• Korean Journal of Child Studies
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• v.27 no.3
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• Journal of Korean Society of Rural Planning
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• v.14 no.3
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• pp.63-73
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• 2008

The purpose of this study is to analyze the determinants of the digital divide at individual level and regional level in Korea, considering interaction between individual and the regional variables. Following results are obtained. First, individual level digital devide in the 16 different regions has been found in terms of Internet use, implying the needs for further analysis on impact of the regional factor in individual Internet use. Second, the result finds the impact of level-l individual variables, "gender, age, education, income and jobs" on digital divide, significantly at level 10% level. Third, the regional variables influencing the individual digital divide were not found at state level. However, regional factors might affect digital devide at county level. Study suggest some plans to reduce digital divide. First, the digital devide at individual level should be remedied by focusing on neglected class of people. Second, we need to approach the digital divide by analyzing in more detail, reflecting interactions of the regional variables and individual variables. Third, we should come up with a policy for mending the digital divide at regional level.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.627-644
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• 2009

In the knowledge-based economy highlighting the importance of human capital, there has been a growing interest in human capital externalities as a fundamental engine of growth and development of a region. The purpose of this study is to analyze human capital externalities using 3-level hierarchical linear model(3-HLM), decomposing determinants of wages into three levels involving workers(level-1) nested within firms(level-2) nested within regions(level-3). This study separately estimates the effect of the average education level on the wages by three different schooling groups on the assumption that the intensity of knowledge spillovers varies with each group's schooling level. The main results are as follows; First, the coefficient of the average education level of a region shows 0.044, indicating that one-year increase in the average level of schooling could increase average individual earnings by 4.4%. Secondly, the external effects of human capital on three different schooling groups are considerably different, raising less than high school graduates' wages by 3.0%, college graduates' wages by 4.7%, and graduate schools' wages by 11.8%, respectively. Thirdly, well educated workers are much more sensitive to the variation of the regional education level than less educated ones when we apply the shares of each schooling group as alternative measures for the average level of education. Such findings of this study draw an implication that local governments could speed up regional economic growth in the knowledge-based economy by not only raising total human capital stock in a region but building the close networks that promote productivity-enhancing human capital external effects.

• Journal of vocational education research
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• v.36 no.2
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• pp.23-50
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• 2017

The purpose of this study was to determine the hierarchical linear relationship among receptivity to organizational change, individual variables of professors and organizational variables in junior colleges. The population for this study was 12,920 professors in 139 junior colleges. Using random sampling method considering subject, 800 professors in 40 colleges were sampled for this study. The data were collected from May 26 to June 13. A total of 445 out of 800 questionnaires were returned of which 441 of 40 junior colleges were used for analysis after data cleaning. These data were analyzed by both descriptive statistics and One-way ANOVA with Random Effects, Ranmdom-Coefficients Regression Model, and Intercepts-and Slopes-as-Outcomes Model of hierarchical linear model(HLM). All data analysis was accomplished using the SPSS 20.0 for windows program and the HLM 6.0 for windows program. An alpha level of 0.05 was established priori for determining the significance. The findings of the study were as follows: First, the level of receptivity to organizational change of professions in junior college was 3.94. Second, 56.5% of total variance in receptivity to organizational change was individual level variance. 43.5% of total variance in receptivity to organizational change was organizational level variance. Third, personal valence about organizational change, psychological ownership, experience of assignment, years of service and job security had positive effects on receptivity to organizational change while years of service had negative effects on receptivity to organizational change. The effect of personal valence about organizational change was highest, and the effect of job security was lowest. Fourth, degree of organizational change, participative decision-making, group culture and accessibility of information related to organizational change had positive effects on receptivity to organizational change. The effect of degree of organizational change was highest, and the effect of accessibility of information related to organizational change was lowest.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.171-181
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• 2012

In the new video coding standard, called high efficiency video coding (HEVC), the coding unit (CU) is adopted as a basic unit of a coded block structure. Therefore, the rate control (RC) methods of H.264/AVC, whose basic unit is a macroblock, cannot be applied directly to HEVC. This paper proposes the largest CU (LCU) level RC method for hierarchical video coding in a HEVC. In the proposed method, the effective bit allocation is performed first based on the hierarchical structure, and the quantization parameters (QP) are then determined using the Cauchy density based rate-quantization (RQ) model. A novel method based on the linear rate model is introduced to estimate the parameters of the Cauchy density based RQ model precisely. The experimental results show that the proposed RC method not only controls the bitrate accurately, but also generates a constant number of bits per second with less degradation of the decoded picture quality than with the fixed QP coding and latest RC method for HEVC.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.957-970
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• 2015

The purpose of this study was to analyze records of Korean pro-basketball using general linear model (two-way ANOVA and hierarchical multiple regression analysis). Korea Basketball League (KBL) informed the records (2014-2015 season) of this study. The eight variables (TA, 2PA, 3PA, 2P, 3P, Ast, TFB, CH) were selected in content validity. SPSS program was used to analyze general linear model. All alpha level was set at 0.05. Major results were as follow. 3PA had significant interaction effect between victory & defeat variable and home & away variable. Victory teams showed that 3PA was higher in home games than away games, and defeat teams was the other. 2PA, AS, TFB, and CH were selected significant variables affecting victory and defeat. In result of hierarchical regression, Ast had significant moderation effect between 3PA and TS. TFB also had significant moderation effect between AS between 2P. The other construct (Ast between 2PA and TS; TFB between AS between 3P) had no significant moderation effect. In the effect of 2PA, 3PA and Ast to TS, CH also had no significant moderation effect.

• Journal of Korea Planning Association
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• v.54 no.5
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• pp.129-138
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• 2019

This study aims to empirically examine the relationship between building energy consumption and building and local factors in Seoul. Building energy issue is an important topic for low carbon and eco-friendly city development. Building physical, socio-economic and environmental factors effect to increasing or decreasing energy consumption. However, there are different characteristic in each area, and this kind of variable has a hierarchical structure. The multi-level model was used to consider the hierarchical structure of the variables. In this study, a multi-level model was applied to confirm the difference between areas. Spatial area is Seoul, Korea and the temporal scope is August, summer season. As the result, in Model 1 (Null Model), ICC is 0.817. This shows that the energy consumption differs by 8.174% due to factors at the Dong level. Model 2 (Random Intercept Model) suggests that building's physical factors and Average age, Household size and Land price in Dong level have significant effects on Building energy consumption. In Model 3 (Random Coefficient Model), random effect variables have intercepts and slopes to vary across groups. This study provides a perspective for policy makers that the building energy reduction policies to be applied for buildings should be differently applied on area. Furthermore, not only physical factors but also socio-economic and environmental factors are important when making energy reduction policy.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.512-520
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• 2021

Purpose: The purpose is to analyze a relationship between urban characteristics and individual characteristics on the health level of individuals. Method: This study analyzed the relationship between urban characteristics and individual characteristics on individual health level in 2016 for local governments in Korea using a hierarchical linear model. Results: It was found that urban characteristics, along with individual characteristics, have a significant effect on the health level of individuals. Although the degree of influence is very large, some variables are not statistically significant, so more detailed research is needed for future urban policy. Conclusion: Although urban characteristics affect an individual's health level, additional research is needed on the variables of individual urban policies.

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.