA) is one of several person-centered approaches that can capture heterogeneity within and between groups. This method is illustrated in the present study, in which LCA is used to explicate differences in symptomatology in a nonclinical, national representative sample of youths. Data (N= 14,738) from the National Longitudinal Study of Adolescent Health were analyzed using externalizing and internalizing behavioral constructs and then validated against a number of sociodemographic characteristics and behavior outcomes typically associated with type and severity of symptomatology. Findings revealed important differences within the externalizing symptomatology construct and class differences across racial and ethnic groups, gender, age categories, and several behavior outcomes. Research and clinical implications on the importance of modeling heterogeneity using a person-centered approach are discussed. KEY WORDS: Add Health; latent class analysis; mixture modeling; person-centered analysis ********** Attention to the variability of human experience is fundamental to social work research and practice. Issues such as differences in prevalence, treatment effects, coping strategies, and normal within-group variations permeate both practice and research agendas. In addition, social work is often concerned with racial and ethnic differences, sociodemographic characteristics, and other variables that may influence or modify focal study relationships (Kataoka, Zhang, & Wells, 2002). Thus, capturing identifiable differences in subpopulations is an important area of social work inquiry. Traditionally, much research, including protocols and evidence-based practice, has been based on variable-oriented methods that capture information about relationships between the variables of interest for the overall sample. In contrast, person-oriented methods capture information at the personal level, enabling researchers to distinguish patterns of characteristics in subgroups (Nurius & Macy, 2008). Person-oriented methods, such as latent class analysis (LCA), enable the researcher to identify important intraindividual and interindividual differences and thus model distinct configurations of heterogeneity within a given sample. Although traditional variable-level studies contain valuable information, they have also been criticized because they obscure diversity and foster the misleading and over-generalized conclusion that study findings represent the overall sample (von Eye & Bergman, 2003). A comment by Bogat, Levendosky, and von Eye (2005) illustrates this obfuscation: "[R]esearchers often write about these analyses 'as it" they say something about individuals, but they are really statements about variables" (p. 50). The importance of significant heterogeneity within subsets of populations has been noted within the larger social sciences (Costello, Mostillo, Erkanli, Keeler, & Angold, 2003). Inadequate attention to the heterogeneity inherent in the complexity of human social activity, such as the variations in symptom manifestations, or the reliance on categorical-based assessments to obtain a particular diagnosis by dichotomizing symptomatology as either being present or not (Krueger & Piasecki, 2002) has resulted in a number of important phenomena left largely unexplored. LCA comes under the rubric of structural equation modeling and is a type of person-centered analysis that uses finite mixture modeling to empirically determine whether interrelationships exist among observed variables that explain the underlying (that is, latent) phenomena (McCutcheon, 1987). Latent variables are statistically inferred from the direct measures, as in factor analysis. …