When checking possible moderation effects, in Model 1 a significant interaction effect between vocational experience and problem-solving activities including goal elaboration and definition was found (β = −.168, p < .001).001
The source of this misfit appeared to be the moderation in the group of older males: moderation effects in all other groups could be constrained to be equal (Model 2a vs Model 1c: χ(6) = 2.77, ns), and could subsequently be dropped from the model (Model 3 vs Model 2a: χ(3) < 1, ns).
Two general conditions are possible when combining mediating and moderating effects together: (a) moderation of a mediated effect, that is, the mediating effect is different for different values of a moderator (e.g., subgroups of clients) and (b) mediation of a moderated effect, that is, a mediation relation explains a significant interaction (moderation effect) in the data.
In testing the moderation effect in Model 4, the joint effects of KMC and ED (β = .371**, p < .01), and KMC and LC (β = .435***, p < .001) were significant on innovation performance.
Study 2 with 106 local council employees used a time-ordered research design (time lag of 10 months) and replicated the TSC moderation effect in the context of an indirect positive effect of task conflict (through relationship conflict) on psychological strain, job burnout, and turnover intentions, but not job dissatisfaction.
This indicates that the moderation effect in the data is generally detected as A × E.
Parameter m refers to the unmoderated part of the means model, while parameter m′ refers to the moderation effect in the means.
We see that the moderation effect in the data is mainly detected as A × E (i.e. power of A × E effect is large, power of A × C effect is small).
When the correlation increases to 0.7 or 1.0, power to detect A × E is small, and power to detect A × C is large, i.e. in this case the moderation effect in the data is generally detected as A × C.
On the other hand, there was no such evidence of a moderation effect in Senegal, highlighting varied influences of women's status and empowerment on maternal health across settings.
Hence, it can be used to study measurement invariance across gender and potential moderation effects of gender in subsequent analyses.
