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

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.99-107
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• 2010

Analyzing mode choice among transportation demand estimate procedures is complicated and understanding characteristics of travelers is also difficult. Generally, it is well known that traveler choose mode considering psychometric factors and characteristic besides socio-demographic indicators. Accordingly, many researches has investigated on methodology that can be applied in mode choice to reflect psychometric factor or specific preference. Latent Class Analysis among various studies is recognized as the theoretically potential approach. This study focuses on class segmented using latent class cluster to analyze impact that included psychometric factors and characteristics on mode choice. It also provides evidence that mode choice model for each class and mode choice model not considering latent class are different. This study based on citizen's stated preference and revealed preference on a new transit on the Han river shows that latent class cluster analysis is the potential approach considering latent preference.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.241-254
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• 2020

This study proposes generalized models of joint latent class analysis (JLCA) for longitudinal data in two approaches, a JLCA with latent profile (JLCPA) and a JLCA with latent transition (JLTA). Our models reflect cross-sectional as well as longitudinal dependence among multiple latent classes and track multiple class-sequences over time. For the identifiability and meaningful inference, EM algorithm produces maximum-likelihood estimates under local independence assumptions. As an empirical analysis, we apply our models to track the joint patterns of adolescent depression and anxiety among US adolescents and show that both JLCPA and JLTA identify three adolescent emotional well-being subgroups. In addition, JLCPA classifies two representative profiles for these emotional well-being subgroups across time, and these profiles have different tendencies according to the parent-adolescent-relationship subgroups.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.4
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• pp.161-184
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• 2011

As movie demands become more and more diversified, it is necessary for movie related firms to segment a whole heterogeneous market into a number of small homogeneous markets in order to identify the specific needs of consumer groups. Relevant market segmentation helps them to develop valuable offer to target segments through effective marketing planning. In this article, we introduce various segmentation methods and compare their advantages and disadvantages. In particular, we analyze "2009~2010 consumer survey data of Korean Film Industry" by using Latent Class Analysis(LCA), a statistical segmentation method which identifies exclusive set of latent classes based on consumers' responses to an observed categorical and numerical variables. It is applied PROC LCA, a new SAS procedure for conducting LCA and finally get the result of 11 distinctive clusters showing unique characteristics on their buying behaviors.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.237-243
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• 2012

The more the satisfied customers increases in customer satisfaction survey, the more the company has difficultly in improving the customer satisfaction. In addition, the effectiveness of practical application of customer satisfaction survey decreases due to its constitution limitation on its data analysis. To overcome these problems, it is necessary to develop a new method to identify the strategy meanings and find the dissatisfied factors of satisfied customers using the satisfied customers reclassification. This study focuses on the satisfied customer segmentation using Latent Class Analysis. The case study shows that the satisfied customers are divided into three subgroups using Latent Class Analysis and we draw meaning results such as satisfaction and dissatisfaction factors through analyzing each group. This study is expected to play the role as the groundwork for the revitalization of customer satisfaction survey.

• Korean Journal of Child Studies
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• v.38 no.2
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• pp.165-176
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• 2017

Objective: Delinquent peers are important predictors of adolescent delinquent behavior. Few studies have classified individuals into groups based on patterns of delinquent behavior among youth and their peers. This study identified latent groups based on adolescent-peer delinquency and examined psychosocial characteristics of each latent group. Methods: First, the study employed latent class analysis based on a nationally representative data of South Korean middle school students (N = 2,277). Both adolescent and peer delinquent behaviors comprised 13 items in the questionnaire that was self-reported by adolescents. Second, the study used multivariate regression models to analyze psychosocial symptoms of latent groups and conducted Wald tests to compare differences among latent groups. Results: Patterns of adolescent-peer delinquency were classified into six latent groups. "Mutual total delinquent group (1.2%)" showed high rates in most delinquent experiences. "Mutual status delinquent group (5.7%)" mainly experienced status delinquency, "Mutual violence delinquent group (5.3%)" showed high rates of violent delinquency. "Peer-only total high delinquent group (3.8%)" reported friends to have engaged in all types of delinquency and "Peer-only total medium delinquent group (11.8%)" reported peer involvement in multiple status and few violent delinquency. Finally, "low risk group (72.2%)" reported low rates of delinquency for themselves and their friends. Regression analysis showed that every "mutual" delinquent group presented significantly worse psychosocial problems than the "low risk group." Conclusion: Using person centered latent class analysis, this study classified six latent classes while considering both delinquent agents and various types of delinquency and investigated specific groups with greater risk of psychosocial problems.

