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Multivariate Applications Series
Handbook of Ethics in Quantitative Methodology
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.
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.
540 pages, Kindle Edition
First published January 1, 2011
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December 14, 2012
Review of Chapter 4, A Statistical Guide for the Ethically Perplexed, by Hubert and Wainer.
Periodically I check for citations to my book, Optimal Data Analysis (APA Books, 2004), so that I can prepare an update including recent citations. I came across this chapter and I have brief comments.
First, Simpson's Paradox represents perhaps the most significant challenge to all empirical research, and deserves thorough coverage. Instead, treatment of this crucial topic in this chapter is incomplete and obsolete, ignoring recent theoretical (e.g., Yarnold, P.R., Characterizing and circumventing Simpson's paradox for ordered bivariate data, Educational and Psychological Measurement, 56, 1996, 430-442) and empirical (e.g., Soltysik, R.C., & Yarnold, P.R., The use of unconfounded climatic data improves atmospheric prediction, Optimal Data Analysis, 1, 2010, 67-100) work which solves the problem.
Second, the Chapter concludes by complaining about the name of a new statistical paradigm, called Optimal Data Analysis, the only statistical methodology which identifies statistical models--for any data configuration--which explicitly maximize accuracy (i.e., the number of correct classifications). For all models derived using this paradigm, exact Type I error is computed, as is jackknife validity (among a host of validity indices). The name "optimal" derives from the roots of this paradigm--the field of operations research. In operations research, when the best (most accurate) possible model is identified, the result is called the optimal solution. This is a tradition of the field. These authors complain that the word optimal implies that other methods are less than optimal. Any model which explicitly finds the maximum solution IS optimal, and any other model IS suboptimal, by definition. This is made obvious in the book which they criticize. To conclude their chapter by warning-off others, in the absence of knowledge or understanding of the subject matter is an unethical act. Alternatively, if the authors were aware of their lack of standing, then to end the chapter with such discussion is an unethical act. In my mind this calls into question the thoroughness and validity of prior work by these authors, as well as the professionalism of the reviewers of this chapter. Of course, it is the editor who must in the end be held culpable.
Paul R. Yarnold, Ph.D.
CEO, Optimal Data Analysis
Periodically I check for citations to my book, Optimal Data Analysis (APA Books, 2004), so that I can prepare an update including recent citations. I came across this chapter and I have brief comments.
First, Simpson's Paradox represents perhaps the most significant challenge to all empirical research, and deserves thorough coverage. Instead, treatment of this crucial topic in this chapter is incomplete and obsolete, ignoring recent theoretical (e.g., Yarnold, P.R., Characterizing and circumventing Simpson's paradox for ordered bivariate data, Educational and Psychological Measurement, 56, 1996, 430-442) and empirical (e.g., Soltysik, R.C., & Yarnold, P.R., The use of unconfounded climatic data improves atmospheric prediction, Optimal Data Analysis, 1, 2010, 67-100) work which solves the problem.
Second, the Chapter concludes by complaining about the name of a new statistical paradigm, called Optimal Data Analysis, the only statistical methodology which identifies statistical models--for any data configuration--which explicitly maximize accuracy (i.e., the number of correct classifications). For all models derived using this paradigm, exact Type I error is computed, as is jackknife validity (among a host of validity indices). The name "optimal" derives from the roots of this paradigm--the field of operations research. In operations research, when the best (most accurate) possible model is identified, the result is called the optimal solution. This is a tradition of the field. These authors complain that the word optimal implies that other methods are less than optimal. Any model which explicitly finds the maximum solution IS optimal, and any other model IS suboptimal, by definition. This is made obvious in the book which they criticize. To conclude their chapter by warning-off others, in the absence of knowledge or understanding of the subject matter is an unethical act. Alternatively, if the authors were aware of their lack of standing, then to end the chapter with such discussion is an unethical act. In my mind this calls into question the thoroughness and validity of prior work by these authors, as well as the professionalism of the reviewers of this chapter. Of course, it is the editor who must in the end be held culpable.
Paul R. Yarnold, Ph.D.
CEO, Optimal Data Analysis
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