The six steps below show you how to analyse your data using a multinomial logistic regression in SPSS Statistics when none of the six assumptions in the previous section, Assumptions, have been violated. At the end of these six steps, we show you how to interpret the results from your multinomial logistic regression. Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes There are several reasons to prefer a random effects model over a traditional fixed effects regression model . First, we may wish to estimate the effect of covariates at the group Running a fixed-effect logit model (-xtlogit, fe) shows highly significant coefficients of my key variables, which would be very beneficial for my study. However, more than 50% of my observations get lost in the regression because of zero within variance. xtlogit— Fixed-effects, random-effects, and population-averaged logit models 9 Underlying this model is the variance components model y it6= 0 ()x it + i+ it>0 where itare i.i.d. logistic distributed with mean zero and variance ?2 = ?2=3, independently of i. Example 1 [] ~ ? ? +? I'll admit that I'm not an SPSS user, so if there are SPSS users out there, feel free to correct this answer. I notice that in your R models, ps and item are separate random intercepts. Skimming the SPSS manual (see e.g. this link, under the section "linear mixed model"), it appears these should be specified separately, e.g. The FIXED subcommand lists the fixed effects. The only really differences are that in GENLINMIXED you have to put EFFECTS= and you have to specify that you do want an intercept. Likewise, the RANDOM subcommand tell SPSS the same thing in both models, but just has slightly different sytnax to do so. MIXED EFFECTS MODELING USING SPSS Logistic regression) Type/Field Fixed Effect Interaction Random Effect Time variant (Level 1- within subjects) Continuous/ Covariate With Level 2 predictor by default (can b