Loglinear Marginal Models

  • Wicher BergsmaEmail author
  • Marcel Croon
  • Jacques A. Hagenaars
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)


Loglinear models provide the most flexible tools for analyzing relationships among categorical variables in complex tables. It will be shown in this chapter how to apply these models in the context of marginal modeling. First, in Section 2.1, the basics of ordinary loglinear modeling will be explained. The main purpose of this section is to introduce terminology and notation and those aspects of loglinear modeling that will be used most in the remainder of this book. It will be assumed that the reader already has some familiarity with loglinear modeling and, therefore, the discussion will be concise. An advanced overview of loglinear models is provided by Agresti (2002); an intermediate one by Hagenaars (1990) and an introduction is given by Knoke and Burke (1980) among many others. In Section 2.2, several motivating examples will be presented showing what types of research questions can be answered by means of loglinear marginal modeling. Finally, in Section 2.3, a general ML estimation procedure will be discussed for testing and estimating loglinear marginal models.


Saturated Model Loglinear Model Full Column Rank Marginal Model Cell Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Wicher Bergsma
    • 1
    Email author
  • Marcel Croon
    • 2
  • Jacques A. Hagenaars
    • 2
  1. 1.Department of StatisticsLondon School of EconomicsLondonUnited Kingdom
  2. 2.Department of MethodologyTilburg University Fac. of Social & Behavioural SciencesTilburgThe Netherlands

Personalised recommendations