In This Article Methodological Approaches for Impact Evaluation in Educational Settings

  • Introduction
  • General Overviews
  • Randomized Control Trials
  • Regression Discontinuity Design
  • Interrupted Time Series Designs
  • Non-Equivalent Comparison Group Designs

Education Methodological Approaches for Impact Evaluation in Educational Settings
by
Kylie L. Anglin, Anandita Krishnamachari, Vivian C. Wong
  • LAST MODIFIED: 26 February 2020
  • DOI: 10.1093/obo/9780199756810-0244

Introduction

Since the start of the War on Poverty in the 1960s, social scientists have developed and refined experimental and quasi-experimental methods for evaluating and understanding the ways in which public policies, programs, and interventions affect people’s lives. The overarching mission of many social scientists is to understand “what works” in education and social policy. These are causal questions about whether an intervention, practice, program, or policy affects some outcome of interest. Although causal questions are not the only relevant questions in program evaluation, they are assumed by many in the fields of public health, economics, social policy, and now education to be the scientific foundation for evidence-based decision making. Fortunately, over the last half-century, two methodological advances have improved the rigor of social science approaches for making causal inferences. The first was acknowledging the primacy of research designs over statistical adjustment procedures. Donald Campbell and colleagues showed how research designs could be used to address many plausible threats to validity. The second methodological advancement was the use of potential outcomes to specify exact causal quantities of interest. This allowed researchers to think systematically about research design assumptions and to develop diagnostic measures for assessing when these assumptions are met. This article reviews important statistical methods for estimating the impact of interventions on outcomes in education settings, particularly programs that are implemented in field, rather than laboratory, settings. We begin by describing the causal inference challenge for evaluating program effects. Then four research designs are discussed that may be used for estimating program impacts. The article highlights what the Campbell tradition identifies as the strongest causal research designs: the randomized experiment and the regression-discontinuity designs. These approaches have the advantage of transparent assumptions for yielding causal effects. The article then discusses weaker but more commonly used approaches estimating effects, including the interrupted time series and the non-equivalent comparison group designs. For the interrupted time series design, differences-in-differences are discussed as a more generalized approach to time series methods; for non-equivalent comparison group designs, the article highlights propensity score matching as a method for creating statistically equivalent groups on the basis of observed covariates. For each research design, references are included that discuss the underlying theory and logic of the method, exemplars of the approach in field settings, and recent methodological extensions to the design. The article concludes with a discussion of practical considerations for evaluating interventions in field settings, including the external validity of estimated effects from impact studies.

General Overviews

The fundamental problem of causal inference is that we cannot observe both what happens to a student when they receive an intervention and what would have occurred in an alternate reality in which the same student did not receive an intervention. For example, researchers can observe what happens to children in a preschool program but cannot observe what would have happened to the same children had they not entered preschool. To study the causal effect of a program or intervention, one needs a counterfactual, or something that is contrary to fact. Given that researchers never observe the counterfactual, we look for approximations (e.g., older siblings, neighborhood children, children in a nationally representative survey, or randomly assigned control children not exposed to the treatment). The Rubin Causal Model introduced in Rubin 1974 formalizes this reasoning mathematically. It is based on the idea that every unit has a potential outcome based on its “assignment” to a treatment or control condition. Using a potential outcomes framework, researchers are able to define a causal estimand of interest for a well-defined treatment and inference population, as well as assumptions required for a research design to yield a valid effect. Campbell and Stanley 1963 demonstrates how these assumptions may be violated in field settings through their list of “validity threats.” Cook and Campbell 1979 and Shadish et al. 2002 extend this idea by introducing four types of validity threats, including threats to internal, external, statistical conclusion, and construct validity. Angrist and Pischke 2009 provides an up-to-date overview of common methodological approaches from an econometric perspective and discusses estimation procedures for producing causal estimates. Angrist and Pischke 2015 offers a more approachable overview of the same material intended for an undergraduate audience. Imbens and Rubin 2015 and Morgan and Winship 2007 straddle the econometric and statistics literature and offer additional insights about causal inference from a potential outcomes perspective and a causal graph theory perspective, respectively. For an overview of key experimental and quasi-experimental designs specific to the field of education, see Murnane and Willett 2011 and Stuart 2007.

  • Angrist, J., and J.-S. Pischke. 2009. Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton Univ. Press.

    DOI: 10.1515/9781400829828E-mail Citation »

    This book is a reference on methods of causal inference using a potential outcomes framework. It covers randomized experiments, statistical matching, instrumental variables, difference-in-differences, and regression discontinuity. The book describes each design and its assumptions formally through a series of proofs and informally through applied examples. Though written for a graduate student audience, it is a useful resource for any evaluator with training in probability and statistics.

