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.

Exploring Differences in Preference Heterogeneity Representation and Their Influence in Product Family Design

2011 ◽  
Author(s):  
Eric Sullivan ◽  
Scott Ferguson ◽  
Joseph Donndelinger
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Multinomial Logit Model ◽  
Hierarchical Bayes ◽  
Product Line ◽  
Combinatorial Design ◽  
Preference Heterogeneity ◽  
Target Market

When using conjoint studies for market-based design, two model types can be fit to represent the heterogeneity present in a target market, discrete or continuous. In this paper, data from a choice-based conjoint study with 2275 respondents is analyzed for a 19-attribute combinatorial design problem with over 1 billion possible product configurations. Customer preferences are inferred from the choice task data using both representations of heterogeneity. The hierarchical Bayes mixed logit model exemplifies the continuous representation of heterogeneity, while the latent class multinomial logit model corresponds to the discrete representation. Product line solutions are generated by each of these model forms and are then explored to determine why differences are observed in both product solutions and market share estimates. These results reveal some potential limitations of the Latent Class model in the masking of preference heterogeneity. Finally, the ramifications of these results on the market-based design process are discussed.

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 How do chocolate lovers balance taste and ethical considerations?

2016 ◽  
Vol 118 (2) ◽  
pp. 343-361 ◽  
Author(s):  
Eline Poelmans ◽  
Sandra Rousseau
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Preference Heterogeneity ◽  
Conditional Logit Model ◽  
Ethical Considerations ◽  
Class Model ◽  
Data Set ◽  
Content Type ◽  
Conditional Logit ◽  
Percent Response Rate

Purpose – The purpose of this paper is to investigate how chocolate lovers balance taste and ethical considerations when selecting chocolate products. Design/methodology/approach – The data set was collected through a survey at the 2014 “Salon du Chocolat” in Brussels, Belgium. The authors distributed 700 copies and received 456 complete responses (65 percent response rate). Choice experiments were used to estimate the relative importance of different chocolate characteristics and to predict respondents’ willingness to pay for marginal changes in those characteristics. The authors estimate both a conditional logit model and a latent class model to take possible preference heterogeneity into account. Findings – On average, respondents were willing to pay 11 euros more for 250 g fairtrade labeled chocolate compared to conventional chocolate. However, taste clearly dominates ethical considerations. The authors could distinguish three consumer segments, each with a different tradeoff between taste and fairtrade. One group clearly valued fairtrade positively, a second group valued fairtrade to a lesser extent and a third group did not seem to value fairtrade. Originality/value – Chocolate can be seen as a self-indulgent treat where taste is likely to dominate other characteristics. Therefore it is unsure to what extent ethical factors are included in consumer decisions. Interestingly the results indicate that a significant share of chocolate buyers still positively value fairtrade characteristics when selecting chocolate varieties.

scholarly journals Farmer preference for improved corn seeds in Chiapas, Mexico: A choice experiment approach

2017 ◽  
Vol 15 (3) ◽  
pp. e0116 ◽  
Author(s):  
Blanca I. Sánchez-Toledano ◽  
Zein Kallas ◽  
José M. Gil-Roig
Keyword(s):  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Multinomial Logit Model ◽  
Marketing Strategies ◽  
Preference Heterogeneity ◽  
Agricultural Policies ◽  
Class Model ◽  
Maize Seeds ◽  
Choice Variable

Appropriate technologies must be developed for adoption of improved seeds based on the farmers’ preferences and needs. Our research identified the farmers’ willingness to pay (WTP) as a key determinant for selecting the improved varieties of maize seeds and landraces in Chiapas, Mexico. This work also analyzed the farmers’ observed heterogeneity on the basis of their socio-economic characteristics. Data were collected using a semi-structured questionnaire from 200 farmers. A proportional choice experiment approach was applied using a proportional choice variable, where farmers were asked to state the percentage of preference for different alternative varieties in a choice set. The generalized multinomial logit model in WTP-space approach was used. The results suggest that the improved seed varieties are preferred over the Creole alternatives, thereby ensuring higher yields, resistance to diseases, and larger ear size. For the preference heterogeneity analyses, a latent class model was applied. Three types of farmers were identified: innovators (60.5%), transition farmers (29.4%), and conservative farmers (10%). An understanding of farmers’ preferences is useful in designing agricultural policies and creating pricing and marketing strategies for the dissemination of quality seeds.

