Big team science initiatives: A catalyst for trustworthy advancements in IO psychology

Keener et al. (2023) raise concerns about the trustworthiness of Industrial/Organizational (IO) Psychology research and related fields due to the low reproducibility and replicability of research findings. The authors provide various solutions to resolve this crisis, such as improving training, realigning incentives, and adopting open science practices. Our commentary elaborates on one solution to which they briefly allude: Big Team Science Initiatives (BTSIs). BTSIs allow scholars to address the trustworthiness of our science by facilitating large sample theory testing, sharing and allocating resources, and selecting appropriate research strategies, all of which support the reproducibility and replication of research. Further, we propose that BTSIs may facilitate researcher training, encourage data sharing and materials, and realign incentives in our field. We discuss how BTSIs could be implemented in IO psychology and related fields, identifying and drawing upon similar BTSIs in related disciplines. Thus, our commentary is an extension of the focal article, encouraging scholars to collaboratively address the “crisis of confidence” facing our field using a big team science approach

Recommendations for reporting sample and measurement information in experience sampling studies

Over the last couple of decades, studies using the experience sampling methodology (ESM) have been used with increasing frequency within the management-related sciences as the method allows researchers the opportunity to investigate questions involving ongoing, dynamic, intra-individual processes. Given the longitudinal nature of the methodology and the resulting multi-level data structure, there are sample- and measurement-related issues that make ESM studies different from other methods commonly used in management research. Consequently, ESM studies have demands for reporting sample- and measurement-related information that differ from more commonly used methods. In the current paper, we review the conceptual foundations of sample and measurement issues in ESM studies and report the findings of a survey of the ESM studies to identify current reporting practices. We then offer clear, easy to implement recommendations for reporting sample- and measurement-related aspects of ESM studies. We hope that these recommendations will improve reporting of ESM studies and allow readers the opportunity to more fully and comprehensively evaluate the research presented