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Journal articles on the topic 'Conditional Multinomial Logit'
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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (July 2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.
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For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.
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Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (September 1, 2021): 182–91. http://dx.doi.org/10.30430/70.2021.3.182-191.
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The paper investigates the influence of different model specifications for interpreting the results of discrete choice experiments when investigating heterogeneous public landscape preferences. Comparing model specifications based on the Mixed Multinomial Logit and the Generalized Multinomial Logit Model reveals that the parameter estimates appear qualitatively comparable. Still, a more in-depth investigation of the conditional estimate distributions of the sample show that parameter interactions in the Generalized Multinomial Logit Model lead to different interpretations compared to the Mixed Multinomial Logit Model. This highlights the potential impact of common model specifications in the results in landscape preference studies.
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Hoffman, Saul D., and Greg J. Duncan. "Multinomial and Conditional Logit Discrete-Choice Models in Demography." Demography 25, no. 3 (August 1988): 415. http://dx.doi.org/10.2307/2061541.
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Jarvis, Benjamin F. "Estimating Multinomial Logit Models with Samples of Alternatives." Sociological Methodology 49, no. 1 (August 30, 2018): 341–48. http://dx.doi.org/10.1177/0081175018793460.
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This comment reconsiders advice offered by Bruch and Mare regarding sampling choice sets in conditional logistic regression models of residential mobility. Contradicting Bruch and Mare’s advice, past econometric research shows that no statistical correction is needed when using simple random sampling of unchosen alternatives to pare down respondents’ choice sets. Using data on stated residential preferences contained in the Los Angeles portion of the Multi-City Study of Urban Inequality, it is shown that following Bruch and Mare’s advice—to implement a statistical correction for simple random choice set sampling—leads to biased coefficient estimates. This bias is all but eliminated if the sampling correction is omitted.
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Cook, Scott J., John Niehaus, and Samantha Zuhlke. "A warning on separation in multinomial logistic models." Research & Politics 5, no. 2 (April 2018): 205316801876951. http://dx.doi.org/10.1177/2053168018769510.
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Oppenheim et al. (2015) provides the first empirical analysis of insurgent defection during armed rebellion, estimating a series of multinomial logit models of continued rebel participation using a survey of ex-combatants in Colombia. Unfortunately, many of the main results from this analysis are an artifact of separation in these data – that is, one or more of the covariates perfectly predicts the outcome. We demonstrate that this can be identified using simple cross tabulations. Furthermore, we show that Oppenheim et al.’s (2015) results are not supported when separation is explicitly accounted for. Using a generalization of Firth’s (1993) penalized-likelihood estimator – a well-known solution for separation – we are unable to reproduce any of their conditional results. While our (re-)analysis focuses on Oppenheim et al. (2015), this problem appears in other research using multinomial logit models as well. We believe that this is both because the discussion on separation in political science has primarily focused on binary-outcome models, and because software (Stata and R) does not warn researchers about seperation in multinomial logit models. Therefore, we encourage researchers using multinomial logit models to be especially vigilant about separation, and discuss simple red flags to consider.
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Hudson, Darren, R. Karina Gallardo, and Terrill R. Hanson. "A Comparison of Choice Experiments and Actual Grocery Store Behavior: An Empirical Application to Seafood Products." Journal of Agricultural and Applied Economics 44, no. 1 (February 2012): 49–62. http://dx.doi.org/10.1017/s107407080000016x.
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In this paper we compare results from an in-store field experiment and a mail survey choice experiment (CE) to investigate CE's capacity in predicting grocery store market share. For the comparison, we used three seafood products: freshwater prawns, marine shrimp, and lobster. CE estimates were obtained via four econometric models: the conditional logit, the random parameter logit, the heteroskedastic extreme value, and the multinomial probit. We found that the level of control in the grocery store experiment and the choice of econometric model influenced the capacity of CE to predict grocery store market shares.
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Cong, Li, and Jeffrey S. Racine. "A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES." Econometric Theory 29, no. 3 (July 30, 2012): 629–41. http://dx.doi.org/10.1017/s0266466612000382.
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We propose a consistent kernel-based specification test for conditional density models when the dependent variable is categorical/discrete. The method is applicable to popular parametric binary choice models such as the logit and probit specification and their multinomial and ordered counterparts, along with parametric count models, among others. The test is valid when the conditional density function contains both categorical and real-valued covariates. Theoretical support for the test and for a bootstrap-based version of the test is provided. Monte Carlo simulations are conducted to assess the finite-sample performance of the proposed method.
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Bradley, Jonathan R., Christopher K. Wikle, and Scott H. Holan. "Spatio‐temporal models for big multinomial data using the conditional multivariate logit‐beta distribution." Journal of Time Series Analysis 40, no. 3 (April 12, 2019): 363–82. http://dx.doi.org/10.1111/jtsa.12468.
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Norets, Andriy, and Debdeep Pati. "ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES." Econometric Theory 33, no. 4 (July 13, 2016): 980–1012. http://dx.doi.org/10.1017/s0266466616000220.
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We consider a nonparametric Bayesian model for conditional densities. The model is a finite mixture of normal distributions with covariate dependent multinomial logit mixing probabilities. A prior for the number of mixture components is specified on positive integers. The marginal distribution of covariates is not modeled. We study asymptotic frequentist behavior of the posterior in this model. Specifically, we show that when the true conditional density has a certain smoothness level, then the posterior contraction rate around the truth is equal up to a log factor to the frequentist minimax rate of estimation. An extension to the case when the covariate space is unbounded is also established. As our result holds without a priori knowledge of the smoothness level of the true density, the established posterior contraction rates are adaptive. Moreover, we show that the rate is not affected by inclusion of irrelevant covariates in the model. In Monte Carlo simulations, a version of the model compares favorably to a cross-validated kernel conditional density estimator.
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Guimarães, Paulo, and Richard C. Lindrooth. "Controlling for overdispersion in grouped conditional logit models: A computationally simple application of Dirichlet‐multinomial regression." Econometrics Journal 10, no. 2 (June 20, 2007): 439–52. http://dx.doi.org/10.1111/j.1368-423x.2007.00215.x.
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Glasgow, Garrett, and Sona N. Golder. "A New Approach to the Study of Parties Entering Government." British Journal of Political Science 45, no. 4 (May 28, 2014): 739–54. http://dx.doi.org/10.1017/s0007123414000015.
