Using Propensity Scores In Quasi Experimental Designs To Equate Groups
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Using Propensity Scores in Quasi-Experimental Designs to Equate Groups
Education research rarely lends itself to large scale experimental research and true randomization, leaving the researcher to quasi-experimental designs. The problem with quasi-experimental research is that underlying factors may impact group selection and lead to potentially biased results. One way to minimize the impact of non-randomization is through the use of propensity scores. First developed by Rosenbaum & Rubin (1983b), these scores allow researchers to balance non-equivalent groups though matching on a singular scalar variable. The present paper will present the theoretical framework behind propensity scores along with a heuristic data set to demonstrate propensity score calculation and evaluation. Appended is: "PASW (v17.0) Syntax for Propensity Score Matching using Matching within Calipers." (Contains 4 tables.).
Using R With Multivariate Statistics
Author: Randall E. Schumacker
language: en
Publisher: SAGE Publications
Release Date: 2015-07-06
Using R with Multivariate Statistics is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis. Designed to serve as a companion to a more comprehensive text on multivariate statistics, this book helps students and researchers in the social and behavioral sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.
Quasi-Experimentation
Author: Charles S. Reichardt
language: en
Publisher: Guilford Publications
Release Date: 2019-09-02
Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest-posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Reichardt compares quasi-experiments to randomized experiments and discusses when and why the former might be a better choice. Modern moethods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility.