Ordinal Log-Linear Models for Contingency Tables

Brzezińska Justyna ()
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Brzezińska Justyna: University of Economics in Katowice, Faculty of Finance and Insurance, Department of Economic and Financial Analysis, 1 Maja 50, 40-287 Katowice, Poland, Poland

Folia Oeconomica Stetinensia, 2016, vol. 16, issue 1, 264-273

Abstract: A log-linear analysis is a method providing a comprehensive scheme to describe the association for categorical variables in a contingency table. The log-linear model specifies how the expected counts depend on the levels of the categorical variables for these cells and provide detailed information on the associations. The aim of this paper is to present theoretical, as well as empirical, aspects of ordinal log-linear models used for contingency tables with ordinal variables. We introduce log-linear models for ordinal variables: linear-by-linear association, row effect model, column effect model and RC Goodman’s model. Algorithm, advantages and disadvantages will be discussed in the paper. An empirical analysis will be conducted with the use of R.

Keywords: association models; ordinal variables; contingency table; log-linear models (search for similar items in EconPapers)
JEL-codes: C35 C52 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:foeste:v:16:y:2016:i:1:p:264-273:n:17

DOI: 10.1515/foli-2016-0017

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