Writing Empirical Articles: Transparency, Reproducibility, Clarity, and Memorability

Abstract

This article provides recommendations for writing empirical journal articles that enable transparency, reproducibility, clarity, and memorability. Recommendations for transparency include preregistering methods, hypotheses, and analyses; submitting registered reports; distinguishing confirmation from exploration; and showing your warts. Recommendations for reproducibility include documenting methods and results fully and cohesively, by taking advantage of open-−science tools, and citing sources responsibly. Recommendations for clarity include writing short paragraphs, composed of short sentences; writing comprehensive abstracts; and seeking feedback from a naive audience. Recommendations for memorability include writing narratively; embracing the hourglass shape of empirical articles; beginning articles with a hook; and synthesizing, rather than Mad Libbing, previous literature.

Bridging Feminist Psychology and Open Science: Feminist Tools and Shared Values Inform Best Practices for Science Reform

“…Though there are researcher degrees of freedom in the writing process, the extent to which methodological and analytical details are disclosed (e.g., sample characteristics, recruitment strategies, procedures, measures, statistical tests) vary and thus can contribute to lackluster contextualizing that is necessary for truly feminist and open research. Most scientists would agree that open and transparent reporting is a shared value (Nosek et al, 2015); To promote honest and accurate reporting of research, researchers should consider standardized statements about transparency and generalizability (e.g., Gernsbacher, 2018;Simons et al, 2017). Some journals require modest versions of transparency statements (e.g., "We report all conditions and measures"), but such a practice reflects the bare minimum of what should be required for the sake of transparency.…”

“…Incentives to publish, the field's favoritism toward significant results, and personal beliefs about psychological theories have been identified as sources of bias (Bakker et al, 2012;. Consequently, open science advocates have promoted tools that allow for more transparency, such as pre-registered reports which disclose researcher's intentions prior to data analysis (i.e., similar to a two-part "proposal and "defense" model; Gernsbacher, 2018;Nosek et al, 2015;Nosek & Lakens, 2014). While these ideas are promising, they do not entirely foster the much-needed reflection by researchers to address their position in the research process.…”

“…The more often researchers include these statements, the more normalized and expected transparency will become. Statements of transparency can be used as a commitment to faithful reporting as well as a way to identify the availability of supplemental material (Gernsbacher, 2018). For example, a thorough transparency statement could confirm which aspects of the study design and materials are included in the current manuscript, whether data are part of a larger project, a commitment to sharing materials with others, and whether supplemental materials exist and how they can be accessed.…”

Easing Into Open Science: A Guide for Graduate Students and Their Advisors

“…Whereas Bem (2004) was once considered a gold standard for writing journal articles, it has since fallen out of favor due to many of his recommendations not supporting transparent and honest writing. Rather, Gernsbacher (2018) provides excellent recommen-Easing Into Open Science: A Guide for Graduate Students and Their Advisors Collabra: Psychology dations that are current and consistent with best practice in open science. Additionally, the American Psychological Association has published Journal Articles Reporting Standards for quantitative studies (Appelbaum et al, 2018) and qualitative/mixed method studies (Levitt et al, 2018).…”

Sound Inference in Complicated Research: A Multi-Strategy Approach

“…Researchers and readers can evaluate a preregistration by how well it does that, as well as whether it is sound on other statistical grounds (including whether contingency plans could unintentionally capitalize on chance). Contrary to some misconceptions, preregistration prevents neither justifiable departures from a pre-data plan nor reporting of additional analyses, which often have great value for discovery (Gernsbacher, 2018). Preregistration makes them transparently distinguishable from the planned analyses that have decision independence, helping authors and readers to calibrate their interpretations.…”