On identification and non-normal simulation in ordinal covariance and item response models
From MaRDI portal
Publication:2177739
DOI10.1007/s11336-019-09688-zzbMath1439.62129OpenAlexW2976806373WikidataQ90341298 ScholiaQ90341298MaRDI QIDQ2177739
Steffen Grønneberg, Njål Foldnes
Publication date: 6 May 2020
Published in: Psychometrika (Search for Journal in Brave)
Full work available at URL: https://hdl.handle.net/11250/2733248
Nonparametric hypothesis testing (62G10) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
Related Items (3)
Partial identification of latent correlations with ordinal data ⋮ Goodman and Kruskal's gamma coefficient for ordinalized bivariate normal distributions ⋮ Partial identification of latent correlations with binary data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Multidimensional item reponse theory.
- How general is the Vale-Maurelli simulation approach?
- Simulating multivariate nonnormal distributions
- On the relationship between item response theory and factor analysis of discretized variables
- Maximum likelihood estimation of the polychoric correlation coefficient
- Factor analysis of dichotomized variables
- Covariance model simulation using regular vines
- Partial identification of probability distributions.
- Vines -- a new graphical model for dependent random variables.
- Limited information estimation and testing of discretized multivariate normal structural models
- A problem with discretizing Vale-Maurelli in simulation studies
- Dependence Modeling with Copulas
- A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence
- A Monte Carlo study comparing PIV, ULS and DWLS in the estimation of dichotomous confirmatory factor analysis
- Testing normality of latent variables in the polychoric correlation
This page was built for publication: On identification and non-normal simulation in ordinal covariance and item response models
