This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442 students collected via parent survey in 2013. All data is currently stored on an online data repository and freely available. Data might be of interest to researchers interested in individual differences in reading development and response to instruction and intervention, as well as to instructors of data analytic methods such as hierarchical linear modeling and psychometrics.

This paper presents a vulnerability framework as a means to contextualize inequities in reading achievement among children who are vulnerable to poor reading outcomes. Models to understand vulnerability have been applied in the social sciences and public health to identify population disparities and design interventions to improve outcomes. Vulnerability is multifaceted and governed by context. Using a vulnerability framework for the science of reading provides an innovative approach for acknowledging multilevel factors contributing to disparities. The ecological considerations of both individual differences in learners and conditions within and outside of schools ensures that scientific advances are realized for learners who are more vulnerable to experiencing reading difficulty in school. K E Y W O R D S minority children, reading, risk, vulnerability INTRODUCTIONThe United States (US) has long been perplexed by persistent inequities in educational experiences and outcomes among school-age children. Whether characterized as achievement gaps or opportunity gaps (Coleman, 1968;Ladson-Billings, 2006;Reardon, 2013), significant differences are routinely observed on most indicators of school achievement and success. In particular, students growing up in poverty and low-income households and

Randomized control trials are considered the pinnacle for causal inference. In many cases, however, randomization of participants in social work research studies is not feasible or ethical. This paper introduces the co-twin control design study as an alternative quasi-experimental design to provide evidence of causal mechanisms when randomization is not possible. This method maximizes the genetic and environmental sameness between twins who are discordant on an “exposure” to provide strong counterfactuals as approximations of causal effects. We describe how the co-twin control design can be used to infer causality and in what type of situations the design might be useful for social work researchers. Finally, we give advantages and limitations to the design, list a set of Twin Registries with data available after application, and provide an example code for data analysis.