Evaluating Research Transparency and Openness in Communication Sciences and Disorders Journals

Conducting high-quality and reliable acoustic analysis: A tutorial focused on training research assistants

Open science practices have led to an increase in available speech datasets for researchers interested in acoustic analysis. Accurate evaluation of these databases frequently requires manual or semi-automated analysis. The time-intensive nature of these analyses makes them ideally suited for research assistants in laboratories focused on speech and voice production. However, the completion of high-quality, consistent, and reliable analyses requires clear rules and guidelines for all research assistants to follow. This tutorial will provide information on training and mentoring research assistants to complete these analyses, covering areas including RA training, ongoing data analysis monitoring, and documentation needed for reliable and re-creatable findings.

Guidance for Increasing Primary Study Inclusion and the Usability of Data in Meta-Analysis: A Reporting Tutorial

Meta-analyses can be used to comprehensively summarize the state of a given literature base, understand development and relations between constructs, and synthesize intervention effects to identify "what works for whom," all of which can directly inform research, practice, and policy. In this tutorial, we first argue that data reporting in primary studies can meaningfully affect the results of meta-analyses and, most importantly, the subsequent interpretations and use of the findings in research, practice, and policy in speech, language, and hearing sciences. Though reporting guidelines have been well established for the results of meta-analyses, there is limited focus on the essentials for primary study reporting that allow for inclusion in meta-analyses. To this end, we provide guidance for primary study authors to ensure their studies can maximize their contributions to research syntheses and, specifically, to meta-analyses. We discuss current and ongoing issues related to reporting, provide data-based examples of instances where lack of reporting or transparency has rendered a study ineligible from inclusion in a meta-analysis, encourage editorial teams and peer reviewers to be flexible in the inclusion of supplemental data reporting based on journal requirements and limits, and suggest being explicit and earnest about why these requests are important to advancing the field. Supplemental Material: https://doi.org/10.23641/asha.23117996.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.