Explore complex social dynamics with ‘Using Time Series to Analyze Long-Range Fractal Patterns’. This essential guide by Matthijs Koopmans introduces social scientists to advanced methods for describing and analyzing dependency and irregularity in long time series data. Move beyond familiar seasonal patterns to understand cycles that repeat over unfixed time scales, revealing fractal patterns in social systems. Koopmans focuses on autoregressive fractionally integrated moving average (ARFIMA) models and power spectral density analysis. Illustrated with simulations and real-world social science examples like unemployment figures, school attendance, teen births, and political orientation data, this book provides the tools and R-scripts (available online) to replicate analyses and uncover hidden structures in your data. Ideal for researchers seeking to quantify and understand irregular, long-range dependencies in social phenomena.