• Journal of Korean Academy of Nursing
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• v.35 no.2
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• pp.239-251
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• 2005

Purpose: This study was designed to construct a structural model for explaining mental health status in middle - aged women. Methods: The data was collected by self - reported questionnaires from 206 middle - aged women in Seoul. Data analysis was done with the SAS pc program for descriptive statistics and a PC - LISREL Program for finding the best fit model which assumes causal relationships among variables. Results: The overall fit of the hypothetical model to the data was good, but paths and variables of the model were modified by considering theoretical implications and statistical significances of parameter estimates. Thus it was modified by excluding 3 paths, The modified model showed was good fit to the data($x^2=177.55$, p=.00), GFI=0.908, AGFI=0.860, RMR=0.013, NFI=0.972, NNFI=0.982). Perceived stress, anger expression method, and self -esteem were found to have direct effects on mental health status in middle - aged women. These predictive variables of mental health status explained $66.6\%$ of the model. Conclusion: Programs to enhance mental health status in middle - aged women should include stress management skill, anger expression skill, and self -esteem enhancement skills to be effective.

• Journal of Korean Academy of Nursing
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• v.32 no.3
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• pp.355-363
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• 2002

The purpose of this study was to examine the level of intention to quit smoking and to identify factors influencing intention to quit among patients with coronary heart disease. Method: The subjects consisted of 80 male patients with coronary heart disease (angina pectoris, myocardial infarction) at three hospitals in Seoul. The data were collected with self reporting in a structured questionnaire. Stepwise multiple regression was used to identify predictors of intention to quit. Included variables were attitudes toward smoking cessation, subjective norms, perceived behavioral control, usefulness of smoking cessation, and previous attempts to quit. Result: 1. The mean score for intention to quit was 11.1($\pm$6.1) which was lower than median score of the scale. 2. There were significant correlations between the all predictive variables and the intention to quit(r=.24-.48, p<.05). 3. usefulness of smoking cessation, perceived behavioral control, and previous attempts to quit explained 34.6% of the variance for intention to quit. Conclusion: usefulness of smoking cessation, perceived behavioral control, and previous attempts to quit were identified as important variables in explaining the intention to quit smoking among patients with coronary heart disease. Thus, it is necessary to try to enhance this factors for increasing intention to quit among patients with coronary heart disease.

• Journal of the Korean Home Economics Association
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• v.47 no.3
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• pp.87-102
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• 2009

This study was conducted to determine which variables of interest could be used to predict the development of infants. The variables of interest in this study were demographic variables regarding to the infants and mothers, maternal knowledge of infant development, maternal self-efficacy, maternal parenting stress, and infant development. The participants consisted of 252 infant-mother pairs and all infants included in this study were 15 months to 36 months of age. The development of Infants was assessed by classroom teachers. Data regarding the mothers’' demographic information, maternal knowledge of infant development, maternal self-efficacy, and maternal stress were gathered by maternal self-reported questionnaires. Data were analyzed by descriptive statistics, t tests, and regression analyses. The primary results demonstrated that family income, maternal infant knowledge, and maternal parenting stress were predictive of the infant development. In addition, different patterns in the results of the stepwise multiple regression were observed among the infant’'s of different age. Implications for research and practice were discussed along with the results of study.

• Korean Journal of Community Nutrition
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• v.13 no.5
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• pp.723-732
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• 2008

The purpose of this study was to identify the influence of diet related factors, such as diet behaviors, food intake, and nutrient intakes, on self-rated health (SRH). Also, in order to determine fitness of classification for SRH reflecting diet related factors, this study surveyed older adults in Gyeongnam province. A total of 101 responses were collected using the interview survey method. The self- rated health of rural older adults was poor as reported by 49.5%. The level of self-rated health was found to be related to the frequencies of coffee and snack, use of sugar and vegetable in diet, the amount of total food intake, and cholesterol intake. The result of discriminant analysis, which was conducted to assess the adequacy of SRH classification and to determine the class of observation, showed frequency of coffee and use of vegetable in diet among 47 variables as predictive variables for explaining SRH. The fitness of self-rated health function was high to 47.7%. Therefore, diet-related factors were ascertained to be important variables to predict SRH.

