testData

datasets

The robustness of many of the statistical techniques, such as factor analysis, applied in
the social sciences rests upon the assumption of item-level normality. However, when dealing
with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
<http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for
large datasets of continuous variables, its application in current software programs is cumbersome
with analysts having to take several steps to normalise each variable. We present an R package
'normalr' that enables researchers to make convenient optimal transformations of multiple variables
in datasets. This R package enables users to quickly and accurately: (1) anchor all of their
variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated,
(3) apply each unique exponent to its variable, (4) rescale resultant values to within their
original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness,
kurtosis, and other inferential assessments of normality.

Kevin Chang

normalr

Normalisation of Multiple Variables in Large-Scale Datasets

Matthew Courtney

testData function

<p>An object of class <code>data.frame</code> with 957 rows and 9 columns.</p>

Format

Test dataset for the paper — testData

Test dataset for the paper

testData: Test dataset for the paper

Description

Usage

Arguments

Format