Section author: Ravi Selker, Jonathon Love, Damian Dropmann

One Sample T-Test

(ttestOneS)

Description

The Student’s One-sample t-test is used to test the null hypothesis that the true mean is equal to a particular value (typically zero). A low p-value suggests that the null hypothesis is not true, and therefore the true mean must be different from the test value.

Usage

ttestOneS(
  data,
  vars,
  students = TRUE,
  bf = FALSE,
  bfPrior = 0.707,
  wilcoxon = FALSE,
  testValue = 0,
  hypothesis = "dt",
  norm = FALSE,
  qq = FALSE,
  meanDiff = FALSE,
  ci = FALSE,
  ciWidth = 95,
  effectSize = FALSE,
  ciES = FALSE,
  ciWidthES = 95,
  desc = FALSE,
  plots = FALSE,
  miss = "perAnalysis",
  mann = FALSE
)

Arguments

data	the data as a data frame
vars	a vector of strings naming the variables of interest 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 2.0 (default 0.707), the prior width to use in calculating Bayes factors
wilcoxon	TRUE or FALSE (default), perform Wilcoxon signed rank tests
testValue	a number specifying the value of the null hypothesis
hypothesis	'dt' (default), 'gt' or 'lt', the alternative hypothesis; different to testValue, greater than testValue, and less than testValue respectively
norm	TRUE or FALSE (default), perform Shapiro-wilk tests of normality
qq	TRUE or FALSE (default), provide a Q-Q plot of residuals
meanDiff	TRUE or FALSE (default), provide means and standard deviations
ci	TRUE or FALSE (default), provide confidence intervals for the mean difference
ciWidth	a number between 50 and 99.9 (default: 95), the width of confidence intervals
effectSize	TRUE or FALSE (default), provide Cohen’s d 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 One-sample t-test assumes that the data are from 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 median is equal to the test value).

Output

A results object containing:

results$ttest	a table containing the t-test results
results$normality	a table containing the normality test results
results$descriptives	a table containing the descriptives
results$plots	an image of the descriptive plots
results$qq	an array of Q-Q 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('ToothGrowth')

ttestOneS(ToothGrowth, vars = vars(len, dose))

#
#  ONE SAMPLE T-TEST
#
#  One Sample T-Test
#  ------------------------------------------------------
#                           statistic    df      p
#  ------------------------------------------------------
#    len     Student's t         19.1    59.0    < .001
#    dose    Student's t         14.4    59.0    < .001
#  ------------------------------------------------------
#