jmv (version 0.9.6.1)

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, effectSize = FALSE, ci = FALSE,
  ciWidth = 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; group 1 different to group 2, group 1 greater than group 2, and group 2 greater than group 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

effectSize

TRUE or FALSE (default), provide effect sizes

ci

TRUE or FALSE (default), provide confidence intervals

ciWidth

a number between 50 and 99.9 (default: 95), the width of confidence intervals

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

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)

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).

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
#  --------------------------------------------------------------
#
# }