Work

Composite Variables

Year: 2012

Type: article

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.

Source: Nursing Research

DOI: 10.1097/nnr.0b013e3182741948

Authors: Mi-Kyung Song, Feng-Chang Lin, Sandra E. Ward, Jason P. Fine

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: University of Wisconsin-Madison, National Institutes of Health, National Institute of Nursing Research

Awards: Improving End-of-Life Communication with ESRD African American Patients, Representational Intervention to Promote End-of-Life Decision-Making Preparation, Mapping Multidimensional Illness Trajectories of Patients With ESKD