Anderson, C.J, & Rutkowski, L. (2007, inpress). Multinomial logistic regression models. In J. Osborne (ed) Best Practices in Quantitative Methods. Thousand Oaks, CA: Sage.

SAS Files for Analyses in the Chapter:

• Description of variables in the High School and Beyond data set.



• HSB data set with SAS code to create SAS data set.



• Simple Models (Table 26.1). Baseline model where mean achievement is the explanatory variable and the separately fit models.



• All Main effects with nominal SES (Table 26.2). Baseline model with mean achievement, SES (nominal), and school type as explanatory variables.



• Plot fitted probabilities and odds (Figures 26.2 and 26.1)...and a few extra. Baseline model with mean achievement, SES (nominal), and school type as explanatory variables.



• All Main effects with ordinal SES (Table 26.3). Baseline model with mean achievement, SES (ordinal), and school type as explanatory variables.



• Conditional Multinominal Models (Table 26.4, 26.5, 26.6). This SAS input files shows you how to
  • Expand the data files into one with 3 lines of data per student.
  • Create indicator variables needed.
  • Fit models already fit but fit them as Poisson log-linear models (not reported in the paper).
  • Fit the baseline models as conditional multinomial models using PROC MDC (Multionmial Discrete Choice models) to a check that the data set is properly set-up (i.e., get the same results as PROC LOGISTIC).
  • Fit model with restrictions on parameters (our "final" model).
  • Fit model needed to test whether general and vocational programs are indistinguishable./li>


• Hosmer-Lemeshow statistic & regression diagnoistic from PROC LOGISTIC. Briefly mentioned and references given in the chapter. See also SAS documentation to PROC LOGISTIC.



• Range of Influence SAS module. Mentioned in the chapter, but not discussed or illustrated. For more information, see Fay, M.P. (2002). Measuring a binary response's range of influence in logistic regression. American Statistican, 56, 5-9.



• Problems & solutions (multicolinearity and separation).

Data for Exercises at the End of the Chapter:

• Exercise 1:
  • Description of variables in the HSB data set.
  
  
  
  • HSB data set with SAS code to create SAS data set.
  
  
  
• Exercise 2:
  • English placement test data. The data are from Lee, H.K. & Anderson, C.J. (2006). Validity and topic generality of a writing performance test. Language Testing, 23, xx-xx.
  
  
  

Report broken links and send email to cja@uiuc.edu

Last revised June 29, 2006