Trying to understand how to analyze my data, sample data follows. I want to
know if the student scores increased from sem1 to sem2 (semesters), and whether
the inGroup scores increase more.
Hereâs what I did with sample data:
students <- c("s1â, âs2â, "s3")
inGroup <- c(T, F, T)
score <- c(4, 3, 4, 6, 4, 6)
time <- as.factor(c("sem1","sem1","sem1", "sem2","sem2","sem2"))
data <- data.frame(students, inGroup, score, time)
#to determine if scores for all students increased over time I did a t.test
t.test(data[data$time=="sem1","score"],data[data$time=="sem2","score"],
paired=T)
#to determine if the inGroup had a greater increase I did a mixed effect anova
library("lme4")
mixedEffect <- lme(score~time, data=data,random=~ 1 | inGroup, na.action =
na.exclude); summary(mixedEffect)
Is this right? in the above the t-Test p-value <.05 so there was a significant
change. the mean of differences was -1.66 so the change was an increase?
For the mixed effect the difference was significant, p < 0.5 timesem2 is this
right? If so how do I know if the inGroup scores increased more?
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