“The book will be interesting for methodologically interested researchers of many different disciplines that are interested in longitudinal research, such as demography, sociology, psychology, educational science, health research, political science, maybe also economics. … sequence analysis reaches out to quantitative as well as qualitative researchers. This book remarks the final position in the series on books dealing with sequence analysis.” (Christian Brzinsky‑Fay, European Journal of Population, Vol. 35, 2019)
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Gilbert Ritschard, PhD in Econometrics, is Professor Emeritus at the Geneva School of Social Sciences of the University of Geneva. He has carried out teaching and research in data analysis and statistical modeling for social sciences, including longitudinal data analysis, event history analysis, and sequence analysis. He is a co-editor of volumes 1 and 2 of "Advances in Knowledge Discovery", (Springer, 2010, 2012), and of "Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences" (Routledge, 2013). He published recently among others in Sociological Methods and Research, the Journal of the Royal Statistical Society A, The American Statistician, and the Journal of Statistical Software. His recent applied contributions concern life course studies. He currently leads a methodological project within the Swiss NCCR “LIVES: Overcoming vulnerability, life course perspectives,” and develops with his team the now worldwide used TraMineR toolkit for the exploration of state and event sequence data.Matthias Studer, PhD in socioeconomics, is a Senior Researcher at the Swiss NCCR program ``LIVES overcoming vulnerability: life course perspectives'' and a Lecturer at the Geneva School of Social Sciences of the University of Geneva. His research interests include quantitative methods for longitudinal data analysis, sequence analysis, gendered career inequalities, labor market and social policy evaluation. He is one of the TraMineR developers, and he recently published on Discrepancy Analysis in Sociological Methods \& Research and a comparison of sequence analysis distance measures in the Journal of the Royal Statistical Society: Series A.