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Previous studies of the factors that influence the ability of parties to join governments have estimated binary choice models using the parties as the unit of analysis, which inappropriately treats each party in a government formation opportunity as an independent observation (a problem that clustered standard errors do not solve) and does not allow researchers to control for important coalition-level effects. This article demonstrates that a preferred methodological approach is to first estimate a standard multinomial choice model (conditional logit or mixed logit) of coalition formation, using government formation opportunities as the unit of analysis and potential governments as the choice alternatives. The probabilities of parties joining governments can then be recovered by simply summing the probabilities for the potential governments that contain each party. An empirical example shows how the substantive conclusions about a party's likelihood of entering office can change depending on the methodological approach taken.
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McElroy, Gail, and Kenneth Benoit. "Party Policy and Group Affiliation in the European Parliament." British Journal of Political Science 40, no. 2 (March 23, 2010): 377–98. http://dx.doi.org/10.1017/s0007123409990469.
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Systematic empirical research has yet to explain how national parties join political groups in the European Parliament. This article first demonstrates, using original empirical measures from expert surveys of party positions, that EP party groups consist of national parties sharing similar policy positions. Secondly, using Bayesian/MCMC methods, the paper estimates the policy determinants of group affiliation using a (conditional) multinomial logit model to explain that ‘party group’ choice is largely driven by policy congruence. Finally, predictions from the model identify national parties not in their ‘ideally congruent’ EP groups. The findings suggest that the organization of and switching between EP groups is driven mainly by a concern to minimize policy incongruence between national and transnational levels.
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Gensch, Dennis H., and Sanjoy Ghose. "Elimination by Dimensions." Journal of Marketing Research 29, no. 4 (November 1992): 417–29. http://dx.doi.org/10.1177/002224379202900403.
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The well-known PRETREE model, though rich in behavioral structural theory, is virtually unused by marketing practitioners. Four problem areas relating to the application of PRETREE are identified. The authors then develop the “elimination-by-dimensions” (EBD) model to address these problems. A key feature of the EBD model is the recognition that most real-life marketing attributes are continuous dimensions, not aspects. The EBD model provides diagnostic information by labeling the specific dimensions consumers use to eliminate alternatives hierarchically. A procedure for naming the key dimensions directly from the data is suggested. Empirical information from both an industrial buying dataset and a consumer product dataset is used to compare the predicted market share estimates of the EBD model with predictions based on Tversky and Sattath's suggested conditional probability approach and the multinomial logit model. The EBD model gives substantially better market share predictions on both datasets than the conditional probability approach. Finally, using only dimensions identified by the disaggregate EBD model, the authors show individual-level predictions to be significantly above the chance level.
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Ouattara, N’Banan, Xueping Xiong, Moussa Bakayoko, Trazié Bertrand Athanase Youan Bi, Dessalegn Anshiso Sedebo, and Zié Ballo. "What Influences Rice Farmers’ Choices of Credit Sources in Côte d’Ivoire? An Econometric Analysis using the Multinomial Conditional Logit Model." Progress in Development Studies 22, no. 2 (January 9, 2022): 149–73. http://dx.doi.org/10.1177/14649934211066453.
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In Côte d’Ivoire, the low participation of smallholder farmers in the credit market remains a matter of concern. This study examines the key determinants of rice farmers’ participation in the credit market. We use a Multinomial Conditional Logit model to consider the characteristics related to the use of different credit sources. A total of 588 rice farmers were randomly sampled from seven rice areas. Our findings reveal that gender, age, education level, experience in rice farming, rice plot size, lowland rice farming, extension contact, membership of a farmer-based organization, marketing of paddy rice, and off-farm income significantly influence the use of different credit sources. While credit requirements such as saving plus collateral, kinship/friendship, membership, favoured client, loan maturity, and the distance between borrowers and lenders are credit source-specific variables that significantly determine rice farmers’ choices between different credit sources. These empirical results show that in addition to farmer-specific variables, policymakers should consider the characteristics of credit sources for developing a credit market suitable for smallholder farmers.
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WAHLBECK, PAUL J., JAMES F. SPRIGGS, and FORREST MALTZMAN. "The Politics of Dissents and Concurrences on the U.S. Supreme Court." American Politics Quarterly 27, no. 4 (October 1999): 488–514. http://dx.doi.org/10.1177/1532673x99027004006.
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Why do justices author or join separate opinions? Most attempts to address the dynamics of concurrence and dissent focus on aggregate patterns across time or courts. In contrast, we explain why an individual justice chooses to author or join a separate opinion. We argue that separate opinions result from justices' pursuit of their policy preferences within both strategic and institutional constraints. Using data from the Burger Court (1969 to 1985 terms), we estimate a multinomial logit model to test the influence of these factors on justices' decisions to join or author a regular concurrence, a special concurrence, or a dissent, as opposed to joining the majority opinion. Our results show that this choice reflects the justices' conditional pursuit of their policy preferences. We also disentangle the decision to join or author separate opinions, and we find that the latter decision is also influenced by the time remaining in the Court's term.
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Dobra, Rebecca Anne, Marco Boeri, Stuart Elborn, Frank Kee, Susan Madge, and Jane C. Davies. "Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials." BMJ Open 11, no. 3 (March 2021): e045803. http://dx.doi.org/10.1136/bmjopen-2020-045803.
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IntroductionEngaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion.Methods and analysisWe have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated.Ethics and disseminationImperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery.PROSPERO registration numberCRD42020184886.
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Jørgensen, O. A., C. Hvingel, P. R. Møller, and M. A. Treble. "Identification and mapping of bottom fish assemblages in Davis Strait and southern Baffin Bay." Canadian Journal of Fisheries and Aquatic Sciences 62, no. 8 (August 1, 2005): 1833–52. http://dx.doi.org/10.1139/f05-101.
