• Journal of Korean Elementary Science Education
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• v.30 no.3
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• pp.353-366
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• 2011

An alternative vision for science inquiry that appears to be important and challenging is model-based inquiry in which students generate, evaluate and revise their explanatory model. Pre-service teachers should be given opportunities to develop and use their mechanistic explanatory models in order to participate in the practice of science and to have a sound understanding of science. With this view, this study described a case of pre-service elementary teachers' scientific modeling in magnetism. The aims of this study were to explore difficulties preservice elementary teachers encountered while they engaged in a model-based inquiry, and to examine how their understandings of the nature of scientific models changed after the model-based inquiry. The data analysis revealed that the pre-service teachers had difficulties in drawing and writing their own thinking because they had little experience of expressing their own science ideas. When asked to predict what would happen, they could not understand what it meant to make a prediction "based on their model". They did not know how to use or consider their model in making a prediction. At the end of the model-based inquiry they reached a final consensus of a best model. However, they were very anxious about whether the model was the "correct" answer. With respect to the nature of scientific models, almost all of the pre-service teachers initially viewed models only as a communication tool among scientists or students and teachers to help understand others' ideas. After the model-based inquiry, however, many of them understood that they could create, test, and revise their "own" models "by themselves". They also realized the key aspects of scientific models that a model can be changed as evidence is accumulated and a model is a knowledge production tool as well as a communication tool. The results indicated that pre-service elementary teachers' understandings of the nature of scientific models and their previous school science experiences could affect their performance on a model-based inquiry, and their experience of scientific modeling could help them enhance their understandings of the nature of scientific models.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.601-604
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• 2006

This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.227-233
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• 2004

This paper presents our work on constructing a model that is intended to predict the probability of attrition at specified points in time among customers of an insurance company. There are some difficulties in building a data-based model because a data set may contain possibly censored observations. In an effort to avoid such kind of problem, we performed logistic regression over specified time intervals while using explanatory variables to construct the proposed model. Then, we developed a Cox-type regression model for estimating the probability of attrition over a specified period of time using time-dependent explanatory variables subject to changes in value over the course of the observations.

• Korean Journal of Adult Nursing
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• v.22 no.1
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• pp.90-102
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• 2010

Purpose: The purpose of this study was to identity factors affecting patient adherence and to develop an explanatory model for patient adherence in patients with spinal cord injury. 8 Variables that were based on the previous research and a review of literature were used to construct hypothetical model. Social support, economic status, perceived barrier, patient provider relationship and rehabilitation related knowledge were the exogenous variables, depression, self-efficacy and patient adherence were the endogenous variables. Methods: Data form 117 patients with SCI were analysed to test the hypothetical model, using SAS and LISREL 8.53 program. Results: The overall fitness of the model was good (GFI=.991, AGFI=.915, NNFI=1.299, NFI=.953, p=.632) Depression, powerlessness, economic status were the strong factors influencing patient adherence. Powerlessness was significant factors for self-efficacy. Conclusion: To improve of patient adherence should focus on nursing intervention for depression, powerlessness and economic status.

• Journal of Korean Elementary Science Education
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• v.40 no.1
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• pp.1-12
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• 2021

This study investigated pre-service elementary teachers' understanding of the planetary revolution movement of Mars and their explanatory models to show how the Sun-Earth-Mars system worked. An assessment item set using five celestial maps drawn from the Stellarium was designed to probe pre-service teachers' understanding of the prograde-retrograde motion of Mars. Among 23 participants, only four showed scientifically accurate understanding of Mars movement and drawing correct explanatory models for the planetary movement. Even the pre-service teachers who construed correctly prograde and retrograde motions of Mars showed a clockwise movement model due to their intuitive perceptions of Mars movement data from the celestial maps. Pre-service teachers with poor understanding of planetary movement also showed weak explanatory models due to their limited observation or lower spatial thinking. Although the planetary motion is not an easy topic for pre-service elementary teachers, it can be argued if the alternative approach, such as using appropriate observational data of a planet and changing the frames of reference between Earth-based view and Space-based view, is employed effectively in teaching planetary motion, pre-service teachers can reach the upper level of leaning planetary motion in terms of the planet's revolution.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.535-542
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• 2012

The consumer price index (CPI) data is one of the important economic measurement of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly CPI data at Seoul, Pusan, Daegu, and Gwangju Cities in Korea, In the ARE model, nine economic variables are used as the explanatory variables for the CPI data set. The nine explanatory variables are CCI (coincident composite index), won-dollar rate, producer price index, oil import price, oil import volume, international current account, import price index, unemployment rate, and amount of currency. The result showed that the monthly ARE models explained about 46-52% for describing the CPI.

• Food Science of Animal Resources
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• v.43 no.1
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• pp.113-123
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• 2023

This study investigated the predictability of cooking loss of pork loin through relatively easy and quick measurable quality properties. The pH, color, moisture, protein content, and cooking loss of 100 pork loins were measured. The explanatory variables included in all linear regression models with an adjust-r² value of ≥0.5 were pH and the protein content. In the linear regression model predicting cooking loss, the highest adjust-r² value was 0.7, with pH, CIE L^*, CIE b^*, moisture, and protein content as the explanatory variables. In 30 pork loins, electrical conductivity was additionally measured, and as a result of linear regression analysis for predicting cooking loss, the highest adjust-r² value was 0.646 with electrical conductivity measured at 40 Hz, with pH and color as the explanatory variables. Ordinal logistic regression analysis was performed to predict the three grades (low, middle, and high) of loin cooking loss using pH, color, and 40 Hz electrical conductivity as the explanatory variables, and the percent concordance was 93.8%. In conclusion, the addition of electrical conductivity as an explanatory variable did not increase the prediction accuracy of the linear regression model for predicting cooking loss; however, it was demonstrated that it is possible to predict and classify the cooking loss grade of pork loin through quality properties that can be measured quickly and easily.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.6
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• pp.963-971
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• 2007

This research aimed to determine the relationship between hotel restaurants' SCM activities and their results. The samples are included exclusive high-end hotels located in the seoul area. To analyze the data, frequency analysis, reliability analysis, factor analysis, and regression analysis were applied. Multiple regression analysis showed that SCM activities (${\beta}$=.342, p<.000), information sharing (${\beta}$=.136, p<.006), and cooperative activities (${\beta}$=.120, p<.015) had a significant impact on financial performance. The explanatory power of this model was 14%, and there was statistical significance in the regression model. SCM activities(${\beta}$=.221, p<.000), information sharing (${\beta}$=.475, p<.000), and cooperative activities (${\beta}$=.172, p<.000) also had a significant impact on non-financial performance, and the explanatory power of this model was 29%, with statistical significance in the regression model.