The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from...

The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from...

This paper examines how including latent variables can benefit propensity score matching. Latent variables can be estimated from the observed manifest variables and used in matching. This paper demonstrates the benefits of such an approach by comparing it with a method where the manifest...

This paper discusses a method to compare progress in reading achievement from primary to secondary school across countries. The method is similar to value-added models that take into account intake levels when comparing student progress in different schools. Value-added models are preferred over...

The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from...

The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from...