• Journal of Families and Better Life
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• v.24 no.4 s.82
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• pp.93-101
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• 2006

This study explored the differences in fathers' involvement in child-rearing according to various family socio-demographic variables (fathers education level, income, mothers employment, preschooler's sex and age) and preschoolers' temperaments and examined the relationship between fathers' involvement in child-rearing and these independent variables (as well as preschooler's temperament). The subjects of the study were 227 fathers whose children were preschoolers between the ages of 3 and 5 attending daycare centers in Keoungbok province, South Korea. Statistical analysis was conducted with the following techniques: two-way ANOVA, interaction effect, Scheffe' test, Pearson's correlation partial correlation, and hierarchical multiple regression (using SPSS 12.0). Results of the study may be summarized as follows. (1) There was a significant difference in fathers' child-rearing involvement according to the fathers' education level, income, and preschoolers' temperaments. (2) There was a significant interaction effect of mothers' employment and preschoolers' temperaments on fathers' child-rearing involvement. (3) Hierarchical multiple regression analysis showed that fathers' education mediated the relationship between income and fathers' involvement in child-rearing, and fathers' education and preschoolers' temperaments was also found to have predictive power over fathers' child-rearing involvement.

• Journal of Ecology and Environment
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• v.35 no.3
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• pp.157-166
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• 2012

Many models intending to explain the latitudinal gradient of increasing species diversity from the poles to the equator are presented, which are a formalisation of the species-energy hypothesis. The model predictions are consistent with patterns of increasing species number with increasing mean air or water temperatures for plants and animals. An increase in species richness is also correlated with net primary production or the Normalised Difference Vegetation Index. This implies that increased availability of resources favours increased diversity capacity. The explanatory variables included in the biodiversity prediction models represent measures of water, energy, water-energy, habitat, history/evolution and biological responses. Water variables tend to be the best predictors when the geographic scope of the data is restricted to tropical and subtropical areas, whereas water-energy variables dominate when colder areas are included. In major models, about 20-35% of species in the various global regions (European, Africa, etc.) will disappear from each grid cell by 2050 and >50% could be vulnerable or threatened by 2080. This study provides good explanations for predictive models and future changes in biodiversity depending on various scenarios.

• Child Health Nursing Research
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• v.13 no.2
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• pp.201-211
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• 2007

Purpose: This study was designed to construct a structural model to explain the quality of life of grandmothers caring for their grandchildren. Method: Data were collected by self-report questionnaires from 232 grandmothers caring for their grandchildren living in Busan. The data collection period was from June to Oct. 2006. Data analysis was done with SAS 9.13 for descriptive statistics and PC-LISREL 8.52 program for Covariance Structural analysis. Results: The findings found that the fit of the hypothetical model to the data was good, but considering theoretical implications and statistical significances of parameter estimates, paths and variables of the model were modified by excluding 2 paths. The Modified Model with 17 paths showed a good fitness to the empirical data ($X^2=15.492$ (df=11, p=.161), GFI=.985 AGFI=.940 NFI=.982 RMSR=.037 RMSEA=.042). Health status, health problems, economical status, life events, caring stress, caring efficacy and life satisfaction had significant effects on quality of life in the grandmother caring their grandchildren, but of these variables, self-esteem was the most essential factor. All predictive variables of quality of life together explained 63.9% of the variance. Conclusion: The derived model in this study was confirmed to be proper in explaining and predicting the quality of life of the grandmothers caring their grandchildren.

• Journal of Korean Academy of Nursing
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• v.29 no.4
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• pp.829-840
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• 1999

This study was conducted to construct a hypothetical model of depression in Korean adolescent women and validate the fit of the model to the empirical data. The data were collected from 345 high school girls in Seoul, from May 1 to June 30, 1998. The instruments were the Body Mass Index, Physical Satisfaction Scale, Family Adaptatibility and Cohesion Evaluation Scale III, Family Satisfaction Scale, CES-D and School Adptation Scale. The data were analyzed using descriptive statistics with the pc -SAS program. The Linear Structural Relationship(LISREL) modeling process was used to find the best fit model which would predict the causal relationships among the variables. The overall fit of the hypothetical model to the data was moderate ［X$^2$＝69.6(df＝17, p＝.000), GFI ＝0.95, AGFI＝0.90, RMR＝0.087, NNFI＝0.86, NFI＝0.90］. The predictable variables, especially menstrual symptoms, physical symptoms and family function, had a significant direct effect on depression. but school life adaptation did not have a significant direct effect. These variables explained 18.1% of the total variance.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.269-284
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• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.