Meta-analyses of positive psychology interventions: The effects are much smaller than previously reported
Bibliographic record
Abstract
For at least four decades, researchers have studied the effectiveness of interventions designed to increase well-being. These interventions have become known as positive psychology interventions (PPIs). Two highly cited meta-analyses examined the effectiveness of PPIs on well-being and depression: Sin and Lyubomirsky (2009) and Bolier et al. (2013). Sin and Lyubomirsky reported larger effects of PPIs on well-being (r = .29) and depression (r = .31) than Bolier et al. reported for subjective well-being (r = .17), psychological well-being (r = .10), and depression (r = .11). A detailed examination of the two meta-analyses reveals that the authors employed different approaches, used different inclusion and exclusion criteria, analyzed different sets of studies, described their methods with insufficient detail to compare them clearly, and did not report or properly account for significant small sample size bias. The first objective of the current study was to reanalyze the studies selected in each of the published meta-analyses, while taking into account small sample size bias. The second objective was to replicate each meta-analysis by extracting relevant effect sizes directly from the primary studies included in the meta-analyses. The present study revealed three key findings: (1) many of the primary studies used a small sample size; (2) small sample size bias was pronounced in many of the analyses; and (3) when small sample size bias was taken into account, the effect of PPIs on well-being were small but significant (approximately r = .10), whereas the effect of PPIs on depression were variable, dependent on outliers, and generally not statistically significant. Future PPI research needs to focus on increasing sample sizes. A future meta-analyses of this research needs to assess cumulative effects from a comprehensive collection of primary studies while being mindful of issues such as small sample size bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.213 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.055 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".