この節の作者: 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
# --------------------------------------------------------------
#