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Distance-based logistic model for cross-classified categorical data
[journal article]
Abstract Logistic regression models are a powerful research tool for the analysis of cross-classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes t... view more
Logistic regression models are a powerful research tool for the analysis of cross-classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to their easy interpretation. In this article we present a distance-based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table. Two configurations are estimated, one for the rows and one for the columns, as the categories of a polytomous predictor and a nominal response variable respectively, such that the local odds ratio and the distances between the predictor and response categories are inversely related. The associations in terms of the odds ratios, or the ratios of the odds to their geometric means, are interpreted through distances for the most common coding schemes of the predictor variable, and the relationship between the distances related to different codings is investigated in its full dimension. The performance of the estimation procedure is analysed with a Monte Carlo experiment. The interpretation of the model and its performance, as well as its comparison with a two-step procedure involving first a logistic regression and then unfolding, is illustrated using real data sets.... view less
Keywords
data; regression analysis; quota; model
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
Rolling Cross-Section-Wahlkampfstudie mit Nachwahl-Panelwelle (GLES 2017) (ZA6803 v4.0.1)
Document language
English
Publication Year
2022
Page/Pages
p. 466-492
Journal
British Journal of Mathematical and Statistical Psychology, 75 (2022) 3
DOI
https://doi.org/10.1111/bmsp.12264
ISSN
2044-8317
Status
Published Version; peer reviewed