• Korean Journal of Human Ecology
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• v.15 no.6
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• pp.1049-1062
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• 2006

The purpose of this study is to find out the image according to formative properties of hat and garment in the fashion collection. For the study, the 96 stimuli found frequently in fashion collection from the S/S season of 1998 to the F/W season of 2004 were selected. Sets of stimulus and response scales (7 point semantic) were used as experimental materials. The stimuli were 96 pictures with the types of hat(4), the lengths of hair(3), the types of garment(3), the relations between the color of garment and hat(4), and the materials(4) and patterns of garment(2). The subjects were 415 women college students majoring fashion design related fields and living in Seoul and Gyeongsangnam-do. As statistical methods for data analysis, Factor Analysis, ANOVA test, and LSD test were used. The items of the adjectives were classified into 5 image dimensions; attractiveness, gracefulness, concentration, cuteness, and hardness and softness. Among these factors, each dimensional image was affected by formative properties of hat and garment. The image of a hat-wearer was perceived differently according to the hair style and the formative properties of hat and garment even if the type of hat was same.

• Journal of Educational Research in Mathematics
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• v.22 no.2
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• pp.137-162
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• 2012

Learning graphical representations for statistical data requires understanding of the context related to measurement in statistical investigation since the choice of representation and the features of the selected graph to represent the data are determined by the purpose and context of data collection and the types of the data collected. This study investigated whether middle school students can think critically about measurement and scales integrating contextual knowledge and statistical knowledge. According to our results, the students lacked critical thinking related to measurement process of data and scales of graphical representations. In particular, the students had a tendency not to question upon information provided from data and graphs. They also lacked competence to critique data and graphs and to make a flexible judgement in light of context including statistical purpose.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.717-736
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• 2023

The purpose of this study is to develop a statistical program using data and artificial intelligence prediction models and apply it to one class in the sixth grade of elementary school to see if it is effective in improving students' statistical literacy. Based on the analysis of problems in today's elementary school statistical education, a total of 15 sessions of the program was developed to encourage elementary students to experience the entire process of statistical problem solving and to make correct predictions by incorporating data, the core in the era of the Fourth Industrial Revolution into AI education. The biggest features of this program are the recognition of the importance of data, which are the key elements of artificial intelligence education, and the collection and analysis activities that take into account context using real-life data provided by public data platforms. In addition, since it consists of activities to predict the future based on data by using engineering tools such as entry and easy statistics, and creating an artificial intelligence prediction model, it is composed of a program focused on the ability to develop communication skills, information processing capabilities, and critical thinking skills. As a result of applying this program, not only did the program positively affect the statistical literacy of elementary school students, but we also observed students' interest, critical inquiry, and mathematical communication in the entire process of statistical problem solving.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.787-806
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• 2016

Mixed-mode (MM) designs in which data are collected by different modes in one design have become increasingly popular. An MM data collection has several advantages such as reductions of coverage error, non-response and cost. However, MM designs may introduce mode effects that are confounded by selection effects and measurement effects, which can make MM data quality poor. In order to investigate mode effects, SRI implemented a concurrent mixed-mode experiment in 2014 where respondents could choose between a self-administrated Web survey and a self-administrated paper survey. This paper separately estimates selection effects and measurement effects. We found that measurement effects on some items are large.

• Journal of the Korean Society of Radiology
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• v.84 no.4
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• pp.866-878
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• 2023

Statistical analysis is an essential component of the medical writing process for research-related articles. Although the importance of statistical testing is emphasized, statistical mistakes continue to appear in journal articles. Major statistical mistakes can occur in any of the three different stages of medical writing, including in the design stage, analysis stage, and interpretation stage. In the design stage, mistakes occur if there is a lack of specificity regarding the research hypothesis or data collection and analysis plans. Discrepancies in the analysis stage occur if the purpose of the study and characteristics of the data are not sufficiently considered, or when an inappropriate analytic procedure is followed. After performing the analysis, the results are interpreted, and an article is written. Statistical analysis mistakes can occur if the underlying methods are incorrectly written or if the results are misinterpreted. In this paper, we describe the statistical mistakes that commonly occur in medical research-related articles and provide advice with the aim to help readers reduce, resolve, and avoid these mistakes in the future.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.705-709
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• 1999

We discuss the paper by Cho, Shin, Lee, and Han on the "information-relate" undergraduate statistics major program from the following perspectives: Recently, Korean universities are under re-structuring turmoil. To effectively confront the situation, we need both the vision and the strategy for statistics and statistics departments. For undergraduate statistics major program, our visions are 1) it should not be preliminary education program targeted for the graduate degrees, 2) it should be responsive to future social demand, and 3) it should incorporate the progressive identity of statistics as information and data science. As strategies, we propose 1) the effective integration and due balance among data collection, management and analysis, 2) the harmony and role development of computers and mathematics as statistical tools, 3) the statistics education through task-oriented problem solving, and 4) the emphasis of team work and communication skills.on skills.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.533-545
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• 2022

Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1269-1273
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• 2010

Typically users prefer to manage and access personal photo collections taken from a cell phone based on events. In this paper we propose an event clustering algorithm that requires low computation cost with high accuracy supporting incremental operation. The proposed method is based on the statistical analysis of the elapsed interval of intra-event photos on the real sample data for the decision of an event boundary. We then incorporate both location and visual information for the ambiguous range to split with only temporal cue. According to test results, we show higher performance compared to existing general clustering approaches.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.239-262
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• 2016

This study aims to explore ways to improve self-censorship tendency in teacher librarians by identifying factors that influence the self-censorship which appears during collection development in elementary school libraries. For this purpose, first, we examined the concept of self-censorship through a review of related literature, then carried out a series of in-depth interviews as a pilot study to develop a questionnaire, which was used for a questionnaire survey. Finally, the survey data was analyzed statistically with SPSS 21.0, a statistical package. As a result, we have discovered statistically significant relationships between self-censorship and the characteristics in collection development policies, school library committees, and the complaints related with the library collections. Based on these results, the factors on the self-censorship were identified in the perception of the school librarians. Based on these results, we suggest to reinforce the function of review and approval of book selection in the school library committee, developed and approved a collection development policy include guidelines for dealing with users' complaints related to library collection, and stipulated a regulation for school library operation to remove inappropriate books from the library collection.