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Psychological Bulletin, 112(1), 155-159. doi:10.1037/0033-2909.112.1.155 Furthermore, as mentioned by Anne Z, four-way ANOVA is not the correct term. … You probably mean one-way ANOVA with four factor levels. …

A three-way ANOVa with factors $A \times B \times C$ sounds pretty reasonable to me. Then you can see whether or not A, B, or C or any interactive combination thereof influences G. …

There are two options on what to do: As you said, use standard repeated measures ANOVA and aggregate over the measurements. afex offers a bunch of convenience functions automatically aggregating the …

Model $\textbf{(1)}$ + $\textbf{(2b)}$ is the standard split-plot ANOVA with a random effects for $B$, the repeated-measures factor $C$ and fixed effect $ \alpha$. …

10.1027/1614-2241.3.1.35 The abstract says: Standard statistics texts indicate that the expected value of the $F$ ratio is $1.0$ (more precisely: $N/(N-2)$) in a completely balanced fixed-effects ANOVA …

Whereas it is true, that the dependent variable in an ANOVA should not be binary, but interval scaled (i.e., continuous), this is not true for the independent variable. …

That seems to be a pretty straightforward thing, an unbalanced design with one DV age and two IVs: gender and kill. You could analyze this easily in R using package afex (for which you will need an id …

I don't know if mathy enough (but it should have the right references to get you started): Gelman, A. (2005). Analysis of Variance: Why It Is More Important than Ever. The Annals of Statistics, 33(1) …

I would guess the explanation is something like this: The higher the correlation between the time points, the lower the proportion of unshared variance. That is, the lower the proportion of variance t …

In a repeated-measures ANOVA you can only have one observation per cell of the design and unit of observation. …

To say something similar to the other answers in slightly different words. I would do the following: Report that the (hopefully expected) interaction is almost or marginal significant or that there i …

If it is only another within-subjets factor (that is how I would read it) you could still use a mixed or split-plot ANOVA (instead of a mixed model). … However, if you want the age-by-training interaction you would have to quantify that with the usual ANOVA effect sizes (e.g., eta-squared) or convert this into r-squared. …

The R-world doesn't like Type 3 SS to much. One of the usually cited references is the "Exegeses on Linear Models" by Bill Venables (2000). I hope I am not misciting him, but I think his main argume …

The overall ANOVA uses the full within subject design. The between-subjects ANOVA just uses the data from the first session and compares those. …

Both functions are basically wrappers for car::Anova(). However, both need a variable coding the subject (i.e., a subject identifier). I don't know what it is in your case. … your data is in a data.frame called dat): nice.anova(ez.glm(id = UNKONW, dv = "x", data = dat, between = "Trt", within = c("Day", "Rep"))) Note, the call to nice.anova, produces a nice ANOVA …