Rejoinder to Fong (2011) and Lenk (2011)'s comments on ‘A comparison of generalized multinomial logit and latent class approaches to studying consumer heterogeneity with some extensions of the generalized multinomial logit model’

2011 ◽  
Vol 27 (6) ◽  
pp. 584-585
Author(s):  
Joseph Pancras ◽  
Dipak K. Dey
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Consumer Heterogeneity ◽  
Approaches To Studying

Identifying Response Styles: A Latent-Class Bilinear Multinomial Logit Model

2010 ◽  
Vol 47 (1) ◽  
pp. 157-172 ◽  
Author(s):  
Joost Van Rosmalen ◽  
Hester Van Herk ◽  
Patrick J.F. Groenen
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Response Styles

scholarly journals The Latent Class Multinomial Logit Model for Modeling Front-Seat Passenger Seatbelt Choice, Considering Seatbelt Status of Driver

2021 ◽  
Vol 1 (3) ◽  
pp. 559-569
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Logit Model ◽  
Goodness Of Fit ◽  
Latent Class ◽  
Real Life ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Point Estimate ◽  
Model Parameters ◽  
Extensive Discussion ◽  
Vehicle Type

The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings.

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