• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.207-216
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• 2005

One of the well-studied problems in data mining is the search for association rules. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. There are three criteria of association rule; support, confidence, lift. The goal of association rule mining is to find all the rules with support and confidence exceeding some user specified thresholds. We can know there is association between two items by the criteria of association rules. But we can not know the degree of association between two items. In this paper we examine the relation between the measures of association and the criteria of association rule for ordinal data.

• Culinary science and hospitality research
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• v.14 no.2
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• pp.215-224
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• 2008

The objective for this research was to clarify the preference for alternative restaurants with different combinations of factor levels: local specialty food, non-local specialty food, very attentive service, moderately attentive service, not attentive service, traditional decoration, modern decoration, \10,000, \15,000, and \20,000. Total 230 copies of questionnaire were completed. Conjoint experiment method was used to develop full restaurant profiles. Ordinal probit model was used to measure the effects of factor levels on the preference. Results of the study demonstrated that the ordinal probit model analysis result for the data also indicated a good model fit. The effects of factor levels on the preference were statistically significant. As expected, the estimates of implicit price to pay were statistically significant. Moreover, the customers were more willing to pay for local specialty than other factor levels. The customers also considered the food factor as a very important factor. This research suggested that the customer's decision-making process for restaurants was best modeled as a conjoint experiment method that combines various factor levels. And it showed the results could be used as good data for understanding the relationships between the factors and preference in choosing food and restaurants in tourist areas.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.69-79
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• 2022

The purpose of the study is to look into the likelihood of private sector enterprises going bankrupt due to COVID-19 pandemic-related issues. The data for this study was taken from the World Bank's Enterprise Survey, which was intended to assess the impact of the COVID-19 pandemic on the business sector. This study uses the Ordinal Logit Method to analyze the model with dependent variables having ordinal values. The determinants reflect business performance, innovation, business relationships, and government support. According to the estimation results, a lower probability of business closures, illiquidity, and payment delays are found in businesses that maintain sales growth, operating hours, temporary workers, product portfolio, consumer demand, and input supply. Meanwhile, the increase in online business activities and receiving support from financial institutions and the government do not help businesses reduce the risk. Moreover, higher survival is found in manufacturing and developing countries. This implies the fragility of businesses in the retail and service sectors, especially for mega-enterprises in developed countries. In addition, the negative impact of the COVID-19 pandemic on businesses in Europe and West Asia is less severe than in other regions. The results imply policies to support the private sector during the pandemic, such as increasing labor market flexibility or rapidly implementing supportive policies.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.30-34
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• 2021

[Purpose] Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes. [Methods] A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models. [Results] The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes. [Conclusion] The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.2
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• pp.165-171
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• 2008

The purpose of this study was to measure tourists' preference for alternative restaurants with different combinations of attribute levels: grown area logo, origin description, traditional food, fusion food, national food, and price. A total of 210 questionnaires were completed. A conjoint experimental method was used to develop hypothetical restaurants, and an ordinal probit model was used to measure the effects of the attribute levels on tourists' preference. The ordinal probit model analysis results for the data indicated an excellent model fit. The effects of the attribute levels on tourists' preferences were statistically significant. As expected, estimates of the marginal willingness to pay were statistically significant Moreover, the tourists were more willing to pay for grown area logo as compared to the other attribute levels. The tourists also considered the grown area logo as a very important attribute. Withe regard to developing and testing conjoint models in the design of choice experiments involving multifactor alternatives, this study may approach a deeper understanding of the conjoint experiment. Greater understanding of the conjoint experiment can improve the managerial diagnoses of the problems as well as the opportunities for different marketing strategies including local branding programs and menu development and marketing communications.

• Journal of the Korean Society of Food Culture
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• v.22 no.6
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• pp.794-800
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• 2007

The purpose of this study was to measure the tourists' preference for alternative restaurants with different combinations of 4 attribute levels: origin description, food type, price and service guarantee. A total of 210 questionnaires were completed from tourists who visited Kwangyang, Suncheon and Yeosu during Jan. 2 - Jan. 15, 2007. Conjoint experiment method was used to develop hypothetical restaurants. Ordinal probit model was used to measure the effects of attribute levels on the tourists' preference. Results of the study demonstrated that the ordinal probit model analysis result for the data indicated excellent model fit. The effects of attribute levels (origin description, traditional food, fusion food, price, service guarantee) on the tourists' preference were statistically significant. As expected, estimates of marginal willingness to pay for origin description(3.063), food type(2.349), and service guarantee(2.356) were statistically significant. Moreover, tourists were more willing to pay for origin description than other attribute levels. Tourists also considered the origin description as the very important attribute. In conclusion, based on conjoint analysis, a model was proposed of marginal willingness to pay of attribute levels. It should be noted that the original model was modified and should, preferably, be validated in future research.

• Korean Journal of Child Studies
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• v.11 no.1
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• pp.15-28
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• 1990

The purpose of this study was (1) to investigate and order the levels of parental authority concepts established by Damon, and (2) to investigate the relationship between the development of parental authority concepts and the child's age and sex. The subjects of this study were 120 children from an elementary school in Seoul. There were 40 subjects (20 males and 20 females) in each of three age groups: 8-, 10-, and 12- year-olds. The subjects were interviewed individually using Damon's(1977) open-ended questions concerning family rules. Responses to the assessment questions were coded as positive or negative, and responses to the judgement concept questions were coded into 6 levels. Statistical analysis of obtained data was by percentage, Spearman correlation using an ordinal scale, two-way analysis of variance, and Duncan test. The results showed that (1) the developmental levels of parental authority concepts established by Damon conformed to an ordinal scale, and (2) the development of parental authority concepts was related to child's age but not to child's sex.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.109-125
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• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.