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The bathymetry of Baffin Bay, with shallow sills both to the north and south, creates a relatively isolated body of deep polar water, unique among the Arctic Seas. During 263 trawl hauls completed during October 1999 and September to November 2001, 116 fish species were collected in Davis Strait and the southern Baffin Bay (61°44.1′ N73°52.8′ N, depths of 1451484 m). The abundance data for the 80 benthic species were used for analyses of the fish fauna diversity and fish assemblages. As a first step, seven assemblages were found by a standard type of cluster analysis. A Bayesian multinomial logit model was then applied to calculate vectors of probabilities defining the likelihood of each haul belonging to each of the seven clusters. The spatial distribution of the conditional probabilities for each cluster (assemblage) was mapped by means of a geostatistical tool. Each assemblage was further defined by indicator species, depth, and temperature. Four of the assemblages were found in Baffin Bay, two in Davis Strait and one mainly in Davis Strait but scattered into Baffin Bay.
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Yamaguchi, Kazuo. "Multigroup Segregation Analyses with Covariates." Sociological Methodology 51, no. 2 (January 6, 2021): 224–52. http://dx.doi.org/10.1177/0081175020981120.
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The author introduces methods for the decomposition analysis of multigroup segregation measured by the index of dissimilarity, the squared coefficient of variation, and Theil’s entropy measure. Using a new causal framework, the author takes a unified approach to the decomposition analysis by specifying conditions that must be satisfied to decompose segregation into unexplained and explained components. Here, the unexplained component represents the direct effects of the group variable on the conditional probability of acquiring a social position—such as a residential district in an analysis of residential segregation or an occupation in an analysis of occupational segregation—and the explained component represents indirect effects of the group variable on the outcome through covariates. The major merit of this approach is its ability to control individual-level covariates for the decomposition analysis of segregation. Two methods, one for semiparametric outcome models with the identity link function and the other for semiparametric outcome models with the multinomial logit link function, are introduced in this unified framework. The application of these methods focuses on occupational segregation among racial/ethnic groups. Father’s occupation, subject’s educational attainment, and the region of interview are included as covariates, using data from the General Social Surveys.
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Shao, Jingyu, Ruipeng Dong, and Zemin Zheng. "Sparse assortment personalization in high dimensions." JUSTC 52, no. 3 (2022): 5. http://dx.doi.org/10.52396/justc-2021-0214.
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The data-driven conditional multinomial logit choice model with customer features performs well in the assortment personalization problem when the low-rank structure of the parameter matrix is considered. However, despite recent theoretical and algorithmic advances, parameter estimation in the choice model still poses a challenging task, especially when there are more predictors than observations. For this reason, we suggest a penalized likelihood approach based on a feature matrix to recover the sparse structure from populations and products toward the assortment. Our proposed method considers simultaneously low-rank and sparsity structures, which can further reduce model complexity and improve its estimation and prediction accuracy. A new algorithm, sparse factorial gradient descent (SFGD), was proposed to estimate the parameter matrix, which has high interpretability and efficient computing performance. As a first-order method, the SFGD works well in high-dimensional scenarios because of the absence of the Hessian matrix. Simulation studies show that the SFGD algorithm outperforms state-of-the-art methods in terms of estimation, sparsity recovery, and average regret. We also demonstrate the effectiveness of our proposed method using advertising behavior data analysis.
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Antonio, Anna Liza M., Robert E. Weiss, Christopher S. Saigal, Ely Dahan, and Catherine M. Crespi. "A Bayesian hierarchical model for discrete choice data in health care." Statistical Methods in Medical Research 27, no. 12 (April 18, 2017): 3544–59. http://dx.doi.org/10.1177/0962280217704226.
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In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.
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Ngangbam, Sapana, and Archana K. Roy. "Determinants of Health-seeking Behaviour in Northeast India." Journal of Health Management 21, no. 2 (May 22, 2019): 234–57. http://dx.doi.org/10.1177/0972063419835118.
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India’s northeast region comprises eight states, which, together, is home to 3.8 per cent of the country’s population. The quality of healthcare and manpower availability remains a cause for concern in the region, affecting the overall health-seeking behaviour of the people. This study attempts to understand the determinants of utilization of healthcare services in Northeast India. Healthcare and morbidity data for this study are based on a Northeast India sample from the National Sample Survey Organization’s (NSSO’s) health consumption data (2014). Probit, multinomial and mixed conditional logit models were employed in the study. In Northeast India, uneducated, higher-aged, Schedule Castes/Schedule Tribes (SCs/STs), Muslims, rural people and district people are served less by medical institutions and because of poor road connectivity they either remain untreated or seek care at underequipped primary healthcare services, while their counterparts utilize private facilities mostly for outpatient care and either public hospital or private facilities for inpatient care. There is also a tendency to substitute alternative healthcare when the cost of an inpatient healthcare service rises. To protect the interest of marginalized people and achieve the target of accessible, affordable and quality healthcare, the government needs to strengthen the primary healthcare in rural areas and improve the quality of healthcare in urban areas without increasing the cost of treatment.
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Oluoch-Aridi, Jackline, Mary B. Adam, Francis Wafula, and Gilbert Kokwaro. "Understanding what women want: eliciting preference for delivery health facility in a rural subcounty in Kenya, a discrete choice experiment." BMJ Open 10, no. 12 (December 2020): e038865. http://dx.doi.org/10.1136/bmjopen-2020-038865.
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ObjectiveTo identify what women want in a delivery health facility and how they rank the attributes that influence the choice of a place of delivery.DesignA discrete choice experiment (DCE) was conducted to elicit rural women’s preferences for choice of delivery health facility. Data were analysed using a conditional logit model to evaluate the relative importance of the selected attributes. A mixed multinomial model evaluated how interactions with sociodemographic variables influence the choice of the selected attributes.SettingSix health facilities in a rural subcounty.ParticipantsWomen aged 18–49 years who had delivered within 6 weeks.Primary outcomeThe DCE required women to select from hypothetical health facility A or B or opt-out alternative.ResultsA total of 474 participants were sampled, 466 participants completed the survey (response rate 98%). The attribute with the strongest association with health facility preference was having a kind and supportive healthcare worker (β=1.184, p<0.001), second availability of medical equipment and drug supplies (β=1.073, p<0.001) and third quality of clinical services (β=0.826, p<0.001). Distance, availability of referral services and costs were ranked fourth, fifth and sixth, respectively (β=0.457, p<0.001; β=0.266, p<0.001; and β=0.000018, p<0.001). The opt-out alternative ranked last suggesting a disutility for home delivery (β=−0.849, p<0.001).ConclusionThe most highly valued attribute was a process indicator of quality of care followed by technical indicators. Policymakers need to consider women’s preferences to inform strategies that are person centred and lead to improvements in quality of care during delivery.
