Using Structural Equation Modeling to Assess Functional Connectivity in the Brain: Power and Sample Size Considerations
Open accessAbstract
The present study assessed the impact of sample size on the power and fit of structural equation modeling applied to functional brain connectivity hypotheses. The data consisted of time-constrained minimum norm estimates of regional brain activity during performance of a reading task obtained with magnetoencephalography. Power analysis was first conducted for an autoregressive model with 5 latent variables (brain regions), each defined by 3 indicators (successive activity time bins). A series of simulations were then run by generating data from an existing pool of 51 typical readers (aged 7.5-12.5 years). Sample sizes ranged between 20 and 1,000 participants and for each sample size 1,000 replications were run. Results were evaluated using chi-square Type I errors, model convergence, mean RMSEA (root mean square error of approximation) values, confidence intervals of the RMSEA, structural path stability, and Δ-Fit index values. Results suggested that 70 to 80 participants were adequate to model relationships reflecting close to not so close fit as per MacCallum et al.’s recommendations. Sample sizes of 50 participants were associated with satisfactory fit. It is concluded that structural equation modeling is a viable methodology to model complex regional interdependencies in brain activation in pediatric populations.
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The sample size used is adequate for estimating the hypothesized model, with a ratio of cases to model parameters higher than 5.
“the sample size used is adequate for estimating the hypothesized model; the ratio of cases (N) to the number of model parameters (q) is higher than 5.”
From "The road to food waste is paved with good intentions": When consumers' goals inhibit the minimization of household food waste (2019) · cites this paper as Sideridis, Simos, Papanicolaou, and Fletcher, 2014
The Importance of the Study and Hypotheses Tested section says sample-size rules of thumb cannot be relied on because they do not account for model complexity and other factors. The Discussion and Conclusions instead gives sample-size recommendations based on simulations of a specific model. The paper does not endorse a cases-to-parameters ratio above 5 as establishing adequacy for the citing study’s model; page numbers are unavailable in our copy. (LLM, conf=0.82)
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References 65 · Cited by 246
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- Authors
- Sideridis, G., Simos, P., Papanicolaou, A., & Fletcher, J.
- Affiliations
- Boston Children's Hospital (US)
- University of Crete (GR)
- University of Tennessee Health Science Center (US)
- University of Houston (US)
- Venue
- Educational and Psychological Measurement
- Publisher
- SAGE Publications
- Published
- 2014-03-04
- Volume
- 74
- Issue
- 5
- Pages
- 733-758
- ISSN
- 0013-1644 (ISSN-L)
- 1552-3888
- PMC
- PMC4245025
- OpenAlex
- W2028563875
- Cited by
- 246
- References
- 65
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 22 · 2023: 30 · 2022: 27 · 2021: 28 · 2020: 18 · 2019: 27 · 2018: 24 · 2017: 22 · 2016: 17 · 2015: 7 · 2014: 1
- FWCI
- 19.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Educational and Psychological Measurement (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD052117)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- structural equation modeling, brain functional connectivity, sample size, statistical power, model fit, reading tasks, magnetoencephalography, autoregressive models, RMSEA
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