• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.577-585
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• 2008

This paper suggests a marginal probability model for analyzing repeated polytomous response data when some factors are nested in others in treatment structures on a larger experimental unit. As a repeated measures factor, time is considered on a smaller experimental unit. So, two different experiment sizes are considered. Each size of experimental unit has its own design structure and treatment structure, and the marginal probability model can be constructed from the structures for each size of experimental unit. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.339-345
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• 2004

This paper deals with a mixed logit model for ordered polytomous data. There are two types of factors affecting the response varable in this paper. One is a fixed factor with finite quantitative levels and the other is a random factor coming from an experimental structure such as a randomized complete block design. It is discussed how to set up the model for analyzing ordered polytomous data and illustrated how to estimate the paramers in the given model.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.597-603
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• 2000

A versatile dimensionality assessment index DETECT has been developed for binary item response data by Kim (1994). The present paper extends the use of DETECT to the polytomously scored item data. A simulation study shows DETECT performs well in differentiating multidimensional data from unidimensional one by yielding a greater value of DETECT in the case of multidimensionality. An additional investigation is necessary for the dimensionally meaningful clustering methods, such as HAC for binary data, particularly sensitive to the polytomous data.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.187-193
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• 2006

This paper shows how to use continuation-ratio logits for the analysis of structured polytomous data. Here, response categories are considered to have a nested binary structure. Thus, conditionally nested binary random variables can be defined in each step. Two types of factors are considered as independent variables affecting response probabilities. For the purpose of analyzing categorical data with binary nested strutures a continuation-ratio mixed model is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed in detail by an example.

• 응용통계연구
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• 제21권4호
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• pp.621-630
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• 2008

본 논문은 개체의 특성으로 다가의 명목형 반응변수가 반복측정 요인인 시간요인에 의해 주기적으로 반복측정 되었을 때, 자료를 분석하기 위한 모형으로 일반화된 주변 로짓모형을 논의하고 있다. 다가의 반응변수에 영향을 미치는 공변량중 일부가 처치로써 상대적으로 큰 크기의 실험단위에 배정되고 반복측정 요인인 시간요인의 수준들이 또한 처치요인으로 비확률화에 의해 상대적으로 작은 크기의 실험단위에 배정될 때 이를 고려한 모형구축과정과 예상되는 공분산 구조의 가정하에서 모수를 추정하기 위한 방법으로 가중최소제곱 방법을 이용할 수 있음을 제시하고 있다.

• 응용통계연구
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• 제9권1호
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• pp.1-15
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• 1996

본 논문은 질병발생집단의 감염율이 질병발생집단내 감염되지 않은 개체들에 대한 어떤 처치효과가 감염율에 어떻게 영향을 받는가를 알아보기 위한 통계적 분석모형으로 연속적 분석모형을 제시하고, 모형내 미지모수들을 추정하기 위한 방법을 논의하고 있다.

• Research in Vestibular Science
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• 제17권4호
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• pp.152-159
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• 2018

Objectives: The Korean Dizziness Handicap Inventory (KDHI), which includes 25 patient-reported items, has been used to assess self-reported dizziness in Korean patients with Parkinson disease (PD). Nevertheless, few studies have examined the KDHI based on item-response theory within this population. The aim of our study was to address the feasibility and clinimetric properties of the KDHI instrument using polytomous Rasch measurement analysis. Methods: The unidimensionality, scale targeting, separation reliability, item difficulty (severity), and response category utility of the KDHI were statistically assessed based on the Andrich rating scale model. The utilities of the orderedresponse categories of the 3-point Likert scale were analyzed with reference to the probability curves of the response categories. The separation reliability of the KDHI was assessed based on person separation reliability (PSR), which is used to measure the capacity to discriminate among groups of patients with different levels of balance deficits. Results: Principal component analyses of residuals revealed that the KDHI had unidimensionality. The KHDI had satisfactory PSR and there were no disordered thresholds in the 3-point rating scale. However, the KDHI showed several issues for inappropriate scale targeting and misfit items (items 1 and 2) for Rasch model. Conclusions: The KDHI provide unidimensional measures of imbalance symptoms in patients with PD with adequate separation reliability. There was no statistical evidence of disorder in polytomous rating scales. The Rasch analysis results suggest that the KDHI is a reliable scale for measuring the imbalance symptoms in PD patients, and identified parts for possible amendments in order to further improve the linear metric scale.

• 한국과학교육학회지
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• 제22권1호
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• pp.122-131
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• 2002

본 연구는 수행 평가 방식의 중요성이 대두됨에 따라 실지로 수행형 평가 문항이 전통적인 선다형 평가문항에 비해 어떤 학생들에게 어떻게 유용한지를 분석한 연구이다. 이를 위하여 제 3차 수학 과학 성취도 국제비교 반복 연구에 사용된 선다형과 수행형 평가문항들을 내용영역별로 분석하여, 수행형의 평가문항과 선다형의 평가문항의 효율성을 비교 분석하였다. 자료분석 결과 수행형의 문항이 선다형의 문항에 비하여 학생들의 능력을 더 정확하게 추정함으로써 효율성이 높은 것으로 나타났다. 특히 환경이나 과학의 본성 영역에 비하여 지구과학, 생물, 물리와 화학 영역에서 수행형의 문항이 선다형의 문항에 비하여 학생들의 능력을 더 정확하게 추정하고 있음을 보여주었다. 또한 선다형의 문항에 비하여 적은 수의 문항으로 학생들의 능력을 추정함에도 불구하고 추정오차가 적어, 적은 수의 수행형 문항으로도 학생들의 능력을 정확하게 추정할 수 있음을 시사하고 있다.

• 수산해양교육연구
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• 제27권6호
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• pp.1546-1555
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• 2015

This study was performed to analyze the item characteristics of competency inventory for designer (CID), which Gil (2011) developed for measurement of design competency. To accomplish the purpose of this study, general partial credit (GPC) model based on polytomous item response theory was applied. The findings were as follows: First, CID is a reliable instrument for measuring design competency. Second, most items of CID have low item discrimination and average item difficulty according to the GPC model. Especially, there are some problems to have low item discrimination in view of validation. To improve the goodness of CID, we will need to examine why CID has low item discrimination.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.