Latent trait models for perceived risk assessment using a Covid-19 data survey

DOI10.1080/02664763.2021.1937584OpenAlexW3171830305WikidataQ115042544 ScholiaQ115042544MaRDI QIDQ6078155

Maria Iannario, Unnamed Author, Silvia Bacci

Publication date: 27 September 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2021.1937584

zbMATH Keywords

item response theoryordinal datagraded response modelCOVID-19latent regression modelrisk-perception analysis

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Editorial to the special issue: statistical perspectives on analytics for COVID-19 data

Cites Work

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• Gini heterogeneity index for detecting uncertainty in ordinal data surveys
• A taxonomy of item response models
• A rating formulation for ordered response categories
• A Rasch model for partial credit scoring
• A generalized Rasch model for manifest predictors
• Estimating the dimension of a model
• MultiLCIRT: an R package for multidimensional latent class item response models
• The Measurement of Observer Agreement for Categorical Data
• Average Partial Association in Three-Way Contingency Tables: A Review and Discussion of Alternative Tests
• A Class of Multidimensional Latent Class IRT Models for Ordinal Polytomous Item Responses
• Chi-Square Tests with One Degree of Freedom; Extensions of the Mantel- Haenszel Procedure
• Sequential item response models with an ordered response
• Some Methods for Strengthening the Common χ 2 Tests

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