Can Short Psychological Interventions Affect Educational Performance? Revisiting the Effect of Self-Affirmation Interventions.

Large amounts of resources are spent annually to improve educational achievement and to close the gender gap in sciences with typically very modest effects. In 2010, a 15-min self-affirmation intervention showed a dramatic reduction in this gender gap. We reanalyzed the original data and found several critical problems. First, the self-affirmation hypothesis stated that women's performance would improve. However, the data showed no improvement for women. There was an interaction effect between self-affirmation and gender caused by a negative effect on men's performance. Second, the findings were based on covariate-adjusted interaction effects, which imply that self-affirmation reduced the gender gap only for the small sample of men and women who did not differ in the covariates. Third, specification-curve analyses with more than 1,500 possible specifications showed that less than one quarter yielded significant interaction effects and less than 3% showed significant improvements among women.

Adjusting for Publication Bias in Meta-Analysis: An Evaluation of Selection Methods and Some Cautionary Notes.

We review and evaluate selection methods, a prominent class of techniques first proposed by Hedges (1984) that assess and adjust for publication bias in meta-analysis, via an extensive simulation study. Our simulation covers both restrictive settings as well as more realistic settings and proceeds across multiple metrics that assess different aspects of model performance. This evaluation is timely in light of two recently proposed approaches, the so-called p-curve and p-uniform approaches, that can be viewed as alternative implementations of the original Hedges selection method approach. We find that the p-curve and p-uniform approaches perform reasonably well but not as well as the original Hedges approach in the restrictive setting for which all three were designed. We also find they perform poorly in more realistic settings, whereas variants of the Hedges approach perform well. We conclude by urging caution in the application of selection methods: Given the idealistic model assumptions underlying selection methods and the sensitivity of population average effect size estimates to them, we advocate that selection methods should be used less for obtaining a single estimate that purports to adjust for publication bias ex post and more for sensitivity analysis-that is, exploring the range of estimates that result from assuming different forms of and severity of publication bias.