Methods Matter: Improving Causal Inference in Educational and Social Science Research

By Richard J. Murnane, J. Willett. 2010

604 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/58c1e7ddce6dd586d0fe1d40fdfeeee67477599d

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Citations in the corpus

Using a Regression Discontinuity Design to Estimate the Impact of Placement Decisions in Developmental Math (2015, background): “The precision of RDD estimates depends on the size of the analysis sample and the degree to which the sample is closely clustered around the cut point of interest (Murnane and Willett 2011; Schochet 2006).”
The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models (2016, background): “Unfortunately, researchers are often interested in exploring or controlling for any number of covariates with the intent of yielding less biased estimates (Murnane & Willett, 2011) and modeling cross-level interactions (Hox, 2010; Raudenbush & Bryk, 2002; Snijders & Bosker, 1999).”
Evaluating the Effectiveness of Correctional Education: A Meta-Analysis of Programs That Provide Education to Incarcerated Adults (2013, background): “Other rigorous comparison-group designs, such as regression discontinuity designs and instrumental variables analysis, attempt to minimize selection bias in nonrandomized studies by capitalizing on arguably random processes, but in doing so, they must satisfy a larger set of assumptions to nullify t”
Does New Teacher Induction Really Improve Retention? (2017, methodology): “Drawing upon the approach used by Murnane and Willett (2011), our propensity score analyses occurred in six phases.”
The Effects of Test Translation on Young English Learners’ Mathematics Performance (2010, unknown): “…approximation to an experimental design that can be obtained from nonexperimental data (Bloom, 2010; Cook, Shadish, & Wong, 2008; Lee & Lemieux, 2009; Murnane & Willett, 2010; Robinson & Reardon, 2009; Schneider, Carnoy, Kilpatrick, Schmidt, & Shavelson, 2007; Shadish, Cook, & Campbell, 2002).”
Feelings of Preparedness Among Alternatively Certified Teachers (2011, methodology): “…to develop complex analytic models to investigate causal questions, given that teachers and students are situated within multiple, overlapping contexts and given that experimental control over education stakeholders and education variables can be difficult to attain (see Murnane & Willett, 2010).”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, background): “…MLMs are flexible and robust, researchers have highlighted that other, often simpler methods can answer a research question of interest while accounting for the clustering effect (Huang, 2014, 2016, 2018b, 2018c; McNeish, 2014; McNeish et al., 2016; Murnane & Willett, 2011).”
Looking beyond Enrollment: The Causal Effect of Need-Based Grants on College Access, Persistence, and Graduation (2016, background): “Oneway to examine this threat empirically is to repeat our analyses using a variety of windowwidths (Angrist and Lavy 1999; Murnane and Willett 2010).”
Improving Educational Outcomes in Developing Countries: Lessons from Rigorous Evaluations (2014, result): “…Banerjee & Duflo, 2011b; Banerjee et al., 2013; Bruns et al., 2011; Glewwe & Kremer, 2006; Glewwe et al., 2011; Kremer & Holla, 2009; McEwan, 2013; Murnane & Willett, 2011), we focused on studies with plausible identification strategies.5 Our review differs from previous efforts in that we do not…”
Can You Really Measure That? Combining Critical Race Theory and Quantitative Methods (2018, background): “As causal inference is a key part of contemporary quantitative research in education (Murnane & Willett, 2011), problems also arise when trying to attribute causes to the socially constructed notion of race (Holland, 2008).”
The Effect of Teacher Coaching on Instruction and Achievement: A Meta-Analysis of the Causal Evidence (2018, methodology): “Third, we required that studies employed an experimental or quasi-experimental research design capable of supporting causal inferences (Murnane & Willett, 2011; Shadish, Cook, & Campbell, 2002).”
Multilevel Modeling and Ordinary Least Squares Regression: How Comparable Are They? (2018, background): “In nonexperimental settings, the assumption of independent and identically distributed error terms has been referred to as “unreasonably strong” (Cameron & Miller, 2015, p. 332) that is often not met in practice (Bell & Jones, 2015; Murnane & Willett, 2011).”