Seeking assistance:
I'm in need of assistance with my analysis. I'm currently conducting a study utilizing a linear mixed-effects model with a 2x3 repeated measures design. Upon examining the summary output, it suggests a potential significance in the interaction among the parameters. However, upon further investigation using the ANOVA function to analyze the interaction effects of the fitted model, no significance is detected. Can anyone shed light on why this inconsistency might occur? Additionally, which of these outputs should I rely on for my analysis?
Like this:
anova(maxmodel2)
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
xing 0.001393 0.0013926 1 29.480 0.4610 0.502462
Jpro 0.031873 0.0159364 2 59.399 5.2753 0.007783 **
xing:Jpro 0.008996 0.0044980 2 113.616 1.4889 0.229983
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.781e+00 1.152e-02 7.386e+01 241.336 < 2e-16 ***
xingL 1.409e-03 6.818e-03 4.704e+01 0.207 0.837162
JproN1 3.404e-02 1.590e-02 6.323e+01 2.141 0.036132 *
JproN2 5.562e-02 1.579e-02 6.683e+01 3.522 0.000778 ***
xingL:JproN1 -4.109e-03 6.584e-03 1.591e+03 -0.624 0.532638
xingL:JproN2 -1.346e-02 7.808e-03 4.098e+01 -1.724 0.092206 .
There is no need that any of the main effects is significant when there is a significant interaction.
Hello Song Qichao In my opinion, you have used two different models for data analysis.
That is, in the fix model (number 2), the factors that caused you to call the model Mix (number 1) (such as random effects or within-subjects) have been removed. Therefore, it cannot be expected to reach the same results in both models.
In my opinion, if you have valid reasons for your analysis to be a mix, the results of a mix model are preferable to a fix model.
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