jmv: ttestPS – R documentation

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ttestPS

Paired Samples T-Test

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).

Value

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

## Not run: 
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
#  --------------------------------------------------------------
#

## End(Not run)

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26