Solved – Converting SPSS syntax for regression to Stata commands

regressionspssstata

I am running a series of regressions to eliminate the effects of IQ, language ability and age from my variables (task performance). To do this, the variable is entered as the dependent variable (e.g. EFT in syntax below) in a regression with IQ, language ability and age as predictor variables, selecting for control group only (GROUP1 EQ 2 is the control group). This regression equation is then applied to the other groups (GROUP1 = 0 & 1) and the residuals are collected.

The main problem I am having is converting the SPSS syntax below into Stata command (as I am relatively new to Stata). I need to use Stata to run the regression as my data are from related individuals and so I need to use the cluster command which isn't available in SPSS.

REGRESSION 
/SELECT=GROUP1 EQ 2 
/MISSING LISTWISE 
/STATISTICS COEFF OUTS R ANOVA 
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN 
/DEPENDENT EFT
/METHOD=ENTER AGE IQ LANG 
/SAVE RESID (rEFT).

Best Answer

It's not clear to me why you want to do this, but anyway... This is a tentative based on what I've understood from your post.

regress eft age iq lang if group1 == 2, vce(cluster clustvar)
predict resid1 if inlist(group1, 0, 1), resid

Where clustvar is your cluster variable

(Please note that using vce(cluster clustvar) or not, won't change the residuals calculated with the second line of code, but of course it changes the standard errors of the linear regression).

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enter image description here

Solved – Random effects in mixed models

Here are my answers to your questions:

1) No, you do not need to account for the grouping here, because the random intercepts are estimated for each cluster (here, each person), not for each group--this is why you have the grouping variable in your fixed effects as a predictor of the intercept (the "main effect") or of the slopes (the interaction terms)

2) The RANDOM line specifies the random effect, which is what you seem to want. The REPEAT line is to allow for a different level-1 residual variance-covariance matrix; for example, you can allow residuals from one timepoint to be correlated with the next (autoregressive structure). In this case however, you do not need a REPEAT line, since you have only two timepoints (there is only one residual correlation to be estimated).

3) Yes, it is correct to use TIME as a fixed factor. The FIXED command simply serves to determine which fixed effects will be estimated, much like in GLM. Therefore, you do want the effect of TIME (mean difference from pre to post, controlling for other effects in your model) to be estimated. Where you decide whether to treat TIME as a factor or as a continuous predictor is in the very first line: BY denotes a categorical factor and will be automatically dummy-coded, and WITH denotes a continuous predictor. Therefore, you'd want your first line to be: MIXED Score BY Group Treatment WITH Time

4) Baseline differences between groups are embedded in the Group fixed effect, which represents the mean difference between the two groups, when $\text{Time}=0$ since there is an interaction term between Group and Time. Therefore, where you decide to center your time variable (i.e. which timepoint gets the value 0) is crucial. By including the Group fixed effect, you are controlling for differences between groups at $\text{Time}=0$.

Best Answer

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