This study undertook a head‐to‐head comparison of best‐worst, best‐best and ranking discrete choice experiments (DCEs) to help decide which method to use if moving beyond traditional single‐best ...DCEs. Respondents were randomized to one of three preference elicitation methods. Rank‐ordered (exploded) mixed logit models and respondent‐reported data were used to compare methods and first and second choices. First choices differed from second choices and preferences differed between elicitation methods, even beyond scale and scale dynamics. First choices of best‐worst had good choice consistency, scale dynamics and statistical efficiency, but this method's second choices performed worst. Ranking performed best on respondent‐reported difficulty and preference; best‐best's second choices on statistical efficiency. All three preference elicitation methods improve efficiency of data collection relative to using first choices only. However, differences in preferences between first and second choices challenge moving beyond single‐best DCE. If nevertheless doing so, best‐best and ranking are preferred over best‐worst DCE.
Labeled discrete choice experiments (DCEs) commonly present all alternatives using a full choice set design (FCSD), which could impose a high cognitive burden on respondents. In the setting of ...employment preferences, this study explored if a partial choice set design (PCSD) reduced cognitive burden whilst maintaining convergent validity compared with a FCSD. Respondents' preferences between the two designs were investigated. In the experimental design, labeled utility functions were rewritten into a single generic utility function using label dummy variables to generate an efficient PCSD with 3 alternatives shown in each choice task (out of 6). The DCE was embedded in a nationwide survey of 790 Australian pharmacy degree holders where respondents were presented with both a block of FCSD and PCSD tasks in random order. The PCSD's impact on error variances was investigated using a heteroscedastic conditional logit model. The convergent validity of PCSD was based on the equality of willingness‐to‐forgo‐expected‐salary estimates from Willingness‐to‐pay‐space mixed logit models. A nested logit model was used combined with respondents' qualitative responses to understand respondents' design preferences. We show a promising future use of PCSD by providing evidence that PCSD can reduce cognitive burden while satisfying convergent validity compared to FCSD.
The active involvement levels of breast cancer patients (BCP) in treatment decisions range from 10% to 81%. We investigated the involvement levels of 179 Israeli breast cancer survivors, aged 30 to ...81, in the choice of hospital, surgeon, surgical procedure, chemotherapy, and radiotherapy. High involvement was documented in location decisions (operating hospital 77%, chemotherapy facility 82%) as opposed to low involvement in treatment decisions (chemotherapy 30%–38%, radiotherapy 25%, surgical procedure 31%). Involvement was influenced by hospital and surgeon reputation, education level, and income. BCPs actively choose their treating facility and staff, and then follow their doctor’s recommendations.
Almost without exception, everything human beings undertake involves a choice. In recent years there has been a growing interest in the development and application of quantitative statistical methods ...to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit and mixed logit. This primer will be an invaluable resource to students as well as of immense value to consultants and professionals, researchers and anyone else interested in choice analysis and modelling.
Abstract Background Respiratory syncytial virus (RSV) circulation dropped markedly early in the COVID-19 pandemic, followed by a resurgence with heightened case counts. The “immunity debt” hypothesis ...proposes that the RSV-naїve pediatric population increased during the period of low transmission. However, the evidence supporting this hypothesis is limited, and the role of changing testing practices in the perceived surge has not been comprehensively evaluated. Methods We conducted a multicenter, retrospective analysis of 342 530 RSV encounters and 980 546 RSV diagnostic tests occurring at 32 US pediatric hospitals in 2013–2023. We used interrupted time series analysis to estimate pandemic-associated changes in RSV patient and test volume and to quantify changes in the proportions of patients requiring hospitalization, intensive care, or mechanical ventilation. We quantified the fraction of the shifts in case counts and in the age of diagnosed patients attributable to changes in testing. Results RSV patient volume increased 2.4-fold (95% confidence interval CI: 1.7, 3.5) in 2021–2023 relative to the pre-pandemic phase and was accompanied by an 18.9-fold increase (95% CI: 15.0, 23.9) in RSV test volume. Shifts in patient volume and in patient age were largely attributable to increased testing. The proportions of patients with RSV that required hospitalization, intensive care, or mechanical ventilation declined significantly across all patient age groups. Conclusions A surge in RSV testing, rather than in viral circulation, likely underlies the increased case counts observed in 2021–2023. These findings warrant a critical assessment of the immunity debt hypothesis and highlight the importance of considering the testing denominator when surveillance strategies are dynamic.
•New data-driven framework for enhancing choice models.•Conservation of standard DCM interpretability while increasing predictive power.•Systematic utility divided into a knowledge-driven and a ...data-driven part.•Demonstration of framework’s effectiveness on the MNL and NL models.•New choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models.•Experiments on publicly available datasets based on revealed or stated preferences.
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.
There is no unified theory that can explain both voter choice and where choices come from. Hinich and Munger fill that gap with their model of political communication based on ideology.
Rather than ...beginning with voters and diffuse, atomistic preferences, Hinich and Munger explore why large groups of voters share preference profiles, why they consider themselves "liberals" or "conservatives." The reasons, they argue, lie in the twin problems of communication and commitment that politicians face. Voters, overloaded with information, ignore specific platform positions. Parties and candidates therefore communicate through simple statements of goals, analogies, and by invoking political symbols. But politicians must also commit to pursuing the actions implied by these analogies and symbols. Commitment requires that ideologies be used consistently, particularly when it is not in the party's short-run interest.
The model Hinich and Munger develop accounts for the choices of voters, the goals of politicians, and the interests of contributors. It is an important addition to political science and essential reading for all in that discipline.
"Hinich and Munger's study of ideology and the theory of political choice is a pioneering effort to integrate ideology into formal political theory. It is a major step in directing attention toward the way in which ideology influences the nature of political choices." --Douglass C. North
". . . represents a significant contribution to the literature on elections, voting behavior, and social choice." --Policy Currents
Melvin Hinich is Professor of Government, University of Texas. Michael C. Munger is Associate Professor of Political Science, University of North Carolina.
Choice Set Size Shapes Self-Expression Cheek, Nathan N.; Schwartz, Barry; Shafir, Eldar
Personality & social psychology bulletin,
02/2023, Letnik:
49, Številka:
2
Journal Article
Recenzirano
Across six studies (total N = 3,549), we find that participants who were randomly assigned to choose from larger assortments thought their choices were more self-expressive, an effect that emerged ...regardless of whether larger sets actually enabled participants to better satisfy their preferences. Studies examining the moderating role of choice domain and cultural context show that the effect of choice set size on perceived self-expression may be particular to contexts in which choices have some initial potential to express choosers’ identities. We then test novel predictions from this theoretical perspective, finding that self-expression mediates the effect of choice set size on choice satisfaction, the likelihood of publicly sharing choices, and the perceived importance of choices. Together, these studies show that choice set size shapes perceived self-expression and illustrate how this meaning-based theoretical lens provides both novel explanations for existing effects and novel predictions for future research.
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