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Titel
What happens when econometrics and psychometrics collide? : an example using the PISA data / John Jerrim (University College London), Luis Alejandro Lopez-Agudo (Universidad de Málaga), Oscar D. Marcenaro-Gutierrez (Universidad de Málaga), Nikki Shure (University College London and IZA) ; IZA Institute of Labor Economics
VerfasserJerrim, John Peter In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach John Peter Jerrim ; Lopez-Agudo, Luis Alejandro In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Luis Alejandro Lopez-Agudo ; Marcenaro-Gutierrez, Oscar D. In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Oscar D. Marcenaro-Gutierrez ; Shure, Nikki In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Nikki Shure
KörperschaftForschungsinstitut zur Zukunft der Arbeit In der Gemeinsamen Normdatei der DNB nachschlagen In Wikipedia suchen nach Forschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, June 2017
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Elektronische Ressource
Umfang1 Online-Ressource (34 Seiten) : Diagramme
SerieDiscussion paper ; no. 10847
URNurn:nbn:de:hbz:5:2-134940 Persistent Identifier (URN)
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What happens when econometrics and psychometrics collide? [0.65 mb]
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Zusammenfassung

International large-scale assessments such as PISA are increasingly being used to benchmark the academic performance of young people across the world. Yet many of the technicalities underpinning these datasets are misunderstood by applied researchers, who sometimes fail to take their complex sample and test designs into account. The aim of this paper is to generate a better understanding amongst economists about how such databases are created, and what this implies for the empirical methodologies one should (or should not) apply. We explain how some of the modelling strategies preferred by economists seem to be at odds with the complex test design, and provide clear advice on the types of robustness tests that are therefore needed when analyzing these datasets. In doing so, we hope to generate a better understanding of international large-scale education databases, and promote better practice in their use.