• Research in Community and Public Health Nursing
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• v.33 no.3
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• pp.299-311
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• 2022

Purpose: Untreated depression in adolescents affects their entire life. It is important to detect and intervene early depression in adolescence considering the characteristics of adolescent's depressive symptoms accompanied by internalization and externalization. The aim of this study was to identify latent classes of depressive symptom trajectories of adolescents and determinants of classes in Korea. Methods: The three time-point (2018~2020) data derived from the Korean Children and Youth Panel Survey 2018 were used (N=2,325). Latent Growth Curve Modeling (LGCM) was conducted to explore the depressive symptom trajectories in all adolescents, and Latent Class Growth Modeling (LCGM) was conducted to identify each latent class. Multinomial logistic regression analysis was performed to confirm the determinants of each latent class. Results: The LGCM results showed that there was no statistically significant change in all adolescents' depressive symptoms for 3 years. However, the LCGM results showed that four latent classes showing different trajectories were distinguished: 1) Low-stable (intercept=14.39, non-significant slope), 2) moderate-increasing (intercept=19.62, significantly increasing slope), 3) high-stable (intercept=26.30, non-significant slope), and 4) high-rapidly decreasing (intercept=26.34, significantly rapidly decreasing slope). The multinomial logistic regression analysis showed that the significant determinants (i.e., gender, self-esteem, aggression, somatization, peer relationship) of each latent class were different. Conclusion: When screening adolescent's depression, it is necessary to monitor not only direct depression symptoms but also self-esteem, aggression, somatization symptoms, and peer relationships. The findings of this study may be valuable for nurses and policy makers to develop mental health programs for adolescents.

• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.69-79
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• 1997

We consider the semiparametric linear errors-in-variables model: yi=(${\alpha}+{\beta}ui+{\varepsilon}i$, xi=ui＋${\varepsilon}i$ i=1, …, n where (xi, yi) stands for an observation vector, (ui) denotes a set of incidental nuisance parameters, (${\alpha}$ , ${\beta}$) is a vector of regression parameters and (${\varepsilon}i$, ${\delta}i$) are mutually uncorrelated measurement errors with zero mean and finite variances but otherwise unknown distributions. On the basis of a simple small-sample low-noise a, pp.oximation, we propose a new method of comparing the mean squared errors(MSE) of the various competing estimators of the true regression parameters ((${\alpha}$ , ${\beta}$). Then we show that a class of estimators including the classical least squares estimator and the maximum likelihood estimator are consistent and first-order efficient within the class of all regular consistent estimators irrespective of type of measurement errors.

• Korean Journal of Child Studies
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• v.15 no.2
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• pp.153-168
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• 1994

The purpose of this study was to examine mother's child rearing practices by mothers of their first-born child as a function of child's sex, temperament, and social class and of mother's marital satisfaction. The subjects were 158 mothers of 3-year-old first-born children in Seoul. Block (1984)'s CRPR, Buss and Plomin (1975)'s EAS, and Roach et al.'s MSS (1981) were used to measure maternal child rearing practices, children's temperament, and mother's marital satisfaction, respectively. The statistical methods for data analysis included t-test, ANOVA, Duncan's multiple range test, multiple regression. The major findings showed there were no sex of child differences in child rearing practices. Social class differences were found in "encouragement of independence", "enjoyment of child" and "openness to experience" in maternal child rearing practices. There were differences in maternal child rearing practices by child's temperament and mother's marital satisfaction. In a multiple regression analysis, it was found that the most significant predictor of maternal child rearing practices was mother's marital satisfaction.

• Journal of the military operations research society of Korea
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• v.18 no.1
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• pp.99-109
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• 1992

It is very important in the navy operation to forecast and allocate appropriate petroleum requirements of a certain class of ships for the next year. The amount of petroleum requirements is very much dependent on the number of operating and training days or the running time of the main engine and the gas turbine of the ship. Two regression models were estimated and analyzed from real data obtained from several ships operated during the past three years. Finally, the amount of petroleum requirements that a ship is expected to spend for the next year can be estimated by using this estimated regression model based on operating hours of main engines and gas turbines of the ship.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.597-604
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• 2011

This research analyzed the runoff patterns and estimated unit loads of selected pollutatnts using monitored data conducted for three years in a bridge area. Three estimating methods; the arithmetic average method, the regression method and the rainfall class method were used to estimate the unit load. Results of three estimating methods were compared with the unit pollutant loads from landuses in Korea and the unit pollutant loads from urban watersheds in Milwaukee, USA. Unit load using the arithmetic mean method were found to be overestimated. In terms of TSS, unit loads of two estimate were half lower than that of USA. Estimated TN and TP unit loads of three estimate were lower than that of Ministry of Environment in Korea.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.247-262
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• 2024

