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The research reported here deals with finding why people make some choices rather than others, why different people make different decisions in objectively similar situations. The book requires that its reader have some basic knowledge of statistical methods, and, since, it cuts across normally separate fields, it requires an adventuresome spirit. But, in return, the reader may expect to gain the use of a powerful tool that can be applied in his own practical projects and social science research.The message is on two levels. On one, the work is a practical handbook for application. On the other, it discusses some fundamental issues in the theory of decision-making and the social sciences.The book presents an application method for measuring assumptions realistic enough for use in management context. In a test-case study, the author uncovered startling diversity in the attributes investors use in picking stocks. More generally, such measures of assumptions are useful in managerial planning and control to aid in decision-making consistence, in learning to revise decision assumptions, and in designing information systems to support decision-making. They are also useful in improving joint decision-making and communication. Still other important applications are possible in consumer market research and in operations research modeling of decision processes. These applications are described with suggestive examples.To the management scientist the author seeks to show the benefits of extending explicitness beyond the traditional bounds of information systems into the realm of subjective decision assumptions. That is, subjective assumptions made explicit in a practical manner are employed as useful inputs to managerial information systems.Such measurement methods as reported here may also have widespread use in building social theory. Individual decision assumptions are key variables in microeconomics, in political science, in organization theory, and in the sociology of knowledge. Their measures play an analogous role in social science to that of thermometers in the development of thermodynamics.The material is developed as follows: First, the problem of discovering the assumptions which underlie decisions is sketched broadly. Alternative possible measurement approaches and theories are then described in logical order. An outline of the method for measuring assumptions is followed by the account of its use in a case study of stock market participants. It is this narrative that provides a practical handbook for the reader's use. A number of prototype applications are shown in some detail. The final chapters propose uses of the method for research in the social sciences and in accounting and the financial markets.

This book focuses on methods of choice in program evaluation. Credible methods choice lies in the assumptions we make about the appropriateness and validity of selected methods and the validity of those assumptions. As evaluators make methodological decisions in various stages of the evaluation process, a number of validity questions arise. Yet unexamined assumptions are a risk to useful evaluation. The first edition of this book discussed the formulation of credible methodological arguments and methods of examining validity assumptions. However, previous publications suggest advantages and disadvantages of using various methods and when to use them. Instead, this book analyzes assumptions underlying actual methodological choices in evaluation studies and how these influence evaluation quality. This analysis is the basis of suggested tools. The second edition extends the review of methodological assumptions to the evaluation of humanitarian assistance. While evaluators of humanitarian action apply conventional research methods and standards, they have to adapt these methods to the challenges and constraints of crisis contexts. For example, the urgency and chaos of humanitarian emergencies makes it hard to obtain program documentation; objectives may be unclear, and early plans may quickly become outdated as the context changes or is clarified. The lack of up-to-date baseline data is not uncommon. Neither is staff turnover. Differences in perspective may intensify and undermine trust. The deviation from ideal circumstances challenges evaluation and calls for methodological innovation. And how do evaluators work with assumptions in non-ideal settings? What tools are most relevant and effective? This revised edition reviews major evaluations of humanitarian action and discusses strategies for working with evaluation assumptions in crises and stable program settings.

This first-rate text explores the theory and methodology of systems engineering in evaluating alternative courses of action and associated decision-making policies. It treats criteria as multidimensional, rather than scalar, in the development of normative theories. These contribute to a behavioral theory of decision making and provide guidance for exercising judgment. An introductory discussion of the systemic approach to judgment and decision is followed by explorations of psychological value measurements, utility, classical decision analysis, and vector optimization theory. The second section chiefly deals with methods of assessing and evaluating alternatives, including both noninteractive and interactive methods. A taxonomy and a comparative evaluation of methods conclude the text.

Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.