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Power Approximation for the Test of Study-Level Categorical Moderators in Meta-Regression With Dependent Effect Sizes
Power Approximation for the Test of Study-Level Categorical Moderators in Meta-Regression With Dependent Effect Sizes
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260311091533.5
- ISBN
- 9798270231828
- DDC
- 519.5
- 서명/저자
- Power Approximation for the Test of Study-Level Categorical Moderators in Meta-Regression With Dependent Effect Sizes / Bethany Hamilton Bhat
- 발행사항
- [Sl] : The University of Texas at Austin, 2025
- 형태사항
- 1 electronic resource (139 pages)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisors: Beretvas, S. Natasha; Pustejovsky, James E. Committee members: Liu, Xiao; Whittaker, Tiffany A.; Pigott, Teresa D.
- 학위논문주기
- - Ph.D. : The University of Texas at Austin, 2025.
- 초록/해제
- 요약Sample 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.
- 언어주기
- English
- 일반주제명
- Statistics
- 일반주제명
- Statistical physics
- 일반주제명
- Psychology
- 키워드
- Power analysis
- 키워드
- CHE+RVE model
- 기타저자
- The University of Texas at Austin Educational Psychology
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr|nu||||||||
■020 ▼a9798270231828
■040 ▼aMiAaPQD▼beng▼cMiAaPQD▼erda
■082 ▼a519.5
■1001 ▼aBhat, Bethany Hamilton▼eauthor.
■24510▼aPower Approximation for the Test of Study-Level Categorical Moderators in Meta-Regression With Dependent Effect Sizes ▼cBethany Hamilton Bhat
■260 ▼a[Sl]▼bThe University of Texas at Austin▼c2025
■264 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a1 electronic resource (139 pages)
■336 ▼atext▼btxt▼2rdacontent
■337 ▼acomputer▼bc▼2rdamedia
■338 ▼aonline resource▼bcr▼2rdacarrier
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisors: Beretvas, S. Natasha; Pustejovsky, James E. Committee members: Liu, Xiao; Whittaker, Tiffany A.; Pigott, Teresa D.
■5021 ▼bPh.D.▼cThe University of Texas at Austin▼d2025.
■520 ▼aSample 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.
■546 ▼aEnglish
■590 ▼aSchool code: 0227
■650 4▼aStatistics
■650 4▼aStatistical physics
■650 4▼aPsychology
■653 ▼aStatistical power
■653 ▼aPower analysis
■653 ▼aCHE+RVE model
■653 ▼aMeta-regression models
■7102 ▼aThe University of Texas at Austin▼bEducational Psychology.▼edegree granting institution.
■7201 ▼aBeretvas, S. Natasha▼edegree supervisor.
■7201 ▼aPustejovsky, James E.▼edegree supervisor.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361200▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
