I have a repeated-measures design, so I use ezANOVA. However, I was interested in a numeric (ordinal) predictor, so I ran two models: with ezANOVA (ez package) and lmer (lme4 + lmerTest). The results seem to be very different, so I don't think they can be attributed to the differences in parameters estimation in two models - unlike other questions on that topic here. For simplicity I left just that numeric predictor.
> ezANOVA(data = mydata, dv = rating, wid = sbj, within = .(block)) Warning: Converting "sbj" to factor for ANOVA. Warning: "block" will be treated as numeric. Warning: Collapsing data to cell means. *IF* the requested effects are a subset of the full design, you must use the "within_full" argument, else results may be inaccurate. Warning: There is at least one numeric within variable, therefore aov() will be used for computation and no assumption checks will be obtained. $ANOVA Effect DFn DFd F p p<.05 ges 1 block 1 79 1.025261 0.3143656 0.01281171
> anova(lmer(rating ~ block + (1|sbj), data = mydata)) Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) block 49.709 9.9419 5 14119 3.304 0.005524 **
Making block (6 levels) an ordered or a factor variable doesn't change much. What can be the reason for such different results?