Fixed Effects Logistic Regression Model

If a researcher wants to know whether watching violent television has an impact on juvenile delinquency, that researcher could compare a student’s delinquency rate when he/she is watching violent television with his/her delinquency rate when not watching. The difference in delinquency rates between the two periods is an estimate of the violent television effect for that student. Similarly, a researcher might want to know how a child’s performance in school differs depending on how much time he/she spends playing video games. The researcher could compare how the child does when spending significant time playing video games versus when he/she does not watch violent television. Fixed effects logistic regression models are presented for both of these scenarios. These models treat each measurement on each subject as a separate observation, and the set of subject coefficients that would appear in an unconditional model is eliminated by conditional methods. This is a conditional, subject-specific model (as opposed to a population-averaged model like the GEE model). We fit this model in SAS, SPSS, and R. An excellent discussion with examples can be found in Allison (Fixed effects regression methods for longitudinal data using SAS, SAS Institute, Cary, NC, 2005).