means_change tests for mean changes across two timepoints for multiple
prepost pairs of variables via dependent two-samples t-tests. The function
also calculates the descriptive statistics for the timepoints and the
standardized mean differences (i.e., Cohen's d) based on either the standard
deviation of the pre-timepoint, pooled standard deviation of the
pre-timepoint and post-timepoint, or the standard deviation of the change
score (post - pre). means_change is simply a wrapper for
t.test plus some extra calculations.
means_change(
data,
prepost.nm.list,
standardizer = "pre",
d.ci.type = "unbiased",
ci.level = 0.95,
check = TRUE
)list of data.frames containing statistical information about the mean
change for each prepost pair of variables (the rownames of the data.frames
are the names of prepost.nm.list): 1) nhst = dependent two-samples
t-test stat info in a data.frame, 2) desc = descriptive statistics stat info
in a data.frame, 3) std = standardized mean difference stat info in a data.frame,
1) nhst = dependent two-samples t-test stat info in a data.frame
mean change estimate (i.e., post - pre)
standard error
t-value
degrees of freedom
two-sided p-value
lower bound of the confidence interval
upper bound of the confidence interval
2) desc = descriptive statistics stat info in a data.frame
mean of the post variable
mean of the pre variable
standard deviation of of the post variable
standard deviation of the pre variable
sample size of the change score
Pearson correlation between the pre and post variables
3) std = standardized mean difference stat info in a data.frame
Cohen's d estimate
Cohen's d standard error
Cohen's d lower bound of the confidence interval
Cohen's d upper bound of the confidence interval
data.frame of data.
list of length-2 character vectors specifying the
colnames from data corresponding to the prepost pairs of variables.
For each element of the list, the character vector should have length 2
where the first element corresponds to the pre-timepoint variable colname
of that prepost pair and the second element corresponds to the
post-timepoint variable colname of that prepost pair. The names of the list
will be the rownames in the data.frames of the return object. See examples.
prepost.nm.list can also be a single length-2 character vector for
the case of a single pre-post pair of variables, which is functionally
equivalent to mean_change.
chararacter vector of length 1 specifying what to use for standardization when computing the standardized mean difference (i.e., Cohen's d). There are three options: 1. "pre" for the standard deviation of the pre-timepoint, 2. "pooled" for the pooled standard deviation of the pre-timepoint and post-timepoint, 3. "change" for the standard deviation of the change score (post - pre). The default is "pre", which I believe makes the most theoretical sense (see Cumming, 2012); however, "change" is the traditional choice originally proposed by Jacob Cohen (Cohen, 1988).
character vector of lenth 1 specifying how to compute the
confidence intervals (and standard errors) of the standardized mean
differences. There are currently two options: 1. "unbiased" which
calculates the unbiased standard error of Cohen's d based on the formulas
in Viechtbauer (2007). If standardizer = "pre" or "pooled", then
equation 36 from Table 2 is used. If standardizer = "change", then
equation 25 from Table 1 is used. A symmetrical confidence interval is then
calculated based on the standard error. 2. "classic" which calculates the
confidence interval of Cohen's d based on the confidence interval of the
mean change itself. The lower and upper confidence bounds are divided by
the standardizer. Technically, this confidence interval is biased
due to not taking into account the uncertainty of the standardizer.
No standard error is calculated for this option and NA is returned for
"d_se" in the return object.
double vector of length 1 specifying the confidence level.
ci.level must range from 0 to 1.
logical vector of length 1 specifying whether the input
arguments should be checked for errors. For example, checking whether
prepost.nm.list is a list of length-2 character vectors. This is a
tradeoff between computational efficiency (FALSE) and more useful error
messages (TRUE).
For each prepost pair of variables, means_change calculates the mean
change as data[[ prepost.nm.list[[i]][2] ]] - data[[
prepost.nm.list[[i]][1] ]] (which corresponds to post - pre) such that
increases over time have a positive mean change estimate and decreases over
time have a negative mean change estimate. This would be as if the
post-timepoint was x and the pre-timepoint y in
t.test(paired = TRUE).
Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale, NJ: Erlbaum.
Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York, NY: Rouledge.
Viechtbauer, W. (2007). Approximate confidence intervals for standardized effect sizes in the two-independent and two-dependent samples design. Journal of Educational and Behavioral Statistics, 32(1), 39-60.
mean_change for a single pair of prepost variables,
t.test fixes the table of contents for some unknown reason,
means_diff for multiple independent two-sample t-tests,
means_test for multiple one-sample t-tests,
# dependent two-sample t-tests
prepost_nm_list <- list("first_pair" = c("disp","hp"), "second_pair" = c("carb","gear"))
means_change(mtcars, prepost.nm.list = prepost_nm_list)
means_change(mtcars, prepost.nm.list = prepost_nm_list, d.ci.type = "classic")
means_change(mtcars, prepost.nm.list = prepost_nm_list, standardizer = "change")
means_change(mtcars, prepost.nm.list = prepost_nm_list, ci.level = 0.99)
# same as intercept-only regression with the change score
means_change(data = mtcars, prepost.nm.list = c("disp","hp"))
lm_obj <- lm(hp - disp ~ 1, data = mtcars)
coef(summary(lm_obj))
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