This paper reports some facts about grading standards across a varied sample of 16 countries participating in the 2003 OCSE-PISA Survey. Our main finding is that in all countries except Ireland and the USA there is conspicuous heterogeneity in standards across schools (Table 3, Figures 1 & 2)....

We address the problem of estimating generalized linear models (GLMs) when the outcome of interest is always observed, the values of some covariates are missing for some observations, but imputations are available to fill-in the missing values. Under certain conditions on the missing-data...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

Data from the 2003 OECD-PISA Survey for Italy reveal a striking difference in the relationship between students' competence (as measured by PISA score in Mathematics) and school grades across regions: a competence level granting bare sufficiency in the North yields excellence grades in the...

We study how the relationship between students' cognitive ability and their school grades depends on institutional contexts. In a simple abstract model, we show that unless competence standards are set at above-school level or the variation of competence across schools is low, students'...

We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the miss- ing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...