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Elewonibi, Bilikisu, Ryoko Sato, Rachel Manongi, Sia Msuya, Iqbal Shah, and David Canning. "The distance-quality trade-off in women’s choice of family planning provider in North Eastern Tanzania." BMJ Global Health 5, no. 2 (February 2020): e002149. http://dx.doi.org/10.1136/bmjgh-2019-002149.
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IntroductionStudies on the determinants of contraceptive use often consider distance to the nearest health facility offering contraception as a key explanatory variable. Women, however, may not seek contraception from the nearest facility, rather opting for a more distant facility with better quality services or to ensure greater privacy and anonymity.MethodsThe dataset used include the name of facility where each women obtained contraception, measures of facility quality, and the distance between each woman’s home and 39 potential facilities she might visit. We use a conditional-multinomial logit model to estimate the determinants of her facility choice to visit and how women tradeoff travelling longer distances to use higher quality facilities.ResultsOnly 33% of woman who received contraception from a health facility used their nearest facility. While the nearest facility was 1.2 km away, the average distance to facility used was 2.9 km, indicating women are willing to travel significantly longer distances for higher quality. Women prefer facilities that specialise in providing contraception, provide a large range of methods, do not suffer from stock outs and do not charge fees. Furthermore, on average, women are willing to travel an additional 2 km for a facility that offers more family planning methods, 4.7 km for a facility without one additional health service, 9 km for a facility without fees for contraception and 11 km for a facility not experiencing stock out of an additional contraception.ConclusionOur results suggest that quality of services provided is an important driver of facility choice in addition to distance to facility.
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Fuertes, Alberto, and Jose María Serena. "How firms borrow in international bond markets." Journal of Financial Regulation and Compliance 26, no. 1 (February 12, 2018): 135–69. http://dx.doi.org/10.1108/jfrc-11-2016-0100.
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Purpose This paper aims to investigate how firms from emerging economies choose among different international bond markets: global, US144A and Eurobond markets. The authors explore if the ranking in regulatory stringency –global bonds have the most stringent regulations and Eurobonds have the most lenient regulations – leads to a segmentation of borrowers. Design/methodology/approach The authors use a novel data set from emerging economy firms, treating them as consolidated entities. The authors also obtain descriptive evidence and perform univariate non-parametric analyses, conditional and multinomial logit analyses to study firms’ marginal debt choice decisions. Findings The authors show that firms with poorer credit quality, less ability to absorb flotation costs and more informational asymmetries issue debt in US144A and Eurobond markets. On the contrary, firms issuing global bonds – subject to full Securities and Exchange Commission requirements – are financially sounder and larger. This exercise also shows that following the global crisis, firms from emerging economies are more likely to tap less regulated debt markets. Originality/value This is, to the authors’ knowledge, the first study that examines if the ranking in stringency of regulation – global bonds have the most stringent regulations and Eurobonds have the most lenient regulations – is consistent with an ordinal choice by firms. The authors also explore if this ranking is monotonic in all determinants or there are firm-specific features which make firms unlikely to borrow in a given market. Finally, the authors analyze if there are any changes in the debt-choice behavior of firms after the global financial crisis.
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Agarwal, Smisha, Udochisom Anaba, Timothy Abuya, Richard Kintu, Alain Casseus, Sharif Hossain, Melvin Obadha, and Charlotte E. Warren. "Understanding incentive preferences of community health workers using discrete choice experiments: a multicountry protocol for Kenya, Uganda, Bangladesh and Haiti." BMJ Open 9, no. 12 (December 2019): e033601. http://dx.doi.org/10.1136/bmjopen-2019-033601.
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IntroductionThere is a renewed global interest in improving community health worker (CHW) programmes. For CHW programmes to be effective, key intervention design factors which contribute to the performance of CHWs need to be identified. The recent WHO guidelines recommends the combination of financial and non-financial incentives to improve CHW performance. However, evidence gaps remain as to what package of incentives will improve their performance in different country contexts. This study aims to evaluate CHW incentive preferences to improve performance and retention which will strengthen CHW programmes and help governments leverage limited resources appropriately.Methods and analysisA discrete choice experiment (DCE) will be conducted with CHWs in Bangladesh, Haiti, Kenya and Uganda with different levels of maturity of CHWs programmes. This will be carried out in two phases. Phase 1 will involve preliminary qualitative research including focus group discussions (FGDs) and key informant interviews to develop the DCE design which will include attributes relevant to the CHW country settings. Phase 2 will involve a DCE survey with CHWs, presenting them with a series of job choices with varying attribute levels. An orthogonal design will be used to generate the choice sets for the surveys. The surveys will be administered in locally-appropriate languages to at least 150 CHWs from each of the cadres in each country. Conditional and mixed multinomial logit (MMNL) models will be used for the estimation of stated preferences.Ethics and disseminationThis study has been reviewed and approved by the Population Council’s Institutional Review Board in New York, and appropriate ethics review boards in Kenya, Uganda, Bangladesh and Haiti. The results of the study will be disseminated through in-country dissemination workshops, meetings with country-level stakeholders and policy working groups, print media, online blogs and peer-reviewed journals.
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Oedingen, Carina, Tim Bartling, and Christian Krauth. "Public, medical professionals’ and patients’ preferences for the allocation of donor organs for transplantation: study protocol for discrete choice experiments." BMJ Open 8, no. 10 (October 2018): e026040. http://dx.doi.org/10.1136/bmjopen-2018-026040.
