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.     

Ethno/racial affiliation,trust, and identification in higher education: the mediating role of perceived injustice in the classroom

Social Psychology of Education - The present study contributes to a vast body of empirical research on the importance of the sense of justice in education. It examines the mediating role of...     

Group heterogeneity and tolerance: The moderating role of conservation values

Does diversity promote tolerance? We propose that reactions to group heterogeneity depend on individual value priorities. In three studies we investigate how values moderate the effect of raising the salience of group heterogeneity (versus homogeneity) on tolerance. As hypothesized, the findings indicate that conservation values moderate the effects of the saliency of the group's heterogeneity. People high on conservation values were more tolerant when the group's homogeneity was made salient than when its heterogeneity was made salient. People low on conservation were either insensitive to information on the group's heterogeneity (Studies 1 and 2), or were more tolerant when the group's heterogeneity was made salient than when its homogeneity was made salient (Study 3). Values are also directly associated with tolerance: conservation values (as well as SDO and RWA in Study 3) were negatively associated whereas openness-to-change and self-transcendence values were positively associated with tolerance. These findings indicate the importance of integrating individual and contextual variables in theories of group processes.     

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.     

Immigrant identity of Israeli adolescents from Ethiopia and the former USSR: culture‐specific principles of organization

The study examines how a sample of 210 high‐school immigrant students (ages 14–15) from Ethiopia and the former USSR socially represent their notion of what klitat aliyah (successful adaptation to Israel) means. Prevalent relevant theories—Berry's model of Acculturation Tendencies (BAT), Social Identity Theory (SIT) and Social Comparison Theory (SCT)—suggest three kinds of patterns by which minority or socially weak groups deal with these kinds of situations. These underlying patterns were tested by a 47‐item questionnaire (with a 4‐point Likert‐like scale), constructed from immigrant narratives regarding their klitat aliyah according to SIT, SCT and BAT categories of adaptation strategies. A Guttman non‐parametric Similarity Structure Analysis (SSA) revealed four‐facet organization of items for both males and females in both sub‐samples. These facets, which reflected social representations of Israeli society, were dubbed: Extended Identity, Rivalry Identity, Secluded Identity and Identity Loss. Results did not confirm the underlying categorization of strategies suggested by SIT and SCT, and partially replicated those suggested by BAT. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

Internet access and use among homeless and indigent drug users in Long Beach, California.

There has been a growing interest in using the Internet to access a variety of populations for prevention education and health care. It is not clear that this is viable for homeless and other marginalized populations. The purpose of this study was to describe Internet access and use among a sample of homeless and indigent drug users. A brief questionnaire was developed to assess computer ownership, use, email, and Internet access and use. Participants were recruited through a community-based human immunodeficiency virus (HIV) prevention program. 265 participants completed the questionnaire. While 55% had ever used a computer in their lifetime, only 25% had ever owned a computer, 24% had ever had their own email account, 33% had access to a working computer in the last 30 days, 10% had sent or received email in the last 30 days, and 19% had surfed or accessed information on the Internet in the last 30 days. Logistic regression predicting lifetime use of a computer showed predictive factors of having completed high school (Odds Ratio [OR] = 4.5; 95% Confidence Interval [CI] = 2.41, 8.38) and number of days used amphetamines in the last 30 days, per 5 days of use (OR = 1.64; 95% CI = 1.18, 2.27). Inversely related were age, per 5 years of age (OR = 0.78; 95% CI = 0.65, 0.93) and number of days used marijuana in the last 30 days, per 5 days of use (OR = 0.97; 95% CI = 0.76, 0.99). Very low access and usage suggest that Internet-based public health education models be reexamined for this population. The association with amphetamine use may indicate a facilitating effect of amphetamine by heightening awareness and increasing wakefulness.