期刊
JOURNAL OF MATHEMATICAL SOCIOLOGY
卷 40, 期 1, 页码 7-20出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/0022250X.2015.1112384
关键词
Generalized ordered logit model; ordered logit model; partial proportional odds; proportional odds assumption; proportional odds model
When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.
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