scholarly journals Domestic airport passenger access mode choice decisions in a multi-airport region of South Africa

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Stephen Carstens
Keyword(s):  
South Africa ◽  
Willingness To Pay ◽  
Latent Class ◽  
Mode Choice ◽  
Latent Class Model ◽  
Preference Heterogeneity ◽  
Access Time ◽  
Class Model ◽  
Access Cost ◽  
Access Mode

The ground access mode used by air passengers to an airport has a vital impact on infrastructural and environmental decisions. An important aspect of a passenger’s mode choice is the sensitivity to factors such as access time and access cost. The objective of this research was to analyse air passenger’s sensitivity to access mode choice attributes, that is,access time, access cost, parking time and parking cost at two airports in Johannesburg, South Africa. A stated choice experiment was used to obtain the information and a latent class model was estimated. In general, discrete choice experiments are designed to reveal respondent(preference) heterogeneity and the latent class model allows for this heterogeneity to be modelled discretely. The estimated results indicated that three latent classes provided the best fit with preference heterogeneity evident from the set of parameter estimates. The access mode used was found to be the only significant covariate in the class assignment model. The respondents’ willingness to pay for a reduction in access time was estimated and it indicated that respondents had the highest access time willingness-to-pay value for the taxi as access mode. In addition, it was estimated that passengers being dropped off at the airport had a higher access time willingness-to-pay than passengers that used their own vehicles to the airport. The research results confirmed the presence of respondent heterogeneity (according to access mode) which resulted in different access time willingness-to-pay values.

scholarly journals Latent Class Model with Heterogeneous Decision Rule for Identification of Factors to the Choice of Drivers’ Seat Belt Use

Computation ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 44
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Decision Rule ◽  
Latent Class ◽  
Latent Class Model ◽  
Seat Belt ◽  
Decision Rules ◽  
Discrete Distribution ◽  
Parameter Estimates ◽  
Vehicle Type ◽  
Seat Belt Use ◽  
A Minor

The choice of not buckling a seat belt has resulted in a high number of deaths worldwide. Although extensive studies have been done to identify factors of seat belt use, most of those studies have ignored the presence of heterogeneity across vehicle occupants. Not accounting for heterogeneity might result in a bias in model outputs. One of the main approaches to capture random heterogeneity is the employment of the latent class (LC) model by means of a discrete distribution. In a standard LC model, the heterogeneity across observations is considered while assuming the homogeneous utility maximization for decision rules. However, that notion ignores the heterogeneity in the decision rule across individual drivers. In other words, while some drivers make a choice of buckling up with some characteristics, others might ignore those factors while making a choice. Those differences could be accommodated for by allowing class allocation to vary based on various socio-economic characteristics and by constraining some of those rules at zeroes across some of the classes. Thus, in this study, in addition to accounting for heterogeneity across individual drivers, we accounted for heterogeneity in the decision rule by varying the parameters for class allocation. Our results showed that the assignment of various observations to classes is a function of factors such as vehicle type, roadway classification, and vehicle license registration. Additionally, the results showed that a minor consideration of the heterogeneous decision rule resulted in a minor gain in model fits, as well as changes in significance and magnitude of the parameter estimates. All of this was despite the challenges of fully identifying exact attributes for class allocation due to the inclusion of high number of attributes. The findings of this study have important implications for the use of an LC model to account for not only the taste heterogeneity but also heterogeneity across the decision rule to enhance model fit and to expand our understanding about the unbiased point estimates of parameters.

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