12/05 12:15 Jake: your poll is about an unbalanced design, but your thread title talks about missing data. i guess that's a big hint about how you would approach this one

12/05 12:16 Jake: do people still care about unbalanced factorial designs or have we reached the 1960s yet

12/05 12:17 spunky: so what do people do in the real world? stick to SPSS's default of Type III sums of squares and move on?

12/05 12:18 spunky: and, even more importantly, if people stick to Type III SSq is that the best approach to this?

12/05 12:19 Jake: most people just stick to type 3. i guess your concern is that the type 3 estimates are slightly less efficient in the unbalanced case?

12/05 12:21 Jake: in theory you could use type 3 estimates and adjust your contrast codes so that they provide estimates equivalent to the type 2 ones. but ive never seen anyone actually do this

12/05 12:21 spunky: something along those lines, true. the other thing is that i see people regularly using this fancy-dandy missing data methods with uber-complicated designs and i can barely find anything related to much more pedestrian methods like regression/ANOVA (particularly ANOVA where i would say having an unbalanced design is more the rule rather than the exception)

12/05 12:22 Dason: a factorial design is uber-complicated?

12/05 12:25 spunky: no. a factorial design is simple. i'm talking about more in the context of SEM where i see these methods used routinely but they don't get used in easy designs (ANOVA/regression)

12/05 12:25 Jake: i see no reason to use missing data methods just because you have an unbalance design... in fact that seems pretty suspect to me

12/05 12:25 spunky: sorry, i kinda meant to imply ANOVA/regression (or the general linear model) = simple. weirdo multivaraite designs with latent variables = compicated

12/05 12:25 Dason: I agree Jake

12/05 12:25 spunky: @Jake why would it be suspect?

12/05 12:26 Dason: give a good reason for doing it in the first place? Because it makes it easier to get the estimates by hand?

12/05 12:26 Jake: because you don't actually have missing data?

12/05 12:27 Jake: if you call an unbalanced design missing data, isn't that kind of like saying "well i recruited 70 subjects, but i wanted to get 100... so i have 30 missing data points!"

12/05 12:27 Dason: I agree

12/05 12:28 spunky: uhm. good point. what if you have attrition in a repeated-measures ANOVA context?

12/05 12:28 spunky: go you mixed models. i answered my own question

12/05 12:29 Jake: haha well yes i would use a mixed model, but i think it would at least be sensible to use missing data methods in that case