4 edition of Latent trait and latent class models found in the catalog.
Latent trait and latent class models
|Other titles||Latent class models.|
|Statement||edited by Rolf Langeheine and Jürgen Rost.|
|Contributions||Langeheine, Rolf., Rost, Jürgen., Universität Kiel. Institut für die Pädagogik der Naturwissenschaften.|
|LC Classifications||QA278.6 .L33 1988|
|The Physical Object|
|Pagination||xv, 311 p. :|
|Number of Pages||311|
|LC Control Number||88004141|
J Am Stat Assoc — The first chart in the chapter explains what Lazarsfeld called a traceline, a function that indicates how the probability of response to an item changes as the "ethnocentrism" of American soldiers varies along a continuum. The aim of this conference-to treat latent trait and latent class models under comparative points of view as well as under application aspects-was realized in many stimulating contributions and very different ways. In this chapter the model that is first discussed is a latent trait model.
The main aim of LCA is to split seemingly heterogeneous data into subclasses of two or more homogeneous groups or classes. Clogg, Clifford C. Appl Psychol Meas — What is now called parameter estimation was formulated as a problem of decomposing a manifest distribution into its homogeneous components. Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error is an excellent book for courses on measurement error and survey methodology at the graduate level. Plenum Press, New York, pp.
Research Report No. I have already noted that the use of discrete categorical manifest variables distinguished latent structure analysis and Guttman scaling from factor analysis. This article, and the publication in of Discrete Multivariate Analysis by Bishop, Fienberg and Holland, brought the LSA model to the attention of mainstream statisticians. Clogg, Clifford C. Log-linear Modelling and Latent Structure Analysis Lazarsfeld considered that the statistical analysis of discrete categorical data had been ignored by the statisticians of his generation, and in a series of historical papers he evaluated the work of the 19th century statistician August Quetelet and the British statistician G.
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Struct Equ Model — In latent trait analysis and latent class analysis, the manifest variables are discrete. Latent trait and latent class models book Houghton Mifflin.
Byhowever, a new approach to had become current. Single-class IRT data sets were generated using different ability distributions and then analyzed with mixture IRT models to determine the impact of these distributions on the extraction of latent classes. The first published explanation of latent structure analysis is "The logical and mathematical foundation of latent structure analysis", chapter 10 of Measurement and Prediction.
Readers who would like to see how the estimation of the parameters in latent trait models plays a role in the correspondence between latent class and latent trait models will find Latent Class and Discrete Latent Trait Models a fascinating read.
This article, and the publication in of Discrete Multivariate Analysis by Bishop, Fienberg and Holland, brought the LSA model to the attention of mainstream statisticians. By thinking of the latent class variable as "missing data" in a cross-classification, a wider range of interpretations and applications became available.
J Am Stat Assoc — Research Report No. Cambridge, MA: Abt Books. The example above takes a different position, by postulating the existence of a relatively small number of homogeneous subpopulations or latent classes.
Latest article,Am. Drawing from his extensive experience in the field of survey methodology, the author examines early models for survey measurement error and identifies their similarities and differences as well as their strengths and weaknesses. Lazarsfeld's ambivalent position between the disciplines of sociology and psychology is shown by his next major publication.
Stat Sci — We asked the authors of these papers to work out their contributions for publication here, not only because many of the papers present new material, but also because the time is ripe for a comprehen sive volume, working up the widespread literature of the past ten years in this field.
As I have already noted, Langeheine and Rost's collection focussed on this very topic.
At the time, however, it made the topic more understandable and acceptable to the social psychologists who were the primary audience of these research studies, the heirs if not in fact the students of Thurstone and Spearman.Latent class, latent trait, and factor mixture models of SCID-II symptoms were estimated, and a latent trait model provided superior fit to the data, supporting a Cited by: A latent trait model with binary xs is similar to a latent class model.
The prior distribution is now continuous and will usually Latent trait and latent class models book taken to be standard normal. The prior distribution is now continuous and will usually be taken to be standard normal.
A general approach for analyzing rating data with latent class models is described, which parallels rating models in the framework of latent trait theory.
A general rating model as well as a two-parameter model with location and dispersion parameters, analogous to Andrich's Dislocmodel are derived, including parameter estimation via the EM Cited by: •The latent class model for p binary variables with C latent classes makes the following pdf i) The n cases are pdf random sample from some population and every case in that population belongs to just one of the C latent classes ii) The probability of giving a positive response to a particular item is the same for all cases in the same.Latent Trait and Latent Class Models by R.
The main aim of LCA is to split seemingly heterogeneous data into subclasses of two or more homogeneous groups or classes. In contrast, LPA is a method that is conducted with continuously scaled data, the focus being on generating profiles of participants.