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THEORY TESTING: COMBINING PSYCHOMETRIC META‐ANALYSIS AND STRUCTURAL EQUATIONS MODELING
Abstract
This paper presents an overview of a useful approach for theory testing in the social sciences that combines the principles of psychometric meta‐analysis and structural equations modeling. In this approach to theory testing, the estimated true score correlations between the constructs of interest are established through the application of meta‐analysis (Hunter & Schmidt, 1990), and structural equations modeling is then applied to the matrix of estimated true score correlations. The potential advantages and limitations of this approach are presented. The approach enables researchers to test complex theories involving several constructs that cannot all be measured in a single study. Decision points are identified, the options available to a researcher are enumerated, and the potential problems as well as the prospects of each are discussed.
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In the citing paper
“We computed harmonic mean sample size (Viswesvaran & Ones, 1995) when conducting hierarchical multiple regression analyses because sample sizes differed across the cells in the meta-analytically derived correlation matrices.Computing harmonic mean sample size will yield more conservative estimates because less weight is given to large samples (Garrett, Miao, Qian, & Bae, 2017;Miao, Coombs, Qian, &”
From A cross-cultural meta-analysis of how leader emotional intelligence influences subordinate task performance and organizational citizenship behavior (2018) · cites this paper as Viswesvaran & Ones, 1995
In the citing paper
“A major advantage of this technique is that not all relationships specified by the theory need to be examined in each primary study, as the population correlations required can be meta-analytically computed (Joseph et al. 2007; Viswesvaran and Ones 1998).”
From Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model (2019) · cites this paper as Viswesvaran and Ones 1998
In the citing paper
“Prior literature suggests various possibilities such as using the minimum sample size, average sample size and harmonic mean sample size (e.g., Tett and Meyer 1993; Viswesvaran and Ones 1998).”
From Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model (2019) · cites this paper as Viswesvaran and Ones 1998
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Metadata
- Authors
- VISWESVARAN, C., & ONES, D. S.
- Affiliations
- Florida International University (US)
- Corresponding author
- VISWESVARAN, CHOCKALINGAM
- Venue
- Personnel Psychology
- Publisher
- Wiley
- Published
- 1995-12
- Volume
- 48
- Issue
- 4
- Pages
- 865-885
- ISSN
- 0031-5826 (ISSN-L)
- 1744-6570
- OpenAlex
- W2051295428
- Cited by
- 926
- References
- 50
- Citations by year
- 2026: 34 · 2025: 42 · 2024: 46 · 2023: 42 · 2022: 66 · 2021: 46 · 2020: 49 · 2019: 48 · 2018: 45 · 2017: 58 · 2016: 45 · 2015: 51 · 2014: 45 · 2013: 36 · 2012: 36
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Personnel Psychology (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- theory testing, structural equation modeling, complex theory
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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