Abstracts
Résumé
Plusieurs champs de recherche en psychoéducation, en psychologie développementale et en sociologie visent à examiner les changements individuels et au sein de groupes dans la population. On cherche, par exemple, à identifier les parcours typiques lors de périodes développementales précises, comme la délinquance à l’adolescence et la transition de l’école au travail au début de l’âge adulte, afin de comprendre celles qui paraissent plus ou moins adaptatives ou optimales. Cet article a comme objectif de présenter une approche algorithmique permettant de tracer de tels parcours sous forme de séquences à partir de variables catégorielles/nominales, comme dont des statuts (p. ex. : occupationnels, maritaux), des états (p. ex. : de santé) ou la présence de comportements (p. ex. : consommation ou pas). Cette approche, l’analyse de séquences, est abondamment utilisée par les chercheurs européens, mais demeure peu connue en Amérique du Nord. L’article présente les fondements et l’application de cette approche analytique, en décrivant chacune des étapes de l’analyse à partir d’un exemple fictif tiré de banques de données portant sur le passage de l’adolescence à l’âge adulte. L’article conclut par une discussion rapportant les forces et les limites de l’analyse de séquences dite algorithmiques en sciences sociales. Le script utilisé pour réaliser les analyses de cet article est également fourni en ligne pour les lecteurs intéressés par cette technique analytique.
Mots-clés :
- analyse de séquences,
- appariement optimal,
- trajectoires et parcours longitudinaux,
- approche algorithmique
Abstract
Several areas of research in psychoeducation, developmental psychology, and sociology are aimed at examining individual and group changes in the population. For example, they seek to identify typical pathways during specific developmental periods, such as delinquency in adolescence and the school to work transition in early adulthood, in order to understand those that appear maladaptive or optimal. The purpose of this paper is to present an algorithmic approach for tracing such pathways as sequences based on categorical/nominal variables, like statuses (e.g., occupational, marital), states (e.g., health), or behaviors (e.g., drinking habits). This approach, named sequence analysis, is widely used by European researchers, but remains little known in North America. This article presents the foundations and application of this analytical approach, describing each step of the analysis using a fictitious example drawn from data banks on the transition from adolescence to adulthood. The article concludes with a discussion of the strengths and limitations of sequence analysis in the social sciences. The script used to perform the analyses in this article is also provided online for readers interested in this analytical technique.
Keywords:
- sequence analysis,
- optimal matching,
- longitudinal pathways and trajectories,
- algorithmic approach
Download the article in PDF to read it.
Download
Appendices
Bibliographie
- Abbott, A. (1995). Sequence analysis: new methods for old ideas. Annual Review of Sociology, 21, 93-113.
- Abbott, A. et Forrest, J. (1986). Optimal matching methods for historical sequences. The Journal of Interdisciplinary History, 16(3), 471-494. https://doi.org/10.2307/204500
- Abbott, A. et Hrycak, A. (1990). Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers. American Journal of Sociology, 96(1), 144-185. https://doi.org/10.1086/229495
- Aisenbrey, S. et 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 & Research, 38(3), 420-462. https://doi.org/10.1177/0049124109357532
- Biemann, T. et Datta, D. K. (2014). Analyzing sequence data:Optimal matching in management research. Organizational Research Methods, 17(1), 51-76. https://doi.org/10.1177/1094428113499408
- Bison, I. (2009). OM natters: The interaction effects between indel and substitution costs. 4(2), 53-67. https://doi.org/10.1177/205979910900400205
- Blossfeld, H.-P. (2009). Comparative Life Course Research. A cross-national and longitudinal perspective. Dans G. H. Elder et J. Z. Giele (dir.), The Craft of Life Course Research (p. 280-306). Guilford Press.
- Brzinsky-Fay, C., Kohler, U. et Luniak, M. (2006). Sequence analysis with stata. The Stata Journal 6(4), 435-460. https://doi.org/10.1177/1536867x0600600401
- Canales Sánchez, D., Bautista Godínez, T., Moreno Salinas, G., García-Minjares, M. et Sánchez-Mendiola, M. (2020). Curricular change in a medical school: a new method for analysis of students academic pathways. medRxivhttps://doi.org/10.1101/2020.04.25.20079715
- Chalmers, A. (2013). What is this thing called science? (4e ed.) Hackett Publishing.
- Collins, L. M., Graham, J. W., Rousculp, S. S. et Hansen, W. B. (1997). Heavy caffeine use and the beginning of the substance use onset process: An illustration of latent transition analysis. Dans Bryant, K.J, Windle, M. et West, S.G. (dir.) The science of prevention: Methodological advances from alcohol and substance abuse research. (p. 79-99). American Psychological Association. https://doi.org/10.1037/10222-003
- Courgeau, D. (2018). Do different approaches in population science lead to divergent or convergent models? Dans G. Ritschard et M. Studer (dir.), Sequence Analysis and Related Approaches: Innovative Methods and Applications (p. 15-33). Springer International Publishing. https://doi.org/10.1007/978-3-319-95420-2_2
- Dlouhy, K. et Biemann, T. (2015). Optimal matching analysis in career research: A review and some best-practice recommendations. Journal of Vocational Behavior, 90, 163-173. https://doi.org/10.1016/j.jvb.2015.04.005
- Dupéré, V., Lacourse, É., Vitaro, F. et Tremblay, R. E. (2007). Méthodes d’analyse du changement fondées sur les trajectoires de développement individuelle : Modèles de régression mixtes paramétriques et non paramétriques/Longitudinal methods based on Individual development trajectories – Parametric and non parametric mixed models. Bulletin de méthodologie sociologique, 95(1), 26-57.
- Dupéré, V., Dion, E., Leventhal, T., Archambault, I., Crosnoe, R. et Janosz, M. (2018). High school dropout in proximal context: The triggering role of stressful life events. Child Development, 89(2), e107-e122. https://doi.org/10.1111/cdev.12792
- Elder, G. H., Jr. (1998). The life course as developmental theory. Child Development, 69(1), 1-12. http://www.jstor.org/stable/1132065
- Elder, G. H., Jr. et Giele, J. (2009). Life course studies: An evolving field. Dans Elder, G. H., Jr. et Giele, J. (dir.)The craft of life course research (p. 1-24). Guilford Press.
- Elder, G. H., Jr. et Shanahan, M. J. (2006). The life course and human development. Dans W. Damon et R. Lerner (dir.), Handbook of Child Psychology (6the éd., vol. 1: Theoretical Models of Human Development, p. 665-715). Wiley. https://doi.org/10.1002/9780470147658.chpsy0112
- Elder Jr, G. H., Shanahan, M. J. et Jennings, J. A. (2015). Human development in time and place. