The DOI matches its Crossref record.
Sequential item response models with an ordered response
Abstract
A stepwise approach to the construction of latent trait models is outlined. As a special case of the derived general sequential model, a sequential Rasch model is considered. The derivation of the models is based on mechanisms of latent random variables. The unconditional maximum likelihood estimation is embedded in the framework of generalized linear models. An alternative estimation procedure which allows for ‘parameter separability’ is considered and its applicability is shown.
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Cited by
brms : An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476
“Families sratio (stopping ratio) and cratio (continuation ratio) are so called sequential models (Tutz 1990).”
BackgroundOrdinal Regression Models in Psychology: A Tutorial
Bürkner, P.-C., & Vuorre, M. (2019) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918823199 · Cited by 689
The Estimation of Item Response Models with the<tt>lmer</tt>Function from the lme4 Package in R
Boeck, P. D., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011) · Journal of Statistical Software · doi:10.18637/jss.v039.i12 · Cited by 338
Measuring progressions: Assessment structures underlying a learning progression
Wilson, M. (2009) · Journal of Research in Science Teaching · doi:10.1002/tea.20318 · Cited by 329
The analysis of ordered categorical data: An overview and a survey of recent developments
Liu, I., & Agresti, A. (2005) · Test · doi:10.1007/bf02595397 · Cited by 301
Metadata
- Authors
- Tutz, G.
- Venue
- British Journal of Mathematical and Statistical Psychology
- Publisher
- Wiley
- Published
- 1990-05
- Volume
- 43
- Issue
- 1
- Pages
- 39-55
- ISSN
- 0007-1102 (ISSN-L)
- 2044-8317
- OpenAlex
- W2019720066
- Cited by
- 223
- References
- 0
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
