Quasi-experimental methods
This book intends to familiarize readers to experimental and quasi-experimental designs used in research.
Regression Discontinuity Design
This paper from IEL examines and discusses the statistical power in the comparative regression discontinuity design.
In this article, IEL describes three main limitations of the regression discontinuity design (RDD) when compared to randomized experiment (RE) and discusses a possible solution to these limitations.
This piece from IEL compares randomized control trial (RCT), the basic regression discontinuity (RD) and comparative regression discontinuity design (CRD) in terms of precision of causal estimates.
Propensity score matching
This paper investigates the use of propensity score methods to approximate factorial experimental designs to analyze the relationship between two variables and an outcome using a Monte Carlo Simulation.
The authors examined whether children receiving special education services displayed (a) greater reading or mathematics skills, (b) more frequent learning-related behaviors, or (c) less frequent externalizing or internalizing problem behaviors than closely matched peers not receiving such services. To do so, they used propensity score matching techniques to analyze data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998—99, a large-scale, nationally representative sample of U.S. schoolchildren.
Interrupted time series
In this article, the author introduces the itsa command, which performs interrupted time-series analysis for single- and multiple-group comparisons. In an interrupted time-series analysis, an outcome variable is observed over multiple, equally spaced time periods before and after the introduction of an intervention that is expected to interrupt its level or trend.
This paper demonstrates a data-analysis using multiple group time-series design.
This study investigated how postpartum parenting education influenced first-time mothers’ mother–infant interaction quality and parenting sense of competence using a a multiple time series design.
This study utilized an interrupted time series design to examine differences in students’ postcounseling academic success compared to their precounseling academic success.
Instrumental variable
The method of instrumental variables provides a framework to study causal effects in both randomized experiments with non-compliance and in observational studies where natural circumstances produce as if random nudges to accept treatment. Traditionally, inference for instrumental variables relied on asymptotic approximations of the distribution of the Wald estimator or two-stage least squares, often with structural modelling assumptions and/or moment conditions. The authors utilize the randomization inference approach to instrumental variables inference and present three different applications from the social sciences.