I believe the primary interest is whether there are differences in perceptions of psychological safety based on the gender of the reviewer and the person rated in pairs of participants working in teams. There are four types of pairs, MM, MF, FM, and FF, where the first letter indicates the gender of the reviewer, and the second one the gender of the person rated, and the desire is to compare mean ratings among these four types of pairs. It becomes more complicated because the same reviewer rates multiple participants, introducing possible correlations among ratings from the same reviewer. There's also the fact that the sets of participants are formed into distinct teams, which is another source of possible variation, and it's thought that it might be the case that the effect of rating pair types is different in different teams. This can be thought of as an interaction between teams and the primary effect of interest, and assuming this exists, comparisons of the primary effect might be done separately within teams, treating teams as a second fixed factor.
There are 372 observations from 137 subjects in 38 teams, with 135 subjects providing sufficiient data to use in the analysis (two reviewers have all missing data for ratings), and 136 of the 137 participants being rated at least once. Not all teams have observations for all reviewers rating all other team members. If both factors are used, with ReviewerGenderToRatedGender nested within TeamNumber, there are 78 fixed-effects parameters being estimated, which is not a small number for only 372 cases. Also, treating TeamNumber as a fixed effect means that you don't want to infer from these teams to a broader (perhaps hypothetical) population of teams, but apply the results only to these teams. This seems unlikely. It's also the case that testing comparisons among the four levels of ReviewerGenderToRatedGender separately for each TeamNumber involves a large number of tests, and if any adjustment for multiple testing is applied not just within each TeamNumber, but also across them (which requires some "manual" adjustment to EMMEANS COMPARE specifications), power is likely to be very poor, and not adjusting across teams provides lots of opportunities for Type I errors. Thus including TeamNumber as a fixed effect has some notable drawbacks.
Note that if TeamNumber is included as a fixed factor, it would need to be listed separately after BY on the MIXED command, and the fixed-effects model including whatever versions of the two factors would be specified on the FIXED subcommand. For example, the part of the syntax that specifies the basic model for nesting ReviewerGenderToRatedGender within TeamNumber would be:
MIXED PS BY ReviewerGenderToRatedGender TeamNumber
/FIXED TeamNumber ReviewerGenderToRatedGender(TeamNumber)
/FIXED TeamNumber ReviewerGenderToRatedGender*TeamNumber
which both fit the same fixed-effects model (which is overall equivalent to a full-factorial model on these two factors). The latter approach has the advantage of allowing specification of the interaction effect on EMMEANS, with COMPARE(ReviewerGenderToRatedGender), giving EMMEANS and comparisons among them for each TeamNumber separately. As noted above, this is probably not a great idea here, but in situations with smaller numbers of levels of the equivalent of the TeamNumber factor, it's reasonable and can be very useful.
Handling the fact that the same reviewers are providing multiple ratings can indeed be done using the REPEATED specification with ReviewerNumber as a subject factor. The default covariance structure for the residual or R matrix in MIXED is DIAG, or diagonal, which actually allows for unequal residual variances across levels of the repeated factor within a subject, but still assumes independence among residuals within subjects. I'll admit it's a bit strange to have a default that assumes independence for repeated measures, but for some reason most mixed modeling procedures have such defaults. Anyway, given that the likely correlated residuals are based on a structure other than time, and in the absence of other specific reasons to posit particular differences among dependence levels for different pairings within subjects, it seems that a single constant covariance for related pairs might make the most sense. In a structure that doesn't assume unequal variances over levels of the repeated factor, this would be the CS or compound symmetric structure. This involves only two parameters, a variance or diagonal offset, and a common covariance. If variances across repeated levels are suspected to be potentially unequal, this can be generalized somewhat by specifying the CSH or heterogeneous compound symmetry structure.
Unfortunately, here with 136 distinct levels of the repeated factor RatedParticipantNumber, the CSH structure has 137 parameters, and the DIAG structure has 136. These are too many parameters to estimate uniquely with the 135 subjects, leading to warnings. There does appear to be some evidence of unequal residual variances over levels of the repeated factor, but it's hard to judge things since unique estimates are not available. Estimating this model also takes a long time, due to the number of covariance parameters.
Standard recommendations for selecting covariance structures for mixed models generally involve comparing models with the same set of fixed effects, either using likelihood-ratio tests of additional parameters in models with more parameters where the smaller models are nested (i.e., the larger model has all the same parameters plus more) by taking differences in -2 log-likelihood values and referring these to chi-square distributions on degrees of freedom equal to the number of additional parameters in the larger model, or using the information criteria to select the better model (smaller values being preferred). Typically, the recommendation is to do these comparisons using the model with the fullest set of fixed effects under consideration. In this case, whether I include the fuller model with both fixed factors, or the simpler model with just ReviewerGenderToRatedGender, the information criteria point in different directions. The LR test, as well as the AIC and AICC measures favor models with the CSH structure over the simpler CS structure, while the CAIC and BIC measures, which penalize additional parameters more, favor the simpler CS structure.
The unfortunate bottom line here is that I'm not sure there's a way to analyze these data that's entirely immune from legitimate criticisms. The simpler models appear not to capture a lot of the systematic variation in the data, while the more complicated models can't be well estimated with the available data. The problems with the R matrix structure for the larger models are intrinsic to the design, as the number of parameters is growing with the number of subjects included in the analysis, so more data won't help. The similar issue with the fixed effects isn't as severe, but as long as the number of members of a team is fixed, the addition of more teams also wouldn't help much (and would just add to the multiple comparisons issue). One approach would be to fit the model with both fixed effects but only look at the averaged main effects of the ReviewerGenderToRatedGender factor, so only the six unique comparisons, using either of the covariance structure models, or doing it with each to see how the results compare. There again, the problems with estimation for the larger models come up, with the trustworthiness of the results being somewhat uncertain.