Determining Sample Size Using Fast and Slow Thinking

ABSTRACT

Using SPSS's bootstrapping procedures, this article demonstrates an approach to determining sample size that combines fast (heuristics or rules-of-thumb) and slow (statistical power analysis) thinking to balance statistical power, precision, and practicality. Sample size is determined for six commonly used statistical procedures: independent groups t-test, one-way ANOVA, one-way MANOVA, Pearson's r correlation, linear regression, and logistic regression. Overall, findings suggest that both approaches may under or over-estimate sample size. Both approaches yielded similar parameter and confidence interval estimates, but varied, sometimes by a factor of two, in their sample size requirements. It is hoped that this study's procedure and results will provide beginning reference points for sample size determination, and encourage researchers continue to search for resolutions for often difficult sample-size decisions.