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I am running a mixed model with a binary dependent variable and a logit link using SPSS 23´s Generalized linear mixed model procedure. The data is multilevel (1 Subject, 5 questions/data points per subject) and I have included subject ID as a random factor with an intercept only. For the group under analysis, I have 65 subjects with a total of 325 observations and the full model includes 10 variables. 4 of these variables are dummy variables, 4 are likert scale confounding variables as well as 1 explanatory variable (interval scale) and 1 interaction term.

My problem is this: Looking at the BIC and AIC, SPSS considers the base model (intercept only) fits best as it comes with the lowest value (~1480). Every variable (whether established explanatory variables or new potential predictors I hypothesized) I add into the model improves prediction accuracy (~70% correctly classified with intercept only, >80% with all explanatory variables), but worsens information criteria despite strong statistical significance of predictors (up to ~1610).

https://s31.postimg.org/fcto36qob/Model_Output.png

Is this a problem with SPSS, did I misunderstand the SPSS output (should it be the highest value I use?) or could there be another explanation for this?

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  • $\begingroup$ Hi Maha and welcome to the site. Could you maybe include a picture of the SPSS-output in your question? Also, how did you measure "predictive capabilities"? $\endgroup$ – COOLSerdash Jul 4 '16 at 15:00
  • $\begingroup$ Hi Serdash, thanks for your response. Edited predictive capabilities -> share of correct classifications and posted a picture of the output I get from SPSS. $\endgroup$ – Maha Jul 4 '16 at 15:13
  • $\begingroup$ BUMP - Still hoping for some support here... $\endgroup$ – Maha Aug 17 '16 at 7:14

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