Fitting Stereotype Logistic Regression Models for Ordinal Response Variables in Educational Research (Stata)

Fitting Stereotype Logistic Regression Models for Ordinal Response Variables in Educational Research (Stata)

The stereotype logistic (SL) model is an alternative to the proportional odds (PO) model for ordinal response variables when the proportional odds assumption is violated. This model seems to be underutilized. One major reason is the constraint of current statistical software packages. Statistical Package for the Social Sciences (SPSS) cannot perform the SL regression analysis, and SAS does not ...

Stereotype Ordinal Regression

There are a number of reasonable approaches to analysing an ordinal outcome variable. One common approach, known as the Proportional Odds (PO) Model, is implemented in Stata as ologit. If the assumptions of the PO model are not satisfied, an alternative is to treat the outcome as categorical, rather than ordinal, and use multinomial logistic regression (mlogit) in Stata. This insert describes a...

Regression Models with Ordinal Variables*

Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables. If ordinal variables are discrete realizations of unmeasured conti...

Regression models for categorical dependent variables using Stata, 2nd Edition

Ordinal logistic regression.

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...