• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.213-217
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• 2011

The research shows misuse cases of quantitative regression analysis used in QC circle activity and six sigma movement which presents guidelines of correct use for quality practitioners. Additionally, the qualitative regression analysis that responses nonconforming ratio of variable y, is reviewed based on misuse cases for proper use by practitioners in the field. In most cases, there are frequent errors that involve the correlation analysis or ANOVA, regardless of using quantitative regression analysis. In addition, qualitative regression analysis for the nonconforming ratio that has dependent variable of discrete and categorical data, is often applied with quantitative regression and result in ineffective quality improvement.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8149-8153
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• 2014

Background: To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Materials and Methods: Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. Results: The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). Conclusions: The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.402-408
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• 2010

Forecasting have qualitative and quantitative methods. Quantitative one analyze macro-economic factors such as the rate of exchange, oil price, interest rate and also predict the micro-economic factors such as sales and demands. Applying various statistical methods depends on the type of data. when data has seasonality and trend, Time Series analysis is proper but when it has casual relation, Regression analysis is good for this. Time Series and Regression can be used together. This study investigate artificial neural networks which is predictive technique for casual relation and try to compare the accuracy of forecasting between regression analysis and artificial neural network.

• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Journal of Families and Better Life
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• v.32 no.3
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• pp.157-177
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• 2014

The purpose of this study was to investigate the contexts in which employed mothers with young children consider leaving the labor force. We used a mixed methods design, which integrates the findings of quantitative and qualitative analyses, to better understand the dynamics underlying employed mothers' intentions to leave the labor force. The participants of both quantitative (N = 324) and qualitative (N = 16) data were married mothers who were employed full-time and had at least one child younger than elementary-school age at the time of data collection. Both the quantitative analysis of logistic regression and the qualitative thematic analysis revealed that the child's age, the husband's income, the utilization of child care by relatives, the mother's job involvement, family-to-work role conflict, and other costs and rewards of participation in the work force were the important contexts where employed mothers considered leaving the labor force. The quantitative analysis uniquely found that being employed at a workplace with flexible work hours were associated with lower odds of considering exit from the labor market. The qualitative analysis highlighted that the decision to leave the labor force or to stay in it is a complicated issue that almost all employed mothers potentially face at some point in their careers. These findings suggest that policy support is warranted to help employed mothers with young children remain in the workforce when they wish to.

• Journal of Korean Academy of Nursing
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• v.45 no.4
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• pp.501-512
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• 2015

Purpose: The purpose of this study, which was guided by the Resiliency Model of Family Stress, Adjustment, and Adaptation, was twofold: (a) to explore family and parental adaptation and factors influencing family adaptation in Korean families of children with Down syndrome (DS) through a quantitative methodology and (b) to understand the life with a Korean child with DS through a qualitative method. Methods: A mixed-methods design was adopted. A total of 147 parents of children with DS completed a package of questionnaires, and 19 parents participated in the in-depth interviews. Quantitative and qualitative data were analyzed using stepwise multiple regression and content analysis respectively. Results: According to the quantitative data, the overall family adaptation scores indicated average family functioning. Financial status was an important variable in understanding both family and parental adaptation. Family adaptation was best explained by family problem solving and coping communication, condition management ability, and family hardiness. Family strains and family hardiness were the family factors with the most influence on parental adaption. Qualitative data analysis showed that family life with a child with DS encompassed both positive and negative aspects and was expressed with 5 themes, 10 categories, and 16 sub-categories. Conclusion: Results of this study expand our limited knowledge and understanding concerning families of children with DS in Korea and can be used to develop effective interventions to improve the adaptation of family as a unit as well as parental adaptation.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

• The Korean Journal of Food & Health Convergence
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• v.8 no.4
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• pp.1-10
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• 2022

The term "Organic Food" is no longer strange to consumers around the world. Many people are concerned about their safety and health, so they have chosen this safe food. However, the decision to buy this product still faces some difficulties and challenges, such as the high price of organic products, short-time use, supply of products and so on. This study conducted an analysis to investigate the determinants of Danang City consumers' intention to purchase organic foods in Vietnam. The results show that, there are 6 influencing factors, including: subjective norm, food safety & health consciousness, consumer knowledge & environment consciousness, price of the product, availability product and trust in brands and certifications. The study uses a combination of 2 qualitative and quantitative methods. Qualitative methods are used through analysis, evaluation and synthesis of previous studies to build research models and scales for variables. Quantitative method with 250 samples applied SPSS 25.0 to test the scale by Cronbach's Alpha coefficients, to analyze the discovery factor EFA and regression analysis. The findings of the study provide useful information for consumers to buy organic foods and for marketers to increase sale of organic foods in Vietnam in general and Danang city in particular.