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IntroductionOrgan transplantation is the treatment of choice for patients with severe organ failure. Nevertheless, donor organs are a scarce resource resulting in a large mismatch between supply and demand. Therefore, priority-setting leads to the dilemma of how these scarce organs should be allocated and who should be considered eligible to receive a suitable organ. In order to improve the supply–demand mismatch in transplantation medicine, this study explores preferences of different stakeholders (general public, medical professionals and patients) for the allocation of donor organs for transplantation in Germany. The aims are (1) to determine criteria and preferences, which are relevant for the allocation of scarce donor organs and (2) to compare the results between the three target groups to derive strategies for health policy.Methods and analysisWe outline the study protocol for discrete choice experiments, where respondents are presented with different choices including attributes with varied attribute levels. They were asked to choose between these choice sets. First, systematic reviews will be conducted to identify the state of art. Subsequently, focus group discussions with the public and patients as well as expert interviews with medical professionals will follow to establish the attributes that are going to be included in the experiments and to verify the results of the systematic reviews. Using this qualitative exploratory work, discrete choice studies will be designed to quantitatively assess preferences. We will use a D-efficient fractional factorial design to survey a total sample of 600 respondents according to the public, medical professionals and patients each. Multinomial conditional logit model and latent class model will be analysed to estimate the final results.Ethics and disseminationThis study has received Ethics Approval from the Hannover Medical School Human Ethics Committee (Vote number: 7921_BO_K_2018). Findings will be disseminated through conference presentations, workshops with stakeholders and peer-reviewed journal articles.
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Nittas, Vasileios, Margot Mütsch, and Milo Alan Puhan. "Preferences for Sun Protection With a Self-Monitoring App: Protocol of a Discrete Choice Experiment Study." JMIR Research Protocols 9, no. 2 (February 14, 2020): e16087. http://dx.doi.org/10.2196/16087.
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Background The incidence of sun-exposure-related skin conditions, such as melanoma, is a gradually increasing and largely preventable public health problem. Simultaneously, the availability of mobile apps that enable the self-monitoring of health behavior and outcomes is ever increasing. Inevitably, recent years have seen an emerging volume of electronic patient-generated health data (PGHD), as well as their targeted application across primary prevention areas, including sun protection and skin health. Despite their preventive potential, the actual impact of these apps relies on the engagement of health care consumers, who are primarily responsible for recording, sharing, and using their PGHD. Exploring preferences is a key step toward facilitating consumer engagement and ultimately realizing their potential. Objective This paper describes an ongoing research project that aims to elicit the preferences of health care consumers for sun protection via app-based self-monitoring. Methods A discrete choice experiment (DCE) will be conducted to explore how healthy consumers choose between two alternative preventive self-monitoring apps. DCE development and attribute selection were built on extensive qualitative work, consisting of the secondary use of a previously conducted scoping review, a rapid review of reviews, 13 expert interviews, and 12 health care consumer interviews, the results of which are reported in this paper. Following D-optimality criteria, a fractional factorial survey design was generated. The final DCE will be administered in the waiting room of a travel clinic, targeting a sample of 200 participants. Choice data will be analyzed with conditional logit and multinomial logit models, accounting for individual participant characteristics. Results An ethics approval was waived by the Ethics Committee Zurich. The study started in September 2019 and estimated data collection and completion is set for January 2020. Five two-level attributes have been selected for inclusion in the DCE, addressing (1) data generation methods, (2) privacy control, (3) data sharing with general practitioner, (4) reminder timing, and (5) costs. Data synthesis, analysis, and reporting are planned for January and February 2020. Results are expected to be submitted for publication by February 2020. Conclusions Our results will target technology developers, health care providers, and policy makers, potentially offering some guidance on how to design or use sun-protection-focused self-monitoring apps in ways that are responsive to consumer preferences. Preferences are ultimately linked to engagement and motivation, which are key elements for the uptake and success of digital health. Our findings will inform the design of person-centered apps, while also inspiring future preference-eliciting research in the field of emerging and complex eHealth services. International Registered Report Identifier (IRRID) PRR1-10.2196/16087
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Goeken, Nils, Peter Kurz, and Winfried Steiner. "Hierarchical Bayes Conjoint Choice Models - Model Framework, Bayesian Inference, Model Selection, and Interpretation of Estimation Results." Marketing ZFP 43, no. 3 (2021): 49–66. http://dx.doi.org/10.15358/0344-1369-2021-3-49.
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Choice-based conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets). Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model due to the existence of closed form solutions for conditional choice probabilities. The popularity of CBC and the MNL model has grown even more since the introduction of hierarchical Bayesian (HB) estimation techniques that accommodate individual consumer heterogeneity in choice data, and which have now become state-of-the-art in marketing theory and practice. Still, researchers and practitioners have to make further decisions under this framework (CBC, MNL, HB estimation), such as how to represent preference heterogeneity. Here, using a normal distribution (and therefore a unimodal distribution) has become the standard approach in the marketing literature. However, the thin tails of the normal distribution suggest that the standard HB-MNL model should not be the “go-to” approach to approximate multimodal preference distributions, because individual preference patterns lying at the tails of the normal distribution (i.e., that do not fit well with the assumption of a unimodal distribution) tend to be shrunk to the population mean. This shrinkage, especially in multimodal data settings, could mask important information (e.g., new or different structures in the data). A mixture of normal distributions avoids this limited flexibility of the most simple continuous approach of assuming a unimodal prior heterogeneity distribution. There are currently two prominent HB-CBC modeling approaches embedding the mixture-of-normals (MoN) approach: the more widespread MoN-HB-MNL model, and the Dirichlet process mixture (DPM)-HB-MNL model. In this article, we review the prominent HB-MNL model (with its normal prior), the MoN-HB-MNL model, and the DPM-HB-MNL model and apply them to an empirical multi-country CBC data set. We compare the statistical performance of the three models in terms of goodness-of-fit and predictive accuracy, show how to include consumer background characteristics in the upper level of these models, and illustrate how to interpret the estimation results (with a special focus on cross-county heterogeneity). In sum, our article serves as a kind of user guide to the estimation and interpretation of Hierarchical Bayes Conjoint Choice Models. For our data, we observed that all three choice models (both with and without consumer background characteristics) resulted in a one-component solution. The DPM-HB-MNL model nevertheless yielded a higher cross-validated hit rate compared to the MoN-HB-MNL and the HB-MNL models due to its even more flexible prior assumptions. The two latter models tended to slightly overfit our empirical data, which was reflected by higher goodness-of-fit statistics but a lower predictive accuracy compared to the DPM-HB-MNL model. We showed that this result could be attributed to the weaker extent of Bayesian shrinkage of these two models. The DPM-HB-MNL model showed a stronger shrinkage effect and seems therefore somewhat more robust against overfitting. Including consumer background characteristics in terms of country of origin information for the respondents did not improve the statistical model performance (especially not the predictive performance). Still, using the country of origin information for respondents in a post-hoc segmentation analysis helped us to explain some differences in brand preferences between the five countries.
