• Journal of Korean Society of Transportation
• /
• v.28 no.4
• /
• pp.119-128
• /
• 2010

Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.

• Communications for Statistical Applications and Methods
• /
• v.27 no.1
• /
• pp.15-35
• /
• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.

• Proceedings of the KSR Conference
• /
• 2004.10a
• /
• pp.1588-1594
• /
• 2004

This study discovers the variables in the railway safety management which give impacts to safety cognition of the employees. Next we do factor analysis to find out latent variables which include safety variables and regression analysis to measure the explanation power of the latent variables. We got the results that expertness latent variable explains the variation of the accidents more than other latest variables

• The Korean Journal of Applied Statistics
• /
• v.30 no.4
• /
• pp.555-566
• /
• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• Communications for Statistical Applications and Methods
• /
• v.24 no.2
• /
• pp.173-191
• /
• 2017

This study develops a new type of latent class analysis (LCA) in order to explain the associations between one latent variable and several other categorical latent variables. Our model postulates that the prevalence of the latent variable of interest is affected by another latent variable composed of other several latent variables. For the parameter estimation, we propose deterministic annealing EM (DAEM) to deal with local maxima problem in the proposed model. We perform simulation study to demonstrate how DAEM can find the set of parameter estimates at the global maximum of the likelihood over the repeated samples. We apply the proposed LCA model in an investigation of the effect of and joint patterns for drug-using behavior to violent behavior among US high school male students using data from the Youth Risk Behavior Surveillance System 2015. Considering the age of male adolescents as a covariate influencing violent behavior, we identified three classes of violent behavior and three classes of drug-using behavior. We also discovered that the prevalence of violent behavior is affected by the type of drug used for drug-using behavior.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.1 s.45
• /
• pp.231-242
• /
• 2007

This study was conducted to identify the relation of bottleneck and satisfaction factors in Korean succession companies. The final goal of this paper is finding some strategies and supporting system for Korean succession companies. According to the results of the study, we found four latent variables of the cause variables and two latent variables of the result variables. Three latent variables of the cause variables have an direct effect on two latent variables of the result variables.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.3
• /
• pp.939-945
• /
• 2014

In most of mobility enhancing plans for the transportation disabled, decisions for the investment priorities are firstly made by the facilities that have lower installation rate or lower satisfaction rate; the decisions are made without analyzing the importance factor (path loading factor) between the facility installation rate and the satisfaction rate together. In this study, a novel method of finding causality between the exogenous latent variables and the endogenous latent variables is provided by using the Structural Equation Model (SEM). The results show that the most influential facilities for the transportation disabled are bus stops, crosswalks and sidewalks in order. Also, a curb height around bus stops, a smoothness of sidewalks and installation of crosswalks traffic light are identified as an important facilities for the Transportation disabled.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.165-178
• /
• 2022

In this study, to explore the psychological and biosocial characteristics of the temperament and character's latent profile group, first, the latent group was identified with the seven variables of the Temperament and Character Inventory(TCI), and second, the difference between the psychological and biosocial characteristics of three identified latent groups. A total of 287 university students participated, and the latent groups was identified through latent profile analysis, a human-centeted statistical method, using Cloninger's TCI, Cognitive Emotion Regulation Questionnaire(CERQ), Positive Affect and Negative Affect Schedule(PANAS), Composite Scale of Moriningness(CSM), Pittsburgh Sleep Qulity Index(PSQI), and Satisfaction With Life Scale(SWLS). As result, first, three latent groups were identified through latent profile analysis using the seven variables of TCI. second, significant differences were identified in CERQ, PANAS, which are psychological variables, CSM, PSQI, and SWLS, which are biosocial variables among the latent groups. In conclusion, the importance of Self-Directedness(SD), a character factor that can be developed rather than Harm-Avoidance(HA), a temperament factor from nature, was confirmed. And the necessity of follow-up studies on psychological and biosocial variables for adaptive and mature personality was discussed.

• International Journal of Highway Engineering
• /
• v.17 no.3
• /
• pp.117-124
• /
• 2015

PURPOSES : The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS : To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS : This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including ${\chi}^2/df$, GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS : Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.

• Journal of The Korean Association For Science Education
• /
• v.17 no.4
• /
• pp.481-492
• /
• 1997

The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.