• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.127-141
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• 2007

The purpose of this research was to analyze the trajectory of child's academic attainment and the effect of family socioeconomic background on the trajectory. Data were part of the Korea Youth Panel Survey 2003-2005(Middle School 2) and were analyzed by Latent Growth Curve Modeling(LGM). The degree of child's academic attainment decreased over 3 years. Socioeconomic status variables that influenced academic trajectory were family poverty, parent's attainments in scholarship, and family structure. Findings from this study suggest that societal support for low socioeconomic status families is needed for improvement of academic attainment of their children.

• The korean journal of orthodontics
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• v.49 no.3
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• pp.170-180
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• 2019

Objective: The purpose of this study was to investigate the relationship between the facial skeletal patterns and the shape of the mandibular symphysis in adults with malocclusion by using a structural equation model (SEM). Methods: Ninety adults who had malocclusion and had records of facial skeletal measurements performed using cone-beam computed tomography were selected for this study. The skeletal measurements were classified into three groups (vertical, anteroposterior, and transverse). Cross-sectional images of the mandibular symphysis were analyzed using generalized Procrustes and principal component (PC) analyses. A SEM was constructed after the factors were extracted via factor analysis. Results: Two factors were extracted from the transverse, vertical, and anteroposterior skeletal measurements. Latent variables were extracted for each factor. PC1, PC2, and PC3 were selected to analyze the variations of the mandibular symphyseal shape. The SEM was constructed using the skeletal variables, PCs, and latent variables. The SEM showed that the vertical latent variable exerted the most influence on the mandibular symphyseal shape. Conclusions: The relationship between the skeletal pattern and the mandibular symphysis was analyzed using a SEM, which showed that the vertical facial skeletal pattern had the highest effect on the shape of the mandibular symphysis.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.5
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• pp.3-10
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• 2019

This study has a purpose to profile local sectors into meaningful groups by using facilities rates of Social Overhead Capital(SOC) for daily life. Comparing SOC for daily life among the meaningful groups, the profiling and comparison results bring the comprehensive understanding about the educational facilities in local sectors. For the research purpose, this study utilized Latent Profile Analysis(LPA) by using variables such as population, road information, SOC for daily life, usage of land, possession of land, and appraised value of land from the 2018 Geographic Information System(GIS) dataset of Gwangjin-gu, where is one of the administrative district of Seoul City. Results showed that there are four latent groups of sectors among 904 local sectors(100 squared-meters sector per each) in Gwangjin-gu. By comparing the four latent groups by using LPA, the results diagnose each sector's status and help to improve the policy about educational facilities. Specifically, by using dataset for SOC of daily life, there are four groups of local sectors and each group has different features. Based on the different features of local sector groups, there can be improved management of educational facilities matching with each group's features.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• Survey Research
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• v.18 no.2
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• pp.67-98
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• 2017

The purposes of this study are to identify the latent classes of school violence depending on bullying and victimized experience by type and the influences of individual-level and school-level variables on determining these latent profiles. For these research goals, the present study utilized data from the Seoul Education Longitudinal Study(SELS) 5th wave, containing data from 2,195 middle school students who experienced school violences more than once. Multilevel latent profile models were applied to examine school violence among students. Our results indicated that there were four latent classes; high-level bullying and victimized group (1.7%), mainly bullying group(2.1%), mainly victimized group(3.7%), verbal bullying and victimized group(92.5%). Gender, resilience, self-control, peer relationship, parental relationship were significant determinants of the latent profiles at student level. Teacher-student relationships, school violence prevention, gender ratio of school were significant determinants of the latent profiles at school level. The present study contributed to extending theoretical discussions by classifying students into groups based on frequency and different forms of bullying and victimization. Moreover, this study examined determinants of student and school level simultaneously by dealing with multilevel data.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

• 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.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.31 no.3
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• pp.237-254
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• 2021

Objectives: This study aims to classify the latent profiles in the exposure to hazard factors of salaried employees and test the determinants. Methods: Latent profile analysis(LPA) was conducted using data from the fifth Korean Working Conditions Survey(KWCS). 30,050 of salaried employees were the subjects of this study. After classifying the employees, multinomial logistic regression was used to test the determinants. Results: Salaried employees were classified with three latent profiles based on the exposure to the hazard factors. Employees included in class 1(32.8%) tend to experience low level of physical hazard factors, moderate level of psychological hazard factors, and high level of office work hazard factors. Employees included in class 2(61.8%) tend to be exposed to the moderate to high level of physical hazard factors, moderate to low level of psychological hazard factors, and low level of office work hazard factors. Employees included in class 3(5.4%) tend to experience high level of psychological and physical hazard factors, and moderate level of office work hazard factors. After classification, the demographic, health-, and employment-related variables were tested. Conclusions: This study clarified the features of each class, and proved that employees in class 3 are quite hazardous in that they are exposed to physical and psychological hazard factors much more frequently than other employees. Thus, this study can be used in predicting the high-risk employees and taking preemptive measures for preventing industrial accidents.

• Korean Journal of Social Welfare
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• v.66 no.3
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• pp.5-28
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• 2014

School absenteeism, one of the early warning signs of behavioral problems, has been known to be a complex and heterogeneous problem. However, much of the research assumes that school absentee adolescents comprise a homogeneous group. This study explored the heterogeneity of school absentee adolescents, based on school adaptation, to provide a more nuanced understanding of school absenteeism and examined predictive and risk factors associated with each typology of school absentee adolescents. Latent profile analysis was conducted using sample 477 middle school students who were reported absent in the previous year from the 3rd wave of Korean Children and Youth Panel Study. Multinomial logistic regression and ANOVA was also employed to examine the effects of predictive variables. As a result, three profiles were identified: low, middle, and high adaptive group. Group membership was found to be associated deferentially with gender, mental health, parenting neglect, delinquent friends, and delinquent behaviors. These findings propose more specific and targeted interventions designed to meet the needs and risk factors associated with the different typologies of school absentee adolescents.