I am new to R and lmer. We have a project examining the effects of mimicking others’ voice (mimicry vs nonmimicry, a between-subjects fixed effect) , modality (reading vs listening, a within-subjects effect), valence (positive vs negative; a within-subjects effect), and category (personality vs appearance,a within-subjects effect ) on the imitator’s judgement of some comments (Pleasantness, continuous, 1-7). We constructed a theoretically full mixed-effects model and wanted to decide the best model. Based on the recommendations of a paper, we tried to use the mixed () function from the afex package to conduct a likelihood-ratio test. The following is the code and summary of the model estimation. Now we are not very sure about how to interpret the result. The summary showed that in Row “10” and “11”, the p values are significant, but in Row “8” are “9”, the p value are not significant. In this case, how can we decide what terms should a best model include? Do we simply include those terms whose p values are significant and drop those whose are not?
Your help and advice will be much appreciated!
Fitting 16 (g)lmer() models:
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Mixed Model Anova Table (Type 3 tests, LRT-method)
Model: Pleasantness ~ mimicry * modality * valence * category + (1 |
Model: Subject)
Data: df
Df full model: 18
Effect df Chisq p.value
1 mimicry 1 6.44 * .011
2 modality 1 19.00 *** <.001
3 valence 1 4493.93 *** <.001
4 category 1 7.86 ** .005
5 mimicry:modality 1 3.74 + .053
6 mimicry:valence 1 0.37 .542
7 modality:valence 1 4.23 * .040
8 mimicry:category 1 2.22 .136
9 modality:category 1 0.68 .409
10 valence:category 1 5.53 * .019
11 mimicry:modality:valence 1 4.12 * .042
12 mimicry:modality:category 1 0.09 .770
13 mimicry:valence:category 1 0.53 .465
14 modality:valence:category 1 0.24 .622
15 mimicry:modality:valence:category 1 0.63 .428
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1