THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Many scientific phenomena are now investigated by complex computer models or codes A computer experiment is a number of runs of the code with various inputs A feature of many computer experiments is that the output is deterministic--rerunning the code with the same inputs gives identical observations Often, the codes are computationally expensive to run, and a common objective of an experiment is to fit a cheaper predictor of the output to the data Our approach is to model the deterministic output as the realization of a stochastic process, thereby providing a statistical basis for designing experiments (choosing the inputs) for efficient prediction With this model, estimates of uncertainty of predictions are also available Recent work in this area is reviewed, a number of applications are discussed, and we demonstrate our methodology with an example

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The design and analysis of computer experiments

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.

The mixed logit model: the state of practice and warnings for the unwary.

Cost Effectiveness in Health and Medicine.

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The mixed logit model: the state of practice

Quick and easy choice sets: Constructing optimal and nearly optimal stated choice experiments

List of Tables. Preface. 1. Typical Stated Choice Experiments. 2. Factorial Designs. 3. The MNL Model and Comparing Designs. 4. Paired Comparison Designs for Binary Attributes. 5. Larger Choice Set Sizes for Binary Attributes. 6. Designs for Asymmetric Attributes. 7. Various Topics. 8. Practical Techniques For Constructing Choice Experiments. Bibliography. Index.

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The Construction of Optimal Stated Choice Experiments: Theory and Methods

The Construction of Optimal Stated Choice Experiments

This book describes direct and recursive methods for the construction of combinatorial designs. It is ideally suited to the statistician through its discussion of how the designs currently used in experimental work have been obtained and through its coverage of other known and potentially useful designs. It is equally suited to the needs of the combinatorialist, with its stress on the statistical motivation for studying particular finite structures and its suggestions of open problems in the construction of new designs with useful properties for the experimentalist. Designs are discussed within a unified framework, showing the interplay between elegant structure and practical use.

Combinatorics of experimental design

In discrete choice experiments, design decisions are crucial for determining data quality and costs. While high statistical efficiency designs are desirable, they may come at a price if they increase the cognitive burden for respondents. We address this problem by designing 44 experiments that systematically vary numbers of attributes and attribute level differences. Our results for two product categories suggest that respondents systematically are less consistent in answering choice questions as statistical efficiency increases. This relationship holds regardless of the number of attributes and is statistically significant even if one accommodates preference heterogeneity. Implications for practice and future research are discussed.

https://www.jstor.org/stable/10.1086/586913

Deborah J. Street

Papers

Quick and easy choice sets: Constructing optimal and nearly optimal stated choice experiments

The Construction of Optimal Stated Choice Experiments: Theory and Methods

The Construction of Optimal Stated Choice Experiments

Combinatorics of experimental design

Designing Discrete Choice Experiments: Do Optimal Designs Come at a Price?