Dynamic Process for Ordinal Data: Simulation and Application

This book is noted for its: elaborate introduction of differential equation modeling; panoramic description of popular longitudinal data analysis methods; organized review of typical approaches dealing with ordinal data; creative solution to adjust bias caused by applying differential equation modeling to ordinal data; two applicable examples in Psychology research, including one survey and one experiment; complete R code for differential equation modeling and bias adjustment solution.