scholarly journals A latent class model for discrete choice analysis: contrasts with mixed logit

2003 ◽  
Vol 37 (8) ◽  
pp. 681-698 ◽  
Author(s):  
William H. Greene ◽  
David A. Hensher
Keyword(s):  
Discrete Choice ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Discrete Choice Analysis ◽  
Class Model ◽  
Choice Analysis

scholarly journals POSITIONING VIETNAM’S PANGASIUS CATFISH IN THE FRENCH MARKET USING DISCRETE CHOICE MODEL

2014 ◽  
Vol 17 (4) ◽  
pp. 96-111
Author(s):  
Thong Tien Nguyen ◽  
Hung Manh Nguyen
Keyword(s):  
Discrete Choice ◽  
Low Income ◽  
Latent Class ◽  
Choice Model ◽  
Latent Class Model ◽  
Random Parameter ◽  
Discrete Choice Model ◽  
Class Model ◽  
Additional Information ◽  
Random Parameter Model

The study used discrete choice model to investigate the position of Vietnam’s Pangasius catfish in the French market. Data was collected via a choice experiment designed for 12 aquaculture species familiar to French consumers. The random parameter model was estimated and used to calculate the share elasticity. The market position of the aquaculture products in this study was calculated based on the competitive clout, vulnerability scores, and ranked-order implicit values. The results show that Vietnam’s Pangasius has a low competitive clout, high vulnarability score, and low ranked-order implicit value. A latent class model was also estimated for comparison and acquisition of additional information. A strong segment of Pangasius (11.9%) is described by low income and education consumers, women at mid-age dominated, and family with children. To improve the Pangasius position and image in the international market, Vietnam needs promotional and marketing campaigns at global level for the product.

scholarly journals Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pengpeng Jiao ◽  
Meiqi Liu ◽  
Jin Guo
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Location Choice ◽  
Rapid Development ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Integrated Model ◽  
Model Framework ◽  
Class Model ◽  
Residence Location

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods.

scholarly journals A Discrete Choice Analysis Of Smallholder Farmers' Preferences And Willingness To Pay For Weather Derivatives: Evidence From Ethiopia

2014 ◽  
Vol 30 (6) ◽  
pp. 1671 ◽  
Author(s):  
Davide Castellani ◽  
Laura Vigan0 ◽  
Belaynesh Tamre
Keyword(s):  
Willingness To Pay ◽  
Financial Markets ◽  
Discrete Choice ◽  
Smallholder Farmers ◽  
Mixed Logit ◽  
Discrete Choice Analysis ◽  
Promising Alternative ◽  
Administrative Costs ◽  
Choice Analysis ◽  
Rational Choices

The ability of Ethiopian farmers to deal with rainfall risk is scanty due to the extension of land plots and incomplete and inefficient financial markets. Traditional drought insurance is flawed by information asymmetries, high administrative costs, and non-diversifiable risks. Insurance based on indexes is a promising alternative. Working on 120 rural households, we estimate the willingness to pay for a drought weather derivative through a mixed logit model allowing for random preferences. The results suggest that the premium, indemnity, and perceived frequency of drought are important determinants of the take-up. Apparent inconsistencies in behavior can be interpreted as rational choices.

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment

10.2196/22841 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e22841
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

Background Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint)

2020 ◽  
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

BACKGROUND Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, <i>P</i> value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, <i>P</i>&lt;.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, <i>P</i>&lt;.001; class 3: OR 1.958, 95% CI 1.769-2.167, <i>P</i>&lt;.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, <i>P</i>=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

scholarly journals Are Consumers Willing to Pay a Premium for Pure Rice Noodles? A Study of Discrete Choice Experiments in Taiwan

2020 ◽  
Vol 12 (15) ◽  
pp. 6144
Author(s):  
Yu-Hui Chen ◽  
Kai-Han Qiu ◽  
Kang Ernest Liu ◽  
Chun-Yuan Chiang
Keyword(s):  
Willingness To Pay ◽  
Marketing Strategy ◽  
Discrete Choice ◽  
Metropolitan Area ◽  
Latent Class ◽  
Corn Starch ◽  
Latent Class Model ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Class Model

