Citation

Bucca M. Socius 2020; 6: 2378023119899219.

Copyright

(Copyright © 2020, SAGE Publishing)

DOI

10.1177/2378023119899219

PMID

unavailable

Abstract

Log-linear models offer a detailed characterization of the association between categorical variables, but the breadth of their outputs is difficult to grasp because of the large number of parameters these models entail. Revisiting seminal findings and data from sociological work on social mobility, the author illustrates the use of heatmaps as a visualization technique to convey the complex patterns of association captured by log-linear models. In particular, turning log odds ratios derived from a model's predicted counts into heatmaps makes it possible to summarize large amounts of information and facilitates comparison across models' outcomes.

Log-linear models for contingency tables play a crucial role in the sociological study of social mobility and assortative mating. The basic goal of these models is to describe the association between categorical variables as a function of two distinct quantities: the marginal distribution of the variables and the net association between them (Agresti 2002). Mobility scholars, for example, want to distinguish temporal changes or cross-country differences in relative mobility from differences in the occupational structure across time and place. Another reason why log-linear models are appealing is that they capture patterns of association between variables, without reducing them to a single summary measure (e.g., correlation coefficients). In this vein, a key finding in mobility research is that patterns of social mobility are remarkably similar across industrialized countries (Erikson and Goldthorpe 1992).

Language: en