Multilevel IRT Modeling in Practice with the Package mlirt. Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals’ outcomes differ substantially when taking the measurement error in the dependent variable of the model into account. The multilevel model can be extended to handle measurement error using an item response theory (IRT) model, leading to a multilevel IRT model. This extended multilevel model is in particular suitable for the analysis of educational response data where students are nested in schools and schools are nested within cities/countries.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Sulis, Isabella; Capursi, Vincenza: Building up adjusted indicators of students’ evaluation of university courses using generalized item response models (2013)
- Sheng, Yanyan; Headrick, Todd C.: A Gibbs sampler for the multidimensional item response model (2012)
- Bacci, Silvia; Caviezel, Valeria: Multilevel IRT models for the university teaching evaluation (2011)
- Paul De Boeck; Marjan Bakker; Robert Zwitser; Michel Nivard; Abe Hofman; Francis Tuerlinckx; Ivailo Partchev: The Estimation of Item Response Models with the lmer Function from the lme4 Package in R (2011) not zbMATH
- Jan de Leeuw; Patrick Mair: An Introduction to the Special Volume on ”Psychometrics in R” (2007) not zbMATH
- Jean-Paul Fox: Multilevel IRT Modeling in Practice with the Package mlirt (2007) not zbMATH