Misunderstandings of Effect Sizes in Message Effects Research
Bibliographic Data
| ID | 12971124 |
|---|---|
| Authors | Daniel J O’keefe (0000-0003-1594-8892, Northwestern University, corresponding author) |
| Year | 2017 |
| Volume | 11 |
| Issue | 3 |
| Pages | 210-219 |
| Publication date | 2017-07-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Communication Methods and Measures (JOURNAL) |
| Journal identifiers | ISSN: 1931-2458 • E-ISSN: 1931-2466 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/19312458.2017.1343812 |
| OpenAlex | W2735973896 |
| Language | EN |
| Citations received | 9 |
| References cited | 23 |
Widespread acknowledgement of the weaknesses of null hypothesis significance testing has led to correspondingly greater appreciation of the central role of effect size estimation in social-scientific research. But in the context of message effects research, it is easy to confuse an effect size—a quantitative representation of the effect of a variable on an outcome—with the size of the effect of a message on an outcome. Illustrations of this misunderstanding are offered, its unhappy consequences specified, and remedies discussed
Acknowledgement · Cognitive psychology · Computer security · Context (archaeology · Data mining · Econometrics · Mathematical economics · Null (SQL · Null hypothesis · Outcome (game theory · Representation (politics · Sample size determination · Statistics · Behavioral Health and Interventions · Computer Science · Mathematics · Media Influence and Health · Psychology · Social and Intergroup Psychology · Social Psychology
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| Unique citing works | 9 |
|---|---|
| Citations per year | 1 |
| Citation span | 2017 - 2023 (7) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 9 |