Excepcionalmente na próxima 3a feira, 13/10/20 às 13:30h

Palestrante: Prof. Caio Lucidius Naberezny Azevedo (Unicamp)

Título: Time series and multilevel modeling for longitudinal item response theory data

Resumo: In this work we consider some stationary and nonstationary time series and multilevel models to represent longitudinal Item Response Theory (IRT) data. We developed a Bayesian inference framework, which includes parameter estimation, model fit assessment and model comparison tools, through MCMC algorithms. Simulation studies are conducted in order to measure the parameter recovery. All computational implementations are made through the WinBUGS program, using the R2WinBUGS package, from the R program. A real data analysis, concerning a longitudinal cognitive study of Mathematics achievement, conducted by the Federal Brazilian government, is performed.

*Joint work with Jean-Paul Fox, University of Twente and Dalton F. Andrade, Universidade Federal de Santa Catarina.

Local: Transmissão online

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