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When Do Effect Sizes Actually Capture Effects? a Meta-Analytic Review.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
When Do Effect Sizes Actually Capture Effects? a Meta-Analytic Review./
作者:
Amari, Paul Matthew.
面頁冊數:
1 online resource (90 pages)
附註:
Source: Masters Abstracts International, Volume: 84-11.
Contained By:
Masters Abstracts International84-11.
標題:
Psychology. -
電子資源:
click for full text (PQDT)
ISBN:
9798379534288
When Do Effect Sizes Actually Capture Effects? a Meta-Analytic Review.
Amari, Paul Matthew.
When Do Effect Sizes Actually Capture Effects? a Meta-Analytic Review.
- 1 online resource (90 pages)
Source: Masters Abstracts International, Volume: 84-11.
Thesis (M.A.)--The University of North Carolina at Charlotte, 2023.
Includes bibliographical references
Effect size benchmarks are used as guidelines for conducting power analysis and Bayesian analysis, guiding theory, interpreting practical significance, and reviewing scientific progress. However, effect size estimates that are correlational directly violate the definition of an "effect", as they do not capture a cause-and-effect relationship. The current work begins with a review of the current state of the literature and presents a continuum of causal-inference strength. Next, to demonstrate this conceptualization, a comprehensive review was conducted of the leadership literature: (1) a second-order meta-analysis of leader individual differences (total k = 1,829; total N = 640,388), (2) meta-analyzed lab and field experiments (total k = 110; total N = 18,402), and (3) a narrative review of effect sizes from quasi-experimental and non-traditional experimental designs. This work concludes with implications for theory and practice, future directions for research, and methodological best practices (e.g., experimental design).
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379534288Subjects--Topical Terms:
555998
Psychology.
Subjects--Index Terms:
Causal inferenceIndex Terms--Genre/Form:
554714
Electronic books.
When Do Effect Sizes Actually Capture Effects? a Meta-Analytic Review.
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Effect size benchmarks are used as guidelines for conducting power analysis and Bayesian analysis, guiding theory, interpreting practical significance, and reviewing scientific progress. However, effect size estimates that are correlational directly violate the definition of an "effect", as they do not capture a cause-and-effect relationship. The current work begins with a review of the current state of the literature and presents a continuum of causal-inference strength. Next, to demonstrate this conceptualization, a comprehensive review was conducted of the leadership literature: (1) a second-order meta-analysis of leader individual differences (total k = 1,829; total N = 640,388), (2) meta-analyzed lab and field experiments (total k = 110; total N = 18,402), and (3) a narrative review of effect sizes from quasi-experimental and non-traditional experimental designs. This work concludes with implications for theory and practice, future directions for research, and methodological best practices (e.g., experimental design).
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