normalr - Normalisation of Multiple Variables in Large-Scale 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.
