DOI: 10.1007/s11336-005-1495-y ITEM RANDOMIZED-RESPONSE MODELS FOR MEASURING NONCOMPLIANCE: RISK-RETURN PERCEPTIONS, SOCIAL INFLUENCES, AND SELF-PROTECTIVE RESPONSES

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. Key words: randomized response, item response theory, cheating, concomitant variable, sensitive behavior, efficiency

Compromise and Attraction Effects under Prevention and Promotion Motivations

This article examines the influence of consumers’ motivational orientations on their susceptibilities to context effects. Prevention‐focused consumers were found to be more sensitive to the compromise effect and less sensitive to the attraction effect than promotion‐focused consumers. In addition, the effects of promotion and prevention motivations were amplified when consumers were asked to justify their choices. Finally, we found that products associated with a prevention concern are more attractive when presented as compromise than asymmetrically dominant options, whereas products associated with a promotion concern are more attractive when presented as asymmetrically dominant options than compromise options

Solving and Testing for Regressor-Error (in)Dependence When no Instrumental Variables are Available: With New Evidence for the Effect of Education on Income

This paper has two main contributions. Firstly, we introduce a new approach, the latent instrumental variables (LIV) method, to estimate regression coefficients consistently in a simple linear regression model where regressor-error correlations (endogeneity) are likely to be present. The LIV method utilizes a discrete latent variable model that accounts for dependencies between regressors and the error term. As a result, additional ‘valid’ observed instrumental variables are not required. Furthermore, we propose a specification test based on Hausman (1978) to test for these regressor-error correlations. A simulation study demonstrates that the LIV method yields consistent estimates and the proposed test-statistic has reasonable power over a wide range of regressor-error correlations and several distributions of the instruments.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47579/1/11129_2005_Article_1177.pd

Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43547/1/11336_2001_Article_958.pd

Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model

In 2004 the Dutch Department of Social Affairs conducted a survey to assess the extent of noncompliance with social security regulations. The survey was conducted among 870 recipients of social security benefits and included a series of sensitive questions about regulatory noncompliance. Due to the sensitive nature of the questions the randomized response design was used. Although randomized response protects the privacy of the respondent, it is unlikely that all respondents followed the design. In this paper we introduce a model that allows for respondents displaying self-protective response behavior by consistently giving the nonincriminating response, irrespective of the outcome of the randomizing device. The dependent variable denoting the total number of incriminating responses is assumed to be generated by the application of randomized response to a latent Poisson variable denoting the true number of rule violations. Since self-protective responses result in an excess of observed zeros in relation to the Poisson randomized response distribution, these are modeled as observed zero-inflation. The model includes predictors of the Poisson parameters, as well as predictors of the probability of self-protective response behavior.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS135 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Impact of Research Design on the Compromise Effect

This work investigates the impact of research design on the results of the compromise effect, using meta-analytic evidence. The findings suggest that experimental characteristics have a major impact on the obtained extremeness aversion results, while sample characteristics have little impact. We discuss implications and methodological recommendations based on our analysis

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47160/1/11002_2005_Article_BF00999208.pd

Modeling Methods for Discrete Choice Analysis

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47225/1/11002_2004_Article_138116.pd