Most consumers in Taiwan have never eaten pure rice noodles (PRNs) and some may mistakenly treat corn starch-based rice noodles as PRNs. This study examines consumers’ willingness to pay (WTP) for PRNs using discrete choice (DC) experiments with a blind tasting test to understand consumers’ ability to identify PRNs with varying rice content on the basis of their appearance and taste. Collecting data from the Taipei metropolitan area, our DC experimental results of both pre- and post-experiment conditions show that Taiwanese consumers do prefer PRNs and their WTP for PRNs was strengthened. A latent class model highlights that attribute preferences tend to differ by group and thus rice content ratios should be properly labeled so that consumers can make a better choice according to their preferences. Our WTP estimates also imply that offering tasting trials to consumers is an effective marketing strategy to encourage potential purchases of PRNs for the rice noodle industry.

scholarly journals Non–Small Cell Lung Cancer Patient Preferences for First-Line Treatment: A Discrete Choice Experiment

2020 ◽  
Vol 5 (1) ◽  
pp. 238146832092220
Author(s):  
Joanna P. MacEwan ◽  
Komal Gupte-Singh ◽  
Lauren M. Zhao ◽  
Karen L. Reckamp
Keyword(s):  
Lung Cancer ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Small Cell ◽  
Small Cell Lung ◽  
First Line ◽  
Class Model ◽  
Treatment Delays

Background. There has been much innovation in the treatment of non–small cell lung cancer (NSCLC) in recent years. In particular, use of immuno-oncology (IO) therapies has been growing. Methods. Patients with NSCLC in the United States were surveyed online using a discrete choice experiment to elicit first-line (1L) treatment preferences across six treatment attributes: survival, adverse events (AEs), mechanism of action (MOA), subsequent treatment options (STOs), genetic testing treatment delay, and out-of-pocket cost (OOPC). Preferences were estimated using a latent-class model. Preference shares were estimated for IO-IO, IO-chemo, and chemo-like regimens. Results. Of the 199 patients who completed the survey, 55% were male, 76% were white, 19% had not begun or were on 1L treatment, and the median age was 43 years. Based on a latent-class model with 3 preference classes, 53.0% of patients considered survival and OOPC alone and were less likely to choose an option with a higher OOPC and lower survival, 12.7% of patients were likely to choose the more expensive option, and for 34.3% of patients, survival, AE risk, and treatment delays all significantly influenced choices. MOA and STOs did not significantly influence treatment choices in any preference class. Approximately 53%, 27%, and 20% of patients preferred IO-IO-like, IO-chemo-like, and chemo-like regimens in 1L, respectively. Respondents were younger, more likely to be Caucasian, and more likely to speak English than the general NSCLC patient population. Conclusions. OOPC, effectiveness, treatment delays, and safety influenced NSCLC patients’ 1L treatment decisions, and most patients preferred an IO-IO followed by IO-chemo-like regimen in 1L. Cancer treatment decisions are complex and patient preferences are unique; therefore, patients’ treatment objectives should be discussed in shared treatment decision making.

scholarly journals Accounting for Attribute Non-Attendance and Common-Metric Aggregation in the Choice of Seat Belt Use, a Latent Class Model with Preference Heterogeneity

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 84 ◽  
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Goodness Of Fit ◽  
Latent Class ◽  
Latent Class Model ◽  
Seat Belt ◽  
Mixed Logit ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Class Model ◽  
Seat Belt Use ◽  
The Right

A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadways, the majority of existing studies ignored the heterogeneity in the data and used a very standard statistical or descriptive method to identify the factors of using a seatbelt. Application of the right statistical method is of crucial importance to unlock the underlying factors of the choice being made by vehicles’ occupants. Thus, this study was conducted to identify the contributory factors to the front-seat passengers’ choice of seat belt usage, while accounting for the choice preference heterogeneity. The latent class model has been offered to replace the mixed logit model by replacing a continuous distribution with a discrete one. However, one of the shortcomings of the latent class model is that the homogeneity is assumed across a same class. A further extension is to relax the assumption of homogeneity by allowing some parameters to vary across the same group. The model could still be extended to overlay some attributes by considering attributes non-attendance (ANA), and aggregation of common-metric attributes (ACMA). Thus, this study was conducted to make a comparison across goodness of fit of the discussed models. Beside a comparison based on goodness of fit, the share of individuals in each class was used to see how it changes based on various model specifications. In summary, the results indicated that adding another layer to account for the heterogeneity within the same class of the latent class (LC) model, and accounting for ANA and ACMA would improve the model fit. It has been discussed in the content of the manuscript that accounting for ANA, ACMA and an extra layer of heterogeneity does not just improve the model goodness of fit, but largely impacts the share of class allocation of the models.

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