Maynard, Rebecca A

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2004 - 200912010 - 20131
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  • Publication
    Programs and Policies in Education, Crime and Justice and Social Welfare: Practical Recommendations Based on 14 Test-bed Reviews
    (2004-01-01) Cottingham, Phoebe; Maynard, Rebecca A; Stagner, Matthew
    Review teams tested the systematic review procedures and principles developed under the Campbell Collaboration. Fourteen review teams selected topics for intervention reviews in social policy, education, and criminal justice. Review protocols gave criteria for the extensive research literature search. Randomised Controlled Trials were selected. Systematic reviewers should give careful attention to defining the review topic, setting study inclusion and exclusion criteria, handling variability in outcome measurement and study reporting, appropriate uses of statistical meta-analysis, and reporting review results. Significant differences in review results were observed based on review criteria and procedures.
  • Publication
    PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies
    (2013-01-01) Maynard, Rebecca A; Dong, Nianbo
    This paper complements existing power analysis tools by offering tools to compute minimum detectable effect sizes (MDES) for existing studies and to estimate minimum required sample sizes (MRSS) for studies under design. The tools that accompany this paper support estimates of MDES or MSSR for 21 different study designs that include 14 random assignment designs (6 designs in which individuals are randomly assigned to treatment or control condition and 8 in which clusters of individuals are randomly assigned to condition, with models differing depending on whether the sample was blocked prior to random assignment and by whether the analytic models assume constant, fixed, or random effects across blocks or assignment clusters); and 7 quasi-experimental designs (an interrupted time series design and 6 regression discontinuity designs that vary depending on whether the sample was blocked prior to randomization, whether individuals or clusters of individuals are assigned to treatment or control condition, and whether the analytic models assume fixed or random effects).