jmv (version 2.5.6)

ttestIS: Independent Samples T-Test

Description

The Student's Independent samples t-test (sometimes called a two-samples t-test) is used to test the null hypothesis that two groups have the same mean. A low p-value suggests that the null hypothesis is not true, and therefore the group means are different.

Usage

ttestIS(data, vars, group, students = TRUE, bf = FALSE,
  bfPrior = 0.707, welchs = FALSE, mann = FALSE,
  hypothesis = "different", norm = FALSE, qq = FALSE, eqv = FALSE,
  meanDiff = FALSE, ci = FALSE, ciWidth = 95, effectSize = FALSE,
  ciES = FALSE, ciWidthES = 95, desc = FALSE, plots = FALSE,
  miss = "perAnalysis", formula)

Value

A results object containing:

results$ttest					a table containing the t-test results
results$assum$norm					a table containing the normality tests
results$assum$eqv					a table containing the homogeneity of variances tests
results$desc					a table containing the group descriptives
results$plots					an array of groups of plots

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

results$ttest$asDF

as.data.frame(results$ttest)

Arguments

data

the data as a data frame

vars

the dependent variables (not necessary when using a formula, see the examples)

group

the grouping variable with two levels (not necessary when using a formula, see the examples)

students

TRUE (default) or FALSE, perform Student's t-tests

bf

TRUE or FALSE (default), provide Bayes factors

bfPrior

a number between 0.01 and 2 (default 0.707), the prior width to use in calculating Bayes factors

welchs

TRUE or FALSE (default), perform Welch's t-tests

mann

TRUE or FALSE (default), perform Mann-Whitney U 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 tests of normality

qq

TRUE or FALSE (default), provide Q-Q plots of residuals

eqv

TRUE or FALSE (default), perform Levene's tests for homogeneity of variances

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.

formula

(optional) the formula to use, see the examples

Details

The Student's independent t-test assumes that the data from each group are from a normal distribution, and that the variances of these groups are equal. If unwilling to assume the groups have equal variances, the Welch's t-test can be used in it's place. If one is additionally unwilling to assume the data from each group are from a normal distribution, the non-parametric Mann-Whitney U test can be used instead (However, note that the Mann-Whitney U test has a slightly different null hypothesis; that the distributions of each group is equal).

Examples

data('ToothGrowth')

ttestIS(formula = len ~ supp, data = ToothGrowth)

#
#  INDEPENDENT SAMPLES T-TEST
#
#  Independent Samples T-Test
#  ----------------------------------------------------
#                          statistic    df      p
#  ----------------------------------------------------
#    len    Student's t         1.92    58.0    0.060
#  ----------------------------------------------------
#