• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.31-37
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• 1996

A hierarchical loglinear model comparison method is developed which is based on the well kmown partitioned likelihood ratio statistiss. For any paels, we can regard the difference of the geedness of fit statistics as the variation explained by a full model, and develop a partial test to compare a full model with a reduced model in that hierarchy. Note that this has similar arguments as that of the regression analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.

• Journal of Distribution Science
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• v.18 no.3
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• pp.53-65
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• 2020

Purpose: This study tests the suitability of a new technology acceptance model for a mobile payment system by checking how statistically significant the change is from the UTAUT (Unified Theory of Acceptance and Use of Technology) and UTAUT 2 models. Research, Data, and Methodology: We surveyed 250 students at Incheon University who are using the mobile payment system. The analysis was conducted on 243 valid questionnaires. The survey was conducted for one month in October 2018. The collected data were analyzed using SPSS and hierarchical regression analysis was applied. Results: Using hierarchical regression analysis, this study confirmed whether the newly added hedonic motivation, switching cost, and perceived risk variables in the UTAUT2 model are good explanatory variables. Mobile payment usage experience was found to have a moderating effect on mobile payment reuse intention. According to the analysis, the UTAUT2 model brought about more influential change than the variables of the UTAUT model. Conclusions: This study found that consumers' psychological factors added in the UTAUT2 model greatly influenced the reuse intention for mobile payment. As an implication of this study, mobile payment providers need to develop strategies that could meet hedonic motivation, switching cost and perceived risk for their customers.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.695-704
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• 2020

Recently, There has been an increasing of utilization IT, and studies have been conducted on predicting learning results. In this study, Learning activity data were collected that could affect learning outcomes by using learning analysis. The survey was conducted at a university in South Chung-Cheong Province from October to December 2018, with 1,062 students taking part in the survey. First, A Hierarchical regression analysis was conducted by organizing a model of individual, academic, and behavioral factors for learning results to ensure the validity of predictors in machine learning. The model of hierarchical regression was significant, and the explanatory power (R²) was shown to increase step by step, so the variables injected were appropriate. In addition, The linear regression analysis method of machine learning was used to determine how predictable learning outcomes are, and its error rate was collected at about 8.4%.

• Journal of the Korean Society of Child Welfare
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• no.26
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• pp.87-111
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• 2008

The purpose of this study is to evaluate the actual conditions of peer victimization and to examine how the various factors of school climate influence peer victimization. Analysis on the relationship between various school climate and peer victimization has not been yet dealt with in Korea. Participants in this study were middle school students chosen from 11 middle schools in Seoul, by convenience sampling. A total of 1,204 surveys were then analyzed. Methods for analysis included Frequencies, Descriptives, Pearson's Correlation, Hierarchical Regression. From the result of the analysis, the level of verbal violence came out to be a relatively high form of peer victimization. The hierarchical regression were conducted in two steps. The second model's descriptive variable was higher by 19.6% than the first model. The variables of interaction between teacher and student in peer violence(${\beta}=.130$), of school facility maintenance(${\beta}=.067$), of safety of school environment(${\beta}=.331$), and economic status and sex out of controlled variables were proved to be of significance, and those variables explained 23.0% of the entire model. Based on the results of this study, practical and effective policy solutions to improve the school climate better have been suggested.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.6
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• pp.928-939
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• 2000

This thesis will study the determinants of consumer satisfaction based on the disconfirmation theory. The proposed questions are first, to find out if desire and expectation are conceptually distinct. Second, to study the effects of desire, expectation, perceived performance, desire congruency, and expectation congruency on clothing satisfaction. The data used in this thesis were obtained from a two stage longitudinal survey. SPSS WIN 8.0 was used for the analysis and the following method such as mean, correlation, t-test, hierarchical regression were applied. The results indicate that first, according to the correlation analysis and crosstab analysis, satisfaction and desire were perceived as two different concepts. Second, using the hierarchical regression analysis to compare the effects of determinants of consumer satisfaction, the model of desire, expectation, performance, desires congruency, expectations congruency best explain the clothing satisfaction. Among them, effects of performance had the strongest impact. Expectation did not influence satisfaction but desire did.

• Korean Journal of Childcare and Education
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• v.20 no.2
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• pp.83-103
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• 2024

Objective: This study aimed to investigate the effects of children's characteristics and childcare teachers' attributes on the frequency and level of imaginative play in two-year-old classrooms. Methods: The study involved 191 toddlers, their mothers, and 32 teachers from childcare centers. Toddler characteristics encompassed temperament along with demographic variables such as gender and age. Teacher' attributes related to play included playfulness, play-support belief, and interactions with toddlers. Data analysis was conducted using SPSS 22.0 and HLM 8.2 software, employing basic analysis, hierarchical linear analysis, and hierarchical regression analysis. Results: First, as toddlers' age increased, both the frequency and level of their imaginative play increased. Second, individual-level model analysis revealed a positive effect of toddlers' extroversion on the level of imaginative play. Third, the class-level model results indicated that teachers' emotions had a negative effect, whereas their encouragement positively influenced the level of imaginative play. Conclusion/Implications: The significance of this study lies in its utilization of a multilayered model analysis, which offers a more robust examination of variable influences by accounting for hierarchical data structures.

• Genomics & Informatics
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• v.16 no.4
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• pp.38.1-38.3
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• 2018

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.