Hi On 26 Sep 2001, Burke Johnson wrote: > R Pretest Treatment Posttest > R Pretest Control Posttest > In the social sciences (e.g., see Pedhazur's popular > regression text), the most popular analysis seems to be to > run a GLM (this version is often called an ANCOVA), where Y > is the posttest measure, X1 is the pretest measure, and X2 is > the treatment variable. Assuming that X1 and X2 do not > interact, ones' estimate of the treatment effect is given by > B2 (i.e., the partial regression coefficient for the > treatment variable which controls for adjusts for pretest > differences).
> Another traditionally popular analysis for the design given > above is to compute a new, gain score variable (posttest > minus pretest) for all cases and then run a GLM (ANOVA) to > see if the difference between the gains (which is the > estimate of the treatment effect) is statistically > significant. > The third, and somewhat less popular (?) way to analyze the > above design is to do a mixed ANOVA model (which is also a > GLM but it is harder to write out), where Y is the posttest, > X1 is "time" which is a repeated measures variable (e.g., > time is 1 for pretest and 2 for posttest for all cases), and > X2 is the between group, treatment variable. In this case one > looks for treatment impact by testing the statistical > significance of the two-way interaction between the time and > the treatment variables. Usually, you ask if the difference > between the means at time two is greater than the difference > at time one (i.e., you hope that the treatment lines will not > be parallel) > Results will vary depending on which of these three > approaches you use, because each approach estimates the > counterfactual in a slightly different way. I believe it was > Reichardt and Mark (in Handbook of Applied Social Research > Methods) that suggested analyzing your data using more than > one of these three statistical methods. Methods 2 and 3 are equivalent to one another. The F for the difference between change scores will equal the F for the interaction. I believe that one way to think of the difference between methods 1 and 2/3 is that in 2/3 you "regress" t2 on t1 assuming slope=1 and intercept=0 (i.e., the "predicted" score is the t1 score), whereas in method 1 you estimate the slope and intercept from the data. Presumably it would be possible to simulate the differences between the two analyses as a function of the magnitude of the difference between means and the relationship between t1 and t2. I don't know if anyone has done that. Best wishes Jim ============================================================================ James M. Clark (204) 786-9757 Department of Psychology (204) 774-4134 Fax University of Winnipeg 4L05D Winnipeg, Manitoba R3B 2E9 [EMAIL PROTECTED] CANADA http://www.uwinnipeg.ca/~clark ============================================================================ ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
