Abstract
This chapter proposes an iterative statistical approach, based on the principles of stepwise and segmented regression, to detect and quantify evolutionary trends and revolutionary changes (breakpoints) in long-term processes. The resulting stepwise segmented regression analysis was initially developed to assess especially complex social systems such as behavioral changes across the Wikipedia editorial community.
Unlike most existing breakpoint detection tools, stepwise segmented regression can detect multiple revolutionary moments occurring in sequence, including those that result in continuous and discontinuous line segments. It is also less sensitive to random noise and heteroscedasticity than tools based upon model selection criteria like BIC, and its model may be expanded to include exponential terms, although such exponential terms should be used with caution. Finally, its use of stepwise-based iteration limits its computational complexity, making it a reasonable choice to examine longer processes with many data points. In sum, this flexible and robust regression-based approach may be used in a far wider range of contexts than any existing breakpoint detection tool, making it ideal for evaluating unknown or complicated social and natural scientific processes.
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Britt, B.C. (2015). Stepwise Segmented Regression Analysis: An Iterative Statistical Algorithm to Detect and Quantify Evolutionary and Revolutionary Transformations in Longitudinal Data. In: Matei, S., Russell, M., Bertino, E. (eds) Transparency in Social Media. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-18552-1_7
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DOI: https://doi.org/10.1007/978-3-319-18552-1_7
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