Bias in Quantitative Nursing Research: Identifying and Mitigating Common Pitfalls
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Abstract: Bias in research is a common issue that can reduce the validity and reliability of study findings. In quantitative nursing research, bias can occur at various stages of the research process, from problem formulation to the reporting of study results. This paper aims to identify common biases in quantitative nursing research and provide strategies to reduce these biases to improve research quality. The types of bias discussed include problem framing bias, theoretical/ conceptual bias, misclassification of research design, sampling bias, selection bias, uncontrolled confounders, inadequate protocol descriptions, measurement bias, low power and analysis bias, and bias in interpretation and reporting and inappropriate implications and recommendations. The paper explains and provides common examples of these biases, along with strategies to mitigate them. If researchers correctly conduct their studies at every stage, it can enhance the quality of nursing research, making the findings more reliable, applicable in practice, and more
suitable for future research.
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