Bias and Beyond: Rethinking Data Practices and Inspiring Collective Dialogue

The rapid expansion of data in every facet of modern life from research and education to public policy and private industry demands a critical re-examination of how data is collected, analyzed, interpreted, and used. Bias and Beyond: Rethinking Data Practices and Inspiring collective dialogue responds to this call by exploring how bias enters the data lifecycle and how inclusive, reflexive, and equity-centered methodologies can improve the validity and impact of data-driven decisions.

Structured around the four stages of the data lifecycle including problem formulation, data collection, analysis, and use, this volume brings together diverse voices to interrogate systemic inequities and offer practical strategies for inclusive data practices. Drawing from qualitative, quantitative, and mixed methods, each chapter highlights the role of researcher positionality, power, and representation in shaping outcomes and knowledge.

Bias and Beyond is anchored in a commitment to Diversity, Equity, and Inclusion (DEI), not only as aspirational values but as methodological imperatives that must inform every aspect of data practice. It will serve as a timely and essential resource for researchers, educators, policymakers, and students committed to data justice and ethical inquiry.

Soyeon Ahn, Ph.D. is a Professor in the Research, Measurement, and Evaluation Program at the University of Miami’s School of Education and Human Development.

April Mann, EdD, is the director of the Writing Center and a senior lecturer in the Department of Writing Studies at the University of Miami.