In essence, I want to do the exact equivalent to what this video does, but in R rather than SPSS. So far I've used the following code, where DV is my continuous dependent variable, FF is a categorical independent variable which is my fixed factor, and RF is a categorical independent variable which is my random factor. My main question are: does this code do the same thing as the above video in SPSS (I wish I had SPSS so I could just test this myself) and how come in SPSS you can use a Tukey test but with this code I need to use a Holm or bonferonni post hoc? Thanks in advance!

model <- lmer(DV ~ FF + (1 | RF), data = Data)


summary(glht(fit.prop2, linfct=mcp(Species = "Tukey")), test = adjusted("holm"))



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