• Journal of Korean Academy of Nursing
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• v.32 no.5
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• pp.599-608
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• 2002

Purpose: The purposes of this study were to test the validity and reliability of FACES III when applied to the only one and two family members, and to use more appropriately in the nursing practice. Method: Data were collected from 105 college students and 105 of their parents in two local nursing colleges. The original questionnaire, which was originally developed by Olson(1989), was modified by based on literature review and analyzed by correlation coefficient, Cronbach's α, Guttmans split coefficients and factor analysis. Result: Cronbach's αof the adaptability and cohesion were .77, .73(Guttmans split coefficient were .76, .71) when applied to the only one family member, and were .81, .77 (Guttmans split coefficient were .81, .77) when applied to two. The Pearson's correlation coefficient of the adaptability and cohesion between two family members were .38, .35. The total-item correlations of the other items except for items 5, 7, 13 were significant. The correlation coefficients between adaptability and cohesion when applied to only one and two were .30, .38(p < .01). When the data was analyzed by principle component analysis and Varimax rotation with the number of factors fixed to two, two factors explained 37.2% of total variance in the case of one member, and 42.2% of total variance in two. Conclusion: These results suggested that the concept and the construction validity of cohesion needed to be more clarified. Also It is required that the reliability and validity of FACES III should be tested in two more family members.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.287-296
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• 2011

Thirty six female students participated in the experiment of the fat mass weight loss. they kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone, and consulted him about fat mass loss once a week for 8 weeks period. Fat mass weight and its related factors of the students had been measured repeatedly every week during 8 weeks, The repeated measurement data were used for applying various random coefficient models. And hence optimal random coefficient model was selected. From the optimal model, the baseline, body mass index, diastolic blood pressure, total cholesterol and time of the fixed factors were very significant. The fixed quadratic time effect existed. The variance components corresponding to the subject effect, linear time effect of the random coefficients were all positive. Thus random coefficients up to the linear terms were considered as the optimal model. The treatment effect reduced the weight loss to an average of 2.1kg at the end of the period.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.1
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• pp.17-26
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• 1998

The purpose of this study was to investigate whether a radiographic estimate of osseous fractal dimension is useful in the characterization of structural changes in bone. Ten specimens of bone were progressively decalcified in fresh 50 ml solutions of 0.1 N hydrochloric acid solution at cummulative timed periods of 5, 10, 20, 30, 60 and 90 minutes, and radiographed from 0 degree projection angle controlled by intraoral parelleling device. The test set of 70 radiographs was digitized and digitally filtered to reduce film -grain noise. I performed one-dimensional variance and fractal analysis of bony profiles or scan lines. Correlation analysis quantified the relationship between variance and fractal dimension. The obtained results were as follow. 1. After the first stage of decalcification variance and fractal dimension of scan line pixel intensities generally decreased with a range of 57.94 to 12.64 and 1.59 to 1.36. 2. Correlation coefficient(r) relating variances to fractal dimensions was consistantly excellent(range r=0.90 to 0.98). 3. Variance and fractal dimension were much alike in ability to discriminate, at leat on a group basis, between control and decalcified specimens.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.215-216
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• 2007

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.1-10
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• 1999

When the component proportions in mixture experiments are restricted by lower and upper bounds multicollinearity appears all too frequently. The ridge regression can be used to stabilize the coefficient estimates in the fitted model. I propose a graphical method for evaluating the mixture component effects of ridge regression estimator with respect to the prediction variance and the prediction bias.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Imaging Science in Dentistry
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• v.30 no.2
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• pp.101-108
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• 2000

Purpose : The effects of step numbers of copper wedge and exposure on the coefficient of determination (r²) of the conversion equation to Cu-equivalent image and on the Cu-equivalent value (mmCu) and it's coefficient of variation measured at each copper step and the mandibular premolar area were evaluated. Method: Digital image analyzing system consisted of scanner, personal computer, and a stepwedge with 10 steps of 0.03 mm copper in thickness as reference material was prepared for quantitative assessment of the bone mineral density. NIH image program was used for analyzing images. Results : The film having moderately high film density showed the discrepancy between the real thickness and the measured Cu-equivalent value of each copper step. The Cu-equivalent image was dependent on the determinational coefficient of the conversion equation than the coefficient of variance of the measured value. Conclusion : Obtaining conversion equation with high coefficient of determination and proper film exposure are supposed to be neccessary for quantitative assessment of bone density. Multiple steps in the range of the corresponding copper thickness to the bone density of the area to be measured should be prepared.

