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Data and Syntax for Investigating Science Education Effect Sizes: Implications for Power Analyses and Programmatic Decisions

Version
1
Resource Type
Dataset
Creator
  • Kowalski, Susan M. (BSCS Science Learning)
  • Taylor, Joseph A. (University of Colorado, Colorado Springs)
Publication Date
2019-01-05
Funding Reference
  • National Science Foundation
    • Award Number: 1118555
Free Keywords
effect size; meta-analysis; program evaluation; science education; students
Description
  • Abstract

    A priori power analyses allow researchers to estimate the number of participants needed to detect the effects of an intervention. However, power analyses are only as valid as the parameter estimates used. One such parameter, the expected effect size, can vary greatly depending on several study characteristics, including the nature of the intervention, developer of the outcome measure, and age of the participants. Researchers should understand this variation when designing studies. Our meta-analysis examines the relationship between science education intervention effect sizes and a host of study characteristics, allowing primary researchers to access better estimates of effect sizes for a priori power analyses. The results of this meta-analysis also support programmatic decisions by setting realistic expectations about the typical magnitude of impacts for science education interventions.

    The primary research question of this meta-analysis is: What is the relationship between the magnitude of the intervention effects and key study characteristics? The study characteristics of interest included the design (randomized studies compared to matched quasi-experimental studies), whether the outcome measure was developed by the study authors, who receives the intervention (e.g., students only, teachers only, both students and teachers), the science discipline targeted by the intervention, the treatment provider’s role (e.g., researcher or teacher), and the grade level of the students.




  • Weighting

    We made several adjustments to the data, including Winsorization of sample sizes, Winsorization of effect sizes, grand mean centering, and weighting effect by the inverse variance.
Temporal Coverage
  • 2001-01-01 / 2014-12-31
    Time Period: Mon Jan 01 00:00:00 EST 2001--Wed Dec 31 00:00:00 EST 2014
Sampled Universe
Manuscripts associated with studies of science education interventions conducted internationally, published in English.
Availability
Download

Update Metadata: 2019-03-23 | Issue Number: 1 | Registration Date: 2019-03-23

Kowalski, Susan M.; Taylor, Joseph A. (2019): Data and Syntax for Investigating Science Education Effect Sizes: Implications for Power Analyses and Programmatic Decisions. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E109061V1