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Courses: Course information Latent Class Regression Analysis at the conference SMABS 2004
Speakers: Jeroen K. Vermunt (Tilburg University, The Netherlands) Summer term 2004, Workshop, Course length: 5.75 hours, Language: English Nowadays, latent class (LC) and other types of finite mixture models belong to the standard research toolbox of social and behavioural scientists. These methods provide elegant solutions to common statistical problems, such as clustering, scaling, and dealing with unobserved heterogeneity, and software for simple and complicated mixture modelling is generally available, e.g., LEM, Mplus, PANMARK, WINMIRA, GLLAMM, GLIMMIX, and Latent GOLD. The traditional application of LC analysis is cluster analysis with categorical response variables (indicators). LC cluster models can, however, not only be used with categorical indicators, but also with continuous indicators and counts, as well as with combinations of indicators of different scale type. A less wellknown variant of the LC model is the LC or mixture regression model, which has been simultaneously proposed in the academic marketing and the statistical literature. The LC regression model is a regression model for twolevel data structures. Depending on the scale type of the dependent variable, one uses another type of model from the generalized linear modelling (GLM) family; that is: a standard linear, a binary, multinomial, ordinal, conditional or exploded logistic, or a (truncated) Poisson regression model. The data has the form of a twolevel data set in which dependent observations belonging to the same unit are linked by an ID variable. These replications can arise from lowerlevel observations that are nested within a higherlevel observation, repeated measures taken from the same individual, or responses on various indicators. The fact that multiple indicators can also be seen as repeated measures illustrates that the LC regression model contains the LC cluster model as a special case. The basic idea of LC regression modelling is that latent classes (of higherlevel units) differ with respect to the size of the regression coefficients. In other words, regression coefficients are assumed to vary across observations. The mixture regression model is, in fact, a randomcoefficients model or, more precisely a nonparametric randomcoefficients model. It has various advantages over standard twolevel modelling approaches, such as imposing less restrictive distributional assumptions and providing much faster and stable estimation with nonlinear regression models. In this workshop, I will introduce the LC regression model using several interesting application types, such as multilevel modelling, longitudinal, growth and survival analysis, and the analysis of data collected by choice experiments and structured tests or questionnaires. I will also show the connection with LC cluster analysis, multilevel regression analysis, and item response theory modelling, as well as illustrate how restrictions yield variants such as zeroinflated and moverstayer models and models with several latent variables. I will also pay attention to recent developments, such as models combining discrete (classes) and continuous (traits) forms of unobserved heterogeneity, the threelevel extension of the LC regression model, and procedures for dealing with complex sampling designs. During the workshop I will make use of the newest versions of the Latent GOLD and Latent GOLD Choice programs. In order to view the videos, you need the which you can download here. The videos are hosted at the Digitale Bibliothek Thüringen (dbt).Click the green link to watch the video or download the file. Material 