Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do…

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an…

Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…

Current practices of reporting a single mean intercoder agreement in meta-analysis leads to systematic bias and overestimates reliability. An alternative is recommended in which average intercoder agreement statistics are calculated within clusters of coded variables. Two studies of intercoder agreement illustrate the model. (SLD)

In this analysis of 1,815 arbitration cases in British Columbia in 1982-85, bias is identified in the bivariate estimates of the probability of management wins by industry and issue. Bivariate estimate errors in these cases can result from failure of estimates to adjust for the effects of omitted variables. (TJH)

To correct for sample selection bias problems, the authors present a mathematical model of decision-making and apply it to young women's decisions regarding college attendance or labor force participation. They find that the two decisions are strongly correlated and together affect the estimated rate of return on college education. (Author/RW)