Sample size and multiple regression analysis

Psychol Methods. 2000 Dec;5(4):434-58. doi: 10.1037/1082-989x.5.4.434.

Abstract

Despite the development of procedures for calculating sample size as a function of relevant effect size parameters, rules of thumb tend to persist in designs of multiple regression studies. One explanation for their persistence may be the difficulty in formulating a reasonable a priori value of an effect size to be detected. This article presents methods for calculating effect sizes in multiple regression from a variety of perspectives and also introduces a new method based on an exchangeability structure among predictor variables. No single method is deemed superior, but rather examples show that a combination of methods is likely to be most valuable in many situations. A simulation provides a 2nd explanation for why rules of thumb for choosing sample size have persisted but also shows that the outcome of such underpowered studies will be a literature consisting of seemingly contradictory results.

MeSH terms

  • Child
  • Humans
  • Models, Statistical
  • Psychological Tests / statistics & numerical data*
  • Psychometrics*
  • Regression Analysis*