Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Paired Samples T-Test (ttestPS)

Description

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.

Usage

ttestPS(
  data,
  pairs,
  students = TRUE,
  bf = FALSE,
  bfPrior = 0.707,
  wilcoxon = FALSE,
  hypothesis = "different",
  norm = FALSE,
  qq = FALSE,
  meanDiff = FALSE,
  ci = FALSE,
  ciWidth = 95,
  effectSize = FALSE,
  ciES = FALSE,
  ciWidthES = 95,
  desc = FALSE,
  plots = FALSE,
  miss = "perAnalysis"
)

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
qq	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

Details

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).

Output

A results object containing:

results$ttest	a table containing the t-test results
results$norm	a table containing the normality test results
results$desc	a table containing the descriptives
results$plots	an array of the descriptive plots

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results$ttest$asDF

as.data.frame(results$ttest)

Examples

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
#  --------------------------------------------------------------
#