Random and Fixed effect model Would you explain a practical situation where random effect model is more appropriate than the fixed effect model?
 A: More information would help us discern the structure of your data. Yet generally speaking, if your data are clustered (e.g., correlated or nested), you should use mixed-effects models that contain both fixed and random effects. For example, you obtained outcome variables:


*

*repeatedly from the same set of subjects. In this case, observations
are nested within subjects and are correlated; or 

*from students nested within classrooms or patients nested within hospitals. Then
the observations taken from the students/patients within each classroom/hospital can be correlated.


Ignoring such a clustering structure of your data can bias your estimates and/or inflate type-I error rates (e.g., Moerbeek, 2004; but see Van Landeghem et al., 2005).  
If your data are obtained from subjects only once, and they are not nested within any grouping/structural variables, you can use fixed-effect models. 
Reference


*

*Moerbeek, M. (2004). The consequence of ignoring a level of nesting in multilevel analysis. Multivariate Behavioral Research, 39, 129–149.

*Van Landeghem, G., De Fraine, B., & Van Damme, J. (2005). The Consequence of Ignoring a Level of Nesting in Multilevel Analysis: A Comment. Multivariate Behavioral Research, 40, 423-434 

