Download or read book Estimating Impacts on Program Related Subgroups Using Propensity Score Matching written by Fatih Unlu and published by . This book was released on 2010 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper addresses methodological issues arising from an experimental study of North Carolina's Early College High School Initiative, a four-year longitudinal experimental study funded by Institute for Education Sciences. North Carolina implemented the Early College High School (ECHS) Initiative in response to low high school graduation rates. The goal of the initiative is to increase the number of students graduating from high school and who continue on and succeed in college. The study has three main goals: (1) Determine the impact of the model on selected student outcomes, including course-taking patterns, achievement, attitudes, and dropout and leaving rates; (2) Determine the extent to which outcomes differ by student characteristics; and (3) Examine the implementation of the model and the extent to which specific model components are associated with positive outcomes. Schools participating in the study identify an eligible pool of student applicants. The research team then randomly assigns students to either the treatment group (attending the ECHS) or the control group (business as usual). The outcomes for students in the two groups are then tracked and compared. The study follows an intent-to-treat model in that once a student is assigned to ECHS, he or she remains in the treatment group regardless of whether he or she ends up enrolling in ECHS, or leaves ECHS. Findings suggest that ECHS has the same impact on pass-rates for the Always-takers as the traditional high school. Where the ECHS are making difference is by increasing the number of students who are taking these courses. (Contains 5 figures, 6 tables and 4 footnotes.).

Download or read book Propensity Score Analysis written by Shenyang Guo and published by SAGE. This book was released on 2015 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.

Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book Using Local Matching to Improve Estimates of Program Impact written by Nathan Jones and published by . This book was released on 2011 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study the authors test whether matching using intact local groups improves causal estimates over those produced using propensity score matching at the student level. Like the recent analysis of Wilde and Hollister (2007), they draw on data from Project STAR to estimate the effect of small class sizes on student achievement. They propose a strategy for intact group matching in which they match treatment cases to control cases in other schools. A secondary goal of this analysis is to determine whether the use of geographic covariates (including latitude and longitude, as well as Census variables) improve the quality of matches over the use of student, teacher, and school covariates alone. They hypothesize that by incorporating physical distance into their matching, they will increase their likelihood of finding maximally similar comparison classrooms.

Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Download or read book Handbook of Matching and Weighting Adjustments for Causal Inference written by José R. Zubizarreta and published by CRC Press. This book was released on 2023-04-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Download or read book From Neurons to Neighborhoods written by National Research Council and published by National Academies Press. This book was released on 2000-11-13 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: How we raise young children is one of today's most highly personalized and sharply politicized issues, in part because each of us can claim some level of "expertise." The debate has intensified as discoveries about our development-in the womb and in the first months and years-have reached the popular media. How can we use our burgeoning knowledge to assure the well-being of all young children, for their own sake as well as for the sake of our nation? Drawing from new findings, this book presents important conclusions about nature-versus-nurture, the impact of being born into a working family, the effect of politics on programs for children, the costs and benefits of intervention, and other issues. The committee issues a series of challenges to decision makers regarding the quality of child care, issues of racial and ethnic diversity, the integration of children's cognitive and emotional development, and more. Authoritative yet accessible, From Neurons to Neighborhoods presents the evidence about "brain wiring" and how kids learn to speak, think, and regulate their behavior. It examines the effect of the climate-family, child care, community-within which the child grows.

Download or read book Treatment Effect Estimation with Propensity Score Matching written by Ricarda Schmidl and published by . This book was released on 2008 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Propensity Score Analysis written by Wei Pan and published by Guilford Publications. This book was released on 2015-04-07 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

Download or read book Using Propensity Scores in Quasi Experimental Designs written by William M. Holmes and published by SAGE Publications. This book was released on 2013-06-10 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of social and behavioral science disciplines.

Download or read book Using Propensity Scores in Quasi Experimental Designs written by William M. Holmes and published by SAGE Publications. This book was released on 2013-06-10 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using Propensity Scores in Quasi-Experimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasi-experimental designs and to fix or improve broken experiments. Requiring minimal use of matrix and vector algebra, the book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of disciplines.

Download or read book Evaluating the Performance of Propensity Score Matching Methods written by and published by . This book was released on 2017 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In education, researchers and evaluators are interested in assessing the impact of programs or interventions. Unfortunately, most education programs do not lend themselves to random assignment; participants generally self-select into programs. Lack of random assignment limits the claims that researchers can make about the impact of the program because individuals who self-select into the program may be qualitatively different from individuals who do not self-select into the program. Propensity score matching allows researchers to mimic random assignment by creating a matched comparison group that is similar to the treatment group on researcher-identified variables. There are a number of matching methods to choose from when employing propensity score matching. Matching methods vary in distance measures, matching algorithms, and rules for comparison group member selection that are used. Thus, the purpose of this study was to examine common matching techniques to determine how they differed in terms of the quantity and quality of matches and whether the results of subsequent group comparisons (e.g., significance test results, estimated effect sizes) varied across the different matching techniques. Differences across effect size, treatment group sample size, comparison-to-treatment ratio, and analysis technique were also examined. To empirically investigate the performance of common matching methods under known and systematically manipulated conditions, data were simulated to reflect values found in higher education, using a recent study by Jacovidis and her colleagues (in press). The choice of matching method dictates both the quality and quantity of the matches obtained and the resulting outcome analyses (e.g., statistical significance tests and estimated effect sizes). Although nearest neighbor matching with calipers produced better quality matches than the other matching methods, it also resulted in the loss of treatment group members. If treatment group members are excluded from the matched groups, representation of the treatment group could be compromised. If this happens, the researcher may want to select a matching method that does not result in a loss of treatment group members. It is up to the researcher to decide how to best balance the quality and quantity of matches, while recognizing that this decision can impact the accuracy of the outcome analyses.

