Donald A. Anderson

Bio: Donald A. Anderson is an academic researcher from University of Wyoming. The author has contributed to research in topics: Fractional factorial design & Factorial experiment. The author has an hindex of 8, co-authored 23 publications receiving 327 citations.

Designs of Discrete Choice Set Experiments for Estimating Both Attribute and Availability Cross Effects

Abstract: Probabilistic discrete choice experiments are frequently applied in strategic policy, management, and marketing problems to model and predict consumer choice behavior. The authors consider the cons...

Efficient Choice Set Designs for Estimating Availability Cross-Effects Models

Abstract: Batsell and Polking proposed a discrete choice model which incorporates the availability (presence or absence) of competing brands into the utility of each brand under study. The information on relative impacts of adding or deleting brands is of strategic interest, and models that do not incorporate such effects may be misleading. The designs suggested by Batsell and Polking have 2m−m−1 choice sets. Even with as few as 10=m brands, this requires over 1000 choice sets. In this paper we provide a catalog of designs for estimating cross effects models in as few as 2m−1 choice sets. This will make cross effects modelling practical in a wide range of academic and commercial settings.

Use of a Fractional Factorial Design to Evaluate Interactions of Environmental Factors Affecting Biodegradation Rates

Abstract: For investigation of main and interactive effects of six experimentally controlled environmental factors on phenol biodegradation in a shake-flask system, a largely neglected statistical procedure was applied. A major benefit resulting from the application of the orthogonal, fractional factorial design is that the number of experiments necessary to evaluate multifactor interactions is limited. In our investigation, the required number of experiments was reduced to 81 from the 324 necessary with conventional factorial designs; information was sacrificed for only 3 of 15 possible two-factor interactions. Six experimentally controlled factors were investigated at two or three treatment levels each; the six factors were (1) amount of phenol substrate, (2) amount of bacterial inoculum, (3) filtration of inoculum, (4) type of basal salts medium, (5) initial pH of basal salts medium, and (6) flask closure. Significant main effects were found for factors 1, 2, and 4; whereas significant interactive effects were found only for factor 2 with factor 3 and for factor 2 with factor 5. Our results suggest that the application of these statistical designs will greatly reduce the number of experiments necessary to evaluate multifactor effects on degradation rates during optimization of both hazard screening systems and waste treatment systems.

The Importance of Utility Balance in Efficient Choice Designs

Abstract: Choice designs traditionally have been built under the assumption that all coefficients are zero. The authors show that if there are reasonable nonzero priors for expected coefficients, then these ...

Patterns of neotropical plant species diversity.

Abstract: Diversity has been given a central role in attempts to develop a general theory of ecology (Johnson and Raven, 1970). Much attention has been focused on large-scale trends, such as the increase in species diversity with decreasing latitude (Pianka, 1966; Mac Arthur, 1965; Fischer, 1960; Tramer, 1974), but there is surprisingly little documentation of the equally striking changes in diversity within the tropics. Moreover, additional information on tropical plant species diversity patterns is critically needed because of their potential importance in resolving some of the fundamental and theoretically significant differences of opinion as to the nature and manner of regulation of species diversity.

Thirty Years of Conjoint Analysis: Reflections and Prospects

Abstract: Conjoint analysis is marketers' favorite methodology for finding out how buyers make trade-offs among competing products and suppliers Conjoint analysts develop and present descriptions of alternative products or services that are prepared from fractional factorial, experimental designs They use various models to infer buyers' part-worths for attribute levels, and enter the part-worths into buyer-choice simulators to predict how buyers will choose among products and services Easy-to-use software has been important for applying these models Thousands of applications of conjoint analysis have been carried out over the past three decades

Efficient Experimental Design with Marketing Research Applications

Abstract: The authors suggest the use of D-efficient experimental designs for conjoint and discrete-choice studies, discussing orthogonal arrays, nonorthogonal designs, relative efficiency, and nonorthogonal...