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Falcó-Gimeno, Albert. "The political geography of government formation: Why regional parties join coalitions." Party Politics 26, no. 2 (January 9, 2018): 91–106. http://dx.doi.org/10.1177/1354068817750865.
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Political parties differ in the geographic distribution of their support. This article argues that a regionalized distribution of a party’s votes facilitates its participation in government, because it produces a tendency to prioritize demands for locally targeted goods that are more conducive to the negotiation of reciprocal logrolling agreements with potential partners. Using a measure based on the Gini coefficient, I empirically evaluate the extent to which the geographic concentration of votes plays a role in the formation of governments, taking Spanish local elections from 1987 to 2011 as a test bed. With around 500 formation opportunities and 20,000 potential governments, multinomial choice models are estimated (conditional and mixed logits) and a very sizable effect is documented: A one-standard deviation increase in the electoral geographic concentration of the members of a potential government almost doubles the likelihood of its formation. These findings are relevant for students of government formation, regional parties, and political geography.
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PAN, HEPING, and LIN LIU. "FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (November 2000): 941–62. http://dx.doi.org/10.1142/s021800140000060x.
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This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
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Khodakarami, Nima, and Khalil Dirani. "Drivers of employee engagement: differences by work area and gender." Industrial and Commercial Training 52, no. 1 (February 13, 2020): 81–91. http://dx.doi.org/10.1108/ict-06-2019-0060.
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Purpose Previous studies have not integrated the impact of the area of study into the notion of employee engagement. The purpose of this study is to empirically measure the association between employee engagement and the two antecedent factors of perceived organizational support (POS) and employee loyalty across different areas of study. Design/methodology/approach A nationally representative survey of 2,408 adults in the USA collected by the worker representation and participation survey (WRPS) was used. A multinomial logit regression was used to estimate the impact of POS and loyalty across different areas of study. Findings The findings of this study consistent with the previous studies showed that POS and employee loyalty are positively and significantly associated with employee engagement. This study found by a decline in the level of support from a “lot of support” to “somewhat support,” the degree of engagement declines by about 50 per cent. Further, it found that the level of engagement changes across different areas of study. For instance, professional and skilled workers are more engaged compared to other groups of workers. The findings were similar for the variables of loyalty to supervisors and loyalty to organizations. Moreover, the findings showed that conditioned on being loyal, women are more engaged than men. Originality/value This is the first study that uses WRPS to understand how the level of engagement varies across different kinds of study.
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Han, Kyung (Chris) Tyek. "Components of the item selection algorithm in computerized adaptive testing." Journal of Educational Evaluation for Health Professions 15 (March 24, 2018): 7. http://dx.doi.org/10.3352/jeehp.2018.15.7.
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Computerized adaptive testing (CAT) greatly improves measurement efficiency in high-stakes testing operations through the selection and administration of test items with the difficulty level that is most relevant to each individual test taker. This paper explains the 3 components of a conventional CAT item selection algorithm: test content balancing, the item selection criterion, and item exposure control. Several noteworthy methodologies underlie each component. The test script method and constrained CAT method are used for test content balancing. Item selection criteria include the maximized Fisher information criterion, the b-matching method, the astratification method, the weighted likelihood information criterion, the efficiency balanced information criterion, and the KullbackLeibler information criterion. The randomesque method, the Sympson-Hetter method, the unconditional and conditional multinomial methods, and the fade-away method are used for item exposure control. Several holistic approaches to CAT use automated test assembly methods, such as the shadow test approach and the weighted deviation model. Item usage and exposure count vary depending on the item selection criterion and exposure control method. Finally, other important factors to consider when determining an appropriate CAT design are the computer resources requirement, the size of item pools, and the test length. The logic of CAT is now being adopted in the field of adaptive learning, which integrates the learning aspect and the (formative) assessment aspect of education into a continuous, individualized learning experience. Therefore, the algorithms and technologies described in this review may be able to help medical health educators and high-stakes test developers to adopt CAT more actively and efficiently.
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Lee, Myoung-jae. "Panel conditional and multinomial logit with time-varying parameters." Studies in Nonlinear Dynamics & Econometrics 19, no. 3 (January 1, 2015). http://dx.doi.org/10.1515/snde-2014-0003.
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AbstractPanel conditional logit estimators (PCLE) in the literature use mostly time-constant parameters. If the panel periods are volatile or long, however, the model parameters can change much. Hence this paper generalizes PCLE with time-constant parameters to PCLE with time-varying parameters; both static and dynamic PCLE are considered for this. The main finding is that time-varying parameters are fully allowed for static PCLE and the dynamic “pseudo” PCLE of [Bartolucci, F. and V. Nigro. 2010. “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a
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Wu, Wangwei, and Ricardo A. Daziano. "On Assignment to Classes in Latent Class Logit Models." Transportation Research Record: Journal of the Transportation Research Board, September 13, 2022, 036119812211212. http://dx.doi.org/10.1177/03611981221121266.
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Random parameter logit models address unobserved preference heterogeneity in discrete choice analysis. The latent class logit model assumes a discrete heterogeneity distribution, by combining a conditional logit model of economic choices with a multinomial logit (MNL) for stochastic assignment to classes. Whereas point estimation of latent class logit models is widely applied in practice, stochastic assignment of individuals to classes needs further analysis. In this paper we analyze the statistical behavior of six competing class assignment strategies, namely: maximum prior MNL probabilities, class drawn from prior MNL probabilities, maximum posterior assignment, drawn posterior assignment, conditional individual-specific estimates, and conditional individual estimates combined with the Krinsky–Robb method to account for uncertainty. Using both a Monte Carlo study and two empirical case studies, we show that assigning individuals to classes based on maximum MNL probabilities behaves better than randomly drawn classes in market share predictions. However, randomly drawn classes have higher accuracy in predicted class shares. Finally, class assignment based on individual-level conditional estimates that account for the sampling distribution of the assignment parameters shows superior behavior for a larger number of choice occasions per individual.
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Uzaheta, Alvaro, Viviana Amati, and Christoph Stadtfeld. "Random effects in dynamic network actor models." Network Science, February 6, 2023, 1–18. http://dx.doi.org/10.1017/nws.2022.37.
