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.

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect ( p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δ r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols ( r = .05) was similar to that of the RP:P protocols ( r = .04) and the original RP:P replications ( r = .11), and smaller than that of the original studies ( r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).

In Experiment 5 of Albarracín et al. (2008), participants primed with words associated with action performed better on a subsequent cognitive task than did participants primed with words associated with inaction. A direct replication attempt by Frank, Kim, and Lee (2016) as part of the Reproducibility Project: Psychology (RP:P) failed to find evidence for this effect. In this article, we discuss several potential explanations for these discrepant findings: the source of participants (Amazon’s Mechanical Turk vs. traditional undergraduate-student pool), the setting of participation (online vs. in lab), and the possible moderating role of affect. We tested Albarracín et al.’s original hypothesis in two new samples: For the first sample, we followed the protocol developed by Frank et al. and recruited participants via Amazon’s Mechanical Turk ( n = 580). For the second sample, we used a revised protocol incorporating feedback from the original authors and recruited participants from eight universities ( n = 884). We did not detect moderation by protocol; patterns in the revised protocol resembled those in our implementation of the RP:P protocol, but the estimate of the focal effect size was smaller than that found originally by Albarracín et al. and larger than that found in Frank et al.’s replication attempt. We discuss these findings and possible explanations.

The comorbidity of psychological disorders with end-stage renal disease (ESRD) presents challenges for renal transplantation, including increased likelihood of medication noncompliance and poorer quality of life. Estimates of rates and severity of affective and anxiety disorders have varied significantly across studies of renal transplant patients, possibly due in part to variation in the methodology and timing of evaluations. To this point, few researchers have examined the psychological condition of patients who are newly referred for renal transplantation. This study examined rates of psychological distress using the Patient Health Questionnaire (PHQ) in a sample of 518 ESRD patients at the specific time point of first contact with the transplant center. In this sample, 15.1% of patients endorsed symptoms consistent with a depressive condition and 7.6% of patients endorsed an anxiety condition. These rates were lower than expected, which may be due to decreased distress in this sample, selection biases, or underreporting of symptoms due to patients' motivation to present themselves positively.