  • Angrist, J., and J. -S. Pischke. 2015. Mastering ’metrics: The path from cause to effect. Princeton, NJ: Princeton Univ. Press.

    E-mail Citation »

    This book provides a more approachable and conversational companion to Angrist and Pischke 2009. While both books describe the same methods of causal inference (randomized control trials, statistical matching, instrumental variables, regression discontinuity, and differences-in-differences designs), this book focuses more on conceptual understanding than on formal proofs—though brief proofs are provided. The book is written as an introduction to causal inference for undergraduate economics students.

  • Campbell, D. T., and J. C. Stanley. 1963. Experimental and quasi-experimental design for research. Boston, MA: Houghton Mifflin.

    E-mail Citation »

    This seminal book outlines the major threats to internal validity (Did the intervention cause the observed effect?) and external validity (To what population, settings, treatments, and outcomes can this effect be generalized?) and provides an overview of how design features can address these threats. While the book discusses quasi-experimental designs, it is best suited for an overview of conceptual challenges related to causal inference rather than for guidance in statistical methods in estimating effects.

  • Cook, T. D., and D. T. Campbell. 1979. Quasi-experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.

    E-mail Citation »

    Similar to Campbell and Stanley 1963, the first chapters of this book introduce the challenge of causal inference and threats to validity. The book updates Campbell and Stanley 1963 by also addressing analytical approaches. Helpfully, the book concludes with a section outlining major obstacles to conducting randomized experiments and describing situations that are particularly conducive to experimental evaluation.

  • Imbens, G., and D. Rubin. 2015. Causal inference for statistics, social, and biomedical sciences: An introduction. Cambridge, UK: Cambridge Univ. Press.

    DOI: 10.1017/CBO9781139025751E-mail Citation »

    This textbook provides a rigorous introduction to the potential outcomes framework. Because the book relies on formal mathematical derivations, it is most appropriate for those with a solid understanding of probability and statistics. The book discusses randomized experiments (including instrumental variables for non-compliance) and matching methods but does not provide an overview of quasi-experimental designs. Applied examples from education, social science, and biomedical science are used to illustrate concepts.

  • Morgan, S., and C. Winship. 2007. Counterfactuals and causal inference: Methods and principles for social research. Cambridge, UK: Cambridge Univ. Press.

    DOI: 10.1017/CBO9780511804564E-mail Citation »

    This textbook discusses how to answer causal questions using observational data rather than data where researchers have the opportunity to manipulate the treatment assignment. The book discusses randomized experiments primarily as a starting point to further understanding on non-experimental research designs, but several concepts, including the potential outcomes framework, are explained in detail with the help of causal diagrams, structural models, and examples from the social sciences.

  • Murnane, R., and J. Willett. 2011. Methods matter: Improving causal inference in educational and social science research. New York: Oxford Univ. Press.

    E-mail Citation »

    This book is a broadly accessible reference to causal inference in education research. It illustrates important concepts in the design and analysis of randomized experiments, quasi-experiments (including the difference-in-difference, regression discontinuity, and instrumental variables approaches), and observational studies. High-quality causal studies in the field of education are used to demonstrate and evaluate the decisions researchers make in the design and analysis of a study.

  • Rubin, D. B. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66.5: 688–701.

    DOI: 10.1037/h0037350E-mail Citation »

    Provides the fundamental building blocks for modern program evaluation. Rubin conceptualizes the fundamental challenge of causal inference using a series of potential outcomes—individual outcomes in the presence of treatment and in the absence of treatment. This conceptualization allows for the formalization of both experimental and non-experimental design assumptions and is often referred to as the Rubin causal model.

  • Shadish, W. R., T. D. Cook, and D. T. Campbell. 2002. Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.

    E-mail Citation »

    This book is a successor to Campbell and Stanley 1963 and Cook and Campbell 1979. Provides a comprehensive discussion of the design elements a researcher may include to improve internal validity and provides the conceptual theory for research design choices. The latter part of the book proposes a theoretical framework for generalized causal inference.

  • Stuart, E. A. 2007. Estimating causal effects using school-level data sets. Educational Researcher 36.4: 187–198.

    DOI: 10.3102/0013189X07303396E-mail Citation »

    Stuart provides a survey of evaluation approaches with school-level data, including randomized experiments, regression discontinuity, interrupted time series, and non-equivalent comparison group designs. The article provides an overview of the National Longitudinal School-Level State Assessment School Database (NLSLASD) and key considerations to keep in mind when using the NLSLASD or other school-level datasets to answer causal questions.

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