Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Multilevel IRT using dichotomous and polytomous response data.

J-P Fox1

  • 1Department of Research Methodology, Measurement and Data Analysis, University of Twente, The Netherlands. fox@edte.utwente.nl

The British Journal of Mathematical and Statistical Psychology
|June 23, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Missing item responses in latent growth analysis: Item response theory versus classical test theory.

Statistical methods in medical research·2020
Same author

Latent growth modeling of IRT versus CTT measured longitudinal latent variables.

Statistical methods in medical research·2019
Same author

Mixture randomized item-response modeling: a smoking behavior validation study.

Statistics in medicine·2013
Same author

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers.

Psychometrika·2009
Same author

A Box-Cox normal model for response times.

The British journal of mathematical and statistical psychology·2009
Same author

Stochastic EM for estimating the parameters of a multilevel IRT model.

The British journal of mathematical and statistical psychology·2003
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

This study introduces a multilevel model using item response theory to analyze unobserved variables from test data. Bayesian methods efficiently estimate parameters and enable model checks for complex educational research.

Area of Science:

  • Statistics
  • Educational Measurement
  • Psychometrics

Background:

  • Multilevel models are essential for analyzing nested data structures common in education.
  • Latent variables, unobserved constructs, are often measured indirectly via test responses.
  • Item response theory (IRT) provides a framework for modeling the relationship between latent traits and observed item responses.

Purpose of the Study:

  • To present a structural multilevel model integrating item response theory for latent variable analysis.
  • To demonstrate the simultaneous estimation of all model parameters using Bayesian methods.
  • To illustrate model checking and comparison techniques within this framework.

Main Methods:

  • Development of a structural multilevel model incorporating IRT.

Related Experiment Videos

  • Application of Bayesian inference via Markov chain Monte Carlo (MCMC) simulation for parameter estimation.
  • Utilizing MCMC output for model diagnostics and comparative analyses.

    • Main Results:

    • The proposed model effectively integrates multilevel structures with latent variable measurement using IRT.
    • Simultaneous estimation of all parameters is achievable through the presented Bayesian MCMC procedure.
    • Model checks and comparisons can be reliably performed using the MCMC output.

    Conclusions:

    • The combined multilevel IRT framework offers a robust approach for analyzing complex educational data.
    • Bayesian MCMC provides a computationally feasible method for estimating and validating such models.
    • The methodology is applicable to real-world educational research, such as analyzing student achievement and teacher/principal characteristics.