scholarly journals Revealing Additional Dimensions of Preference Heterogeneity in a Latent Class Mixed Multinomial Logit Model

2011 ◽  
Author(s):  
William H. Greene ◽  
David A. Hensher
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Preference Heterogeneity ◽  
Mixed Multinomial Logit Model

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 Older Adults’ Choice of Patterns of Outdoor Physical Activity Duration: A Mixed Multinomial Logit Model

2021 ◽  
Vol 18 (15) ◽  
pp. 8199
Author(s):  
Zhengying Liu ◽  
Wenli Huang ◽  
Yuan Lu ◽  
You Peng
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Exercise Duration ◽  
Multinomial Logit ◽  
Activity Levels ◽  
Activity Duration ◽  
Mixed Multinomial Logit Model ◽  
Physical Activity Duration

Outdoor physical activity duration is a key component of outdoor physical activity behavior of older adults, and therefore, an important determinant of their total physical activity levels. In order to develop a successful outdoor physical activity program, it is important to identify any heterogeneity in preferences for outdoor physical activity duration patterns among older adults. In addition, more insight is needed in the influence of environmental characteristics on duration choice for creating supportive neighborhood environments matching individuals’ preferences. To this end, a mixed multinomial logit model is estimated based on one-week data collected among 336 respondents aged 60 or over in 2017 in Dalian, China. The present model formulation accounts for heterogeneity in individuals’ preferences and allows for the analysis of substitution and complementary relationships between the different patterns of outdoor physical activity duration. Results indicate that older adults vary significantly in their preferences for each outdoor physical activity duration pattern. Moreover, short walking duration, short exercise duration and medium exercise duration are substitutes for medium walking duration while short walking duration and short exercise duration are complements for medium exercise duration in terms of individuals’ outdoor physical activity duration preferences. In addition, we find that distance to the nearest park, footpath conditions and neighborhood aesthetics are associated with older adults’ outdoor physical activity duration choice.

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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