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.

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.

scholarly journals A Comparison of Mixture Modeling Approaches in Latent Class Models With External Variables Under Small Samples

2017 ◽  
Vol 78 (6) ◽  
pp. 925-951 ◽  
Author(s):  
Unkyung No ◽  
Sehee Hong
Keyword(s):  
Sample Size ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixture Modeling ◽  
Small Sample ◽  
Outcome Variable ◽  
Parameter Estimates ◽  
Class Model ◽  
Modeling Approaches ◽  
Distal Outcome

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the three-step approaches even in poorly manipulated conditions indicate the advantage of these approaches.

Relating Latent Class Assignments to External Variables: Standard Errors for Correct Inference

2014 ◽  
Vol 22 (4) ◽  
pp. 520-540 ◽  
Author(s):  
Zsuzsa Bakk ◽  
Daniel L. Oberski ◽  
Jeroen K. Vermunt
Keyword(s):  
Latent Class ◽  
Asymptotic Variance ◽  
Latent Class Model ◽  
Monte Carlo Study ◽  
Correction Method ◽  
Standard Errors ◽  
Classification Error ◽  
Parameter Estimates ◽  
Science Literature ◽  
External Variables

Latent class analysis is used in the political science literature in both substantive applications and as a tool to estimate measurement error. Many studies in the social and political sciences relate estimated class assignments from a latent class model to external variables. Although common, such a “three-step” procedure effectively ignores classification error in the class assignments; Vermunt (2010, “Latent class modeling with covariates: Two improved three-step approaches,” Political Analysis 18:450–69) showed that this leads to inconsistent parameter estimates and proposed a correction. Although this correction for bias is now implemented in standard software, inconsistency is not the only consequence of classification error. We demonstrate that the correction method introduces an additional source of variance in the estimates, so that standard errors and confidence intervals are overly optimistic when not taking this into account. We derive the asymptotic variance of the third-step estimates of interest, as well as several candidate-corrected sample estimators of the standard errors. These corrected standard error estimators are evaluated using a Monte Carlo study, and we provide practical advice to researchers as to which should be used so that valid inferences can be obtained when relating estimated class membership to external variables.

Road Traffic Violations and Seat Belt Use in Kuwait: Study of Driver Behavior in Motion

1998 ◽  
Vol 1640 (1) ◽  
pp. 17-22 ◽  
Author(s):  
P. A. Koushki ◽  
S. Y. Ali ◽  
O. Al-Saleh
Keyword(s):  
Road Traffic ◽  
Driver Behavior ◽  
Seat Belt ◽  
Time Of Day ◽  
Vehicle Type ◽  
Young Drivers ◽  
Trip Time ◽  
Traffic Violations ◽  
Seat Belt Use ◽  
Age Range

Despite heavy investments in the transportation infrastructure and the existence of a young vehicle fleet, road safety in the affluent State of Kuwait continues to decline. Poor driver behavior and lack of enforcement of traffic regulations are believed to be the main causes of the unsafe driving environment. The findings of a research project designed to examine the relationship between seat belt use and road traffic violations in Kuwait are reported. The traffic violation behavior of 821 randomly selected drivers was recorded while the drivers were followed to their destinations. Factors of nationality, age range, gender, roadway type, vehicle type, time of day, trip time, and trip distance were also monitored. The average sample nonuser of seat belts made more than twice as many violations both per kilometer of travel and per minute of trip time than did seat belt users. Seat belt nonusers and young drivers (especially Kuwaitis) were found to be overrepresented in the violating groups, and discriminant analysis successfully discriminated between the high and low violators of traffic rules.

Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators

2004 ◽  
Vol 12 (1) ◽  
pp. 3-27 ◽  
Author(s):  
Annabel Bolck ◽  
Marcel Croon ◽  
Jacques Hagenaars
Keyword(s):  
Latent Variables ◽  
Latent Class ◽  
Latent Class Model ◽  
Measurement Model ◽  
Latent Structure ◽  
Categorical Variables ◽  
Parameter Estimates ◽  
Common Source ◽  
Systematic Bias ◽  
Structural Part

We study the properties of a three-step approach to estimating the parameters of a latent structure model for categorical data and propose a simple correction for a common source of bias. Such models have a measurement part (essentially the latent class model) and a structural (causal) part (essentially a system of logit equations). In the three-step approach, a stand-alone measurement model is first defined and its parameters are estimated. Individual predicted scores on the latent variables are then computed from the parameter estimates of the measurement model and the individual observed scoring patterns on the indicators. Finally, these predicted scores are used in the causal part and treated as observed variables. We show that such a naive use of predicted latent scores cannot be recommended since it leads to a systematic underestimation of the strength of the association among the variables in the structural part of the models. However, a simple correction procedure can eliminate this systematic bias. This approach is illustrated on simulated and real data. A method that uses multiple imputation to account for the fact that the predicted latent variables are random variables can produce standard errors for the parameters in the structural part of the model.

The Evolutionary Dynamics of Decision Rule Selection

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Positive Feedback ◽  
Party System ◽  
Evolutionary Dynamics ◽  
Decision Rules ◽  
Past Performance ◽  
Party Leaders ◽  
Track Record ◽  
Long Run ◽  
Survival Of The Fittest

This chapter extends the survival-of-the-fittest evolutionary environment to consider the possibility that new political parties, when they first come into existence, do not pick decision rules at random but instead choose rules that have a track record of past success. This is done by adding replicator-mutator dynamics to the model, according to which the probability that each rule is selected by a new party is an evolving but noisy function of that rule's past performance. Estimating characteristic outputs when this type of positive feedback enters the dynamic model creates new methodological challenges. The simulation results show that it is very rare for one decision rule to drive out all others over the long run. While the diversity of decision rules used by party leaders is drastically reduced with such positive feedback in the party system, and while some particular decision rule is typically prominent over a certain period of time, party systems in which party leaders use different decision rules are sustained over substantial periods.

Endogenous Parties, Interaction of Different Decision Rules

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Decision Rules ◽  
Party Competition ◽  
Competitive Environment ◽  
Party Leaders ◽  
Robert Axelrod

This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either the same decision rule or some quite different rule. The chapter is most interested in how a decision rule performs against anything the competitive environment might throw against it, including agents using decision rules that are difficult to anticipate and/or comprehend.

scholarly journals Consumer Preferences for Private Label Brand vs. National Brand Organic Juice and Eggs: A Latent Class Approach

2021 ◽  
Vol 13 (13) ◽  
pp. 7028
Author(s):  
Ellen J. Van Loo ◽  
Fien Minnens ◽  
Wim Verbeke
Keyword(s):  
Orange Juice ◽  
Latent Class ◽  
Consumer Preferences ◽  
Latent Class Model ◽  
Organic Food ◽  
Food Products ◽  
Organic Production ◽  
Private Label ◽  
National Brand ◽  
Major Barrier

Many retailers have expanded and diversified their private label food product assortment by offering premium-quality private label food products such as organic products. With price being identified as the major barrier for organic food purchases, private label organic food products could be a suitable and more affordable alternative for many consumers. While numerous studies have examined consumer preferences for organic food, very few organic food studies have incorporated the concept of private labels. This study addresses this research gap by studying consumer preferences and willingness to pay for national brand and private label organic food using a latent class model. Specifically, this study analyzes consumer preferences for organic eggs and orange juice and the effect of national branding versus private label. Findings show heterogeneity in consumer preferences for production method and brand, with three consumer segments being identified based on their preferences for both juice and eggs. For eggs, about half of the consumers prefer private label and organic production, whereas one-quarter clearly prefers organic, and another quarter is indifferent about the brand and the organic production. For orange juice, the majority (75%) prefer the national brand. In addition, one-quarter of the consumers prefers organic juice, and about one-third values both organic and the national brand.

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