Power and Sample Size Determination for Linear Models

This presentation describes the steps involved in performing sample size analyses for a variety of linear models, both univariate and multivariate. As an analyst you must gather and synthesize the information needed, but you should be able to rely on the analytical tools to accommodate the numerous ways in which you can characterize and solve problems. Examples illustrate these principles and review relevant methods. User-written, SAS  software-based programs already handle a wide variety of problems in linear models. Now, SAS Institute itself is developing software that will handle a rich array of sample size analyses, including all those discussed in this paper.

[1]  Keith E. Muller,et al.  BIAS IN LINEAR MODEL POWER AND SAMPLE SIZE DUE TO ESTIMATING VARIANCE. , 1997, Communications in statistics: theory and methods.

[2]  R W Helms,et al.  Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs. , 1992, Statistics in medicine.

[3]  C Gatsonis,et al.  Multiple correlation: exact power and sample size calculations. , 1989, Psychological bulletin.

[4]  Russell D. Wolfinger,et al.  Multiple Comparisons and Multiple Tests Using the SAS System , 1999 .

[5]  J. Castelloe Paper 265-25 Sample Size Computations and Power Analysis with the SAS System , 2000 .

[6]  K. Muller,et al.  Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. , 1992, Journal of the American Statistical Association.

[7]  Keith E. Muller,et al.  Approximate Power for Repeated-Measures ANOVA Lacking Sphericity , 1989 .

[8]  Keith E. Muller,et al.  Unified power analysis for t-tests through multivariate hypotheses. , 1993 .

[9]  Douglas J. Taylor,et al.  BIAS IN LINEAR MODEL POWER AND SAMPLE SIZE CALCULATION DUE TO ESTIMATING NONCENTRALITY. , 1996, Communications in statistics: theory and methods.

[10]  S. Maxwell Sample size and multiple regression analysis. , 2000, Psychological methods.

[11]  Gwowen Shieh,et al.  Pragmatic, Unifying Algorithm Gives Power Probabilities for Common F Tests of the Multivariate General Linear Hypothesis , 1999 .

[12]  Keith E. Muller,et al.  Practical methods for computing power in testing the multivariate general linear hypothesis , 1984 .

[13]  R. O’brien,et al.  A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis , 1998 .