Hyunjoo Kim

2023

Jin, I.H., Yun, J.H., Kim, H.J., and Jeon, M.J. (2023) Latent Space Accumulator Model for Analyzing Bipartite Networks with Its Connection Time and Its Applications to Item Response Data with Response Time
Stat https://doi.org/10.1002/sta4.632

• Identified the effect of response time on network structure in the competing risk modeling framework
• Preprocessed and analyzed data from the language learning application ’Duolingo’
• Contributed to the drafting of the first version and provided proofreading, editing, and approval for the final draft before submission
• Related-codes link
  
• ABSTRACT

Response time has attracted increased interest in educational and psychological assessment for, e.g., measuring test takers' processing speed, improving the measurement accuracy of ability, and understanding aberrant response behavior. Most models for response time analysis are based on a parametric assumption about the response time distribution. The Cox proportional hazard model has been utilized for response time analysis for the advantages of not requiring a distributional assumption of response time and enabling meaningful interpretations with respect to response processes. In this paper, we present a new version of the proportional hazard model, called a latent space accumulator model, for cognitive assessment data based on accumulators for two competing response outcomes, such as correct vs. incorrect responses. The proposed model extends a previous accumulator model by capturing dependencies between respondents and test items across accumulators in the form of distances in a two-dimensional Euclidean space. A fully Bayesian approach is developed to estimate the proposed model. The utilities of the proposed model are illustrated with two real data examples.

2022

Kim, H.J., Jeon, Y.J., Kim, H.C., Jin, I.H., and Jung, S.J. (2022) Application of latent space item response model to clustering stressful life events Beck and Depression Inventory-II: results from Korean epidemiological survey data
Epidemiology and Health https://doi.org/10.4178/epih.e2022093

• Devised the application model based on the Latent Space Item Response Model and conducted data analysis
• Contributed to the drafting of the first version and provided proofreading, editing, and approval for the final draft before submission
• Related-codes link
  
• ABSTRACT

OBJECTIVES: According to previous findings, stressful life events (SLEs) and their subtypes are associated with depressive symptoms. However, few studies have explored potential models for these events and incidental symptoms of depression.
METHODS: Participants (3,966 men; 5,709 women) were recruited from the Cardiovascular and Metabolic Diseases Etiology Research Center cohort. SLEs were measured using a 47-item Life Experiences Survey (LES) with a standardized protocol. Depressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II). Joint latent space item response models were applied by gender and age group (<50 vs. ≥50 years old).
RESULTS: Among the LES items, death or illness of close relatives, legal problems, sexual difficulties, family relationships, and social relationships shared latent positions with major depressive symptoms regardless of gender or age. We also observed a gender-specific domain: occupational and family-related items.
CONCLUSIONS: By projecting LES and BDI-II data onto the same interaction map for each subgroup, we could specify the associations between specific LES items and depressive symptoms.

Identifiability of Q-Matrices for the DINA Model: Implications for Estimation Algorithm

University of Illinois-Urbana Champaign

Research with Dr. Hans-Friedrich Koehn (UIUC), Dr. Chia-Yi Chiu

• Conducted a large-scale simulation study for the validation of identifiability conditions as they are imposed on several MCMC algorithms for estimating the parameters of diagnostic classification models
• Was presented at the International Meeting of the Psychometric Society 2023
• A paper is in preparation

A Two-Step Q-Matrix Estimation Algorithm

University of Illinois-Urbana Champaign

Research with Dr. Chia-Yi Chiu, Dr. Hans-Friedrich Koehn (UIUC)

• Ran computational experiments analyzing and comparing the performance of MCMC algorithms, regularized EM-based algorithms, and restricted Boltzmann Machines for estimating the Q-matrices of cognitively diagnostic assessments based on the DINA and G-DINA models, which were part of the evaluation of a nonparametric algorithm for estimating the Q-matrices of cognitively diagnostic assessments
• A paper is accepted.