Corrections
On the page 169, the chi square difference test of equal actor and partner effects should be 12.14, not 12.06.
The figure heading for Figure 7.3 on the page 171 should be "Campbell et al." and not "Simpson et al." Also in that figure the paths from Em and Ef should be be one.
On the bottom of page 176 and top of 177, there are two sets of SPSS syntax. For both, the " | NOINT " goes on the /FIXED line. For the set of SPSS syntax of page 177, the second line should be:
DISTRESS BY GENDER WITH ACT_NEURO PART_NEURO
Again the " | NOINT " goes on the /FIXED line.
On page 180, in the two equations, there should be some space between the plus sign and the lines on either side.
On page 183, we mean NLMIXED not NMIXED.
Elaborations
There are several papers in preparation that discuss mediation of the APIM. One is by Ledermann, Macho, and Bodenmann of the University of Fribourg Switzerland. When these papers are available, information will be posted.
Using SEM to estimate the APIM with distinguishable dyads (p. 178-179) is a saturated model and so it has zero degrees of freedom and no measures of fit can be computed. Normally in SEM with latent variables, models have non-zero degrees of freedom and are over-identified. The use of SEM to estimate the APIM is to estimate two regression equations with correlated error terms. It is then not problematic that the base model is saturated and no measures of fit can be obtained.
It is possible to test for indistinguishability using MLM. Click here to download a document that describes how to do this.
When using SEM with the APIM, one usually does include fit statistics such as the RMSEA or the CFI, because one is using SEM to compute constrained regression analyses. Moreover, the RMSEA can be misleading when the df are small. For example, Marga Korporaal found a chi square of 2.098 with 1 degree of freedom and an RMSEA =.126. As the example shows, a non-significant chi square can yield a large RMSEA. Note also sometimes the CFI can be very small, despite big fit, if the relationships between variables are not strong. For these reasons, fit measures need not be reported for SEM-APIM analyses.
When conducting an APIM within multilevel modeling, one may wish to know the standardized or beta weight. One can use the Z-score option in SPSS on the pairwise data set. To this click: Analyze, Descriptive, then select the variables you want Z scored, and then click the box "save Z scores". One then runs the analysis using these new variables.
When using structural equation modeling with distinguishable dyads as in Figure 7.3, it is advisable to estimate the intercepts for each of the members on the outcome variable. For the example, the difference in intercepts estimates the gender difference. It is then important that causal variables have a meaningful zero and if not they should be centered.
How do you compute R squared when dyad members are distinguishable, say by gender, when using multilevel modeling? For distinguishable dyads, there are two error variances, one for males and one for females, assuming you have asked for heterogeneous variance by using CSH with SAS or SPSS. There are two R squared values, one for the males and one for the females. For each, you would compute 1 – EV(M)/EV(E) where EV(M) is the error variance for the model with the actor and partner effects and EV(E) is the empty model, a model with no actor and partner effects. However, for the empty model, it is advisable to have gender in the model, so it is not entirely “empty.”
Data and Files
Artificial Roommate Data in Table 7.1
SPSS (Page 161):
Data File,
Syntax, and
Output
SAS (Page 160):
Data File,
Syntax, and
Output
MLwin (Page 165):
Run of the Roommate Data
HLM (Page 164):
HLM File, and
Output.
Campbell et al. Study: Page 171-177 (There appears to be minor error in the centering of neuroticism which changes the intercepts in some analyses.):
Campbell et al. 
