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Activity Number: 70 - Utilizing High-Dimensional and Complex Data in Personalized Medicine
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Mental Health Statistics Section
Abstract #323837 View Presentation
Title: Assessing Latent Dimensionality of a Symptom Battery in a Sample with Many Subjects Exhibiting No Symptoms
Author(s): William Christensen* and Melanie M Wall and Irini Moustaki
Companies: Brigham Young University and Columbia University and London School of Economics
Keywords: factor analysis ; latent variable model ; zero-inflated ; dimensionality
Abstract:

Common methods for determining the number of latent dimensions underlying a symptom battery or item set include eigenvalue analysis and examination of fit statistics for factor analysis models with varying number of factors. Given a set of dichotomous items, we will demonstrate that these empirical assessments of dimensionality are likely to misestimate the number of dimensions when the data represent a mixed sample of pathological and healthy subjects. We demonstrate the problem is exacerbated when there is a preponderance of individuals in the sample with no symptoms. Several dimension-assessment tools are considered and simulated data experiments are conducted to demonstrate their relative utility when dealing with a mixed-status sample. Recommended approaches are illustrated using an example assessing the dimensionality of a social anxiety disorder symptom battery.


Authors who are presenting talks have a * after their name.

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