Abbott, A. (1983). Sequences of social events: Concepts and methods for the analysis of order in social processes. Historical Methods, 16(4), 129–147.
CrossRef
Google Scholar
Abbott, A., & Forrest, J. (1986). Optimal matching methods for historical sequences. Journal of Interdisciplinary History, 16, 471–494.
CrossRef
Google Scholar
Abbott, A., & Tsay, A. (2000). Sequence analysis and optimal matching methods in sociology, Review and prospect. Sociological Methods and Research, 29(1), 3–33. (With discussion, pp 34–76).
CrossRef
Google Scholar
Aisenbrey, S., & Fasang, A. E. (2010). New life for old ideas: The “second wave” of sequence analysis bringing the “course” back into the life course. Sociological Methods and Research, 38(3), 430–462.
CrossRef
Google Scholar
Barban, N., & Billari, F. C. (2012). Classifying life course trajectories: A comparison of latent class and sequence analysis. Journal of the Royal Statistical Society. Series C (Applied Statistics), 61(5), 765–784.
CrossRef
Google Scholar
Billari, F. C. (2005). Life course analysis: Two (complementary) cultures? Some reflections with examples from the analysis of transition to adulthood. In R. Levy, P. Ghisletta, J.-M. Le Goff, D. Spini, & E. Widmer (Eds.), Towards an interdisciplinary perspective on the life course (Advances in life course research, Vol. 10, pp. 267–288). Amsterdam: Elsevier.
Google Scholar
Bison, I. (2014). Sequence as network: An attempt to apply network analysis to sequence analysis. In P. Blanchard, F. Bühlmann, & J.-A. Gauthier (Eds.), Advances in sequence analysis: Theory, method, applications (pp. 231–248). Cham: Springer.
Google Scholar
Bison, I., & Scalcon, A. (2018). From 07.00 to 22.00: A dual-earner couple’s typical day in Italy. Old questions and new evidence from social sequence analysis. In Ritschard and Studer (2018) (this volume).
Google Scholar
Bolano, D., Berchtold, A., & Ritschard, G. (2016). A discussion on hidden Markov models for life course data. In Proceedings of the International Conference on Sequence Analysis and Related Methods, Lausanne, 8–10 June 2016.
Google Scholar
Borgna, C., & Struffolino, E. (2018). Unpacking configurational dynamics: Sequence analysis and qualitative comparative analysis as a mixed-method design. In Ritschard and Studer (2018) (this volume).
Google Scholar
Brzinsky-Fay, C. (2007). Lost in transition? Labour market entry sequences of school leavers in Europe. European Sociological Review, 23(4), 409–422.
CrossRef
Google Scholar
Brzinsky-Fay, C., Kohler, U., & Luniak, M. (2006). Sequence analysis with Stata. The Stata Journal, 6(4), 435–460.
CrossRef
Google Scholar
Bürgin, R., & Ritschard, G. (2014). A decorated parallel coordinate plot for categorical longitudinal data. The American Statistician, 68(2), 98–103.
CrossRef
Google Scholar
Butts, C. T., & Pixley, J. E. (2004). A structural approach to the representation of life history data. The Journal of Mathematical Sociology, 28(2), 81–124.
CrossRef
Google Scholar
Collas, T. (2018). Multiphase sequence analysis. In Ritschard and Studer (2018) (this volume).
Google Scholar
Cornwell, B. (2018). Network analysis of sequence structures. In Ritschard and Studer (2018) (this volume).
Google Scholar
Cornwell, B., & Watkins, K. (2015). Sequence-network analysis: A new framework for studying action in groups. In S. R. Thye & E. J. Lawler (Eds.), Advances in group processes (Vol. 32, pp. 31–63). Bingley: Emerald Group Publishing Limited.
CrossRef
Google Scholar
Courgeau, D. (2018). Do different approaches in population science lead to divergent or convergent models? In Ritschard and Studer (2018) (this volume).
Google Scholar
Eerola, M. (2018). Case studies of combining sequence analysis and modelling. In Ritschard and Studer (2018) (this volume).
Google Scholar
Elzinga, C. H. (2010). Complexity of categorical time series. Sociological Methods & Research, 38(3), 463–481.
CrossRef
Google Scholar
Elzinga, C. H., & Liefbroer, A. C. (2007). De-standardization of family-life trajectories of young adults: A cross-national comparison using sequence analysis. European Journal of Population, 23, 225–250.
CrossRef
Google Scholar
Fasang, A. E., & Liao, T. F. (2014). Visualizing sequences in the social sciences: Relative frequency sequence plots. Sociological Methods & Research, 43(4), 643–676.
CrossRef
Google Scholar
Gabadinho, A., & Ritschard, G. (2013). Searching for typical life trajectories applied to childbirth histories. In R. Levy & E. Widmer (Eds.), Gendered life courses – Between individualization and standardization. A European approach applied to Switzerland (pp. 287–312). Vienna: LIT-Verlag.
Google Scholar
Gabadinho, A., & Ritschard, G. (2016). Analysing state sequences with probabilistic suffix trees: The PST R library. Journal of Statistical Software, 72(3), 1–39.