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

• Research in Community and Public Health Nursing
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• v.30 no.4
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• pp.449-459
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• 2019

Purpose: The aim of this study is to explore different types of self-rated health trajectories among one-person households in Korea. Methods: We used five time-point data derived from Korea Health Panel (2011~2015). A latent growth curve modeling was used to assess the overall feature of self-rated health trajectory in one-person households, and a latent class growth modeling was used to determine the number and shape of trajectories. We then applied multinomial logistic regression on each class to explore the predicting variables. Results: We found that the overall slope of self-rated health in one-person households decreases. In addition, latent class analysis demonstrated three classes: 1) High-Decreasing class (i.e., high intercept, significantly decreasing slope), 2) Moderate-Decreasing class (i.e., average intercept, significantly decreasing slope), and 3) Low-Stable class (i.e., low intercept, flat and nonsignificant slope). The multinomial logistic regression analysis showed that the predictors of each class were different. Especially, one-person households with poor health condition early were at greater risk of being Low-Stable class compared with High-Decreasing class group. Conclusion: The findings of this study demonstrate that more attentions to one-person households are needed to promote their health status. Policymakers may develop different health and welfare programs depending on different characteristics of one-person household trajectory groups in Korea.

• Research in Community and Public Health Nursing
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• v.33 no.3
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• pp.299-311
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• 2022

Purpose: Untreated depression in adolescents affects their entire life. It is important to detect and intervene early depression in adolescence considering the characteristics of adolescent's depressive symptoms accompanied by internalization and externalization. The aim of this study was to identify latent classes of depressive symptom trajectories of adolescents and determinants of classes in Korea. Methods: The three time-point (2018~2020) data derived from the Korean Children and Youth Panel Survey 2018 were used (N=2,325). Latent Growth Curve Modeling (LGCM) was conducted to explore the depressive symptom trajectories in all adolescents, and Latent Class Growth Modeling (LCGM) was conducted to identify each latent class. Multinomial logistic regression analysis was performed to confirm the determinants of each latent class. Results: The LGCM results showed that there was no statistically significant change in all adolescents' depressive symptoms for 3 years. However, the LCGM results showed that four latent classes showing different trajectories were distinguished: 1) Low-stable (intercept=14.39, non-significant slope), 2) moderate-increasing (intercept=19.62, significantly increasing slope), 3) high-stable (intercept=26.30, non-significant slope), and 4) high-rapidly decreasing (intercept=26.34, significantly rapidly decreasing slope). The multinomial logistic regression analysis showed that the significant determinants (i.e., gender, self-esteem, aggression, somatization, peer relationship) of each latent class were different. Conclusion: When screening adolescent's depression, it is necessary to monitor not only direct depression symptoms but also self-esteem, aggression, somatization symptoms, and peer relationships. The findings of this study may be valuable for nurses and policy makers to develop mental health programs for adolescents.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.145-153
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• 2020

Students' engagement is very important for effect science learning. Multidimensional approaches on students' engagement defines engagement in three ways which includes cognitive, behavioral, and cognitive engagement. Based on the multidimensional approaches on students' engagement, this study identified latent groups of elementary students characterized by patterns of cognitive, behavioral, and emotional engagement in science classes. This study also compared students' perceptions of their engagement in general science classes and small-group activities by the latent groups. 377 elementary students were involved in this study. 5-scale Likert survey were used in order to investigate students' engagement in science classes. Latent class analysis using Mplus program identified 3 latent groups of students engagement in science classes: Highly engaged, moderately engaged, and minimally engaged in three ways of engagement. The mean scores of cognitive, behavioral, and emotional engagement were significantly different by three latent groups. In addition, there were significant difference in students perceptions on participating experiments activities and carefully listening of teacher among latent groups. However, there was no significant difference in students' perceptions on their actions during small-group activities. Educational implications were discussed.