• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.577-585
• /
• 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
• /
• v.15 no.2
• /
• pp.339-345
• /
• 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
• /
• v.7 no.2
• /
• pp.597-603
• /
• 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
• /
• v.17 no.1
• /
• pp.187-193
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.621-630
• /
• 2008

This paper discusses how to construct a generalized marginal logit model for analyzing repeated polytomous response data when some factors are applied to larger experimental units as treatments and time to a smaller experimental unit as a repeated measures factor. So, two different experimental sizes are considered. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• The Korean Journal of Applied Statistics
• /
• v.9 no.1
• /
• pp.1-15
• /
• 1996

This paper discusses the effectiveness of an infection rate under a certain disease on an immunity rate by a protective inoculation. A sequence of dependense models concerning the infection rate is derived by defining conditionally nested binary random variables for the analysis of polytomous data with hierarchical response scale. Maximum likelihood estimates based on the marginal log-likelihood functin are obtained numerically in the Nelder and Mead's(1965) simplex method.

• Research in Vestibular Science
• /
• v.17 no.4
• /
• pp.152-159
• /
• 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.

• Journal of The Korean Association For Science Education
• /
• v.22 no.1
• /
• pp.122-131
• /
• 2002

Recently, performance assessment that makes growing use of free response items in a large scale assessment has been emphasized. This study is an empirical examination of the effectiveness of free response items in comparison with multiple choice items. Using the information function in Item Response Theory (IRT) framework, item information of free response items and multiple-choice items from the Third International Mathematics and Science Study-Repeat (TlMSS-R) were obtained. Test information of the whole science area as well as each area of science contents was computed. On average, free response items yielded more information than multiple choice items, especially in earth science, physics, chemistry, and life science. This study also showed that free response items were appropriate for students in high science ability. Also, free response items estimated students' science ability more accurately than multiple choice items with smaller number of free response items.

• Journal of Fisheries and Marine Sciences Education
• /
• v.27 no.6
• /
• pp.1546-1555
• /
• 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
• /
• v.30 no.6
• /
• pp.577-587
• /
• 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.