Loglinear Models with Latent Variables (Quantitative Applications in the Social Sciences)

Hagenaars, Jacques A. P.

ISBN 10: 0803943105 / ISBN 13: 9780803943100

Published by SAGE Publications, Inc, 1993

About this Item

***GOOD CONDITION***CLEAN PAGES*** Front cover bottom corner bend. Moderate edge wear to cover. Signed by previous owner. Small stain mark atop front cover. We specialize in non-fiction books. Follow us on Facebook and Instagram.

Seller Inventory # mon0000011878

Bibliographic Details

• Title: Loglinear Models with Latent Variables (Quantitative Applications in the Social Sciences)
• Publisher: SAGE Publications, Inc
• Publication Date: 1993
• Language: English
• ISBN 10: 0803943105
• ISBN 13: 9780803943100
• Binding: Paperback
• Condition: Good

About this title

Synopsis

Sociologists with a quantitative bent will doubtless find it useful. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research "Loglinear Models with Latent Variables, by Jacques A. Hagenaars, is a timely contribution to the literature that serves to inform researchers of the richness of loglinear approaches to analyzing latent categorical variables. . . . The author provides a clear exposition of the loglinear model." --Scott L. Hershberger in Structural Equation Modeling Since the 1980s, the loglinear model has become the dominant form of categorical data analysis as researchers have expanded it into new directions. Jacques A. Hagenaars′ book shows researchers the applications of one of these new developments--how uniting ordinary loglinear analysis and latent class analysis into a general loglinear model with latent variables can result in a modified LISREL approach. This modified LISREL model will enable researchers to analyze categorical data in the same way that they have been able to use LISREL to analyze continuous data. Beginning with an introduction to ordinary loglinear modeling and standard latent class analysis, Hagenaars explains the general principles of loglinear modeling with latent variables; the application of loglinear models with latent variables as a causal model, as well as a tool for the analysis of categorical longitudinal data; the strengths and limitations of this technique; and lastly, a summary of computer programs that are available for executing this technique.