English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This article deals with the latent class analysis of models with error of measurement. If the latent variable is ordinal and manifest variables are nominal, an approach to handle the restrictions is given for latent class analysis of the models with error of measurement using log linear models. By this way, we include ordinal nature of the latent variable into the analysis. Therefore, overall uncertainty is decreased, and our inferences become more precise. The new approach is applied to a women's liberation data set. Latent class analysis is frequently used in social sciences and education. Main aim of the analysis is to explain the association structure between manifest vari- ables by using unobserved variables, namely latent variables. Latent class analy- sis is a categorical analogous of the factor analysis when the latent and manifest variables are categorical. Log-linear models are widely used for the analysis of contingency tables. It is possible to represent a latent class model as a log-linear model using conditional response probabilities. This representation is called as log-linear parametrization. This is a special case of Formann's linear logstic latent class analysis (Formann, 1992). Error of measurement models are probabilistic versions of Guttman scale (Guttman, 1950), and considered as restricted latent class models. In the log- linear parametrization of the latent class models, types of manifest and latent variables are important issues because latent class models specialize according to the typology of the variables. For example, if the latent variable is metrical and manifest variables are nominal then appropriate analysis is to carry on latent class analysis with linear restrictions or use nominal response models (Heinen, 1996). In our concerned models with error of measurement, manifest variables

Latent Class Analysis for Models with Error of Measurement Using Log-Linear Models and An Application to Women’s Liberation Data