RT Book, Section
A1 Portney, Leslie G.
A1 Watkins, Mary P.
SR Print(0)
ID 1138249779
T1 Transformation of Data
T2 Foundations of Clinical Research: Applications to Practice, 3e
YR 2017
FD 2017
PB McGraw-Hill Education
PP New York, NY
SN 9780803646575
LK fadavispt.mhmedical.com/content.aspx?aid=1138249779
RD 2020/06/04
AB Many statistical procedures, like the t-test, analysis of variance and linear regression are based on assumptions about homogeneity of variance and normality that should be met to ensure the validity of the test. Although most parametric statistical procedures are considered robust to moderate violations of these assumptions, some modification to the analysis is usually necessary with striking departures. When this occurs, the researcher can choose one of two approaches to accommodate the analysis. The analytic procedure can be modified, by using nonparametric statistics or nonlinear regression, or the dependent variable, X, can be transformed to a new variable, X', which more closely satisfies the necessary assumptions. The new variable is created by changing the scale of measurement for X. In this appendix we introduce five approaches to data transformation.