Monday Symposium in Measurement and Statistics (MSMS)
Department of Human Development and Quantitative Methodology
Fall 2021
On Networks and Online Russian Trolls: How Can the Total Entropy Fit Index Be Applied to Optimize the Number of Embedded Dimensions Used in Dynamic Exploratory Graph Analysis, and Why Does It Matter
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The current presentation will show how a new fit index for dimensionality analysis termed total entropy fit index can be applied to tune the number of embedded dimensions used in the dynamic exploratory graph analysis (DynEGA) technique. DynEGA uses dynamical systems and network psychometrics to estimate the number of (dynamic) latent factors in multivariate time-series of continuous or categorical data. In a Monte-Carlo simulation, we show that the total entropy fit index can be used in a grid search to find the optimal number of embedded dimensions. In an applied example, we performed DynEGA with the TEFI optimization on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 U.S. presidential election. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.
Dr. Hudson Golino’s research focuses on quantitative methods, psychometrics and machine learning applied in the fields of psychology, health and education. He is particularly interested in new ways to assess the number of dimensions (i.e. latent variables) underlying multivariate data using network psychometrics.