William R. Dillon

Offending Estimates in Covariance Structure Analysis: Comments on the Causes of and Solutions to Heywood Cases

In this article we discuss, illustrate, and compare the relative efficacy of three recommended approaches for handling negative error variance estimates (i.e., Heywood cases): (a) setting the offending estimate to zero, (b) adopting a model parameterization that ensures positive error variance estimates, and (c) using models with equality constraints that ensure nonnegative (but possibly zero) error variance estimates. The three approaches are evaluated in two distinct situations: Heywood cases caused by lack of fit and misspecification error, and Heywood cases induced from sampling fluctuations. The results indicate that in the case of sampling fluctuations the simple approach of setting the offending estimate to zero works reasonably well. In the case of lack of fit and misspecification error, the theoretical difficulties that give rise to negative error variance estimates have no ready-made methodological solutions.

Extending Discrete Choice Models to Incorporate Attitudinal and Other Latent Variables

One of the nagging issues in using discrete choice models is how softer attributes, such as attitudes and perceptions, that are not explicitly manipulated within the context of the choice experiment can be accommodated. In many cases, it is reasonable to expect that the choice of a particular alternative may be influenced by non–product-related attributes. For example, latent attitudes and perceptions may play as much of a role in shaping choice as the attributes that have been manipulated and used to define the alternative offerings. In this article, the authors present several full information models that can accommodate latent variables such as attitudes and satisfaction within the context of binary and multinomial choice models. The models proposed are particularly useful when the focus is on understanding how softer attributes can influence choice decisions. The authors accomplish this by integrating structural equation models within the basic framework of binary and multinomial choice models. Two empirical applications are provided. In addition to illustrating the proposed models, these applications provide insights into the circumstances under which the simultaneous factor–choice modeling approach makes a difference.

On the Use of Component Scores in the Presence of Group Structure

Examination of the properties of component scores in the presence of group structure shows that the first few components extracted, typically viewed as most informative regarding total variance, do not necessarily contain the most information across group differences. A method for identifying informative components that account for across group differences is presented and illustrated. Copyright 1989 by the University of Chicago.

GLOBAL PRODUCT QUALITY AND CORPORATE SOCIAL RESPONSIBILITY PERCEPTIONS: A CROSS-NATIONAL STUDY OF HALO EFFECTS

Attribute ratings often contain a holistic or global impression of the brand, commonly referred to as “halo.” A halo response can occur when perceptions of a brands performance on an attribute are influenced by performance perceptions on another attribute or by a global impression of the brand. Using cross-national survey data from consumers in Argentina, China, Spain, and the United States, the authors examine the extent to which a halo response introduces bias to product quality and corporate social responsibility perceptions of competing brands. The findings show that halo is more pervasive for product quality than for corporate social responsibility associations, varies across brands and markets, and is strongly related to brand recommendations. Examining cross-national brand performance and halo perceptions can help international marketing managers understand key perceptual similarities and differences between and across markets, which can inform strategic considerations such as whether to pursue global, panregional, or national branding, positioning, and advertising strategies.

Decision Issues in Building Perceptual Product Spaces with Multi-Attribute Rating Data

This paper considers decisions that face consumer researchers as they implement a perceptual product space analysis based on multi-attribute rating data. Decisions that affect the structure of the derived perceptual product space solution can be grouped into six major categories relating to issues of (1) data input, (2) mode, (3) preprocessing transformation, (4) choice/preference modeling, (5) technique, and (6) solution. The major difficulties of each decision area are explicated, and specific recommendations are provided whenever possible.

A Probabilistic Latent Class Model for Assessing Inter-Judge Reliability.

Increasingly behavioral researchers are soliciting cognitive responses in addition to standard attitudinal measures when attempting to assess the effects of persuasive communications. The coding of the elicited cognitive responses generally involves some sort of categorization, typically undertaken by independent judges, and the quality of the data is, to a large degree, evaluated in terms of some reliability coefficient which reflects the extent to which the independent judges agreed. The purpose of this paper is to present and illustrate a probabilistic model for assessing inter-judge reliability. The proposed probabilistic model allows one to (a) use formal test statistics to evaluate the extent and character of inter-judge reliability, (b) estimate the assignment error rates and their standard errors, and (c) test for simultaneous agreement for more than two judges. The probabilistic model is operationalized in terms of restricted latent class models.

On the Performance of Some Multinomial Classification Rules

Abstract This article presents and discusses a new multinomial classification procedure based on a discrete distributional distance. Its performance along with other commonly used classification procedures is assessed through Monte Carlo sampling experiments under different population structures. In addition to reporting results consistent with the work of Gilbert (1968) and Moore (1973), the article describes sampling experiments which show that the new distance procedure is generally superior, in terms of both the mean actual and mean apparent errors, to the usual full multinomial rule in situations of disproportionate sample sizes.

The Impact of Negative Marketing Communications: The Consumers Union/Chrysler Controversy

Abstract Negative publicity about products and companies has become increasingly problematic for many firms. This study took the Chrysler/Consumers Union controversy concerning the alleged handling problems of the Plymouth Horizon and Dodge Omni automobiles into the laboratory to examine its effects. Original videotapes of the negative news story, the companys reply, and product advertisements were obtained and edited to form experimental conditions reflecting the actual news story and potential company response strategies. Measurements obtained immediately and two weeks after exposure indicate that the detrimental effects of the news story suggested by depressed market shares can be replicated in the laboratory. The use of public relations replies and product advertising are assessed as possible response strategies.

An integrative look at the use of additive and multiplicative covariance structure models in the analysis of MTMM data.

First-order confirmatory factor analytic models have had widespread use in the analysis of multitrait-multimethod (MTMM) data. In contrast to the usual first-order confirmatory factor analytic mode...

Inferring Latent Brand Dependencies

In this article, the authors develop a class of models to reconstruct brand-transition probabilities when individual brand purchase sequence information is not available. The authors introduce two general model forms by assuming different underlying mechanisms for individual heterogeneity in brand switching. The first model form captures individual heterogeneity by a latent class structure. The second model form captures individual heterogeneity by postulating that the brand-choice probabilities follow a Dirichlet distribution, which yields the popular Dirichlet multinomial formulation. Monte Carlo simulations are performed with a view toward assessing whether individual transition probabilities can be captured from knowledge of only aggregated brand choices. Results indicate that the proposed method can indeed capture individual brand-transition probabilities under several different conditions. An empirical application illustrates how these models can be used to provide important information on individual brand transitions and the role of marketing-related covariates.