This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter:
- Explores the ethics of a particular topic
- Identifies prevailing methodological issues
- Reviews strategies and approaches for handling such issues and their ethical implications
- Provides one or more case examples
- Outlines plausible approaches to the issue including best-practice solutions.
Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process.
This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.
A.T. Panter is the Bowman and Gordon Gray Distinguished Professor of Psychology at the L. L. Thurstone Psychometric Laboratory at University of North Carolina, Chapel Hill. She develops instruments, research designs, and data-analytic strategies for applied research questions in health and education. Her publications are in survey methodology, measurement and testing, advanced quantitative methods, program evaluation, and individual differences. She has received numerous teaching awards including APA's Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring. She has significant national service in disability assessment, testing in higher education, women in science, and the advancement of quantitative psychology.
Sonya K. Sterba is an Assistant Professor in the Quantitative Psychology Program at Vanderbilt University. She received her Ph.D. in Quantitative Psychology and her M.A. in Child Clinical Psychology from the University of North Carolina at Chapel Hill. Her research evaluates how traditional structural equation and multilevel models can be adapted to handle methodological issues that arise in developmental psychopathology research.
