#Simple main effect spss code code#

GrSPSS <- reshape(data = gr, timevar = 'stage', idvar = c('ID', 'group'), direction = 'wide') Gr <- rbind(grA_pre, grA_post, grB_pre, grB_post) GrB_post <- ame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=absMean, sd=sdCustom), group=factor('B'), stage = factor('post')) GrB_pre <- ame(ID = seq(N+1,2*N), lvl=rnorm(N, mean=-absMean, sd=sdCustom), group=factor('B'), stage = factor('pre')) GrA_post <- ame(ID = seq(N), lvl=rnorm(N, mean=-absMean, sd=sdCustom), group=factor('A'), stage = factor('post')) GrA_pre <- ame(ID = seq(N), lvl=rnorm(N, mean=absMean, sd=sdCustom), group=factor('A'), stage = factor('pre')) performs simple effect analysis (there is probably a mistake) N <- 100 prepares data to csv format to import to SPSS

#Simple main effect spss code code#

The code below will do everything for you. I would be very thankful if you can help me.

There were no difference between the results in main effects, but results of simple effects analysis do not match when comparing between levels of variable which introduced repeated measures (GROUP A: PRE vs POST GROUP B: PRE vs POST). To test that I generated artificial data from a normal distribution to simulate the interaction between two independent variables.

The SPSS script is correct, but in R script there is a mistake somewhere. I prepared a mixed 2x2 ANOVA design analysis both in SPSS and in R.