- Download the School readiness data from blackboard (Part 4 – Joost van Ginkel). The data set consists of the following variables: The child’s Gender, Mother’s ethnicity, School readiness, Mother’s depression score, Mother’s educational level, Mother’s age, Family income, Mother’s IQ, and the Child’s IQ. The data set contains missing values which have to be multiply imputed before carrying out statistical analyses.

- Carry out multiple imputation (Rubin, 1987), using all variables in the imputation procedure. Also include the two-way interactions among categorical predictors (see, computer lab session 2). Before running the imputation procedure, paste the command in the syntax first. The data set is too large to carry out a multiple imputation using the default settings in SPSS. The settings may only be changed using the syntax. You can change the settings in the syntax by adding an additional command to the /IMPUTE subcommand: MAXMODELPARAM=100000.

Next, we are interested in how the child’s school readiness is predicted from mother’s depression score, mother’s age, family income, mother’s IQ, and the child’s IQ. Carry out this regression analysis. Note that some of the numeric variables are highly skewed which have to be log transformed (In SPSS: COMPUTE [new variable name] = ln([old variable name])) before they can be used for the statistical analysis.

Carry out the regression analysis using Mixed Models rather than the standard regression option in SPSS, and save both the regression coefficients and the covariance matrices of the regression coefficients to an SPSS data file using the OMS option (see the manual MI-mul2manual.pdf of the MI-mul2.sps syntax file by Van Ginkel (2010), pp. 5-8). We need this output for later analyses.

The following things are important to think about:

**Whether you perform the log transformation before or after multiply imputing the data.**Note: Don’t try to find the answer to this question in the literature because the literature will not be of any help here. The idea is that you try to reason*yourself*which option will be correct in this case and which option will be incorrect, and what exactly will go wrong when you choose the incorrect option.