In recent years, methods of analyzing latent variables have become increasingly popular in social work research. Techniques such as structural equation modeling (SEM) and confirmatory factor analysis (CFA) have been introduced into the curriculum of social work doctoral programs (Gillespie, Alsup, & Rubio, 1995). Many social worker researchers are now familiar with these techniques. Another useful but less well known approach to analyzing latent variables is latent class (LC) modeling. LC models are person-centered methods. These approaches can be quite useful when investigating problems that affect diverse groups of people. LC models estimate a latent variable that explains an aspect of heterogeneity (or diversity) in a population. This is particularly important when the aspect of heterogeneity is elusive and difficult to capture with a single measure (for example, religiosity, attitudes about moral issues) (McCutcheon, 1987a). LC modeling is not a particularly new concept-techniques have been described for more than 50 years (Green, 1951, 1952). Early examples of LC modeling include the use of General Social Survey data from the 1970s to identify classes of racial prejudice and lack of racial prejudice among white respondents (Tuch, 1981) and to identify classes of respondents regarding sexual morality and values (McCutcheon, 1987b). The recent development of more efficient and useable statistical methods, described later in this article, has made the application of LC models to social science problems a more realistic possibility (Goodman, 2002).