title: Composite variables: when and how.
abstract: BACKGROUND
  Use of composite variables is a common practice, but knowledge about what researchers should consider when creating composite variables is lacking.
  
  
  OBJECTIVE
  The purpose of this paper was to present methods used to create composite variables with attention to advantages and disadvantages.
  
  
  METHODS
  Methods of simple averaging, weighted averaging, and meaningful grouping to create composite variables are described briefly, and the context in which one method might be more suitable than the others is discussed. Study examples and comparisons of statistical power among these methods as well as Bonferroni correction are described.
  
  
  CONCLUSIONS
  Each approach to creating composite variables has advantages and disadvantages that researchers should weigh carefully. With normally distributed data, composite variables provide the greatest increases in power when the original variables (that make up the composite variable) have similar associations with the outside outcome variable.
authors.affiliation: School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. songm@email.unc.edu
authors.forename: Mi-Kyung
authors.initials: MK
authors.lastname: Song
authors.forename: Feng-Chang
authors.initials: FC
authors.lastname: Lin
authors.forename: Sandra E
authors.initials: SE
authors.lastname: Ward
authors.forename: Jason P
authors.initials: JP
authors.lastname: Fine
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D006801:Humans; D015399:Nursing Research; D025341:Principal Component Analysis; D012016:Reference Values; D012107:Research Design