Project Activities
The project team will conduct a systematic review of interventions that target MAST constructs across five phases. The research team will search, screen, and select relevant studies; code studies, estimate study effect sizes, and build the core dataset to be used for the meta-analysis; collect original intervention materials from all eligible studies to conduct a scoping review of intervention practices; conduct iterative coding and thematic analysis to generate a parsimonious set of practices aligned with MAST and determine their relative effects; and develop a conceptual framework blueprint to guide future research. Findings will also inform the creation of the MAST thesaurus, a standardized set of terms to describe database content in a consistent fashion.
Structured Abstract
Setting
To be included in the meta-analysis, studies must examine interventions with one or more components aligned with MAST to improve education outcomes for K–12 students with disabilities in the United States; measure at least one of the four primary outcomes of interest (academic; social, emotional, or behavioral; functional; and secondary outcomes related to transition and progression through school systems); be published in English after 1990; and use data collected through experimental, quasi-experimental, or single-case design research.
Sample
The studies included in this meta-analysis will include K-12 students with disabilities.
Factors
The primary factor under investigation is the use of a MAST intervention. In addition, the type of intervention (for example, whether it is a targeted or a multicomponent intervention), student demographics (disability category, grade, race/ethnicity), and type of implementer (teacher, researcher, technology) will be examined as factors that may moderate the effects.
Research design and methods
In phase 1, researchers will identify studies that meet eligibility criteria using the following systematic search methods: electronic databases searches, reference harvesting, journal hand searches, and examination of previously conducted reviews. The research team will screen titles and abstracts and then review full-text articles to select studies for inclusion. Project staff will be trained to 90 percent reliability to ensure accuracy of search, screening, and selection procedures. In phase 2, the researchers will code all eligible studies, extract data to calculate effect sizes, and conduct the planned meta-analysis. In phase 3, the team will conduct a scoping review of original intervention materials from eligible studies. They will contact study authors, and review and inventory materials. In phase 4, they will identify intervention practices through an iterative coding and thematic analysis process, followed by the planned analyses. Finally, in phase 5 they will develop publications detailing key findings, refine a conceptual framework to guide future research and intervention development, and archive the final dataset in a data sharing repository.
Control condition
Due to the nature of the research design, there is no control condition.
Key measures
Education outcomes will include academic (such as literacy or STEM), social, emotional, behavioral, functional, and secondary/transition outcomes. The measures will vary depending on whatever measure was used in a given study.
Data analytic strategy
To identify special education interventions that target MAST-driven constructs and determine their effects for K–12 students with disabilities, robust variance estimation methods will be used to estimate separate mean effect sizes for each education outcome after controlling for various factors. A multi-level meta-analysis model will be used to provide omnibus effect size estimates across cases (as appropriate for single-case design studies), participants, and studies. Meta-regression analyses will be used to examine whether moderators of interest influence effects of MAST interventions. To examine the degree to which different intervention practices are targeting similar MAST constructs, a bubble plot analysis will be used to visualize data patterns. To identify the relative effects of different intervention practices and determine the optimal MAST intervention to improve education outcomes for K–12 students with disabilities, the research team will use Bayesian network meta-analysis (B-NMA) models. The model with the best fit will be used as the primary analysis model, treating others as sensitivity analyses. Network meta-regression will be conducted separately for each moderator.
Products and publications
Products: The project will result in knowledge about the applications of MAST interventions to inform decision-making about what works, for whom, and under what conditions in peer-reviewed publications and presentations, as well as dissemination products for researchers, practitioners, policymakers, and other stakeholders. The project will also produce and disseminate a MAST thesaurus—a standardized set of terms within a database that describe the content in a consistent fashion—which can reduce misconceptions in the literature. The final dataset derived from this project will be archived and openly shared.
ERIC Citations: Find available citations in ERIC for this award here.
Supplemental information
Co-Principal Investigator: Shogren, Karrie
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.