• International Area Studies Review
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• v.21 no.4
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• pp.173-194
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• 2017

The research study conducted logistics regression analysis through HLM (Hierarchical Linear Modeling) and presented the value of the outcome in order to investigate characteristic factors of demanders which influence the intention to move into public rental housing. (1) The results of the regression analysis of characteristic factors of household and housing were analyzed as having the significant effect on the intention to move in public rental housing, according to residents moving in monthly rent housing, residents' occupations, rental housing, the number of household, the location of surrounding public rental housing, monthly average income, children's educational level, the number of children, the types of housing and one's own house, in order, out of the types of housing tenure. (2) The results of the regression analysis of characteristic factors of the conditions of location were analyzed that out of the conditions of location of the top five areas in public rental rates, what influences significant effects on the intention to move in public rental housing is the location of surrounding rental housing, income, the number of household and children, children's educational level, job state, housing types, ones' own house, rent housing, monthly rent housing, in order. (3) In case of Seoul, Expanding public rental housing is inevitable in order to stabilize ordinary people's housing stability, owing to the high and rental prices of private housing. Nevertheless, an accurate analysis of the intention to move in public rental housing has not been conducted. Eventually, the research was, thus, conducted, based on the fact that the preference on public housing is low. According to the analytic results of the study, it is required for the government institutions and agencies should consider individual and local characteristics and provide an alternative that meets the real situation, in order to help ordinary citizens with low incomes stabilize housing.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.115-126
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• 2016

The purpose of this study is to identify Hierarchical Culture of construction enterprises, Empowerment, and Organizational Effectiveness through empirical research. To perform this study, we carried out a survey targeting executives of construction companies in Seoul, metropolitan area, and non-metropolitan area. For analyzing, SPSS Statistical Techniques, Reliability analysis, Factor analysis, Multiple Regression analysis were used. The results of the study are as follows. First, the Hierarchy of the Culture affects significantly positive (+) on Organizational Effectiveness. Second, Hierarchical Culture affects the significantly positive (+) to the Empowerment. Third, Hierarchies and Cultural Empowerment was found to significantly affect the positive (+) on Organizational Effectiveness. Based on the conclusions, Hierarchical Culture of the domestic construction industry has a significant impact on Self-determination and the goal of consciousness among employees. In addition, this is helpful in Job Performance and Organizational Commitment.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• Knowledge Management Research
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• v.16 no.3
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• pp.81-102
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• 2015

With the rapid development of digital media, there has been a huge change in a way of communication, a process of information diffusion and a role of traditional media. Not like mass media, social media enables users to generate and tap into the opinions of a larger world. From that reason, social media is impacting marketing strategies. However, still social media marketing researches just focus on case study, analysis of users motivation or analysis of power user's usage pattern. Word-of-mouth has always been important especially in marketing area. In social media, word-of-mouth depends on each user that's why this research focuses on individual user's activity in SNS. I defined 4 factors (produce, diffusion, network size, activity of network size enlarge) that are effect on activity and verified hypothesis by multiple regression analysis, hierarchical regression analysis and moderated multiple regression.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.683-694
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• 2011

This study analysed impact of service quality on customer satisfaction and loyalty in the take-out coffee shop. The switching barrier was also studied as a moderating effect. Particularly, this study focused on relationships between customer loyalty and switching barriers. A lot of previous studies interest only in customers satisfaction. This study also analysed relationships among service quality, customer satisfaction, switching barriers and brand loyalty. Eventually, service quality significantly affects customer satisfaction, moderating effects, brand loyalty and marketing performance. SERVQUAL model which was established by PZB (1988) was used as a service quality factors. The impact on customer satisfaction was analysed using multiple regression analysis. Simple regression analysis was used to find effects of customer satisfaction and customer loyalty. Additional factors of switching barriers was classified based on previous studies. Hierarchical multiple regression analysis was used to find factors of customer loyalty among switching barriers. In the result, we can find that the importance of tangibles, responsiveness in service quality factors and contract cost, search cost and continuous cost in moderating effects.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1513-1521
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• 2015

Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.143-151
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• 2021

This study was a cross-sectional descriptive research to analyze the effects of sub-factors of cognitive emotional control on happiness levels. The participants of the study were 201 men and women in their 20s, and data were collected online from January 1 to 15 collected data were, 2001 using structured cognitive control and happiness level questionnaires. The collected data were conducted Independent t-test, Pearson correlation analysis, simple regression analysis, multiple regression Analysis, hierarchical regression analysis using SPSS 18.0 statistic program. As a result, the study appeared that the level of happiness by gender does not differ, and cognitive emotional control affected 58.5%. The average of cognitive emotional control was higher for all men, but women were higher than men in criticized others. Also, acceptance was the sub-factor of emotional control that most affected the level of happiness (β=-.587, p<0.01). Based on the results of this study, it is suggested that a systematic program on subject of acceptance, a sub-factor of cognitive emotional control, should be developed to improve the level of happiness.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.8
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• pp.1352-1363
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• 2010

This study analyzes how the organizational culture of TV home shopping companies influence the job satisfaction, commitment, and intention of turnover. It ascertains the differences the job satisfaction, commitment, and intention of turnover according to tenure of office and turnover time. The survey was conducted from May $3^{rd}$ to $31^{rt}$ in 2010, and 356 responses were used in the data analysis. The statistical analysis methods were frequency analysis, factor analysis, reliability analysis, ANOVA, and multiple regression analysis. As a result, the organizational culture of TV home shopping companies was classified by the innovation culture, group culture, rational culture, and hierarchical culture. The innovation culture, group culture, and hierarchical culture of TV home shopping companies influenced job satisfaction and commitment. The rational culture and hierarchical culture of TV home shopping companies influenced the intention of turnover. There were differences in the intention of turnover according to the tenure of office and the job commitment according to turnover time.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.506-514
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• 2015

In this study, a new analytical techniques is proposed for seeking policy alternatives aimed at objectives of TDM, increasing the transit rideshare. Determinants of travel mode such as personal characteristics, lifestyle, and urban spatial characteristics are interdependent and have combined effect on decision. In addition, individuals, groups, and regional characteristics have interdependencies at different levels. Unlike traditional regression analysis, hierarchical analysis model has the advantage of identifying interdependencies and complex relationship between the combined impact factors. This analysis technique is expected to be a significant contribution to seek a more efficient TOD policy.