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Abstract Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial logit model. The DyNAM assumes that the parameters are constant over the actors and the context. This homogeneity assumption, albeit statistically and computationally convenient, is difficult to justify, e.g., in the presence of unobserved differences between actors or contexts. In this paper, we extend DyNAMs by including random-effects parameters that vary across actors or contexts and allow controlling for unknown sources of heterogeneity. We illustrate the model by analyzing relational events among the users of an online community of aspiring and professional digital and graphic designers.
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Zha, Dao-Shun, Tian-Tian Feng, and Jia-Jie Kong. "Effects of enterprise carbon trading mechanism design on willingness to participate——Evidence from China." Frontiers in Environmental Science 10 (August 24, 2022). http://dx.doi.org/10.3389/fenvs.2022.986862.
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At this stage, the air pollution problem represented by climate warming has become very serious, and it is important to establish a carbon emission trading system as an innovative policy tool to promote energy savings and emission reduction. China’s carbon trading market is currently in its initial stage, and only power generation enterprises are included in the market. When faced with changes in the carbon trading market mechanism, enterprises are often unable to make rapid adjustments, making it difficult to reflect the carbon market’s role in reducing emissions. Current research on the design of carbon trading mechanisms of enterprises has lacked investigation into the influence of the design of carbon trading mechanisms on the willingness of enterprises to participate from the perspective of enterprises. In this paper, we use choice experiments, a conditional logit model and a multinomial logit model to explore the impacts of changes in the design of enterprise carbon trading mechanisms on enterprise willingness to participate in carbon trading. The impacts of all attributes on the willingness to participate in national carbon trading is ranked from largest to smallest: offset mechanism > carbon product trading > government penalty > carbon quota allocation. Based on the research results, relevant policy recommendations and corporate countermeasures are proposed to improve corporate willingness to participate in carbon trading and encourage active cooperation in energy saving and emission reduction activities.
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Zhang, Xuan, Huiying Wen, Toshiyuki Yamamoto, and Qiang Zeng. "Investigating hazardous factors affecting freeway crash injury severity with real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors." Journal of Safety Research, January 2021. http://dx.doi.org/10.1016/j.jsr.2020.12.014.
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Aristei, David, Silvia Bacci, Francesco Bartolucci, and Silvia Pandolfi. "A bivariate finite mixture growth model with selection." Advances in Data Analysis and Classification, December 29, 2020. http://dx.doi.org/10.1007/s11634-020-00433-4.
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AbstractA model is proposed to analyze longitudinal data where two response variables are available, one of which is a binary indicator of selection and the other is continuous and observed only if the first is equal to 1. The model also accounts for individual covariates and may be considered as a bivariate finite mixture growth model as it is based on three submodels: (i) a probit model for the selection variable; (ii) a linear model for the continuous variable; and (iii) a multinomial logit model for the class membership. To suitably address endogeneity, the first two components rely on correlated errors as in a standard selection model. The proposed approach is applied to the analysis of the dynamics of household portfolio choices based on an unbalanced panel dataset of Italian households over the 1998–2014 period. For this dataset, we identify three latent classes of households with specific investment behaviors and we assess the effect of individual characteristics on households’ portfolio choices. Our empirical findings also confirm the need to jointly model risky asset market participation and the conditional portfolio share to properly analyze investment behaviors over the life-cycle.
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Kirui, Leonard K., Nathaniel D. Jensen, Gideon A. Obare, Isaac M. Kariuki, Philemon K. Chelanga, and Munenobu Ikegami. "Pastoral livelihood pathways transitions in northern Kenya: The process and impact of drought." Pastoralism 12, no. 1 (June 1, 2022). http://dx.doi.org/10.1186/s13570-022-00240-w.
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AbstractRecurrent droughts and long-term changes to climate, social structures, and the economy in the world’s arid and semi-arid lands have impacted pastoralists’ livelihood activities over time, resulting in different livelihood pathways. Some pastoralists continue to follow more traditional strategies of herd accumulation, while others frequently engage in livestock markets and value addition activities, and others still are dropping out of pastoralism. Using data collected over 6 years from 924 households in northern Kenya and applying a generalized structural dynamic multinomial logit model, this study quantitatively determined the dynamic transitions between livelihood categories conditional on drought incidences. From the results, there were considerable and frequent transitions between livelihood pathways within the panel period (2009–2015). Notably, many households that started in the low-cash income, larger herd size category denoted as hanging in, were transitioning to lower cash income with small herds (dropping out). At the same time, there was a great deal of back and forth between the category with low-cash income and small herd size (dropping out) and the category with higher cash income and small herd size (moving out), indicating that moving out was the only way out of poverty. Also, an increase in vegetation index from a drought season where the index was at a 10% level to a good season where the level was 90% decreased the likelihood of households dropping out from a predicted probability of 37.9 to 28.7% and increased the likelihood of moving up and moving out from 22.2 to 25.0% and 22.6 to 34.3%, respectively, unconditionally. The study findings imply that any livelihood interventions aimed at reducing the impact of drought and alleviating poverty among pastoral households should support the transition to market-oriented, relatively successful pathways and also protect households from falling back into the ranks of poverty by dropping out.
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Howard, Kirsten, Kate Anderson, Joan Cunningham, Alan Cass, Julie Ratcliffe, Lisa J. Whop, Michelle Dickson, et al. "What Matters 2 Adults: a study protocol to develop a new preference-based wellbeing measure with Aboriginal and Torres Strait Islander adults (WM2Adults)." BMC Public Health 20, no. 1 (November 17, 2020). http://dx.doi.org/10.1186/s12889-020-09821-z.
