evaluating bias in VA is to test for balance in lagged values of the outcome, based on the intuition that today's inputs cannot …, tests for balance using lagged outcomes do not provide robust information about the degree of bias in value-added models for … structure even in large datasets. We conclude that bias in VA models is better evaluated using techniques that are less …

In this paper, I present a simple characterization of the sample selection bias problem that is also applicable to the … problem of sample selection bias is fit within the conventional specification error framework of Griliches and Theil. A simple … estimator is discussed that enables analysts to utilize ordinary regression methods to estimate models free of selection bias …

In this paper we show that omitted variables and publication bias lead to severely biased estimates of the value of a …

observations in empirical analyses. Errors in such measures may bias parameter estimation, but it remains unclear how large such … multiple imputation, a standard statistical imputation technique so far untested in this setting, effectively reduces bias and …

magnitude of the bias depends upon the nature of the measurement error in the region of educational attainment affected by the …

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such …