Introduction to special issue on quasi-causal methods

Background Over the past 50 years, ILSAs have experienced marked development in terms of scope, participants, and sophistication. The first such study, the International Association for the Evaluation of Educational Achievement’s Pilot Twelve Country Study was completed in 1961 and measured achievement in math, reading, geography, science, and so-called non-verbal ability (Forshay et al. 1962). Reported achievement was based on fairly simple statistics and item response theory and related methods were relative new-comers and not often used in educational research. This seemingly modest accomplishment of measuring educational achievement in six subjects and twelve countries is really monumental when we consider the state-of-the-science at the time: there was no internet or email and calculating regression parameter estimates could take up to 24 h (Ramcharan 2006). In contrast, current international assessments use highly sophisticated designs, with rotated booklets, complex estimation methods, and computerized platforms for administration; participating systems number in the dozens; and national policy makers wait with bated breath for the results of each study cycle. And commensurate with these developments, a natural interest in understanding variation in achievement has emerged. Perhaps more importantly, researchers and policy makers want to know what, if anything can be done to improve achievement overall and for particular groups of test takers. This, in turn, has motivated interest in making connections between a host of potential causes and achievement; although it is important to note that this interest isn’t strictly limited to the achievement domain. In this special issue of Large-Scale Assessments in Education, we offer several papers on the topic of causal inferences with international large-scale assessment (ILSA) data. The papers here are primarily empirical analyses of ILSA data that feature different methods, all with an aim toward estimating the effect of some cause. Each paper also includes a brief introduction to the method at hand along with a discussion of the important statistical assumptions that underpin each method and whether or not the assumptions are plausible in the given circumstance. Although the treatment is selective, the methods featured here are commonly used in practice and serve as a useful introduction to specific methods of data analysis applied in a quasi-experimental context. Important to keep in mind is that the implemented methods are applied to observational data, most of which are also cross-sectional. The final paper (Rutkowski and Delandshere, this issue) Open Access