Categorical causal modeling:Latent class analysis and directed log-linear models with latent variables  

Oleh:

Hagenaars, Jacques A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 26 no. 04 (May 1998), page 436-486.
Topik: interval; ordinal; nominal; measurement

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Latent class analysis (LCA) is an extremely useful and flexible technique for the analysis of categorical data, measured at the nominal, ordinal, or interval level (the latter with fixed or estimated scores). It is, first, a general measurement model, a particular kind of latent structure model that can be used for the investigation of the reliability and validity of categorical measurements, taking both random and systematic response errors into account. When dealing with test-retest effects, response consistency effects, unobserved heterogeneity, response effects from varying survey (interviewing) conditions, and so on, it is useful to take a consistent causal modeling point of view and to integrate LCA into a general causal log-linear model with latent variables. The resulting directed log-linear-modeling approach integrates insights from Goodman's modified path approach, the modified LISREL approach, and directed graphical modeling.

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