neg.indvars {negligible}

Negligible Effect Test for Variances of Independent Populations

Description

This function allows researchers to test whether the difference in the variances of independent populations is negligible, where negligible represents the smallest meaningful effect size (MMES, where in this case the effect is the difference in population variances)

Usage

neg.indvars(dv, iv, eps = 0.5, alpha = 0.05, na.rm = TRUE, data = NULL, ...)

## S3 method for class 'neg.indvars'
print(x, ...)

Arguments

dv

Outcome Variable

iv

Independent Variable

eps

Used to Establish the Equivalence Bound (Conservative: .25; Liberal: .50, according to Wellek, 2010)

alpha

Nominal Type I Error Rate

na.rm

Missing Data Treatment

data

Dataset containing dv and iv

...

Extra arguments

x

object of class neg.indvars

Details

This function evaluates whether the difference in the population variances of J independent groups can be considered negligible (i.e., the population variances can be considered equivalent).

The user provides the name of the outcome/dependent variable (should be continuous) and the name of Independent Variable (predictor, should be a factor), as well as the epsilon value (eps) which determines the smallest difference in variances that can be considered non-negligible.

Wellek (2010) suggests liberal and conservative values of eps = .50 and eps = .25, respectively. See Wellek, 2010, pp. 16, 17, 22, for details.

See Mara & Cribbie (2018): https://doi.org/10.1080/00220973.2017.1301356

Value

A list including the following:

• vars Sample variances

• sds Sample standard deviations

• mads Sample median absolute deviations

• ratio Ratio of the largest to smallest variance

• eps Epsilon (e) can be described as the maximum difference in the variances that one would consider to be unimportant (see Details).

• LWW_md Levene-Wellek-Welch statistic based on the median.

• crit_LWW_md Critical value for the Levene-Wellek-Welch statistic based on the median.

• alpha Nominal Type I error rate

Author(s)

Rob Cribbie [e-mail] and Constance Mara [e-mail]

Examples

#Two Group Example
indvar<-rep(c("a","b"),c(10,12))
depvar<-rnorm(22)
d<-data.frame(indvar,depvar)
neg.indvars(depvar,indvar)
neg.indvars(dv=depvar,iv=indvar,eps=.25,data=d)
neg.indvars(dv=depvar,iv=indvar,eps=.5)

#Four Group Example
indvar<-rep(c("a","b","c","d"),c(10,12,15,13))
depvar<-rnorm(50)
d<-data.frame(indvar,depvar)
neg.indvars(dv=depvar,iv=indvar,eps=.25,data=d)
neg.indvars(dv=depvar,iv=indvar)