The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
A critical review of structural equation modeling applications in construction research
Open accessAbstract
Structural equation modeling (SEM) is a versatile multivariate statistical technique, and applications have been increasing since its introduction in the 1980s. This paper provides a critical review of 84 articles involving the use of SEM to address construction related problems over the period 1998–2012 including, but not limited to, seven top construction research journals. After conducting a yearly publication trend analysis, it is found that SEM applications have been accelerating over time. However, there are inconsistencies in the various recorded applications and several recurring problems exist. The important issues that need to be considered are examined in research design, model development and model evaluation and are discussed in detail with reference to current applications. A particularly important issue concerns the construct validity. Relevant topics for efficient research design also include longitudinal or cross-sectional studies, mediation and moderation effects, sample size issues and software selection. A guideline framework is provided to help future researchers in construction SEM applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Coefficient Alpha and the Internal Structure of Tests
Cronbach, L. J. (1951) · Psychometrika · doi:10.1007/bf02310555 · Cited by 43,931
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
Power analysis and determination of sample size for covariance structure modeling.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.2.130 · Cited by 10,418
Structural Equation Modelling: Guidelines for Determining Model Fit
Hooper, D., Coughlan, J., & Mullen, M. R. (2008) · Dublin Institute of Technology · doi:10.21427/d7cf7r · Cited by 8,746
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003) · Journal of Consumer Research · doi:10.1086/376806 · Cited by 5,807
Practical issues in structural modeling
BENTLER, P. M., & CHOU, C.-P. (1987) · Sociological Methods & Research · doi:10.1177/0049124187016001004 · Cited by 5,804
Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Shuai, C., Shen, L., Jiao, L., Wu, Y., & Tan, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.11.029 · Cited by 364
Zeng, N., Liu, Y., Gong, P., Hertogh, M., & König, M. (2021) · Frontiers of Engineering Management · doi:10.1007/s42524-021-0153-5 · Cited by 300
Xu, K., Shen, G. Q., Liu, G., & Martek, I. (2019) · Sustainability · doi:10.3390/su11020491 · Cited by 64
Hosseini, M. R., Banihashemi, S., Rameezdeen, R., Golizadeh, H., Arashpour, M., & Ma, L. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.08.200 · Cited by 48
Metadata
- Authors
- Xiong, B., Skitmore, M., & Xia, B.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 49
- Pages
- 59-70
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W1500144733
- Cited by
- 427
- References
- 108
- FWCI
- 22.18
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A critical review of structural equation modeling applications in construction research. https://app.cochat.ai/papers/10.1016/j.autcon.2014.09.006
- The paper
- Xiong, B., Skitmore, M., & Xia, B. (2015). A critical review of structural equation modeling applications in construction research. Automation in Construction. https://doi.org/10.1016/j.autcon.2014.09.006
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