Download or read book Impact Evaluation in Practice Second Edition written by Paul J. Gertler and published by World Bank Publications. This book was released on 2016-09-12 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policy makers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluations and the best ways to use them to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation. The handbook is divided into four sections: Part One discusses what to evaluate and why; Part Two presents the main impact evaluation methods; Part Three addresses how to manage impact evaluations; Part Four reviews impact evaluation sampling and data collection. Case studies illustrate different applications of impact evaluations. The book links to complementary instructional material available online, including an applied case as well as questions and answers. The updated second edition will be a valuable resource for the international development community, universities, and policy makers looking to build better evidence around what works in development.

Download or read book Using a Two Staged Propensity Score Matching Strategy and Multilevel Modeling to Estimate Treatment Effects in a Multisite Observational Study written by Jordan H. Rickles and published by . This book was released on 2012 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study is designed to demonstrate and test the utility of the proposed two-stage matching method compared to other analytic methods traditionally employed for multisite observational studies. More specifically, the study addresses the following research questions: (1) How do different specifications of the matching method influence covariate balance? (2) How do different specifications in the matching method influence inferences about treatment effect and effect heterogeneity? The different matching method specifications include differences in the propensity score model and whether a between-site match, within-site match, or two-stage matching process is used. The simulation results indicate that the two-stage matching method balances the desire for within-site covariate balance and the desire to retain as many treatment units in the analysis as possible. Relative to more straightforward matching methods, however, the two-stage matching method does not result in greater covariate balance nor less biased effect estimation. As a result, more straightforward methods that address the nested data structure--such as within-site matching or pooled matching with a random-intercept-and-slope propensity score model--might be preferable to the more complex two-stage matching method. These conclusions are based on a finite set of data generating conditions, with a small set of important confounders at both the unit and site level and a reasonable within-site sample size for matching. Future research should examine the performance of various propensity score model and matching methods under more extreme data conditions. (Contains 2 tables and 5 figures.).

Download or read book Propensity Score Methods for Estimating Causal Effects from Complex Survey Data written by Robert D. Ashmead and published by . This book was released on 2014 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propensity score based adjustments are popular in analyzing observational data. To obtain valid causal estimates, we often assume that the sample is a simple random sample from the population of interest or that the treatment effect is homogeneous across the population. When data from surveys with complex design are used, ad-hoc adjustments to incorporate survey weights are often applied without rigorous justification. In this dissertation, we propose a super population framework, which includes a pair of potential outcomes for every unit in the population, to streamline the propensity score analysis for complex survey data. Based on the proposed framework, we develop propensity score stratification, weighting, and matching estimators along with a new class of hybrid estimators and corresponding variance estimators that adjust for survey design features. Additionally, we argue that in this context we should estimate the propensity scores by a weighted logistic regression using the sampling weights. Various estimators are compared in simulation studies that calculate the bias, mean-squared error, and coverage of the estimators. As the treatment effect becomes more heterogeneous, the gains of adjusting for the survey design increase. Lastly, we demonstrate the proposed methods using a real data example that estimates the effect of health insurance on self-rated health for adults in Ohio who may be eligible for tax credits to purchase medical insurance from the healthcare insurance exchange.

Download or read book Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score written by Keisuke Hirano and published by . This book was released on 2000 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the pre-treatment variables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pre-treatment, the propensity score, also removes the entire bias associated with differences in pre-treatment variables. Thus it is possible to obtain unbiased estimates of the treatment effect without conditioning on a possibly high-dimensional vector of pre-treatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency. We show that weighting with the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects. This result holds whether the pre-treatment variables have discrete or continuous distributions. We provide intuition for this result in a number of ways. First we show that with discrete covariates, exact adjustment for the estimated propensity score is identical to adjustment for the pre-treatment variables. Second, we show that weighting by the inverse of the estimated propensity score can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score. Finally, we make a connection to results to other results on efficient estimation through weighting in the context of variable probability sampling.

Download or read book Practical Propensity Score Methods Using R written by Walter Leite and published by SAGE Publications. This book was released on 2016-10-28 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Propensity Score Methods Using R by Walter Leite is a practical book that uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book’s free online resources help them apply the text’s concepts to the analysis of their own data.