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 2024/04/18
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.