TIMSS results
In antecipation of TIMSS results release 8 Dec, it may be informative to read our book chapter on what PISA and TIMSS measure and how: Read here.
O Eclipse da Relatividade
Revista da Ciência Elementar, Outubro 2019.
Nuno Crato e Luis Tirapicos
Há na história da ciência algumas datas que marcam revoluções no nosso conhecimento do Universo. O ano de 1666 constitui, certamente, uma dessas datas. Foi então que Isaac Newton, na altura com 23 anos, criou o cálculo integral e diferencial, a teoria da gravitação universal e a teoria das cores. O ano de 1905, em que Einstein publicou quatro trabalhos revolucionários, constitui uma outra dessas datas-chave. E deve-se a este físico ainda uma outra data-chave: o ano de 1915, em que formulou a teoria da relatividade geral, por vezes considerada a criação científica mais fabulosa da mente humana.
Assessment Background: What PISA Measures and How
Luísa Araújo, Patrícia Costa and Nuno Crato
November, 2020
This chapter provides a short description of what the Programme for
International Student Assessment (PISA) measures and how it measures it. First, it details the concepts associated with the measurement of student performance and the concepts associated with capturing student and school characteristics and explains how they compare with some other International Large-Scale Assessments (ILSA). Second, it provides information on the assessment of reading, the main domain in PISA 2018. Third, it provides information on the technical aspects of the measurements in PISA. Lastly, it offers specific examples of PISA 2018 cognitive items, corresponding domains (mathematics, science, and reading), and related
performance levels.
Read here the complete article.
Tests for comparing time series of unequal lengths
Journal of Statistical Computation and Simulation, Maio 2011.
Jorge Caiado, Nuno Crato and Daniel Peña.
This paper deals with hypothesis testing for independent time series with unequal length. It proposes a spectral test based on the distance between the periodogram ordinates and a parametric test based on the distance between the parameter estimates of fitted autoregressive moving average models. Both tests are compared with a likelihood ratio test based on the pooled spectra. In all cases, the null hypothesis is that the two series under consideration are generated by the same stochastic process. The performance of the three tests is investigated by a Monte Carlo simulation study.
Tests for comparing time series of unequal lengths
Journal of Statistical Computation and Simulation
Jorge Caiado, Nuno Crato and Daniel Peña
This paper deals with hypothesis testing for independent time series with unequal length. It proposes a spectral test based on the distance between the periodogram ordinates and a parametric test based on the distance between the parameter estimates of fitted autoregressive moving average models. Both tests are compared with a likelihood ratio test based on the pooled spectra. In all cases, the null hypothesis is that the two series under consideration are generated by the same stochastic process. The performance of the three tests is investigated by a Monte Carlo simulation study.
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