Loading…
New developments in categorical data analysis for the social and behavioral sciences /
A collection of studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting datasets. A prominent breakthrough in categorical data analysis is the development and use of latent variable models.
| Call Number: | Libro Electrónico |
|---|---|
| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Mahwah, N.J. :
L. Erlbaum Associates,
©2005.
|
| Series: | Quantitative methodology series.
|
| Subjects: | |
| Online Access: | Texto completo |
Table of Contents:
- Statistical models for categorical variables / L. Andries van der Ark, Marcel A. Croon, and Klaas Sijtsma
- Misclassification phenomena in categorical data analysis : regression toward the mean and tendency toward the mode / Jacques A. Hagenaars
- Factor analysis with categorical indicators : a comparison between traditional and latent class approaches / Jeroen K. Vermunt and Jay Magidson
- Bayesian computational methods for inequality constrained latent class analysis / Olav Laudy, Jan Boom, and Herbert Hoijtink
- Analyzing categorical data by marginal models / Wicher P. Bergsma and Marcel A. Croon
- Computational aspects of the E-M and Bayesian estimation in latent variable models / Irini Moustaki and Martin Knott
- Logistic models for single-subject time series / Peter W. van Rijn and Peter C.M. Molenaar
- The effect of missing data imputation on Mokken scale analysis / L. Andrew van der Ark and Klaas Sijtsma
- Building IRT models from scratch : graphical models, exchangeability, marginal freedom, scale types, and latent traits / Henk Kelderman
- The Nedlesky model for multiple-choice items / Timo M. Bechger [and others]
- Application of the polytomous saltus model to stage-like proportional reasoning data / Karen Draney and Mark Wilson
- Multilevel IRT model assessment / Jean-Paul Fox.