Optimal Aging Colloquium Series presents a Lecture by Dr. Steven M. Boker, PhD, Professor University of Virginia
"Three dimensions of Cattell's persons by variables by time data box are discussed in the context of three types of researchers each wanting to answer their own categorically different question. The example of the well-known speed-accuracy trade off is used to illustrate why these exemplify three different categories of statistical question. A conceptual model is presented for the speed-accuracy trade off example that could account for cross-sectional between persons effects, short term dynamics, and long term learning effects. Two fundamental differences between the time axis and the other two axes of the data box include ordering and time scaling. In addition, non-stationarity in human systems poses a pervasive problem along the time dimension of the data box. To illustrate this, the difference in non-stationarity between dancing and conversation is discussed in the context of the interaction between theory, methods, and data. An information theoretic argument is presented that the theory-methods-data interaction is better understood when viewed as a conversation than as a dance. Entropy changes in the development of a theory-methods-data conversation provide one metric for evaluating scientific progress."
Reception follows the lecture.
About the Speaker
Dr. Boker is Professor of Quantitative Psychology, Director of the Human Dynamics Laboratory, PI of the OpenMx SEM Software project, Speaker-Director of the UVA section of the International Max Planck Research School on the Life Course (LIFE), and Area Head of the Quantitative Psychology Area at the University of Virginia Department of Psychology.
Dr. Boker's research interests include the application of dynamical systems analytic techniques to psychological and physiological data. His contributions include methods for examining change in multivariate mixed cross-sectional and longitudinal data include the Statistical Vector Field method, Differential Structural Equation Modeling using local linear approximation of derivatives, and the Latent Differential Equations method for fitting differential equations models to multivariate multiple occasion data. He is currently pursuing research into methods for estimating models for nonstationary data -- data for which model parameters or model structures change over time.