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Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse – Grantee Submission, 2022
Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units…
Descriptors: Causal Models, Statistical Inference, Computation, Evaluation Methods
Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Pashley, Nicole E.; Miratrix, Luke W. – Grantee Submission, 2019
In the causal inference literature, evaluating blocking from a potential outcomes perspective has two main branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2016
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Descriptors: Causal Models, Markov Processes, Longitudinal Studies, Probability
Gelman, Andrew; Imbens, Guido – National Bureau of Economic Research, 2014
It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…
Descriptors: Regression (Statistics), Mathematical Models, Causal Models, Research Methodology
Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P. – MDRC, 2014
A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…
Descriptors: Regression (Statistics), Research Design, Quasiexperimental Design, Research Methodology
Finch, W. Holmes; French, Brian F. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible…
Descriptors: Causal Models, Computation, Data, Sampling
Raudenbush, Stephen W.; Reardon, Sean F.; Nomi, Takako – Journal of Research on Educational Effectiveness, 2012
Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…
Descriptors: Program Evaluation, Statistical Analysis, Hierarchical Linear Modeling, Computation
Bollen, Kenneth A.; Davis, Walter R. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
We discuss the identification, estimation, and testing of structural equation models that have causal indicators. We first provide 2 rules of identification that are particularly helpful in models with causal indicators--the 2C emitted paths rule and the exogenous X rule. We demonstrate how these rules can help us distinguish identified from…
Descriptors: Structural Equation Models, Testing, Identification, Statistical Significance
Schluchter, Mark D. – Multivariate Behavioral Research, 2008
In behavioral research, interest is often in examining the degree to which the effect of an independent variable X on an outcome Y is mediated by an intermediary or mediator variable M. This article illustrates how generalized estimating equations (GEE) modeling can be used to estimate the indirect or mediated effect, defined as the amount by…
Descriptors: Intervals, Predictor Variables, Equations (Mathematics), Computation
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation