Best Practices in Data Collection and Preparation: Recommendations for Reviewers, Editors, and Authors

By Herman Aguinis, N. S. Hill, J. Bailey. Organizational Research Methods, 2019

170 citations recorded by the source corpus.

We offer best-practice recommendations for journal reviewers, editors, and authors regarding data collection and preparation. Our recommendations are applicable to research adopting different epistemological and ontological perspectives—including both quantitative and qualitative approaches—as well as research addressing micro (i.e., individuals, teams) and macro (i.e., organizations, industries) levels of analysis. Our recommendations regarding data collection address (a) type of research design, (b) control variables, (c) sampling procedures, and (d) missing data management. Our recommendations regarding data preparation address (e) outlier management, (f) use of corrections for statistical and methodological artifacts, and (g) data transformations. Our recommendations address best practices as well as transparency issues. The formal implementation of our recommendations in the manuscript review process will likely motivate authors to increase transparency because failure to disclose necessary information may lead to a manuscript rejection decision. Also, reviewers can use our recommendations for developmental purposes to highlight which particular issues should be improved in a revised version of a manuscript and in future research. Taken together, the implementation of our recommendations in the form of checklists can help address current challenges regarding results and inferential reproducibility as well as enhance the credibility, trustworthiness, and usefulness of the scholarly knowledge that is produced.

Subfields: Artificial Intelligence, Management Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/650048c30b44e7345deffde4e43faaf51b9f3df0

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Citations in the corpus

Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2021, background): “Against this background, the recent claims that the use of transformed variables for testing hypotheses would be problematic are simply incorrect (Aguinis et al., 2019; Becker et al., 2019; Maula & Stam, 2020).”
Editorial Essay: The Tumult over Transparency: Decoupling Transparency from Replication in Establishing Trustworthy Qualitative Research* (2019, background): “…replication in the pursuit of trustworthiness are spilling over to qualitative research in unhelpful and potentially even dangerous ways (e.g., Aguinis, Cascio, and Ramani, 2017; Aguinis, Ramani, and Alabduljader, 2018; Aguinis, Hill, and Bailey, 2019; Aguinis and Solarino, 2019; Bamberger, 2019).”