Increasing the Power of Your Study by Increasing the Effect Size
Bibliographic Data
| ID | 2237523 |
|---|---|
| Authors | Tom Meyvis (0000-0003-2380-7416, New York University, corresponding author), Stijn M J Van Osselaer (0000-0002-9141-3040, Samuel Curtis Johnson Graduate School of Management, Cornell SC Johnson College of Business, Ithaca, NY 14853) |
| Editors | Dahl Darren, Eileen Fischer (0000-0002-8212-0414), Gita Johar, Vicki Morwitz |
| Year | 2018 |
| Volume | 44 |
| Issue | 5 |
| Pages | 1157-1173 |
| Publication date | 2018-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Consumer Research (JOURNAL) |
| Journal identifiers | ISSN: 0093-5301 • E-ISSN: 1537-5277 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/jcr/ucx110 |
| OpenAlex | W2769187086 |
| Language | EN |
| Citations received | 56 |
| References cited | 21 |
As in other social sciences, published findings in consumer research tend to overestimate the size of the effect being investigated, due to both file drawer effects and abuse of researcher degrees of freedom, including opportunistic analysis decisions. Given that most effect sizes are substantially smaller than would be apparent from published research, there has been a widespread call to increase power by increasing sample size. We propose that, aside from increasing sample size, researchers can also increase power by boosting the effect size. If done correctly, removing participants, using covariates, and optimizing experimental designs, stimuli, and measures can boost effect size without inflating researcher degrees of freedom. In fact, careful planning of studies and analyses to maximize effect size is essential to be able to study many psychologically interesting phenomena when massive sample sizes are not feasible
Aside · Boosting (machine learning) · Econometrics · Physics · Power (physics) · Sample (material) · Sample size determination · Statistical power · Statistics · Artificial Intelligence · Behavioral Health and Interventions · Computer Science · Consumer Behavior in Brand Consumption and Identification · Decision-Making and Behavioral Economics · Mathematics · Psychology · Social Psychology
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| Unique citing works | 56 |
|---|---|
| Citations per year | 7 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 49 |