A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential components of microeconomic theory including convex indifference curves, constrained utility maximization, demand functions, et cetera. This paper presents a new stochastic MDS procedure called MICROSCALE that attempts to operationalize many of these traditional microeconomic concepts. First, we briefly review several existing MDS models that operate on paired comparisons data, noting the particular nature of the utility functions implied by each class of models. These utility assumptions are then directly contrasted to those of microeconomic theory. The new maximum likelihood based procedure, MICROSCALE, is presented, as well as the technical details of the estimation procedure. The results of a Monte Carlo analysis investigating the performance of the algorithm as a number of model, data, and error factors are experimentally manipulated are provided. Finally, an illustration in consumer psychology concerning a convenience sample of thirty consumers providing paired comparisons judgments for some fourteen brands of over-the-counter analgesics is discussed.     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.     

Decision-making in rectal and colorectal cancer: systematic review and qualitative analysis of surgeons’ preferences

Surgeons are experiencing difficulties implementing recommendations not only owing to incomplete, confusing or conflicting information but also to the increasing involvement of patients in decisions relating to their health. This study sought to establish which common factors including heuristic factors guide surgeons’ decision-making in colon and rectal cancers. We conducted a systematic literature review of surgeons’ decision-making factors related to colon and rectal cancer treatment. Eleven of 349 identified publications were eligible for data analyses. Using the IRaMuTeQ (Interface of R for the Multidimensional Analyses of Texts and Questionnaire), we carried out a qualitative analysis of the significant factors collected in the studies reviewed. Several validation procedures were applied to control the robustness of the findings. Five categories of factors (i.e. patient, surgeon, treatment, tumor and organizational cues) were found to influence surgeons’ decision-making. Specifically, all decision criteria including biomedical (e.g. tumor information) and heuristic (e.g. surgeons’ dispositional factors) criteria converged towards the factor ‘age of patient’ in the similarity analysis. In the light of the results, we propose an explanatory model showing the impact of heuristic criteria on medical issues (i.e. diagnosis, prognosis, treatment features, etc.) and thus on decision-making. Finally, the psychosocial complexity involved in decision-making is discussed and a medico-psycho-social grid for use in multidisciplinary meetings is proposed.     

An analytic tableau calculus for a temporalised belief logic

A tableau is a refutation-based decision procedure for a related logic, and is among the most popular proof procedures for modal logics. In this paper, we present a labelled tableau calculus for a temporalised belief logic called TML⁺, which is obtained by adding a linear-time temporal logic onto a belief logic by the temporalisation method of Finger and Gabbay. We first establish the soundness and the completeness of the labelled tableau calculus based on the soundness and completeness results of its constituent logics. We then sketch a resolution-type proof procedure that complements the tableau calculus and also propose a model checking algorithm for TML⁺ based on the recent results for model checking procedures for temporalised logics. TML⁺ is suitable for formalising trust and agent beliefs and reasoning about their evolution for agent-based systems. Based on the logic TML⁺, the proposed labelled tableau calculus could be used for analysis, design and verification of agent-based systems operating in dynamic environments.     

Inferring latent class lexicographic rules from choice data

A lexicographic rule orders multi-attribute alternatives in the same way as a dictionary orders words. Although no utility function can represent lexicographic preference over continuous, real-valued attributes, a constrained linear model suffices for representing such preferences over discrete attributes. We present an algorithm for inferring lexicographic structures from choice data. The primary difficulty in using such data is that it is seldom possible to obtain sufficient information to estimate individual-level preference functions. Instead, one needs to pool the data across latent clusters of individuals. We propose a method that identifies latent clusters of subjects, and estimates a lexicographic rule for each cluster. We describe an application of the method using data collected by a manufacturer of television sets. We compare the predictions of the model with those obtained from a finite-mixture, multinomial-logit model.     

Probabilistic Subset Conjunction

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only main effects or only interaction effects. This results in a confounding of the main and interaction effects when there is little or no response error. With greater response error, a logistic regression, even if it gives a good fit to data, can produce parameter estimates that do not reflect the underlying decision process. The authors propose a model in which the binary classification of alternatives into acceptable/unacceptable categories is based on a probabilistic implementation of a subset-conjunctive process. The satisfaction of decision criteria biases the odds toward one outcome or the other. The authors then describe a two-stage choice model in which a (possibly large) set of alternatives is first reduced using a subset-conjunctive rule, after which an alternative is selected from this reduced set of items. They describe methods for estimating the unobserved consideration probabilities from classification and choice data, and illustrate the use of the models for cancer diagnosis and consumer choice. They report the results of simulations investigating estimation accuracy, incidence of local optima, and model fit. The authors thank the Editor, the Associate Editor, and three anonymous reviewers for their constructive suggestions, and also thank Asim Ansari and Raghuram Iyengar for their helpful comments. They also thank Sawtooth Software, McKinsey and Company, and Intelliquest for providing the PC choice data, and the University of Wisconsin for making the breast-cancer data available at the machine learning archives.     

Bayesian factor analysis for multilevel binary observations

Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropolis-Hastings methods can be used to perform Bayesian inference, model checking and model comparison without the need for multidimensional numerical integration. We illustrate the proposed estimation methods using three simulation studies and an application involving student's achievement results in different areas of mathematics. The authors thank Ian Westbury, University of Illinois at Urbana Champaign for kindly providing the SIMS data for the application.     

Examination of the Hierarchical Structure of the Brief COPE in a French Sample: Empirical and Theoretical Convergences

This study aimed to determine whether the various factors of coping as measured by the Brief COPE could be integrated into a more parsimonious hierarchical structure. To identify a higher structure for the Brief COPE, several measurement models based on prior theoretical and hierarchical conceptions of coping were tested. First, confirmatory factor analysis (CFA) results revealed that the Brief COPE's 14 original factors could be represented more parsimoniously with 5 higher order dimensions: problem-solving, support-seeking, avoidance, cognitive restructuring, and distraction (N = 2,187). Measurement invariance across gender was also shown. Second, results provided strong support for the cross-validation and the concurrent validity of the hierarchical structure of the Brief COPE (N = 584). Results indicated statistically significant correlations between Brief COPE factors and trait anxiety and perceived stress. Limitations and theoretical and methodological implications of these results are discussed.     

Voice and persuasion in a banking telemarketing context

Voice has been neglected in research on advertising and persuasion. The present study examined the influence of voice and sex on the credibility of the voice source in a banking telemarketing context as well as with regards to the attitude toward the advertisement, and subjects' behavioral intention. An experiment using voices of a man and a woman was conducted. A recorded mock-telemarketing message consisted of an advertisement for an ATM card offered by a Canadian bank. Subjects were undergraduate students (N=399; 71.6% women, 28.4% men; M age=26.5 yr., SD = 7.4). They completed a questionnaire after hearing the message in telemarketing conditions. Analysis indicated a moderate intensity, an unmarked intonation, and a fast speech rate are associated with a more credible source than the other combinations. Sex was not a significant moderator in the relationship between voice characteristics and source credibility. Voice characteristics significantly affected attitudes toward the advertisement and behavioral intention.