I want to move from using repeat measure ANOVAs to linear mixed models (LMM). However, where I have good intuitions about ANOVAs, LMMs are new to me. I'm using python's StatsModels as my package. Here's the form of my data:
participant_ID Condition_1 Condition_2 dependent_var
1 1 1 0.71
1 2 1 0.43
2 1 1 0.77
2 2 1 0.37
3 1 1 0.58
3 2 1 0.69
4 2 1 0.72
4 1 1 0.12
26 2 2 0.91
26 1 2 0.53
27 1 2 0.29
27 2 2 0.39
28 2 2 0.75
28 1 2 0.51
29 1 2 0.42
29 2 2 0.31
As you can seen, this is a classic repeat-measures ANOVA design, with fixed effects nested in participants. What I wish to do is establish (1) the independent effects of Condition_1 and Condition_2, and (2) the effect of their interaction, all on dependent_var. My statsmodels code is as follows:
md = smf.mixedlm("dependent_var ~ C(Condition_1)+C(Condition_2) + C(Condition_1):C(Condition_2)", toy_data, groups=toy_data["participant_ID]).fit()
This outputs the following summary.
Allowing that this data is contrived, and p values are meaningless, etc, etc, am I correct to read this as saying that neither variable is significant as a main effect, and neither is their interaction?
I appreciate that LMMs aren't ANOVAs and I should avoid translating them into ANOVAs, but my actual data was arranged for an ANOVA design, and I wish to be confident in my interpretation.