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Tipton, Elizabeth – American Journal of Evaluation, 2022
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to…
Descriptors: Sampling, Sample Size, Selection, Randomized Controlled Trials
Xinran Li; Peng Ding; Donald B. Rubin – Grantee Submission, 2020
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of…
Descriptors: Experiments, Research Design, Randomized Controlled Trials, Sampling
Natesan, Prathiba; Hedges, Larry V. – Grantee Submission, 2016
Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in SCDs, no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in…
Descriptors: Bayesian Statistics, Statistical Inference, Research Design, Models
Hedges, Larry V.; Pustejovsky, James E.; Shadish, William R. – Online Submission, 2012
Single case designs are a set of research methods for evaluating treatment effects by assigning different treatments to the same individual and measuring outcomes over time and are used across fields such as behavior analysis, clinical psychology, special education, and medicine. Emerging standards for single case designs have focused attention on…
Descriptors: Research Design, Effect Size, Meta Analysis, Computation

Lunneborg, Clifford E.; Tousignant, James P. – Multivariate Behavioral Research, 1985
This paper illustrates an application of Efron's bootstrap to the repeated measures design. While this approach does not require parametric assumptions, it does utilize distributional information in the sample. By appropriately resampling from study data, the bootstrap may determine accurate sampling distributions for estimators, effects, or…
Descriptors: Hypothesis Testing, Research Design, Research Methodology, Sampling

Orwin, Robert G. – Journal of Educational Statistics, 1983
Rosenthan's (1979) concept of fail-safe N has thus far been applied to probability levels exclusively. This note introduces a fail-safe N for effect size. (Author)
Descriptors: Effect Size, Meta Analysis, Research Design, Research Problems

Olejnik, Stephen F.; Algina, James – 1985
This paper examined the rank transformation approach to analysis of variance as a solution to the Behrens-Fisher problem. Using simulation methodology four parameters were manipulated for the two group design: (1) ratio of population variances; (2) distribution form; (3) sample size and (4) population mean difference. The results indicated that…
Descriptors: Analysis of Variance, Computer Simulation, Error of Measurement, Hypothesis Testing
Johnson, Colleen Cook – 1993
The purpose of this study is to help define the precise nature and limits of the tolerable range in which a researcher may be relatively confident about the statistical validity of his or her research findings, focusing specifically on the statistical validity of results when violating the assumptions associated with the one-way, fixed-effects…
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Computer Simulation