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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. OverviewPsychologists and other behavioral scientists are using structural covariance analysis with increasing regularity (Bagozzi,

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.

Although brand ratings capture the favorability of brand associations, they often do not enable marketing managers to disentangle brand-specific associations from other effects. In this article, the authors present a decompositional model for analyzing brand ratings that addresses this nagging problem and provide insights for understanding the sources of brand equity. Starting with consumers' perceived level of a brand on an attribute, the authors decompose the rating into two components: brandspecific associations and general brand impressions. Brand-specific associations refer to features, attributes, or benefits that consumers link to a brand and that differentiate it from the competition. General brand impressions refer to general impressions about the brand that are based on a more holistic view of the brand. In this article, the authors focus on two principal issues: (1) How can the sources of bias that may be present in brand ratings be disentangled? and (2) Do these putatively biasing effects, if present, have any managerial implications for brand equity?The authors demonstrate the properties and advantages of the model in the context of three empirical applications.

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