• Journal of Korean Academy of Nursing
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• v.28 no.1
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• pp.17-25
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• 1998

This study was done to evaluate the quality of clinical nursing care using the variables of structure, process, and outcome and to analyze the relationship between the variables. This study also explored which variables are validating indicators to evaluate the quality of nursing care. The results analyzed by multiple regression showed that, generally structural variables did not contribute to the variance in outcome scores, but process variables of nursing care contributed significantly to the outcome variable of patient satisfaction. A combination of structure and process variables explained outcome variables more than structural variables alone. Also, patient satisfaction and hospital preference were significantly related to each other. Therefore, if nursing quality evaluation relies solely upon on structural variables such as number of available nurses and workload, it would be inaccurate because process variables of nursing care are strongly related to outcome variables and the two categories of structure and process variables helped to strengthen the relationships. Thus, it is important to focus on variables of structure, process, ant outcome together in evaluating nursing care quality.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.163-175
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• 2002

An extended version of the minimax eccentricity factor estimation for multiple set case is proposed. In addition, two more simple methods for multiple set factor analysis exploiting the concept of generalized canonical correlation analysis is suggested. Finally, a certain connection between the generalized canonical correlation analysis and the multiple set factor analysis is derived which helps us clarify the relationship.

• Korean Journal of Health Education and Promotion
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• v.4 no.2
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• pp.9-31
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• 1987

This study was conducted because of the investigator's concern for the high incidence and fatal nature of cancer in prime years of human life. The purpose of this study was to investigate risk factors on compilance for preventive health behavior of cancer. The data on which the analysis was based come from a survey of 828 married men & women, 40-59 years old. The instrument of the study were 'Health Belief Model' by Becker. The Data was analyzed using X--test, t-test, ANOVA, Pearson's Correlation Coefficient, Stepwise Multiple Regression. The followings were the result; 1. The examined group had a higher scores than the non-examined group in health belief variables. (p<0.001) 2. The higher level of health belief variables, the higher level of compliance for preventive health behavior is. (p<0.001) 3. The Stepwise Multiple Regression of compliance for preventive health behavior on the variables in the health belief model; Approximataly 65.5% of the variance of compliance for preventive health behavior was accounted for by health concern, susceptibility and barriers in combination. This meant that other factors seemed to influence preventive health behavior since the linear combination of variables failed to explain the remaining 34.5% of preventive health behavior of cancer. It tended to cost doubt on the usefulness of 5 variables in this model. Therefore further study to investigate the influential factors preventive health behavior of cancer is necessary.

• Journal of Families and Better Life
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• v.8 no.2
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• pp.101-118
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• 1990

The purpose of this study is to examine the stress level and factors percepted by the married women during family life, and to identify the related variables influencing on stress level. For this purpose, questionnaires were distributed to the 650 married women who have been living in seoul. Among them 463 data were selected. For data analysis, the statistical method such as the factor analysis frequency distribution percentile , t-test, ANOVA, Duncan's Multiple Range Test and Stepwise Regression Analysis were used. the major findings were summarized as follows; 1) The general tendancy of the stress level percepted by the married women during family life was relatively low. 2) The household background variables(age, level of education, income, type of family, number of children, status of employment ) have turned out to be significant on the stress level of married women except family type. 3) All the social-psychological variables(socio-economic status, communication interaction, resources perception, psychological satisfaction) showed significant differences according to the stress level percepted by the married women during family life. 4) The level of work identity was the important factor on the stress level percepted by the married women during family life. 5) In Stepwise Multiple Regression Analysis, the married women's stress level during family life was greatly influenced by variables such as the resource perception and the psychological satisfaction.

• Korean Journal of Occupational Health Nursing
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• v.24 no.2
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• pp.114-121
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• 2015

Purpose: The purpose of this study was to assess the psychosocial work environment of hospital nurses to identify influences of psychosocial work environment on stress, depression, sleep disorder, and burnout. Methods: A total of 219 nurses working in one hospital were surveyed by using the Korean version of the Copenhagen Psychosocial Questionnaire (COPSOQ-K) mental health and psychosocial work environment. The impact of the psychosocial work environment on mental health was analyzed using multiple regression. Results: Mental health variables are correlated with each other. The psychosocial work environment variables and mental health variables are mostly correlated. To assess the psychosocial work environment that affects mental health the most, multiple regression was used. Work-family conflict was the most powerful explanation of all the mental health variables. Work pace, social community at work, mutual trust among employees, predictability, and influence were found to be affecting some mental health variables. Conclusion: To improve the mental health of nurses, it is necessary to consider work pace, social community at work, mutual trust among employees, predictability, influence focus on work-family conflict.

• Korean Journal of Child Studies
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• v.27 no.6
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• pp.249-261
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• 2006

This study investigated the influence of socio-environmental variables and cognitive variables on adolescent depression. Subjects were 494 middle and high school students of Deagu. The instrument was a self-report questionnaire; data were analyzed by t-test, stepwise multiple regression and path analysis. Among Socio-environmental variables, social support variables had a stronger effect on depression than stress. Among cognitive variables, automatic thought had a stronger effect on depression than cognitive distortion and socio-environmental variables. In path analysis, social support had a direct effect on cognitive distortion and automatic thought. Automatic thought served as a mediater of the relation between social support and depression. Although adolescent stress resulted in high depression, its effect on depression varied with the level of perceived social support.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.11 s.242
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• pp.1472-1479
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• 2005

Multiple surface crack distributed randomly along a weld toe influences strongly on the fatigue crack propagation life of welded joint. It is investigated by using statistical approaches based on series of systematic experiments. From the statistical results, initial crack numbers and its locations follow the normal distribution, and the probability of initial crack depths and lengths can be described well by tile Weibull distribution. These characteristics are used to calculate the fatigue crack propagation life, in which the mechanisms of mutual interaction and coalescence of the multiple cracks are considered as well as the Mk-factors obtained from a parametric study on the crack depths and lengths. The automatic calculation is achieved by the NESUSS, where the parameters such as the number, location and size of the cracks are all treated as random variables. The random variables are dealt through the Monte-Carlo simulation with sampling random numbers of 2,000. The simulation results show that the multiple cracks lead to much shorter crack propagation life compared with those in single crack situation. The sum of the simulation and tile fatigue crack initiation life derived by the notch strain approach agrees well with the experiments.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.11
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• pp.761-769
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• 2007

The purpose of this study is to present a simplified model of predicted mean vote (PMV) using multiple regression analysis. We performed the experiments and the numerical calculations in the lecture room during summer and winter to simplify PMV. And the multiple regression analysis on PMV was conducted to estimate the contribution of independent variables toward PMV. From the multiple regression analysis, we found that the effect of independent variables on PMV followed in order, clo value>air temperatur>air velocity>mean radiant temperature>relative humidity. And the simplified PMV was proposed through a few assumptions and then was compared with original PMV. They had a good agreement with each other. Additionally, we compared the simplified PMV with EDT. We expected that the simplified PMV can be more useful than EDT to evaluate the thermal comfort in the place, where radiation is dominant. But the comfort range of the simplified PMV should be adjusted to predict the exact thermal comfort in the future.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.