We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion ( p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2,696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences-conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects. Word count = 151Keywords: social psychology; cognitive psychology; replication; participant pool; individual differences; sampling effects; situational effects 4 Many Labs 3: Evaluating participant pool quality across the academic semester via replication University participant pools provide access to participants for a great deal of published behavioral research. The typical participant pool consists of undergraduates enrolled in introductory psychology courses that require students to complete some number of experiments over the course of the academic semester. Common variations might include using other courses to recruit participants or making study participation an option for extra credit rather than a pedagogical requirement. Research-intensive universities often have a highly organized participant pool with a participant management system for signing up for studies and assigning credit. Smaller or teaching-oriented institutions often have more informal participant pools that are organized ad hoc each semester or for an individual class.To avoid selection bias based on study content, most participant pools have procedures to avoid disclosing the content or purpose of individual studies during the sign-up process.However, students are usually free to choose the time during the semester that they sign up to complete the studies. This may introduce a selection bias in which data collection on different dates occurs with different kinds of participants, or in different situational circumstances (e.g., the carefree semester beginning versus the exam-stressed semester end).If participant characteristics differ across time during the academic semester, then the results of studies may be moderated by the time at which data collection occurs. Indeed, among behavioral researchers there are widespread intuitions, superstitions, and anecdotes about the "best" time to collect data in order to minimize error and maximize power. It is common, for example, to hear stories of an effect being obtained in the first part of the semester that then "d...

Many Labs 3 is a crowdsourced project that systematically evaluated time-of-semester effects across many participant pools. See the Wiki for a table of contents of files and to download the manuscript.

We conducted preregistered replications of 28 classic and contemporary published findings with protocols that were peer reviewed in advance to examine variation in effect magnitudes across sample and setting. Each protocol was administered to approximately half of 125 samples and 15,305 total participants from 36 countries and territories. Using conventional statistical significance (p &lt; .05), fifteen (54%) of the replications provided evidence in the same direction and statistically significant as the original finding. With a strict significance criterion (p &lt; .0001), fourteen (50%) provide such evidence reflecting the extremely high powered design. Seven (25%) of the replications had effect sizes larger than the original finding and 21 (75%) had effect sizes smaller than the original finding. The median comparable Cohen’s d effect sizes for original findings was 0.60 and for replications was 0.15. Sixteen replications (57%) had small effect sizes (&lt; .20) and 9 (32%) were in the opposite direction from the original finding. Across settings, 11 (39%) showed significant heterogeneity using the Q statistic and most of those were among the findings eliciting the largest overall effect sizes; only one effect that was near zero in the aggregate showed significant heterogeneity. Only one effect showed a Tau &gt; 0.20 indicating moderate heterogeneity. Nine others had a Tau near or slightly above 0.10 indicating slight heterogeneity. In moderation tests, very little heterogeneity was attributable to task order, administration in lab versus online, and exploratory WEIRD versus less WEIRD culture comparisons. Cumulatively, variability in observed effect sizes was more attributable to the effect being studied than the sample or setting in which it was studied.

Objective: Although there is mounting evidence that massed treatment for PTSD is both feasible and effective, many questions remain about the optimal length of intensive treatment programs (ITPs), as well as the role of adjunctive services, such as psychoeducation, mindfulness, and yoga. Our setting recently transitioned from a three-week ITP to a two-week program. Adjunctive services were reduced, but the amount of individual CPT between programs remained similar. The present study examined the effectiveness of a two-week ITP based on twice daily individual CPT sessions and evaluated the program's noninferiority to an established three-week ITP using a Bayesian analytical approach. Method: Bayesian linear mixed regression models were used to explore PTSD and depression changes over time, as well as predictors of change. Noninferiority of the two-week ITP to a three-week ITP was also established using a Bayes factor approach. Results: Results indicate that program participants change meaningfully in both PTSD and depression severity over the course of treatment, and that changes in posttraumatic cognitions predict subsequent changes in these outcomes. Further, the two-week ITP can be considered noninferior to the three-week ITP in both clinical outcomes and overall satisfaction. Conclusions: In the context of intensive PTSD treatment, the content of the ITP appears to matter more than its overall length. Shorter programs have the potential to increase access and treatment capacity. Our findings demonstrate the importance of continuous and rigorous program evaluation. Limitations as well as future directions for research, such as identifying the most effective treatment components, are discussed. Clinical Impact StatementThe present study compared 2 intensive treatment programs for veterans with posttraumatic stress disorder (PTSD). The 2-and 3-week-long programs were based on daily Cognitive Processing Therapy (CPT) as well as adjunctive services. The two-week program had fewer overall service hours but offered a comparable amount of CPT. Both programs produced large and equivalent PTSD and depression severity reductions. The findings suggest that the more/longer treatment is not necessarily better and that content of the intensive PTSD treatment programs appears to matter more than its overall length. Shorter PTSD treatment programs have the potential to increase access and capacity.