Why is there a dramatic difference between aov and lmer? <p>I have a mixed model and the data looks like this:</p> <pre><code>&gt; head(pce.ddply) subject Condition errorType errors 1 j202 G O 0.00000000 2 j202 G P 0.00000000 3 j203 G O 0.08333333 4 j203 G P 0.00000000 5 j205 G O 0.16666667 6 j205 G P 0.00000000 </code></pre> <p>Each subject provides two datapoints for errorType (O or P) and each subject is in either Condition G (N=30) or N (N=33). errorType is a repeated variable and Condition is a between variable. I'm interested in both main effects and the interactions. So, first an anova:</p> <pre><code>&gt; summary(aov(errors ~ Condition * errorType + Error(subject/(errorType)), data = pce.ddply)) Error: subject Df Sum Sq Mean Sq F value Pr(&gt;F) Condition 1 0.00507 0.005065 2.465 0.122 Residuals 61 0.12534 0.002055 Error: subject:errorType Df Sum Sq Mean Sq F value Pr(&gt;F) errorType 1 0.03199 0.03199 10.52 0.001919 ** Condition:errorType 1 0.04010 0.04010 13.19 0.000579 *** Residuals 61 0.18552 0.00304 </code></pre> <p>Condition is not significant, but errorType is, as well as the interaction.</p> <p>However, when I use lmer, I get a totally different set of results:</p> <pre><code>&gt; lmer(errors ~ Condition * errorType + (1 | subject), data = pce.ddply) Linear mixed model fit by REML Formula: errors ~ Condition * errorType + (1 | subject) Data: pce.ddply AIC BIC logLik deviance REMLdev -356.6 -339.6 184.3 -399 -368.6 Random effects: Groups Name Variance Std.Dev. subject (Intercept) 0.000000 0.000000 Residual 0.002548 0.050477 Number of obs: 126, groups: subject, 63 Fixed effects: Estimate Std. Error t value (Intercept) 0.028030 0.009216 3.042 ConditionN 0.048416 0.012734 3.802 errorTypeP 0.005556 0.013033 0.426 ConditionN:errorTypeP -0.071442 0.018008 -3.967 Correlation of Fixed Effects: (Intr) CndtnN errrTP ConditionN -0.724 errorTypeP -0.707 0.512 CndtnN:rrTP 0.512 -0.707 -0.724 </code></pre> <p>So for lmer, Condition and the interaction are significant, but errorType is not.</p> <p>Also, the lmer result is exactly the same as a glm result, leading me to believe something is wrong.</p> <p>Can someone please help me understand why they are so different? I suspect I am using lmer incorrectly (though I've tried many other versions like (errorType | subject) with similar results.</p>