2022/02/15 by Daniel R. Isbell, DANIEL R. ISBELL, Dan Brown +14 · 1 voice · 94 citations
Computer Science · Engineering · Medicine · Psychology · Social Sciences · #Academic integrity and plagiarism #Alternative medicine #Computer science #Data collection #Engineering #Engineering ethics #Explainable Artificial Intelligence (XAI) #Imbalanced Data Classification Techniques #Law #Medical education #Medicine #Misconduct #Pathology #Political science #Psychology #Relation (database) #Research ethics #Research integrity #Scientific integrity #Scientific misconduct #Social science #Sociology
paper · doi:10.1111/modl.12760
published in Modern Language Journal 106(1), 172-195 (Wiley)
openalex publication_date 2022/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract Scientific progress depends on the integrity of data and research findings. Intentionally distorting research data and findings constitutes scientific misconduct and introduces falsehoods into the scientific record. Unintentional distortions arising from questionable research practices (QRPs), such as unsystematically deleting outliers, pose similar obstacles to knowledge advancement. To investigate the extent of misconduct and QRPs in quantitative applied linguistics research, we surveyed 351 applied linguists who conduct quantitative research about their practices related to data handling and reporting. We found that 17% of respondents (approximately 1 in 6) admitted to 1 or more forms of scientific misconduct and that 94% admitted to 1 or more QRPs relevant to quantitative research. We also examined these practices in relation to participant background and training. Researchers admitting to misconduct tended to be earlier in their careers and had experienced publication rejection due to lack of statistically significant results. Quantitative training had generally desirable associations with QRPs. Publication rate and experience with publication rejection were associated with admission of several QRPs related to omitting statistical results. We discuss these findings and offer 5 recommendations for the field of applied linguistics to improve ethical quantitative data handling and reporting in research.