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This paper addresses the critical issue of common method bias (CMB) in social and behavioral research, emphasizing its impact on the validity and reliability of quantitative studies, particularly in the context of doctoral-level research. CMB arises when data collection methods in doctoral research, such as self-report surveys, artificially inflate or deflate relationships between variables, leading to distorted findings. This can result in misleading conclusions, such as overstating the link between job satisfaction and employee performance, which may have serious implications for both academic research and policymaking. The paper offers a comprehensive overview of the sources and effects of CMB, along with strategies to detect, prevent, and control it. It highlights key statistical techniques, including Harman’s single-factor test, marker variable approaches, and latent variable modeling, to minimize bias during data analysis. Additionally, it outlines best practices for study design, such as temporal separation, varying measurement methods, and ensuring data collection anonymity, to reduce CMB from the outset. By implementing these strategies, researchers can enhance the validity and generalizability of their findings, ensuring that results reflect genuine relationships rather than artifacts of the research process. Received: 7 September 2024 | Revised: 31 October 2024 | Accepted: 23 Januray 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Mohammad Rashed Hasan Polas: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Mohammad Rashed Hasan Polas (Fri,) studied this question.