Mapping unobserved item-respondent interactions: a latent space item response model with interaction map

DOI10.1007/s11336-021-09762-5zbMath1477.62342arXiv2007.08719OpenAlexW3157225845MaRDI QIDQ2066583

Minjeong Jeon, Ick Hoon Jin, Michael Schweinberger, Samuel Baugh

Publication date: 14 January 2022

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.08719

zbMATH Keywords

interactionsnetwork modellatent space modelbipartite networkinteraction mapitem response data

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (8)

Commentary on: ``Explore conditional dependencies in item response tree data ⋮ A latent space diffusion item response theory model to explore conditional dependence between responses and response times ⋮ Structure learning of exponential family graphical model with false discovery rate control ⋮ Nondecomposable item response theory models: fundamental measurement in psychometrics ⋮ Identifying and supporting academically low-performing schools in a developing country: an application of a specialized multilevel IRT model to PISA-D assessment data ⋮ How social networks influence human behavior: an integrated latent space approach for differential social influence ⋮ Bayesian model selection for high-dimensional Ising models, with applications to educational data ⋮ Joint Latent Space Model for Social Networks with Multivariate Attributes

Uses Software

• lme4
• psych
• mirt

Cites Work

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• A simple general method for oblique rotation
• Generalized Procrustes analysis
• Inference from iterative simulation using multiple sequences
• Rasch models for item bundles
• Additive and multiplicative effects network models
• The geometry of continuous latent space models for network data
• A doubly latent space joint model for local item and person dependence in the analysis of item response data
• Spike and slab variable selection: frequentist and Bayesian strategies
• Bayesian estimation of a multilevel IRT model using Gibbs sampling
• Latent Space Approaches to Social Network Analysis
• Latent Space Models for Dynamic Networks
• Bilinear Mixed-Effects Models for Dyadic Data

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