Meta-analyses of positive psychology interventions: The effects are much smaller than previously reported

Carmela A. White, Bob Uttl, Mark D. Holder (2019)

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 wellbeing (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. Read full paper.