• Human Ecology Research
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• v.55 no.3
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• pp.221-232
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• 2017

This study analyzes the motivation for selecting child-care profession, job environment, director's transformational leadership associated with child-care center teacher's (lead teacher and head teacher) role performance. The subjects of this study were 336 teachers (lead teacher and head teacher) who worked in a child-care center located in Seoul and Gyeonggi-do. Data were collected through self-report questionnaires. Collected data were analyzed using the IBM SPSS Statistics ver. 23.0 program using t-test, F-test, analysis of variance, post-hoc analysis (Duncan), Pearson's correlation analysis, and multiple regression analysis. The results of the study are as follows. First, the study inquired on if the child-care center teacher's general characteristics (year) influence the child-care center teacher's role performance. Consequently, significant differences were not found in overall role performance according to teacher's career but not in the child-care center teacher's age, academic ability, and licensing. Second, the motivation for selecting profession (teaching aptitude, teacher's desire for social respect, and possibility of self-realization), job environment, and director's transformational leadership had a significant positive correlation with overall role performance. The motivation for selecting profession (without motives) had a significant negative correlation with overall role performance. Third, the strongest predictors of 'overall role performance' were teaching aptitude, variable of motivation for selecting child-care profession, and director's transformational leadership variable.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.563-571
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• 2001

In this paper, we study selecting a transformation so that the transformed variable is nearly symmetrically distributed. The large sample properties of an M-estimator of transformation parameter that is obtained by minimizing the integrated square of the imaginary part of the empirical characteristic function are investigated when a random sample is selected from some unspecified distribution. According to influence function calculations and Monte Carlo simulations, these estimates are less sensitive, than the normal model maximum likelihood estimates, to a few outliers.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.809-824
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• 2002

we propose two methods which separate the variable selection step and the split-point selection step. We call these two algorithms as CHITES method and F&CHITES method. They adapted some of the best characteristics of CART, CHAID, and QUEST. In the first step the variable, which is most significant to predict the target class values, is selected. In the second step, the exhaustive search method is applied to find the splitting point based on the selected variable in the first step. We compared the proposed methods, CART, and QUEST in terms of variable selection bias and power, error rates, and training times. The proposed methods are not only unbiased in the null case, but also powerful for selecting correct variables in non-null cases.

• Physical Therapy Rehabilitation Science
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• v.2 no.1
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• pp.49-56
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• 2013

Objective: To examine the relations between satisfaction in major, academic achievement and five personality factors of physical therapy students. Design: Questionnaire study. Methods: In order for a complete enumeration when selecting study subjects, we selected five representative schools through raffles. For about three weeks from May 21st to June 16th 2012, we distributed self-administered questionnaires comprised of questions related to five personality factor characteristics, satisfaction in major and academic achievement. Total of 510 questionnaires were distributed and 442 questionnaires were returned. Except the castle is not answered or unanswered call 73 questionnaire collected data from the 369 call. And 369 questionnaires were used for analysis. The frequency analysis was conducted to examine general characteristics of subjects. Results: In the analysis of differences in personality factors for each individual variable in accordance with sex, women had higher degree of neuroticism than men (p<0.05). Also men showed higher openness than women (p<0.05). In the analysis of differences in personality factors for each individual variable in accordance with age, the lower the age was, the higher the degree of neuroticism was (p<0.05). For satisfaction in major, "Satisfaction in school life" and "Motive for selecting the major"were significant factors (p<0.05). academic achievement, "School type" and "Motive for selecting the major" were significant factors (p<0.05). Conclusions: In regards to the satisfaction in major and academic achievement, "Motive for selecting the major" was the major significant factor. Students who had high interest in their majors expressed higher satisfaction, which the in turn correlated with higher academic achievement.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.138-146
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• 2003

A critical issue in area-based stereo matching lies in selecting a fixed rectangular window size. Previous stereo methods doesn't deal effectively with occluding boundary due to inevitable window-based problems, and so give inaccurate and noisy matching results in areas with steep disparity variations. In this paper, a variable window approach is presented to estimate accurate, detailed and smooth disparities for three-dimensional structure reconstruction. It makes the smoothing of depth discontinuity reduced by evaluating corresponding correlation values and intensity gradient-based similarity in the three-dimensional disparity space. In addition, it investigates maximum connected match candidate points and then devise the novel arbitrarily shaped variable window representative of a same disparity to treat with disparity variations of various structure shapes. We demonstrate the performance of the proposed variable window method with synthetic images, and show how our results improve on those of closely related techniques for accuracy, robustness, matching density and computing speed.

• 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 the Korean Data and Information Science Society
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• v.21 no.5
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• pp.959-965
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• 2010

For multiclassification, it is often the case that some variables are not important while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables for multiclassification. This algorithm is base on multiclass least squares support vector machine (LS-SVM), which uses results of multiclass LS-SVM using one-vs-all method. Experimental results are then presented which indicate the performance of the proposed method.