%0 Journal Article %1 Song2013 %A Song, Mi-Kyung %A Lin, Feng-Chang %A Ward, Sandra E %A Fine, Jason P %D 2013 %J Nursing research %K AnalysisofVariance Bias(Epidemiology) Humans NursingResearch PrincipalComponentAnalysis ReferenceValues ResearchDesign %N 1 %P 45-9 %R 10.1097/NNR.0b013e3182741948 %T Composite variables: when and how. %U http://www.ncbi.nlm.nih.gov/pubmed/23114795 %V 62 %X 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. DISCUSSION: 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. %@ 1538-9847; 0029-6562

@article{Song2013, 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. DISCUSSION: 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.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Song, Mi-Kyung and Lin, Feng-Chang and Ward, Sandra E and Fine, Jason P}, biburl = {https://www.bibsonomy.org/bibtex/2358951bf77228352ac5c6c8aa4f2524b/jepcastel}, city = {School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. songm@email.unc.edu}, doi = {10.1097/NNR.0b013e3182741948}, interhash = {9ea846e60219e2c217c8c1d98a34dcfd}, intrahash = {358951bf77228352ac5c6c8aa4f2524b}, isbn = {1538-9847; 0029-6562}, issn = {1538-9847}, journal = {Nursing research}, keywords = {AnalysisofVariance Bias(Epidemiology) Humans NursingResearch PrincipalComponentAnalysis ReferenceValues ResearchDesign}, note = {GR: R01NR011464/NR/NINR NIH HHS/United States; GR: R01NR013359/NR/NINR NIH HHS/United States; JID: 0376404; ppublish;<br/>Comparacions múltiples; Composite endpoints}, number = 1, pages = {45-9}, pmid = {23114795}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Composite variables: when and how.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23114795}, volume = 62, year = 2013 }