Several options exist for conducting inference on indirect effects in mediation analysis. Although methods that use bootstrapping are the preferred inferential approach for testing mediation, they are time-consuming when the test must be performed many times for a power analysis. Alternatives that are more computationally efficient are not as robust, meaning accuracy of the inferences from these methods is more affected by nonnormal and heteroskedastic data. Previous research has shown that different sample sizes are needed to achieve the same amount of statistical power for different inferential approaches with data that meet all the statistical assumptions of linear regression. By contrast, we explore how similar power estimates are at the same sample size, including when assumptions are violated. We compare the power estimates from six inferential methods for between-subjects mediation using a Monte Carlo simulation study. We varied the path coefficients, inferential methods for the indirect effect, and degree to which assumptions are met. We found that when the assumptions of linear regression are met, three inferential methods consistently perform similarly: the joint significance test, the Monte Carlo confidence interval, and the percentile bootstrap confidence interval. When the assumptions were violated, the nonbootstrapping methods tended to have vastly different power estimates compared with the bootstrapping methods. On the basis of these results, we recommend using the more computationally efficient joint significance test for power analysis only when no assumption violations are hypothesized a priori. We also recommend the joint significance test to pick an optimal starting sample size value for power analysis using the percentile bootstrap confidence interval when assumption violations are suspected.

Registered reports are a new publication workflow where the decision to publish is made prior to data collection and analysis and thus cannot be dependent on the outcome of the study. An increasing number of journals have adopted this new mechanism, but previous research suggests that submission rates are still relatively low. We conducted a census of journals publishing registered reports (N = 278) using independent coders to collect information from submission guidelines, with the goal of documenting journals’ early adoption of registered reports. Our results show that the majority of journals adopting registered reports are in psychology, and it typically takes about a year to publish the first registered report after adopting. Still, many journals have not published their first registered report. There is high variability in impact of journals adopting registered reports. Many journals do not include concrete information about policies that address concerns about registered reports (e.g., exploratory analysis); however, those that do typically allow these practices with some restrictions. Additionally, other open science practices are commonly encouraged or required as part of the registered report process, especially open data and materials. Overall, many journals did not include many of the fields coded by the research team, which could be a barrier to submission for some authors. Though the majority of journals allow authors to be anonymous during the review process, a sizable portion do not, which could also be a barrier to submission. We conclude with future directions and implications for authors of registered reports, journals that have already adopted registered reports, and journals that may consider adopting registered reports in the future.

OBJECTIVE: Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN). METHOD: Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics. RESULTS: For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample. CONCLUSIONS: Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).