• 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.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.615-632
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• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.386-394
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• 2011

Recently it is difficult to draw an improvement for customer satisfaction because the ratio of satisfied customers increases in customer satisfaction survey. In addition, the effectiveness of practical application of customer satisfaction survey decreases due to its constitution limitation on its data analysis. In order to solve these problems, it is necessary to develop a novel research to identify the strategy meanings and find dissatisfied factors of satisfied customers using the satisfied customers' reclassification. This study focuses on the satisfied customer segmentation based on Latent Class Analysis (LCA). The case study with high-speed internet service customers show that the satisfied customers are divided into three subgroups using LCA 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 as well as improving customer satisfaction management.

• 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.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.17-39
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• 2020

The purpose of this study is to analyze the mathematical beliefs of students and teachers by Latent Class Analysis(LCA). This study surveyed 60 teachers about beliefs of 'nature of mathematics', 'mathematic teaching', 'mathematical ability' and also asked 1850 students about beliefs of 'school mathematics', 'mathematic problem solving', 'mathematic learning' and 'mathematical self-concept'. Also, this study classified each student and teacher into a class that are in a similar response, analyzed the belief systems and built a profile of the classes. As a result, teachers were classified into three types of belief classes about 'nature of mathematics' and two types of belief classes about 'teaching mathematics' and 'mathematical ability' respectively. Also, students were classfied into three types of belief classes about 'self concept' and two types of classes about 'School Mathematics', 'Mathematics Problem Solving' and 'Mathematics Learning' respectively. This study classified the mathematics belief systems in which students were categorized into 9 categories and teachers into 7 categories by LCA. The belief categories analyzed through these inductive observations were found to have statistical validity. The latent class analysis(LCA) used in this study is a new way of inductively categorizing the mathematical beliefs of teachers and students. The belief analysis method(LCA) used in this study may be the basis for statistically analyzing the relationship between teachers' and students' beliefs.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.721-732
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• 2018

Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.485-506
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• 2020

The purpose of this study is to analyze of the effect in Mathematics Teachers beliefs on their students beliefs by Latent Class Regression Model (LCRM). For this analysis, the study used the findings and surveys of Kang, Hong (2020) who developed a belief profile by analyzing the mathematical beliefs of 60 high school teachers and 1,850 second-year high school students learning from them through the Latent Class Analysis (LCA). As a result It was observed that 'Nature of Mathematics', 'Mathematic Teaching' and 'Mathematical Ability' of mathematics teachers beliefs influence the mathematical beliefs of students. The teacher's belief of 'Nature of Mathematics' statistically significant effects on students' beliefs in 'School Mathematics', 'Problem Solving', 'Mathematics Learning'. The teacher's belief of 'Teaching Mathematics', 'Mathematical Ability' statistically significant effects on students' beliefs in 'School Mathematics', 'Problem Solving', 'Self-Concept'. The results of this study can give a preview of the phenomenon in which teacher's mathematical beliefs are reproduced into student's mathematical beliefs. In addition, the results of observation of this study can be used to the contents that can achieve the purpose of reorientation for mathematics teachers.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.113-123
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• 2017

The purpose of this study is to investigate engineering students' attitude toward convergence and relevance with engineering education accreditation, gender, and academic year and attitude toward convergence. To be specific, fist, we examined whether the instrument for measuring attitudes toward convergence were reliable and valid for engineering students. Second, we compared levels of attitudes toward convergence in terms of engineering education accreditation, gender and academic year. Third, latent classes, which were distinguished in terms of attitudes toward convergence, were identified. Participants were 2076 engineering students. By using factor analysis and Rasch analysis, validity and reliability of instrument measuring attitudes toward convergence were confirmed. The differences in attitude toward convergence in terms of engineering education accreditation experience, gender, and academic year were examined by independent t-test and ANOVA. There were significant differences in attitude towards convergence in terms of engineering education accreditation, gender, and academic year. Students who experience engineering education accreditation program and male and high academic year have higher levels of attitude toward convergence than others. Lastly latent class analysis (LCA) was conducted to identify subgroups underlying engineering students in terms of attitude toward convergence and five latent classes were identified. In addition, the chi-square results showed that there were significant relationships between identified latent classes and engineering education accreditation, gender, and academic year. Based on these results, engineering education considering students' characteristics and diversity in attitude toward convergence were discussed.

• 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.

• Journal of Korean Academy of Nursing
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• v.49 no.6
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• pp.701-712
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• 2019

Purpose: Despite the high drinking rates and the complexity of drinking behaviors in adolescents, insufficient attention has been paid to their drinking patterns. Therefore, we aimed to identify patterns of adolescent drinking behaviors and factors predicting the distinct subgroups of adolescent drinking behaviors. Methods: We analyzed nationally representative secondary data obtained in 2017. Our final sample included 24,417 Korean adolescents who had consumed at least one glass of alcohol in their lifetime. To investigate patterns of drinking behaviors, we conducted a latent class analysis using nine alcohol-related characteristics, including alcohol consumption levels, solitary drinking, timing of drinking initiation, and negative consequences of drinking. Furthermore, we investigated differences in demographics, mental health status, and characteristics of substance use across the latent classes identified in our study. To do so, we used the PROC LCA with COVARIATES statement in the SAS software. Results: We identified three latent classes of drinking behaviors: current non-drinkers (CND), binge drinkers (BD), and problem drinkers (PD). Compared to the CND class, both BD and PD classes were strongly associated with higher academic year, lower academic performance, higher levels of stress, suicidal ideation, lifetime conventional or electronic cigarette use, and lifetime use of other drugs. Conclusion: Health professionals should develop and implement intervention strategies targeting individual subgroups of drinking behaviors to obtain better outcomes. In particular, health professionals should consider different characteristics across subgroups of adolescent drinking behaviors when developing the interventions, such as poor mental health status and other substance use among binge and problem drinkers.