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Abstract Background Understandings of health and wellbeing are culturally bound. Many Aboriginal and Torres Strait Islander people perceive wellbeing and quality of life (QOL) differently from the Western biomedical models of health underpinning existing QOL instruments. Any instrument to measure the wellbeing of Aboriginal and Torres Strait Islander people should be culturally appropriate and safe, include relevant dimensions, and be informed by their own values and preferences. Existing QOL instruments do not meet these standards. This study will generate a new preference-based wellbeing measure, WM2Adults, for Aboriginal and Torres Strait Islander adults, underpinned by their values and preferences. Methods A mixed methods approach will be used; we will employ decolonising methodologies, privilege Aboriginal and Torres Strait Islander voices and perspectives, and adopt a strengths-based approach rather than a deficit lens. Yarning Circles will be conducted with Aboriginal and Torres Strait Islander people across Australia. A candidate item pool will be developed from these data, on which psychometric analysis and validity testing will be undertaken to develop a descriptive system. Following finalisation of the descriptive system, wellbeing states will be valued using a quantitative preference-based approach (best-worst scaling) with a diverse sample of Aboriginal and Torres Strait Islander adults (n = 1000). A multinomial (conditional) logit framework will be used to analyse responses and generate a scoring algorithm for the new preference-based WM2Adults measure. Discussion The new wellbeing measure will have wide applicability in assessing the effectiveness and cost-effectiveness of new programs and services for Aboriginal and Torres Strait Islander people. Results will be disseminated through journals, conferences and policy forums, and will be shared with Aboriginal and Torres Strait Islander communities, organisations and research participants.
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Girotra, Saket, Mary S. Vaughan-Sarrazin, Peter Cram, and Ioana Popescu. "Abstract P153: Factors Influencing Admissions to America's Lowest Performing Hospitals for AMI Care." Circulation: Cardiovascular Quality and Outcomes 4, suppl_1 (November 2011). http://dx.doi.org/10.1161/circoutcomes.4.suppl_1.ap153.
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Background Research has identified a group of hospitals with consistently poor performance for acute myocardial infarction (AMI) care as compared to other US hospitals. Factors influencing patients' selection of these hospitals are unknown. Methods We used 2004-06 Hospital Compare data to identify hospitals reporting AMI process performance measures. We calculated composite AMI performance scores and classified hospitals as low performing (lowest score decile for all 3 years, n = 100), top performing (top score decile for all 3 years, n = 64) and intermediate (all others, n = 2595). We used 2004-05 Medicare data to identify AMI patients living within 5 miles of low performing hospitals and alternative hospitals within 30 miles of patient residence. We used conditional multinomial logit models to evaluate the likelihood of choosing a low performing hospital over alternative hospitals. Models accounted for distance from patient residence to available hospitals, provision of revascularization (based on Medicare CABG volumes), and patient characteristics including age, race and residential social disadvantage (derived from US Census data). Results AMI patients admitted to low performing hospitals were older (mean age 80 vs. 79, p <.01), more likely female (56% vs. 51%, p < .01). These patients were more likely to live in zip codes with high (top quartile) social disadvantage (46% vs. 34%, p<.01) and closer to a low performing hospital (1.6 vs. 2.9 miles, p < .01). In multivariate models adjusting for distance and the presence of hospital revascularization services, patients were overall less likely to be admitted to low performing hospitals over alternative hospitals (HR 0.28, 95%CI 0.25 - 0.31, p < .01). However, patients living in areas with high disadvantage were relatively more likely to be admitted to low performing hospitals as compared to patients from intermediate and low disadvantage areas (HR 2.72 95% CI 2.24 - 3.30, p < .01), even after adjusting for age, race, and sex. Conclusion AMI patients are less likely to seek care at low performing hospitals except patients who live in zip code areas with high social disadvantage. Disincentives for poor performance against low performing hospitals could adversely impact care for this vulnerable population.
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Moreno, Jorge O., Cinthya G. Caamal-Olvera, and Edgar M. Luna. "Mobility and sustainable transportation in higher education: evidence from Monterrey Metropolitan Area in Mexico." International Journal of Sustainability in Higher Education, March 29, 2022. http://dx.doi.org/10.1108/ijshe-07-2021-0276.
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Purpose This paper aims to analyze the demand for mobility in higher education to understand the critical elements of students' mobility and the potential impact of accessing sustainable alternatives. The demand for different means of transportation accounts for socioeconomic variables such as household income and size, gender, age, among other travel factors such as distance, time, speed and direct cost. Design/methodology/approach This research uses a unique data set encompassing a representative sample of households across gender and municipalities in the Monterrey Metropolitan Area in Mexico. This paper uses the value of transportation time savings approach to estimate the derived demand for mobility in higher education across different means. The empirical strategy uses a linear utility framework in a multinomial non-ordered logit model to analyze the conditional selection of transportation as a function of sociodemographics, direct monetary costs and travel determinants such as travel time, distance and speed. Findings Men students are more likely to use an unsustained transportation mode such as a car or cab than women. Income is a crucial determinant for transportation demand. There is a negative relationship between household income and walking or riding the bus but positive with driving a car. An increase in the time of the trip decreases the probability of walking while increasing the likelihood of using a car or riding the bus to school. Students who feel safe while riding a bus are more likely to use the bus or walk as transportation than using a car. Finally, having access to better quality sidewalks significantly incentive walking over other transportation options. Practical implications Results show that individuals would almost always choose private vehicles because of the personal comforts and time savings. Social implications Understanding the demand for higher education and access to sustainable transportation might provide new insights into campus planning and development. Access to public transportation options can reduce the travel costs for students with lower incomes and enlarge options for students who have to commute longer distances by car. Sustainable transportation access plays a role in promoting equality and economic development. Originality/value This study’s results bring new light on how transportation becomes an essential component of the opportunity cost of college education and could define its role in promoting equality or increasing inequality. This study’s finding is of particular interest for cities where housing location seems to be independent of access to school, work or other amenities.
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Cole, Katrina, Jason Rydberg, and Michael Cassidy. "Going Off the Grid: Assessing Sentencing Departures for Individuals Convicted of Sexual Offenses." Crime & Delinquency, November 26, 2020, 001112872097745. http://dx.doi.org/10.1177/0011128720977451.
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This research examines factors associated with departures from sentencing guidelines for a sample of individuals convicted of sexual offenses in Pennsylvania between 2004 and 2015 ( N = 26,093). We contribute to the literature on the sentencing of these cases by considering whether the impact of individual and contextual factors is conditioned by the type of sexual offense being sentenced (e.g., assaultive, child pornography, failure to comply with registration). Bayesian multilevel multinomial logit models suggested a number of legal, extralegal, and contextual correlates of departure likelihood, and indicated that the effects of offense severity, prior record, a previous sex offense conviction, multiple concurrent convictions, and court size vary across different subsets of sexual convictions.