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Ledford, Jennifer R. – American Journal of Evaluation, 2018
Randomization of large number of participants to different treatment groups is often not a feasible or preferable way to answer questions of immediate interest to professional practice. Single case designs (SCDs) are a class of research designs that are experimental in nature but require only a few participants, all of whom receive the…
Descriptors: Research Design, Randomized Controlled Trials, Experimental Groups, Control Groups
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Bell, Stephen H.; Peck, Laura R. – American Journal of Evaluation, 2013
To answer "what works?" questions about policy interventions based on an experimental design, Peck (2003) proposes to use baseline characteristics to symmetrically divide treatment and control group members into subgroups defined by endogenously determined postrandom assignment events. Symmetric prediction of these subgroups in both…
Descriptors: Program Effectiveness, Experimental Groups, Control Groups, Program Evaluation
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St.Clair, Travis; Cook, Thomas D.; Hallberg, Kelly – American Journal of Evaluation, 2014
Although evaluators often use an interrupted time series (ITS) design to test hypotheses about program effects, there are few empirical tests of the design's validity. We take a randomized experiment on an educational topic and compare its effects to those from a comparative ITS (CITS) design that uses the same treatment group as the experiment…
Descriptors: Time, Evaluation Methods, Measurement Techniques, Research Design
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Mueller, Christoph Emanuel; Gaus, Hansjoerg – American Journal of Evaluation, 2015
In this article, we test an alternative approach to creating a counterfactual basis for estimating individual and average treatment effects. Instead of using control/comparison groups or before-measures, the so-called Counterfactual as Self-Estimated by Program Participants (CSEPP) relies on program participants' self-estimations of their own…
Descriptors: Intervention, Research Design, Research Methodology, Program Evaluation
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Azzam, Tarek; Jacobson, Miriam R. – American Journal of Evaluation, 2013
This article explores the viability of online crowdsourcing for creating matched-comparison groups. This exploratory study compares survey results from a randomized control group to survey results from a matched-comparison group created from Amazon.com's MTurk crowdsourcing service to determine their comparability. Study findings indicate…
Descriptors: Matched Groups, Control Groups, Comparative Analysis, Evaluation
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Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen – American Journal of Evaluation, 2016
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Descriptors: Intervention, Multivariate Analysis, Mixed Methods Research, Models
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Harvill, Eleanor L.; Peck, Laura R.; Bell, Stephen H. – American Journal of Evaluation, 2013
Using exogenous characteristics to identify endogenous subgroups, the approach discussed in this method note creates symmetric subsets within treatment and control groups, allowing the analysis to take advantage of an experimental design. In order to maintain treatment--control symmetry, however, prior work has posited that it is necessary to use…
Descriptors: Experimental Groups, Control Groups, Research Design, Sampling
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Peck, Laura R. – American Journal of Evaluation, 2007
This article uses propensity scores to identify subgroups of individuals most likely to experience a reduction in cash benefits because of sanctions in some of the programs that make up the National Evaluation of Welfare-to-Work Strategies. It extends program evaluation methodology by using propensity scoring to identify the subgroups of…
Descriptors: Program Evaluation, Control Groups, Welfare Recipients, Research Design