Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models
Bibliographic Data
| ID | 19289679 |
|---|---|
| Authors | Lara Fontanella (0000-0002-5441-0035, University of Chieti-Pescara), Sara Fontanella (0000-0003-1681-9873, Imperial College London), Pasquale Valentini (0000-0003-0611-2073, University of Chieti-Pescara, corresponding author), Nickolay Trendafilov (The Open University), Nickolay T Trendafilov (0000-0001-9309-1375, The Open University) |
| Year | 2019 |
| Volume | 54 |
| Issue | 1 |
| Pages | 100-112 |
| Publication date | 2019-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2018.1496317 |
| PMID | 30403887 |
| OpenAlex | W2889284200 |
| Language | EN |
| Citations received | 2 |
| References cited | 37 |
In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions
Algorithm · Bayesian probability · Item response theory · Machine learning · Markov chain Monte Carlo · Pattern recognition (psychology) · Psychometrics · Simple (philosophy) · Statistics · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Mathematics · Optimal Experimental Design Methods · Sensory Analysis and Statistical Methods
Bayesian Item Response Modeling
The Multidimensional Random Coefficients Multinomial Logit Model
Using Confirmatory Factor Analysis for Construct Validation
Mirt
Estimation of Latent Ability Using a Response Pattern of Graded Scores
On the Relationship between iTem Response Theory and Factor Analysis of Discretized Variables
Generalized Procrustes Analysis
Bayesian Analysis of Binary and Polychotomous Response Data
Factor analysis in the development and refinement of clinical assessment instruments.
Comparing Multiunidimensional and Unidimensional Item Response Theory Models
Use of Exploratory Factor Analysis in Published Research
Further Empirical Results on Parametric Versus Non-Parametric IRT Modeling of Likert-Type Personality Data
An Overview of Analytic Rotation in Exploratory Factor Analysis
Individual differences in uses of humor and their relation to psychological well-being
| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |