Determining Sample Size Using Fast and Slow Thinking
Bibliographic Data
| ID | 11214602 |
|---|---|
| Authors | Patrick Dattalo (0000-0002-6760-9035, Virginia Commonwealth University, corresponding author) |
| Year | 2018 |
| Volume | 44 |
| Issue | 2 |
| Pages | 180-190 |
| Publication date | 2018-03-15 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Social Service Research (JOURNAL) |
| Journal identifiers | ISSN: 0148-8376 • E-ISSN: 1540-7314 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/01488376.2018.1436632 |
| OpenAlex | W2793337416 |
| Language | EN |
| References cited | 24 |
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
Bootstrapping (finance · Confidence interval · Econometrics · Heuristics · Logistic regression · Multivariate analysis of variance · Rule of thumb · Sample (material · Sample size determination · Statistical hypothesis testing · Statistical power · Statistics · Variance (accounting · Big Data and Business Intelligence · Computer Science · Data Analysis with R · Mathematics · Statistics Education and Methodologies
The Jackknife, the Bootstrap and Other Resampling Plans
The role and interpretation of pilot studies in clinical research
Understanding Power and Rules of Thumb for Determining Sample Sizes
A simulation study of the number of events per variable in logistic regression analysis
Correct Confidence Intervals for Various Regression Effect Sizes and Parameters
A History of Effect Size Indices
How Large Should the Sample be? A Question with no Simple Answer? or
How Many Subjects
Determining Sample Size
How Many Subjects Does It Take To Do A Regression Analysis
The problem of sample size estimation
Nonprobability Sampling in Social Work Research
A power primer
Consequences of prejudice against the null hypothesis
| Citation velocity | historical |
|---|---|
| Highly cited | No |