I conducted a study with 28 subjects to measure the effect of an intervention. Thus, I have 3 data points from each subject: pre-treatment, treatment, and post-treatment.
I tried running a generalized mixed-effect linear model, since my data is binomial and non-independent. In this model, I included subject as a random effect (together with three fixed effects I wanted to look at). My problem is that the model would not converge. So I was wondering whether using a generalized linear model instead would be a good alternative, as this one does converge. I know it is not the best solution, but my knowledge of statistics is pretty limited and I cannot think of a better alternative. Even though I would not be able to account for those random effects, would this model still give me some useful information/results?
Note: I tried using different optimizers and none of them worked, since I still got convergence warnings.
More information about my study:
It's a teaching-intervention study. I looked at whether explicit instruction during a language class led to better use of two particular linguistic structures. I also had two groups (corresponding to 2 different classes: intermediate learners and advanced learners).
lm_general = lme4::glmer(TARGET~CONSTRUCTION*PHASE*TYPE_OF_SPEAKER + (1|SUBJECT) + (1|ITEM), data = my_df, family = "binomial")
My results (for fixed effects):
Estimate Std. Error z value Pr(>|z|) (Intercept) 1.0461 0.3352 3.120 0.00181 ** CONSTRUCTIONpassives -2.8949 0.2050 -14.121 < 2e-16 *** PHASEpretreatment -0.1810 0.2321 -0.780 0.43548 PHASEposttreatment -0.3527 0.2300 -1.534 0.12504 TYPE_OF_SPEAKERintermediate -2.3805 0.3903 -6.099 1.07e-09 *** CONSTRUCTIONpassives:PHASEpretreatment -18.0473 1319.5225 -0.014 0.98909 CONSTRUCTIONpassives:PHASEposttreatment -17.8300 1302.0983 -0.014 0.98907 CONSTRUCTIONpassives:TYPE_OF_SPEAKERintermediate 2.6915 0.2687 10.018 < 2e-16 *** PHASEpretreatment:TYPE_OF_SPEAKERintermediate -0.9765 0.3584 -2.725 0.00643 ** PHASEposttreatment:TYPE_OF_SPEAKERintermediate 0.6715 0.3100 2.166 0.03028 * CONSTRUCTIONpassives:PHASEpretreatment:TYPE_OF_SPEAKERintermediate 0.4458 1744.3590 0.000 0.99980 CONSTRUCTIONpassives:PHASEposttreatment:TYPE_OF_SPEAKERintermediate -1.2249 1729.7634 -0.001 0.99943
My warning: "Model failed to converge: degenerate Hessian with 1 negative eigenvalues"