Given decision-makers often prioritize causal research that identifies the impact of treatments on the people they serve, a key question in education research is, "Does it work?". Today, however, researchers are paying increasing attention to successive questions that are equally important from a practical standpoint--not only does it…

Causal evaluations in teacher education are rare. Underlying reasons include a lack of clearly defined treatments, a lack of research designs that can work in the context of teacher education programs, and a lack of resources for enacting these designs. This article provides a framework for how to fill these gaps. We first propose an approach to…

Regression discontinuity (RD) designs have gained significant popularity as a quasi-experimental device for evaluating education programs and policies. In this paper, we present a comprehensive review of RD designs, focusing on the continuity-based framework, the most widely adopted RD framework. We first review the fundamental aspects of RD…

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental…

Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer" in PER, this paper discusses some of the fundamental issues in statistical causal inference. In…

Despite calls for more evidence regarding the effectiveness of teacher education practices, causal research in the field remains rare. One reason is that we lack designs and measurement approaches that appropriately meet the challenges of causal inference in the context of teacher education programs. This article provides a framework for how to…

Identifying causal mechanisms is an essential aspect of disparities research. Rigorous investigations of mechanisms can open the black box and clarify linkages in the causal chain, indicating how effects occur. Studies of mechanisms can also illuminate novel ways to reduce inequality when intervention at the root cause of unequal outcomes is not…

The learning sciences inherently involve interdisciplinary research with an overarching objective of advancing theories of learning and to inform the design and implementation of effective instructional methods and learning technologies. In these endeavors, learning sciences encompass diverse constructs, measures, processes, and outcomes…

Researchers commonly interpret effect sizes by applying benchmarks proposed by Jacob Cohen over a half century ago. However, effects that are small by Cohen's standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability.…

This article addresses the gap between what works in research and what works in practice. Currently, research in evidence-based education policy and practice focuses on randomized controlled trials. These can support causal ascriptions ("It worked") but provide little basis for local effectiveness predictions ("It will work…

The central purpose of this article is to provide an overview of the many ways in which special educators can generate and think about causal inference to inform policy and practice. Consideration of causality across different lenses can be carried out by engaging in multiple method and mixed methods ways of thinking about inference. This article…

Design research is strongly associated with the learning sciences community, and in the 2 decades since its conception it has become broadly accepted. Yet within and without the learning sciences there remains confusion about how to do design research, with most scholarship on the approach describing what it is rather than how to do it. This…

We contribute to debate about causal inferences in educational research in two ways. First, we quantify how much bias there must be in an estimate to invalidate an inference. Second, we utilize Rubin's causal model to interpret the bias necessary to invalidate an inference in terms of sample replacement. We apply our analysis to an inference…

Structural equation modelling (SEM) is a method for analysis of multivariate data from both non-experimental and experimental research. The method combines a structural model linking latent variables and a measurement model linking observed variables with latent variables. Its use in social science and educational research has grown since the…

In education, most naturally occurring data are clustered within contexts. Students are clustered within classrooms, classrooms are clustered within schools, and schools are clustered within districts. When people are clustered within naturally occurring organizational units such as schools, classrooms, or districts, the responses of people from…