In the introduction we give a brief characterization of the usual measures for indicating the quality of diagnostic procedures (sensitivity, specificity and predictive value) and we refer to their relationship to parameters of the latent class model. Different variants of latent class analysis (LCA) for dichotomous data are described in the following: the basic (unconstrained) model, models with parameters fixed to given values and with equality constraints on parameters, multigroup LCA including mixed-group validation, and linear logistic LCA including its relationship to the Rasch model and to the measurement of change in latent subgroups. The problem with the identifiability of latent class models and the possibilities for statistically testing their fit are outlined. The second part refers to latent class models for polytomous data. Special attention is paid to simple variants having fixed and/or equated parameters and to log-linear extension of LCA with its possibility for including on the latent level. Several examples are presented to illustrate typical applications of the model. The paper ends with some warnings that should be taken into consideration by potential users of LCA.

Latent class analysis in medical research.

To understand one developmental process, it is often helpful to investigate its relations with other developmental processes. Statistical methods that model development in multiple processes simultaneously over time include latent growth curve models with time-varying covariates, multivariate latent growth curve models, and dual trajectory models. These models are designed for growth represented by continuous, unidimensional trajectories. The purpose of this article is to present a flexible approach to modeling relations in development among two or more discrete, multidimensional latent variables based on the general framework of loglinear modeling with latent variables called associative latent transition analysis (ALTA). Focus is given to the substantive interpretation of different associative latent transition models, and exactly what hypotheses are expressed in each model. An empirical demonstration of ALTA is presented to examine the association between the development of alcohol use and sexual risk ...

Modeling Relations among Discrete Developmental Processes: A General Approach to Associative Latent Transition Analysis

http://www.scielo.br/pdf/rbp/v40n2/1516-4446-rbp-1516444620162139.pdf

Screening for common mental disorders using the SRQ-20 in Brazil: what are the alternative strategies for analysis?

/pdf/examining-gambling-and-substance-use-applications-of-34zadfev2z.pdf

Examining gambling and substance use: Applications of advanced latent class modeling techniques for cross -sectional and longitudinal data

Umweltforschung produziert Daten. Diese mussen statistisch analysiert und mit Blick auf Handlungsoptionen bewertet werden. Die Entwicklung und Anwendung neuer statistischer Methoden gilt in diesem Zusammenhang als ein wichtiger Arbeitsschwerpunkt (JAGER 1987: 57).

Korrespondenzanalytische Standortstypisierung der alten Bundesländer

Data collected by social and behavioral scientists very often consist of large multidimensional tables of subjects cross-classified according to the values or states of several categorical variables. For example, Table 1 shows a set of data on suicide victims in which the method of committing suicide is cross-classified by sex and age group (Van der Heijden & de Leeuw, 1985) and Table 2 shows counts of subjects resulting from a survey of the political attitudes of a sample from the British electorate (Butler & Stokes, 1974). The analysis of such data should clearly depend on the substantive questions posed by the researcher involved, although in many cases these questions will be rather vague. The research worker may be interested in such notions as “pattern” and “structure” but it will often be left to the statistician to clarify what is meant by such concepts and whether they are present in the investigator’s data. Finally, the statistician has the often difficult task of explaining the results.

B. S. Everiti

Papers

Log-Linear Modeling, Latent Class Analysis, or Correspondence Analysis Which Method Should Be Used for the Analysis of Categorical Data?