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ERIC Number: EJ826685
Record Type: Journal
Publication Date: 2009-Jan
Pages: 16
Abstractor: As Provided
ISSN: ISSN-1070-5511
Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling
Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J.
Structural Equation Modeling: A Multidisciplinary Journal, v16 n1 p147-162 Jan 2009
The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a "true" or population path model was then developed using Bayesian structural equation modeling. To evaluate the impact of sample size on parameter estimation bias, proportion of parameter replication coverage, and statistical power, a 2 group (clinical/control) x 6 (sample size: N = 10, N = 15, N = 20, N = 25, N = 50, N = 100) Markov chain Monte Carlo study was conducted. Results indicate that using a sample size of less than N = 15 per group will produce parameter estimates exhibiting bias greater than 5% and statistical power below 0.80. (Contains 5 tables and 1 figure.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A