Paired Samples T-Test
The Student’s paired samples t-test (sometimes called a dependent-samples t-test) is used to test the null hypothesis that the difference between pairs of measurements is equal to zero. A low p-value suggests that the null hypothesis is not true, and that the difference between the measurement pairs is not zero.
The Student’s paired samples t-test assumes that pair differences follow a normal distribution – in the case that one is unwilling to assume this, the non-parametric Wilcoxon signed-rank can be used in it’s place (However, note that the Wilcoxon signed-rank has a slightly different null hypothesis; that the two groups of measurements follow the same distribution).
Example usage
data('bugs', package = 'jmv')
ttestPS(bugs, pairs = list(
list(i1 = 'LDLF', i2 = 'LDHF')))
#
# PAIRED SAMPLES T-TEST
#
# Paired Samples T-Test
# ──────────────────────────────────────────────────────────────
# statistic df p
# ──────────────────────────────────────────────────────────────
# LDLF LDHF Student's t -6.65 90.0 < .001
# ──────────────────────────────────────────────────────────────
#
Arguments
| data | the data as a data frame |
| pairs | a list of lists specifying the pairs of measurement in data |
| students | TRUE (default) or FALSE, perform Student's t-tests |
| bf | TRUE or FALSE (default), provide Bayes factors |
| bfPrior | a number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors |
| wilcoxon | TRUE or FALSE (default), perform Wilcoxon signed rank tests |
| hypothesis | 'different' (default), 'oneGreater' or 'twoGreater', the alternative hypothesis; measure 1 different to measure 2, measure 1 greater than measure 2, and measure 2 greater than measure 1 respectively |
| norm | TRUE or FALSE (default), perform Shapiro-wilk normality tests |
| TRUE or FALSE (default), provide a Q-Q plot of residuals | |
| meanDiff | TRUE or FALSE (default), provide means and standard errors |
| ci | TRUE or FALSE (default), provide confidence intervals |
| ciWidth | a number between 50 and 99.9 (default: 95), the width of confidence intervals |
| effectSize | TRUE or FALSE (default), provide effect sizes |
| ciES | TRUE or FALSE (default), provide confidence intervals for the effect-sizes |
| ciWidthES | a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes |
| desc | TRUE or FALSE (default), provide descriptive statistics |
| plots | TRUE or FALSE (default), provide descriptive plots |
| miss | 'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' excludes a row from all analyses if one of its entries is missing |
Returns
A results object containing:
| results$ttest | a table |
| results$norm | a table |
| results$desc | a table |
| results$plots | an array of groups |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$ttest$asDF
as.data.frame(results$ttest)
Elements in arrays can be accessed with [[n]]. For example:
results$plots[[1]] # accesses the first element