means_change: Mean Changes Across Two Timepoints For Multiple PrePost Pairs...
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means_change

R Documentation

Mean Changes Across Two Timepoints For Multiple PrePost Pairs of Variables (dependent two-samples t-tests)

Description

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.

Usage

means_change(
  data,
  prepost.nm.list,
  standardizer = "pre",
  d.ci.type = "unbiased",
  ci.level = 0.95,
  check = TRUE
)

Arguments

`data`	data.frame of data.
`prepost.nm.list`	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`.
`standardizer`	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).
`d.ci.type`	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.
`ci.level`	double vector of length 1 specifying the confidence level. `ci.level` must range from 0 to 1.
`check`	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).

Details

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).

Value

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

est: mean change estimate (i.e., post - pre)
se: standard error
t: t-value
df: degrees of freedom
p: two-sided p-value
lwr: lower bound of the confidence interval
upr: upper bound of the confidence interval

2) desc = descriptive statistics stat info in a data.frame

mean_post: mean of the post variable
mean_pre: mean of the pre variable
sd_post: standard deviation of of the post variable
sd_pre: standard deviation of the pre variable
n: sample size of the change score
r: Pearson correlation between the pre and post variables

3) std = standardized mean difference stat info in a data.frame

d_est: Cohen's d estimate
d_se: Cohen's d standard error
d_lwr: Cohen's d lower bound of the confidence interval
d_upr: Cohen's d upper bound of the confidence interval

References

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.

Examples


# 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))

quest documentation built on May 29, 2024, 4:59 a.m.