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In this article, I show how item response models can be used to capture multiple response processes in psychological applications. Intuitive and analytical responses, agree-disagree answers, response refusals, socially desirable responding, differential item functioning, and choices among multiple options are considered. In each of these cases, I show that the response processes can be measured via pseudoitems derived from the observed responses. The estimation of these models via standard software programs that allow for missing data is also discussed. The article concludes with two detailed applications that illustrate the prevalence of multiple response processes. (Contains 9 tables and 6 figures.)

In a number of psychological studies, answers to reasoning vignettes have been shown to result from both intuitive and deliberate response processes. This paper utilizes a psychometric model to separate these two response tendencies. An experimental application shows that the proposed model facilitates the analysis of dual-process item responses and the assessment of individual-difference factors, as well as conditions that favor one response tendency over another one.

Loss aversion and reference dependence are 2 keystones of behavioral theories of choice, but little is known about their underlying cognitive processes. We suggest an additional account for loss aversion that supplements the current account of the value encoding of attributes as gains or losses relative to a reference point, introducing a value construction account. Value construction suggests that loss aversion results from biased evaluations during information search and comparison processes. We develop hypotheses that identify the influence of both accounts and examine process-tracing data for evidence. Our data suggest that loss aversion is the result of the initial direct encoding of losses that leads to the subsequent process of directional comparisons distorting attribute valuations and the final choice. (Contains 7 figures, 2 footnotes and 8 tables.)

Recent research has suggested that perceived control and a person's perceptions of their neighborhood environment may mediate the association between socioeconomic status (SES) and health. This cross-sectional study assessed whether perceptions of informal social control mediated the association between SES and self-reported health, and if these two constructs represented distinct mechanisms linking SES with self-reported health. The sample consisted of 869 adults residing in 300 census tracts in Montreal, Canada. Multilevel methods were used to assess the associations among self-reported health, SES, perceived control, and perceived informal social control adjusting for sociodemographic variables. Perceived control (mediation estimate=-0.16, p less than 0.001) and perceived informal social control (mediation estimate=-0.05, p less than 0.05) partially mediated the association between SES and self-reported health. Perceived control did not mediate the association of perceived informal social control with self-reported health. Perceived informal social control may act alongside but distinct from perceived control as a mechanism linking SES to self-reported health. (Contains 3 tables and 1 figure.)

Randomized response (RR) is a well-known method for measuring sensitive behavior. Yet this method is not often applied because: (i) of its lower efficiency and the resulting need for larger sample sizes which make applications of RR costly; (ii) despite its privacy-protection mechanism the RR design may not be followed by every respondent; and (iii) the incorrect belief that RR yields estimates only of aggregate-level behavior but that these estimates cannot be linked to individual-level covariates. This paper addresses the efficiency problem by applying item randomized-response (IRR) models for the analysis of multivariate RR data. In these models, a person parameter is estimated based on multiple measures of a sensitive behavior under study which allow for more powerful analyses of individual differences than available from univariate RR data. Response behavior that does not follow the RR design is approached by introducing mixture components in the IRR models with one component consisting of respondents who answer truthfully and another component consisting of respondents who do not provide truthful responses. An analysis of data from two large-scale Dutch surveys conducted among recipients of invalidity insurance benefits shows that the willingness of a respondent to answer truthfully is related to the educational level of the respondents and the perceived clarity of the instructions. A person is more willing to comply when the expected benefits of noncompliance are minor and social control is strong.

Current psychometric models of choice behavior are strongly influenced by Thurstone's (1927, 1931) experimental and statistical work on measuring and scaling preferences. Aided by advances in computational techniques, choice models can now accommodate a wide range of different data types and sources of preference variability among respondents induced by such diverse factors as person-specific choice sets or different functional forms for the underlying utility representations. At the same time, these models are increasingly challenged by behavioral work demonstrating the prevalence of choice behavior that is not consistent with the underlying assumptions of these models. I discuss new modeling avenues that can account for such seemingly inconsistent choice behavior and conclude by emphasizing the interdisciplinary frontiers in the study of choice behavior and the resulting challenges for psychometricians.

This paper presents a hierarchical Bayes circumplex model for ordinal ratings data. The circumplex model was proposed to represent the circular ordering of items in psychological testing by imposing inequalities on the correlations of the items. We provide a specification of the circumplex, propose identifying constraints and conjugate priors for the angular parameters, and accommodate theory-driven constraints in the form of inequalities. We investigate the performance of the proposed MCMC algorithm and apply the model to the analysis of value priorities data obtained from a representative sample of Dutch citizens.

L. L. Thurstone's (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart summarizes the model selection process. The authors show how to embed these models within a more familiar structural equation modeling (SEM) framework. The different special cases of Thurstone's model can be estimated with a popular SEM statistical package, including factor analysis models for paired comparisons and rankings. Only minor modifications are needed to accommodate both types of data. As a result, complex models for comparative judgments can be both estimated and tested efficiently.

Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent Markov models to allow for the analysis of rating data that are collected at arbitrary points in time. This extension offers new ways of investigating change processes by focusing explicitly on the durations that are spent in latent states. In an experience sampling application the author shows that such duration analyses can provide valuable insights about chronometric features of emotions.

