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

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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  • A cross-sectional sampling approach was used, so possible causal effects between the study latent factors cannot be identified with certainty.

    “A cross-sectional sampling approach was also used in this paper. As a result, while possible causal effects between the study latent factors can be inferred cautiously, they cannot be identified with certainty, therefore, a longitudinal research design will be advantageous in confirming the study's possible causal relationships [82].”

    From The Impact of Religiosity and Food Consumption Culture on Food Waste Intention in Saudi Arabia (2021) · cites this paper as [82]

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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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