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Cited by 237Psychometric Methodologies and TestingAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

The DOI matches its Crossref record; 69 of the 70 references it cites were found in a registry or an index.

Causation issues in structural equation modeling research

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Bullock, Heather E., Harlow, Lisa L., Mulaik, Stanley A.·1994·Structural Equation Modeling: A Multidisciplinary Journal
DOI 10.1080/10705519409539977
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Abstract

As the use of structural equation modeling (SEM) has increased, confusion has grown concerning the correct use of and the conclusions that can be legitimately drawn from these methodologies. It appears that much of the controversy surrounding SEM is related to the degree of certainty with which causal statements can be drawn from these procedures. SEM is discussed in relation to the conditions necessary for providing causal evidence. Both the weaknesses and the strengths of SEM are examined. Although structural modeling cannot ensure that necessary causal conditions have been met, it is argued that SEM methods may offer the potential for tentative causal inferences to be drawn when used with carefully specified and controlled designs. Keeping in mind that no statistical methodology can in and of itself determine causality, specific guidelines are suggested to help researchers approach a potential for providing causal evidence with SEM procedures.

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Citations

Cites

Show all 70 references

Research works printed but not linked: 13

  1. 10.1037/0022-3514.45.6.1289 · DOI
  2. 10.1080/08098130809478203 · DOI
  3. 10.3102/10769986012002206 · DOI
  4. Games , P. A. 1988 . Invited commentary: “Correlation and causation: An alternative view.” . The Score , XI April : 9 – 10 .
  5. 10.3102/0013189X015009012 · DOI
  6. 10.1037/0022-3514.40.1.180 · DOI
  7. 10.1037/0022-3514.43.3.604 · DOI
  8. 10.1037/0022-3514.43.3.598 · DOI
  9. 10.2307/1130289 · DOI
  10. Mulaik , S. A. . A conception of causality for structural modeling . Paper presented at the Symposium on Methodological Issues in Causal Modeling . March , Tuscaloosa : University of Alabama .
  11. 10.2307/1130288 · DOI
  12. Mulaik , S. A. . An analysis of the conditions under which the estimation of parameters inflate goodness of fit indices as measures of model validity . Paper presented at the meeting of the Psychometric Society . Princeton, NJ. June ,
  13. 10.1037/0022-0663.84.3.371 · DOI

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Metadata

Authors
Bullock, H. E., Harlow, L. L., & Mulaik, S. A.
Affiliations
  • University of Rhode Island (US)
  • Georgia Institute of Technology (US)
ORCID
Harlow, Lisa L.
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Publisher
Informa UK Limited
Published
1994-01
Volume
1
Issue
3
Pages
253-267
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
DOI
10.1080/10705519409539977
OpenAlex
W2000776953
Cited by
237
References
70
Citations by year
2026: 6 · 2025: 9 · 2024: 6 · 2023: 12 · 2022: 14 · 2021: 18 · 2020: 20 · 2019: 7 · 2018: 11 · 2017: 11 · 2016: 10 · 2015: 6 · 2014: 11 · 2013: 8 · 2012: 10
FWCI
6.20
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
  • Journal of Media Literacy Education (submittedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
structural equation modeling, causality, causal inference, causal evidence, research design, methodological limitations, research guidelines

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