Using Propensity Scores in Quasi Experimental Designs

Author: William M. Holmes
Publisher: SAGE Publications
ISBN: 1483310817
Format: PDF, ePub, Docs
Download Now
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.

A Comparison of Propensity Score Estimation and Adjustment Methods on Simulated Data

Author: Jason K. Luellen
Publisher:
ISBN: 9780549020097
Format: PDF, Kindle
Download Now
Faced with potential selection bias resulting from nonequivalent groups, researchers employing quasi-experimental designs have become increasingly interested in statistical adjustments to the estimates of treatment effects based upon the propensity score. Propensity score analysis is the process of trying to balance nonequivalent groups by estimating each participant's conditional probability of treatment assignment using observed covariates and then using these probabilities (i.e., propensity scores) for case matching, stratification, covariance adjustment, or weighting of observations. Numerous propensity score methods have been proposed in the literature. This study used simulated data to examine the relative performance of five methods of estimating propensity scores (logistic regression, classification trees, bootstrap aggregation, boosted regression, and random forests) crossed with four types of adjustments that utilize propensity scores (matching, stratification, covariance adjustment, and weighting) at two levels of sample sizes (N = 200 and N = 1,000). One thousand Monte Carlo replicates were used per level of sample size. All combinations of propensity score methods led to at least some average reduction in selection bias, and for most combinations of methods these reductions were statistically significant. However, this seemingly promising finding is tempered by the fact that bias was actually introduced in many replicates, especially when the level of sample size was 200. The traditional approach to estimating propensity scores, logistic regression, worked well at reducing selection bias, on average, at both sample sizes and tended to result in more precise estimates of the treatment effect with less potential for introducing bias. Other combinations of propensity score methods performed better than logistic regression, on average, but with less precision in the estimates and greater potential for introducing bias. These included random forests at N = 200 and boosted regression and random forests at N = 1,000. Of the ensemble methods, boosted regression, in particular, might be a useful alternative to logistic regression for large sample sizes once the default settings have been changed to favor PSA. With regard to methods of adjusting outcomes using propensity scores, weighting tended to perform poorly. Otherwise, matching, stratification, and covariance adjustment were fairly competitive and a clear favorite was not discerned.

Propensity Score Analysis

Author: Wei Pan
Publisher: Guilford Publications
ISBN: 1462519490
Format: PDF, ePub
Download Now
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).

The SAGE Handbook of Social Research Methods

Author: Pertti Alasuutari
Publisher: SAGE
ISBN: 1412919924
Format: PDF, Docs
Download Now
The SAGE Handbook for Social Research Methods is a must for every social-science researcher. It charts the new and evolving terrain of social research methodology, covering qualitative, quantitative, and mixed methods in one volume. The Handbook includes chapters on each phase of the research process: research design, methods of data collection, and the processes of analyzing and interpreting data. As its editors maintain, there is much more to research than learning skills and techniques; methodology involves the fit between theory, research questions, research design, and analysis.

Propensity Score Analysis

Author: Shenyang Guo
Publisher: SAGE
ISBN: 1452235007
Format: PDF, ePub, Mobi
Download Now
Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

Practical Propensity Score Methods Using R

Author: Walter Leite
Publisher: SAGE Publications
ISBN: 1483313395
Format: PDF, ePub, Mobi
Download Now
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.

The SAGE Encyclopedia of Social Science Research Methods

Author: Michael Lewis-Beck
Publisher: SAGE
ISBN: 9780761923633
Format: PDF, Mobi
Download Now
"The first encyclopedia to cover inclusively both quantitative and qualitative research approaches, this set provides clear explanations of 1,000 methodologies, avoiding mathematical equations when possible with liberal cross-referencing and bibliographies. Each volume includes a list of works cited, and the third contains a comprehensive index and lists of person names, organizations, books, tests, software, major concepts, surveys, and methodologies."--"Reference that rocks," American Libraries, May 2005.

Designing a Quasi Experimental Study to Test the Community College Penalty Using Propensity Score Matching Methods

Author: Dietrich
Publisher:
ISBN: 9781473956551
Format: PDF
Download Now
We present a case study of the process through which a methodology was developed and applied to a quasi-experimental research study that employed propensity score matching. Methodological decisions are discussed and summarized, including an explanation of the approaches selected for each step in the study as well as rationales for these selections. Examples include identification and creation of treatment and control groups, application of relational database software and methods, calculation of propensity scores, accounting for multilevel effects, post-treatment changes and identification of post-treatment adjustment, and selection of a propensity matching algorithm. We demonstrate that much of the propensity score matching process focuses on creating a valid counterfactual or control group. Thus, propensity score matching allows researchers to focus on creating conditions that help show the impact of the treatment, rather than on other factors that may be related to the outcome of interest. Additional items discussed include decisions about missing data, use of balancing diagnostics, determination of the effect of the treatment on the outcome of interest, and sensitivity analysis. The authors propose that an appropriate methodology for such a study is best arrived at through an iterative, experimental process.

International Development

Author: Bruce Currie-Alder
Publisher: OUP Oxford
ISBN: 0191651699
Format: PDF, ePub, Mobi
Download Now
Thinking on development informs and inspires the actions of people, organizations, and states in their continuous effort to invent a better world. This volume examines the ideas behind development: their origins, how they have changed and spread over time, and how they may evolve over the coming decades. It also examines how the real-life experiences of different countries and organizations have been inspired by, and contributed to, thinking on development. The extent to which development 'works' depends in part on particular local, historical, or institutional contexts. General policy prescriptions fail when the necessary conditions that make them work are either absent, ignored, or poorly understood. There is a need to grasp how people understand their own development experience. If the countries of the world are varied in every way, from their initial conditions to the degree of their openness to outside money and influence, and success is not centred in any one group, it stands to reason that there cannot be a single recipe for development. Each chapter provides an analytical survey of thinking about development that highlights debates and takes into account critical perspectives. It includes contributions from scholars and practitioners from the global North and the global South, spanning at least two generations and multiple disciplines. It will be a key reference on the concepts and theories of development - their origins, evolution, and trajectories - and act as a resource for scholars, graduate students, and practitioners.