In this paper, we explore nonlinear sufficient dimension reduction (SDR) methods, with a primary focus on establishing a foundational framework that integrates various nonlinear SDR methods. We illustrate the generalized sliced inverse regression (GSIR) and the generalized sliced average variance estimation (GSAVE) which are fitted by the framework. Further, we delve into nonlinear extensions of inverse moments through the kernel trick, specifically examining the kernel sliced inverse regression (KSIR) and kernel canonical correlation analysis (KCCA), and explore their relationships within the established framework. We also briefly explain the nonlinear SDR for functional data. In addition, we present practical aspects such as algorithmic implementations. This paper concludes with remarks on the dimensionality problem of the target function class.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.2
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• pp.185-197
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• 2020

Purpose: The purpose of this study was to investigate the mediating effect of self-regulated learning ability on the relationship between experiencing a good class and problem solving ability in nursing college students. Methods: A structured self-report questionnaire was used to measure experiencing a good class, self-regulated learning ability, and problem solving ability. During June, 2019, data were collected from 130 nursing students in D city. Data were analyzed using t-test, One-way ANOVA, Pearson's correlation coefficients, and hierarchical multiple linear regression with SPSS/WIN 23.0. Results: Importance of good class (r=.50, p<.001), satisfaction of good class (r=.42, p<.001), and self-regulated learning ability (r=.71, p<.001) were positively correlated with the problem solving ability of participants. Also, self-regulated learning ability had a partial mediating effect on the relationship between experiencing a good class and problem solving ability. Conclusion: Considering the findings of this study, developing programs that can improve the self-regulated learning ability of nursing students who experience a good class are needed to increase their level of problem solving ability.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.177-179
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• 2023

본 연구에서는 사회복지시설 종사사의 직무만족도 유형을 살펴보고 유형별 예측변인과의 영향관계를 검증하였다. 이러한 연구목적을 검증하기 위해 보건복지부의 '사회복지시설 실태조사'(2014년) 데이터에서 직무만족도 변인에 모두 응답한 11,660명을 최종 분석하였다. 잠재프로파일 분석결과, 사회복지사의 직무만족도 유형은 4집단으로 나타나 '최상 직무만족도집단', '중상 직무만족도집단', '중간 직무만족도집단', '최하 직무만족도집단'으로 명명하였다. 다항로지스틱 분석결과, CLASS4(최상 직무만족도집단)를 준거집단으로 하여 CLASS1(최하 직무만족도집단)과 비교해 노동강도대비 보수수준 평가, 타직종대비 보수수준 평가, 시설안전도, 인권보장도를 높게 인식할수록 CLASS4(최상 직무만족도집단)에 속할 확률이 높아지는 것으로 나타났다. 다만, 이직의사는 낮을수록 CLASS4(최상 직무만족도집단)에 속할 확률이 높아지는 것으로 나타났다. CLASS4를 준거집단으로 하여 CLASS2집단, CLASS3집단도 비교분석 하였다.

• Journal of the Korea Fashion and Costume Design Association
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• v.26 no.2
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• pp.45-56
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• 2024

The purpose of this study is to verify the effects of the instructor's communication type on class satisfaction. To this end, a survey was conducted on 204 people taking classes related to beauty design, and regression analysis was conducted on the collected data using SPSS 25.0. It was found that the instructor's communication type had a significant effect on the formation of rapport with learners and class satisfaction. As the instructor's verbal and non-verbal communication positively affects the formation of rapport and enhances class satisfaction, efforts are be needed to improve these aspects. In addition, it can be seen that the instructor's communication type is a variable that positively affects class satisfaction through the formation of rapport. Therefore, instructors need to use appropriate communication types, and the effect of class satisfaction with learners can be enhanced through non-verbal cues, such as exchanging gazes, while having a natural posture, and smiling.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3523-3529
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• 1999

In this paper, we propose an improved regression testing method, which use method as the basic unit of changing. The testing method consists of three steps. We represent the relationship of classes using the notation of UML(Unified Modeling Language), find the nodes of the modified methods and affected methods by node analysis, and then select changed test cases from the original test cases. The proposed object-oriented regression testing method can reduce the number of test cases, testing time and cost through reuse of test cases.