Skip to main content
Log in

A Unified Approach to Power Calculation and Sample Size Determination for Random Regression Models

  • Theory and Methods
  • Published:
Psychometrika Aims and scope Submit manuscript

Abstract

The underlying statistical models for multiple regression analysis are typically attributed to two types of modeling: fixed and random. The procedures for calculating power and sample size under the fixed regression models are well known. However, the literature on random regression models is limited and has been confined to the case of all variables having a joint multivariate normal distribution. This paper presents a unified approach to determining power and sample size for random regression models with arbitrary distribution configurations for explanatory variables. Numerical examples are provided to illustrate the usefulness of the proposed method and Monte Carlo simulation studies are also conducted to assess the accuracy. The results show that the proposed method performs well for various model specifications and explanatory variable distributions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aguinis, H., Beaty, J.C., Boik, R.J., & Pierce, C.A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90, 94–107.

    Article  PubMed  Google Scholar 

  • Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks: Sage.

    Google Scholar 

  • Algina, J., & Olejnik, S. (2000). Determining sample size for accurate estimation of the squared multiple correlation coefficient. Multivariate Behavioral Research, 35, 119–137.

    Article  Google Scholar 

  • Anderson, T.W. (1999). Asymptotic distribution of the reduced rank regression estimator under general conditions. Annals of Statistics, 27, 1141–1154.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum.

    Google Scholar 

  • Gatsonis, C., & Sampson, A.R. (1989). Multiple correlation: Exact power and sample size calculations. Psychological Bulletin, 106, 516–524.

    Article  PubMed  Google Scholar 

  • Kelley, K., & Maxwell, S.E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8, 305–321.

    Article  PubMed  Google Scholar 

  • Mendoza, J.L., & Stafford, K.L. (2001). Confidence interval, power calculation, and sample size estimation for the squared multiple correlation coefficient under the fixed and random regression models: A computer program and useful standard tables. Educational and Psychological Measurement, 61, 650–667.

    Article  Google Scholar 

  • Muirhead, R.J. (1982). Aspects of multivariate statistical theory. New York: Wiley.

    Book  Google Scholar 

  • Raudenbush, S.W., & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5, 199–213.

    Article  PubMed  Google Scholar 

  • Raudenbush, S.W., & Liu, X. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6, 387–401.

    Article  PubMed  Google Scholar 

  • Rencher, A.C. (2000). Linear models in statistics. New York: Wiley.

    Google Scholar 

  • Sampson, A.R. (1974). A tale of two regressions. Journal of the American Statistical Association, 69, 682–689.

    Article  Google Scholar 

  • SAS Institute (2003). SAS/IML user’s guide, Version 8. Cary, NC: SAS Institute Inc.

  • Shieh, G. (2003). A comparative study of power and sample size calculations for multivariate general linear models. Multivariate Behavioral Research, 38, 285–307.

    Article  Google Scholar 

  • Shieh, G. (2005). Power and sample size calculations for multivariate linear models with random explanatory variables. Psychometrika, 70, 347–358.

    Article  Google Scholar 

  • Shieh, G. (2006). Exact interval estimation, power calculation and sample size determination in normal correlation analysis. Psychometrika, 71, 529–540.

    Article  Google Scholar 

  • Steiger, J.H., & Fouladi, R.T. (1992). R2: A computer program for interval estimation, power calculations, sample size estimation, and hypothesis testing in multiple regression. Behavioral Research Methods, Instruments, and Computers, 24, 581–582.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gwowen Shieh.

Additional information

The author would like to thank the editor, the associate editor, and the referees for drawing attention to pertinent references that led to improved presentation. This research was partially supported by National Science Council grant NSC-94-2118-M-009-004.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shieh, G. A Unified Approach to Power Calculation and Sample Size Determination for Random Regression Models. Psychometrika 72, 347–360 (2007). https://doi.org/10.1007/s11336-007-9012-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11336-007-9012-5

Keywords

Navigation