CrossRef
Google Scholar
Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2010). Indice de complexité pour le tri et la comparaison de séquences catégorielles. Revue des nouvelles technologies de l’information RNTI, E-19, 61–66.
Google Scholar
Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37.
CrossRef
Google Scholar
Halpin, B. (2014). SADI: Sequence analysis tools for Stata. Department of Sociology Working Paper Series WP2014-03, University of Limerick.
Google Scholar
Halpin, B. (2015). MICT: Multiple imputation for categorical time-series. Department of Sociology Working Paper Series WP2015-02, University of Limerick, Ireland.
Google Scholar
Hamberger, K. (2018). Relational sequence networks as a tool for studying gendered mobility patterns. In Ritschard and Studer (2018) (this volume).
Google Scholar
Hamming, R. W. (1950). Error detecting and error correcting codes. Bell System Technical Journal, 26(2), 147–160.
CrossRef
Google Scholar
Helske, S., & Helske, J. (2017). Mixture hidden Markov models for sequence data: The seqHMM package in R. Vignette of the seqHMM package, CRAN.
Google Scholar
Helske, S., Helske, J., & Eerola, M. (2018). Combining sequence analysis and hidden Markov models in the analysis of complex life sequence data. In Ritschard and Studer (2018) (this volume).
Google Scholar
Levenshtein, V. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10, 707–710.
Google Scholar
Lundevaller, E. H., Vikström, L., & Haage, H. (2018). Modelling mortality using life trajectories of disabled and non-disabled individuals in 19th-century Sweden. In Ritschard and Studer (2018) (this volume).
Google Scholar
Malin, L., & Wise, R. (2018). Glass ceilings, glass escalators and revolving doors: Comparing gendered occupational trajectories and the upward mobility of men and women in West Germany. In Ritschard and Studer (2018) (this volume).
Google Scholar
Manzoni, A., & Mooi-Reci, I. (2018). Measuring sequence quality. In Ritschard and Studer (2018) (this volume).
Google Scholar
McVicar, D., & Anyadike-Danes, M. (2002). Predicting successful and unsuccessful transitions from school to work using sequence methods. Journal of the Royal Statistical Society A, 165(2), 317–334.
CrossRef
Google Scholar
Needleman, S., & Wunsch, C. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48, 443–453.
CrossRef
Google Scholar
Piccarreta, R. (2017). Joint sequence analysis: Association and clustering. Sociological Methods & Research, 46(2), 252–287.
CrossRef
Google Scholar
Piccarreta, R., & Elzinga, C. H. (2013). Mining for association between life course domains. In J. J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (Quantitative methodology, pp. 190–220). New York: Routledge.
Google Scholar
Pollock, G. (2007). Holistic trajectories: A study of combined employment, housing and family careers by using multiple-sequence analysis. Journal of the Royal Statistical Society A, 170(1), 167–183.
CrossRef
Google Scholar
Ritschard, G., & Studer, M. (Eds.) (2018). Sequence analysis and related approaches: Innovative methods and applications. (Life course research and social policies). Cham: Springer.
Google Scholar
Ritschard, G., Bussi, M., & O’Reilly, J. (2018). An index of precarity for measuring early employment insecurity. In Ritschard and Studer (2018) (this volume).
Google Scholar
Rossignon, F., Studer, M., Gauthier, J.-A., & Le Goff, J.-M. (2018). Sequence history analysis (SHA): Estimating the effect of past trajectories on an upcoming event. In Ritschard and Studer (2018) (this volume).
Google Scholar
Sankoff, D., & Kruskal, J. B. (Eds.) (1983). Time warps, string edits, and macro-molecules: The theory and practice of sequence comparison. Reading: Addison-Wesley.
Google Scholar
Scherer, S. (2001). Early career patterns: A comparison of Great Britain and West Germany. European Sociological Review, 17(2), 119–144.
CrossRef
Google Scholar
Studer, M. (2018). Divisive property-based and fuzzy clustering for sequence analysis. In Ritschard and Studer (2018) (this volume).
Google Scholar
Studer, M., & Ritschard, G. (2016). What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures. Journal of the Royal Statistical Society, Series A, 179(2), 481–511.
CrossRef
Google Scholar
Studer, M., Ritschard, G., Gabadinho, A., & Müller, N. S. (2011). Discrepancy analysis of state sequences. Sociological Methods and Research, 40(3), 471–510.
CrossRef
Google Scholar
Studer, M., Liefbroer, A. C., & Mooyaart, J. E. (2018a). Understanding trends in family formation trajectories: An application of competing trajectories analysis (CTA). Advances in Life Course Research, 36, 1–12.
CrossRef
Google Scholar
Studer, M., Struffolino, E., & Fasang, A. E. (2018b). Estimating the relationship between time-varying covariates and trajectories: The sequence analysis multistate model procedure. Sociological Methodology. (First Published Online).
CrossRef
Google Scholar
Taushanov, Z., & Berchtold, A. (2018). Markovian-based clustering of internet addiction trajectories. In Ritschard and Studer (2018) (this volume).
Google Scholar
Vermunt, J., Tran, B., & Magidson, J. (2008). Latent class models in longitudinal research. In S. Menard (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 373–385). Burlington, MA: Elsevier.
Google Scholar