Usage
meanDiff(x, y=NULL, paired = FALSE, r.prepost = NULL, var.equal = "test", conf.level = .95, plot = FALSE, digits = 2, envir = parent.frame())
Arguments
x
Dichotomous factor: variable 1; can also be a formula of the form y ~ x, where x must be a factor with two levels (i.e. dichotomous).
y
Numeric vector: variable 2; can be empty if x is a formula.
paired
Boolean; are x & y independent or dependent? Note that if x & y are dependent, they need to have the same length.
r.prepost
Correlation between the pre- and post-test in the case of a paired samples
t-test. This is required to compute Cohen's d using the formula on page
29 of Borenstein et al. (2009). If NULL, the correlation is simply
computed from the provided scores (but of course it will then be lower
if these is an effect - this will lead to an underestimate of the
within-groups variance, and therefore, of the standard error of Cohen's d,
and therefore, to confidence intervals that are too narrow (too liberal).
Also, of course, when using this data to compute the within-groups
correlation, random variations will also impact that correlation, which
means that confidence intervals may in practice deviate from the null
hypothesis significance testing p-value in either direction (i.e. the
p-value may indicate a significant association while the confidence
interval contains 0, or the other way around). Therefore, if the
test-retest correlation of the relevant measure is known, please provide
this here to enable computation of accurate confidence intervals.
var.equal
String; only relevant if x & y are independent; can be "test" (default; test whether x & y have different variances), "no" (assume x & y have different variances; see the Warning below!), or "yes" (assume x & y have the same variance)
conf.level
Confidence of confidence intervals you want.
plot
Whether to print a dlvPlot.
digits
With what precision you want the results to print.
envir
The environment where to search for the variables (useful when calling meanDiff from a function where the vectors are defined in that functions environment).
Warning
Note that when different variances are assumed for the t-test (i.e. the null-hypothesis test), the values of Cohen's d are still based on the assumption that the variance is equal. In this case, the confidence interval might, for example, not contain zero even though the NHST has a non-significant p-value (the reverse can probably happen, too).