# lmer model building--include random effects?

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## lmer model building--include random effects?

 Hello, This is a follow up question to my previous one http://tolstoy.newcastle.edu.au/R/e4/help/08/02/3600.htmlI am attempting to model relationship satisfaction (MAT) scores   (measurements at 5 time points), using participant (spouseID) and   couple id (ID) as grouping variables, and time (years) and conflict   (MCI.c) as predictors. I have been instructed to include random   effects for the slopes of both predictors as well as the intercepts,   and then to drop non-significant random effects from the model. The   instructor and the rest of the class is using HLM 6.0, which gives p- values for random effects, and the procedure is simply to run a model,   note which random effects are not significant, and drop them from the   model. I was hoping I could to something analogous by using the anova   function to compare models with and without a particular random   effect, but I get dramatically different results than those obtained   with HLM 6.0. For example, I wanted to determine if I should include a random effect   for the variable "MCI.c" (at the couple level), so I created two   models, one with and one without, and compared them:  > m.3 <- lmer(MAT ~ 1 + years + MCI.c + (1 + years | spouseID) + (1 +   years + MCI.c | ID), data=Data, method = "ML")  > m.1 <- lmer(MAT ~ 1 + years + MCI.c  + (1 + years + MCI.c |   spouseID) + (1 + years + MCI.c | ID), data=Data, method = "ML")  > anova(m.1, m.3) Data: Data Models: m.3: MAT ~ 1 + years + MCI.c + (1 + years | spouseID) + (1 + years + m.1:     MCI.c | ID) m.3: MAT ~ 1 + years + MCI.c + (1 + years + MCI.c | spouseID) + (1 + m.1:     years + MCI.c | ID)      Df     AIC     BIC  logLik  Chisq Chi Df Pr(>Chisq) m.3 12  5777.8  5832.7 -2876.9 m.1 15  5780.9  5849.5 -2875.4 2.9428      3     0.4005 The corresponding output from HLM 6.0 reads   Random Effect           Standard      Variance     df    Chi- square   P-value                           Deviation     Component     ------------------------------------------------------------------------------   INTRCPT1,       R0      6.80961      46.37075      60       112.80914    0.000      YEARS slope, R1      1.49329       2.22991      60       59.38729    >.500        MCI slope, R2      5.45608      29.76881      60       90.57615    0.007     ------------------------------------------------------------------------------ To me, this seems to indicate that HLM 6.0 is suggesting that the   random effect should be included in the model, while R is suggesting   that it need not be. This is not (quite) a "why do I get different   results with X" post, but rather an "I'm worried that I might be doing   something wrong" post. Does what I've done look reasonable? Is there a   better way to go about it? Thank you very much for reading this, and for any advice. -Ista ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.