{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/134793"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/134793","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"Power approximation for the test of study-level categorical moderators in meta-regression with dependent effect sizes","abstract":"Sample size and statistical power are key considerations when planning a research synthesis. While power analysis methods for the tests of moderators have been established for fixed- and random-effects models for independent effects, there is currently no methodology for conducting power analysis for moderator tests in meta-regression models that account for dependence. Building on a previous study that evaluated power approximations for the test of an average effect size (Vembye et al., 2023), I propose a new approximation formula specifically for testing study-level categorical moderators using the correlated-hierarchical effects model with robust variance estimation (CHE+RVE). Additionally, I conduct a Monte Carlo simulation to validate this power approximation formula against the true simulated power of a test of multiple contrasts from a CHE+RVE model. I also examine the Type I error rates and power of a test of multiple contrasts corrected for small samples from a CHE+RVE model. The results from my study show that the power approximation formula is accurate when there is a small number of contrasts, but it could be inaccurate in conditions with a larger number of contrasts and small degrees of freedom. Additionally, I replicate past findings that the small-sample adjusted test of multiple contrasts using RVE is conservative when there is a higher number of contrasts and a small number of studies.","abstract_html":"Sample size and statistical power are key considerations when planning a research synthesis. While power analysis methods for the tests of moderators have been established for fixed- and random-effects models for independent effects, there is currently no methodology for conducting power analysis for moderator tests in meta-regression models that account for dependence. Building on a previous study that evaluated power approximations for the test of an average effect size (Vembye et al., 2023), I propose a new approximation formula specifically for testing study-level categorical moderators using the correlated-hierarchical effects model with robust variance estimation (CHE+RVE). Additionally, I conduct a Monte Carlo simulation to validate this power approximation formula against the true simulated power of a test of multiple contrasts from a CHE+RVE model. I also examine the Type I error rates and power of a test of multiple contrasts corrected for small samples from a CHE+RVE model. The results from my study show that the power approximation formula is accurate when there is a small number of contrasts, but it could be inaccurate in conditions with a larger number of contrasts and small degrees of freedom. Additionally, I replicate past findings that the small-sample adjusted test of multiple contrasts using RVE is conservative when there is a higher number of contrasts and a small number of studies.","abstract_has_math":false,"creators":["Bhat, Bethany Hamilton"],"institution":"The University of Texas at Austin","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":[],"advisors":["Beretvas, Susan Natasha","Pustejovsky, James E."],"committee_chairs":[],"committee_members":["Xiao Liu","Tiffany A. Whittaker","Therese D. Pigott"],"year":2025,"date_issued":"2025-08","date_published":"2025-08","updated_at":"2026-07-24T05:01:12Z","subjects":["Robust variance estimation","Categorical moderators","Meta-analysis","Dependent effect sizes","Power","Study-level moderators","Meta-regression"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.26153/tsw/62115"],"render_values":[{"text":"https://doi.org/10.26153/tsw/62115","href":"https://doi.org/10.26153/tsw/62115","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/2152/134793","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Beretvas, Susan Natasha","Pustejovsky, James E."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Xiao Liu","Tiffany A. Whittaker","Therese D. Pigott"]},{"key":"dc:creator","label":"Author","values":["Bhat, Bethany Hamilton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-11-12T17:44:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational Psychology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Texas at Austin"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robust variance estimation","Categorical moderators","Meta-analysis","Dependent effect sizes","Power","Study-level moderators","Meta-regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2152/134793","https://doi.org/10.26153/tsw/62115"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Sample size and statistical power are key considerations when planning a research synthesis. While power analysis methods for the tests of moderators have been established for fixed- and random-effects models for independent effects, there is currently no methodology for conducting power analysis for moderator tests in meta-regression models that account for dependence. Building on a previous study that evaluated power approximations for the test of an average effect size (Vembye et al., 2023), I propose a new approximation formula specifically for testing study-level categorical moderators using the correlated-hierarchical effects model with robust variance estimation (CHE+RVE). Additionally, I conduct a Monte Carlo simulation to validate this power approximation formula against the true simulated power of a test of multiple contrasts from a CHE+RVE model. I also examine the Type I error rates and power of a test of multiple contrasts corrected for small samples from a CHE+RVE model. The results from my study show that the power approximation formula is accurate when there is a small number of contrasts, but it could be inaccurate in conditions with a larger number of contrasts and small degrees of freedom. Additionally, I replicate past findings that the small-sample adjusted test of multiple contrasts using RVE is conservative when there is a higher number of contrasts and a small number of studies."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Power approximation for the test of study-level categorical moderators in meta-regression with dependent effect sizes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Beretvas, Susan Natasha","Pustejovsky, James E."],"dc:contributor.committeemember":["Xiao Liu","Tiffany A. Whittaker","Therese D. Pigott"],"dc:creator":["Bhat, Bethany Hamilton"],"dc:date.accessioned":["2025-11-12T17:44:55Z"],"dc:date.issued":["2025-08"],"dc:description.abstract":["Sample size and statistical power are key considerations when planning a research synthesis. While power analysis methods for the tests of moderators have been established for fixed- and random-effects models for independent effects, there is currently no methodology for conducting power analysis for moderator tests in meta-regression models that account for dependence. Building on a previous study that evaluated power approximations for the test of an average effect size (Vembye et al., 2023), I propose a new approximation formula specifically for testing study-level categorical moderators using the correlated-hierarchical effects model with robust variance estimation (CHE+RVE). Additionally, I conduct a Monte Carlo simulation to validate this power approximation formula against the true simulated power of a test of multiple contrasts from a CHE+RVE model. I also examine the Type I error rates and power of a test of multiple contrasts corrected for small samples from a CHE+RVE model. The results from my study show that the power approximation formula is accurate when there is a small number of contrasts, but it could be inaccurate in conditions with a larger number of contrasts and small degrees of freedom. Additionally, I replicate past findings that the small-sample adjusted test of multiple contrasts using RVE is conservative when there is a higher number of contrasts and a small number of studies."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2152/134793","https://doi.org/10.26153/tsw/62115"],"dc:language.iso":["English"],"dc:subject":["Robust variance estimation","Categorical moderators","Meta-analysis","Dependent effect sizes","Power","Study-level moderators","Meta-regression"],"dc:title":["Power approximation for the test of study-level categorical moderators in meta-regression with dependent effect sizes"],"dc:type":["Thesis"],"thesis:degree_discipline":["Educational Psychology"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The University of Texas at Austin"]},"updated_at":"2026-07-24T05:01:12Z"}