Quantifying uncertainty in the meta-analytic lower bound estimate.

In meta-analyses, it is customary to compute a confidence interval for the overall mean effect (ρ̄ or δ̄), but not for the underlying standard deviation (τ) or the lower bound of the credibility value (90%CV), even though the latter entities are often as important to the interpretation as is the overall mean. We introduce 2 methods of computing confidence intervals for the lower bound (Lawless and bootstrap). We compare both methods using 3 lower bound estimators (Schmidt-Hunter, Schmidt-Hunter with k correction, and Morris/Hedges, labeled HOVr/HOVd) in 2 Monte Carlo studies (1 for correlations and 1 for standardized mean differences) and illustrate their application to published meta-analyses. For correlations, we found that HOVr bootstrap confidence intervals yielded coverage greater than 90% across a wide variety of conditions provided that there were at least 25 studies. For the standardized mean difference, all 3 methods produced acceptable results using the bootstrap for the lower bound confidence interval provided that there were at least 20 studies with an average sample size of at least 50. When the number of studies was small (k = 8 or 10), coverage was less than 90% and confidence intervals were very wide. Even with larger numbers of studies, if there was indirect range restriction coupled with a small underlying between-studies variance, the between-studies variance was poorly estimated and coverage of the lower bound suffered. We provide software to allow meta-analysts to compute bootstrap confidence intervals for the estimators described in the article. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Stress Outcomes of Four Types of Perceived Interruptions.

Objective We sought to define and measure four types of perceived interruptions and to examine their relationships with stress outcomes. Background Interruptions have been defined and measured in a variety of inconsistent ways. No study has simultaneously examined the subjective experience of all types of interruptions. Method First, we provide a synthesized definition and model of interruptions that aligns interruptions along two qualities: origin and degree of multitasking. Second, we create and validate a self-report measure of these four types of perceived interruptions within two samples (working undergraduate students and working engineers). Last, we correlate this measure with self-reported psychological and physical stress outcomes. Results Our results support the four-factor model of interruptions. Results further support the link between each of the four types of interruptions (intrusions, breaks, distractions, and a specific type of ruminations, discrepancies) and stress outcomes. Specifically, results suggest that distractions explain a unique portion of variance in stress outcomes above and beyond the shared variance explained by intrusions, breaks, and discrepancies. Conclusion The synthesized four-factor model of interruptions is an adequate representation of the overall construct of interruptions. Further, perceived interruptions can be measured and are significantly related to stress outcomes. Application Measuring interruptions by observation can be intrusive and resource intensive. Additionally, some types of interruptions may be internal and therefore unobservable. Our survey measure offers a practical alternative method for practitioners and researchers interested in the outcomes of interruptions, especially stress outcomes.