Presents a synthesis of the nominal categories model of R. Bock (1972) and the choice model for mixed-effects analysis of rank-ordered data by R. Luce (1959). Shows that the proposed ranking model is both parsimonious and flexible in accounting for preference heterogeneity as well as fixed and random effects of covariates. Discusses relationships to other approaches. (SLD)

Presents a hierarchical framework for the analysis of paired comparison data with three response categories that allow judges to be indifferent or undecided. The approach is viewed as a stochastic representation of the semiorder of R. Luce (1956). Illustrates the usefulness of this multilevel approach through the analysis of a survey study. (SLD)

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach. (Author/SLD)

This paper presents two probabilistic models based on the logistic and the normal distribution for the analysis of dependencies in individual paired comparison judgments such as might occur in educational research. Three applications illustrate the approach. (SLD)

A reparameterization of a latent class model is presented to classify and scale nomial and ordered categorical choice data simultaneously. The model extension represents a nonhomogeneous population as a mixture of homogeneous subpopulations. Simulated data and data from a magazine preference survey of 347 college students illustrate the model. (SLD)

This paper proposes a generalization of Thurstonian probabilistic choice models for analyzing both multiple preference responses and their relationships. The approach is illustrated by modeling data from two multivariate preference experiments. Preliminary data analyses show that the extension can yield an adequate representation of multivariate paired comparison data. (TJH)

A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)

The issues of soft versus hard data and segment level versus individual marketing models are discussed.

Byline: Moshe Ben-Akiva (1), Daniel Mcfadden (2), Makoto Abe (3), Ulf Bockenholt (4), Denis Bolduc (5), Dinesh Gopinath (6), Takayuki Morikawa (7), Venkatram Ramaswamy (8), Vithala Rao (9), David Revelt (10), Dan Steinberg (11) Keywords: discrete choice models; multinomial probit; simulation estimation; sample design Abstract: This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems. Author Affiliation: (1) Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139 (2) Department of Economics, University of California, 655 Evans Hall, Berkeley, CA, 94720 (3) Department of Marketing, University of Illinois at Chicago, 601 S. Morgan Street, MC 243, Chicago, IL, 60607 (4) Department of Psychology, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL, 61820 (5) Departement d'economique, Universite Laval, Pavillon J.-A. De Seve, Sainte-Foy, Quebec, G1K 7P4, Canada (6) Mercer Management Consulting, 33 Hayden Avenue, Lexington, MA, 02173 (7) Department of Civil Engineering, Nagoya University, Chikusa-ku, Nagoya, 464-01, Japan (8) NBD Bancorp, University of Michigan, 701 Tappan Street, Ann Arbor, MI, 48109 (9) Johnson Graduate School of Management, Cornell University, 529 Malott Hall, Ithaca, NY, 14853 (10) Department of Economics, University of California at Berkeley, USA (11) Department of Economics, San Diego State University, San Diego, CA, 92182 Article History: Registration Date: 22/09/2004

The use of qualitative evaluations and comparisons entailed by heuristic choice strategies may account for the occurrence of decision anomalies such as certainty effects, possibility effects, and preference reversals. The strong reduction of these effects when gambles are represented as being played many times suggests that the multiple-play representation leads to an increase in the use of integration strategies that are more quantitative. This hypothesis was supported by subjects' written justifications for choosing or refusing to play a gamble 1 or 100 times. Justifications for single-play choices tended to focus on single attributes of the gamble, whereas justifications for multiple play choices tended to reflect strategies that integrated probabilities and outcomes., Traditionally, psychological models of risky decision making have been founded upon the assumption that people employ quantitative reasoning strategies when they consider risky prospects. For example, the many variants of [...]

Physicians generated diagnostic hypotheses for case histories for which 2 types of diagnoses were plausible, with one having a higher population base rate but less severe clinical consequences than the other. The number of clinical and background symptoms pointing towards the 2 diagnoses was factorially manipulated. The order and frequency with which physicians generated hypotheses varied with the amount of relevant clinical and background information and as a function of population incidence rates, with little evidence of base rate neglect. Availability of a hypothesis, made possible by diagnosis of a similar case before, also made doctors generate this diagnosis earlier and more frequently. Physicians' experience affected hypothesis generation solely by increasing the availability of similar cases. The results are consistent with the use of similarity-based hypothesis generation processes that operate on memory for prior cases.

An analysis of a study which examined the possibility of consumer choice overload is presented. It is believed that the study would benefit from a theory-based meta-analysis that tests a conceptual model of choice's validity. The importance of further theory-guided research in identifying the theoretical underpinnings of the effect of assortment size on consumer choice is also explained.

This study explored how preference reversals, induced by changes in response mode (choice vs. pricing), are moderated by how lotteries are represented, as being played 1, 10, or 100 times. Ss chose which of a pair of gambles they preferred to play and determined the minimum selling price of each gamble. Replicating previous research, the preference reversal was obtained for single-play representations: Ss tended to choose the gambles with the higher probabilities of winning, but priced them lower. However, for multiple-play representations, preference reversals were reduced, and consistency between pricing and choice behaviors was increased. Both response modes were sensitive to differences in the expected values of gambles, but sensitivity did not vary significantly with the number of plays. These results support the hypothesis that violations of expected utility theory are reduced for multiple-play gambles.

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. [ABSTRACT FROM AUTHOR]

This paper presents finite mixture versions of integer-valued autoregressive (INAR) Poisson regression models for investigating regularity and predictability of purchase behavior over time. The approach facilitates the analysis of heterogeneity and serial correlation effects as well as conditional and marginal analyses of the effects of covariates. An application to scanner panel data of detergents yields substantive insights into sources of autodependencies in individual category purchases. Keywords: Autoregression; Binomial thinning; Count data; Finite mixture