Hi everyone,
I'm working with the following data frame using R. It consists of
measurements obtained from 7 subjects with two independent variables (IV1
and IV2) with two levels each (OFF/ON, ALT/ISO, respectively):
>myData
Subject DV IV1 IV2
1 2.567839 OFF ALT
1 58.708027 ON ALT
1 44.504265 OFF ISO
1 109.555701 ON ISO
2 99.043735 OFF ALT
2 75.958737 ON ALT
2 182.727396 OFF ISO
2 364.725795 ON ISO
3 45.788988 OFF ALT
3 52.941263 ON ALT
3 54.719013 OFF ISO
3 41.909909 ON ISO
4 116.145279 OFF ALT
4 162.927971 ON ALT
4 34.162077 OFF ISO
4 74.029748 ON ISO
5 114.412913 OFF ALT
5 121.127983 ON ALT
5 192.379708 OFF ISO
5 229.192453 ON ISO
6 213.421076 OFF ALT
6 526.739206 ON ALT
6 150.596812 OFF ISO
6 217.931951 ON ISO
7 117.931273 OFF ALT
7 102.467813 ON ALT
7 57.823062 OFF ISO
7 85.181033 ON ISO
(1) Is this a repeated measures (RM) design? Some folks have mentioned that
it is not since it isn't a longitudinal study, but I thought that as long
as there are measurements from each experimental unit for every single
level of a factor, one can say this as a RM design. What is correct? Also,
is an RM design synonymous with having a within-subject factor?
(2) I'm interested in both the main and the interaction effects of IV1 and
IV2, but due to having measurements from each subject for all level
combinations, I think I have to include Subject as a random effect. I have
looked at aov and lmer but I'm confused about the difference in syntax:
This cheat sheet recommends:
m1<-aov(DV ~ IV1*IV2 + Error(Subject/(IV1*IV2)), myData)
However it's not clear to me whether Error(x/(y*z)) means x is a random
effect and y and z are nested in x. Is this interpretation correct? If so,
would m1 be inappropriate for my data since my data isn't nested, but fully
crossed? And if so, would
m2<-aov(DV ~ IV1*IV2 + Error(Subject), myData)
be the correct syntax? I have also been told that in m2 the Error term
should be dropped - is this correct?
(3) In a previous question I was told the linear mixed effects model
m3<-lmer(DV ~ IV1*IV2 + (1|Subject), myData)
was appropriate more my data. Just to better understand lmer syntax: if I
had n subjects and for each subject measurements were obtained for both
levels of IV2 but half of the subjects were OFF and the other half ON,
would the model be
m4<-lmer(DV ~ IV1*IV2 +(1|Subject/IV1), data=myData) ?
And if there was only one measurement per IV1*IV2 combination, would that
mean this is no longer a repeated-measures design and therefore the model
is just
m5<-lmer(DV ~ IV1*IV2, data=myData) ? In which case lm would probably
suffice.
Any help would be greatly appreciated,
Uri Ramirez
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