ds = dataset you are currently using.
DV = dependent variable of interest
IV = independent variable of interest
XYXY = dummy name for a variable, matrix, or data frame into which you are moving information.
Performing a T-Test with between-subjects data
Reminder: Always begin by performing a test of homogeneity of variance. The car package includes Lavene's test.
leveneTest(DV ~ IV, center = mean, data=ds)
If p<.05 you have a situation where group variances are different (var.equal=FALSE). If p>.05, there is insufficient evidence to draw such a conclusion (var.equal=TRUE).
"paired" is set to FALSE to indicate that the data are between-subjects, and thus an independent-samples test should be run.
If your IV has only two levels
XYXY <- t.test(DV ~ IV, var.equal=??????, paired = FALSE, data=ds)
If your IV has more than two levels
r has a command (pairwise.t.test) that can perform multiple tests concurrently. However, this does not provide t values or df information. Adding a subset command to t.test will allow you to test specified levels, while producing critical output. Just remember to adjust for multiple tests.
%in% will pair your IV name with each of the components in the c() list. Make sure to put the names in "".
L1, L2, L3 are the names you may have given to the different levels of the IV
XYXY1 <- t.test(DV ~ IV, var.equal=??????, paired = FALSE, data=ds, subset = IV %in% c("L1", "L2"))
XYXY2 <- t.test(DV ~ IV, var.equal=??????, paired = FALSE, data=ds, subset = IV %in% c("L1", "L3"))
XYXY3 <- t.test(DV ~ IV, var.equal=??????, paired = FALSE, data=ds, subset = IV %in% c("L2", "L3"))
Performing a T-Test with within-subjects data
"paired" is set to TRUE to indicate that the data are within-subjects, and thus an paired-samples test should be run.
XYXY <- t.test(DV ~ IV, paired = TRUE, data=ds)
Post-Hoc T-Tests using the results of a one-way ANOVA *not complete*
The lsr package can use the results of an aov file to perform multiple t-tests and make correct for multiple comparisons.
At present, this function is a demo, but it will provide fast-access to p-values.
XYXY <-posthocPairwiseT(x=aovFile, p.adjust.method="bonferroni")