Suchergebnisse

The standard mixture model, the concomitant variable mixture model, the mixture regression model and the concomitant variable mixture regression model all enable simultaneous identification and description of groups of observations. This study reviews the different ways in which dependencies among the variables involved in these models are accommodated. It is demonstrated that the standard and concomitant variable mixture models identify groups of observations and at the same time discriminate them analogous, respectively, to discriminant analysis and logistic regression. While the mixture regression model is shown to have limited use for classifying new observations. An extension of it, called the saturated mixture regression model, is shown to be more useful in that respect. Advantages of that model in model estimation when missing data are present and as a framework for model selection are also discussed.

This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and allows us to augment the data for all subjects in a company's transaction database with soft data collected in a survey among a sample of those subjects. We propose a bootstrap‐based imputation approach, which is appealing to use in combination with the factor model, since it allows one to include estimation uncertainty in the imputation procedure in a simple, yet adequate manner. We provide an empirical case study of the performance of the approach to a transaction data base of a bank.

ABSTRACTA latent class model for identifying classes of subjects in paired comparison choice experiments is developed. The model simultaneously estimates a probabilistic classification of subjects and the logit models' coefficients relating characteristics of objects to choices for each respective group among two alternatives in paired comparison experiments. A modest Monte Carlo analysis of algorithm performance is presented. The proposed model is illustrated with empirical data from a consumer psychology experiment that examines the determinants of perceived consumer risk. The predictive validity of the method is assessed and compared to that of several other procedures. The sensitivity of the method to (randomly) eliminate comparisons, which is important in view of reducing respondent fatigue in the task, is investigated.

Abstract
Vast sums of money are still spent on TV advertising. In an environment of rising perviewer rates for advertisers and increased skipping past ads by consumers it is necessary for advertising managers to understand the determinants of commercial avoidance. In order to optimize brand exposure they need information on how to best retain consumers' attention from moment-to-moment during television advertising. This large-scale eye tracking study shows that the decision to zap or not to zap depends on how the brand is presented within the commercial. First, the ability of a commercial to concentrate consumers' visual attention reduced avoidance significantly. Second, the likelihood that viewers will zap can be decreased with a "pulsing strategy" in which brand images are shown more frequently for a shorter period of time within the commercial instead of longer at the beginning or end.

The objectives of this paper are (1) to review methods that can be used to test for different types of random effects and regressor dependencies, (2) to present results from Monte Carlo studies designed to investigate the performance of these methods, and (3) to discuss estimation methods that can be used when some but not all of the random effects and regressor independence assumptions, are violated. Because current methods are limited in various ways, we will also present a list of open problems and suggest solutions for some of them. As we will show, the issue of regressor random‐effects independence has received some attention in the econometrics literature, but this important work has had little impact on current research practices in the social and behavioral sciences.