• Journal of Korean Academy of Nursing
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• v.28 no.3
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• pp.550-562
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• 1998

The purpose of this study was to test the Theory of Planned Behavior in the prediction of contraceptive behavior among married women. This study used a descriptive correlational design to examine the relationships among the study variables. Eighty married women in Seoul and Kyungki-do participated in this study, Research instruments used were the tool for measuring TPB variables search as attitude toward contraception, subjective norm, perceived behavioral control, and intention ; and the tool for measuring contraceptive behavior. The former was modified by the researcher according to Ajzen & Fishbein(1980)'s guidelines for tool development and Jee (1993)'s tool. The latter was developed by the researcher Data was collected from July 20, 1996 to October 25, 1996. The results are as follows ; The three factors, attitude, subjective norm and perceived behavioral control of contraception can explain 30% of the variance in contraceptive intention. Inspection of path coefficient for each of the three predictor variables revealed that subjective norm and perceived behavioral control were the predictor variables on intention, while attitude was not. ; and intention and percevied behavioral control factors can explain 42% of the variance in contraceptive behavior. Inspection of path coefficient for each of the two predictor variables revealed that intention and perceived behavioral control were the predictor variables on behavior. In conclusion, this study identified that Theory of Planned Behavior was a useful model in the prediction of contraceptive behavior, and the contraceptive service program based on the TPB variables would be an effective nursing intervention for the change in contraceptive behavior.

• Journal of Korean Academy of Nursing
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• v.15 no.2
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• pp.5-15
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• 1985

This study was conducted for the purpose of fin-ding out the variance explaining the medical facilities utilization behavior, which is defined adaptation behavior Process by focal, contextual, residual stimuli in Roy's Adaptation Model. What kinds of characteristics can explain adaptation behavior in Roy's Model? And which is the relative importance of input variables? For this analysis, stepwise multiple regression and path analysis was used. The data come from the 1981 Baseline Household Interview Survey in remote rural area. The findings of the analysis can be summarized as follows: First, Total variance of independant variables for adaptation behavior, that is medical facilities utilization including clinic, drug store, health center, herb medicine was shown 16.2 percent. The most important variable which explain the dependent variable was the occurance of illness with the Ra of value 0.112. The illness symptom, living level, regular care source was shown important variables with relatively high the R²value and significant beta coefficient. Second, in the path analysis of variables which is selected important variables, the occurance of illness was shown variable which has the highest direct effect which 0.297 path coefficient. Also the education level of household was shown variable which has the highest indirect effect through living level and the occurance of illness in causal model. Third, This analysis suggests that the occurance of illness belonging focal stimuli are more influenced than others. To sum up, It is seem to the occurance of illness, illness symptom belonging focal stimuli have high explanation ability through direct effect, education level of household among contextual stimuli have explanation ability through indirect effect.

• Journal of Korean Academy of Nursing
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• v.34 no.5
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• pp.693-701
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• 2004

Purpose: The purpose of this study was to validate Quality of Life Index-Cancer (Q.L.I.-C) developed by Ferrans (1990) among Korean cancer patients. Method: This study design was exploratory factor analysis methodology. Q.L.I.-C was translated into Korean and reverse-translated into English. The subjects were 357 Korean patients with various cancers. Data were collected by questionnaires from May to August, 2000 and was analyzed by descriptive statistics, Principal Component Analysis for construct validity and Cronbach's alpha coefficient for reliability. Result: The range of factor loadings was .446～.841. The explained variance from the 5 extracted factors was 63.7% of the total variance. The first factor 'family' was 35.5%, and 'health & physical functioning', 'psychological', 'spiritual', and 'economic' factors were 11.5%, 6.9%, 5.6%, and 4.2% respectively. Because of cultural difference between Americans and Koreans, certain items such as sexuality, job status, and education were deleted from the extraction of factors in this study. The Cronbach's alpha coefficient was .9253 among the 28 items. Conclusion: Q.L.I.-C could be applied in measuring quality of life of Korean cancer patients. It also recommend to do further studiesfor validation of Q.L.I.-C American and Korean versions relating to cultural differences.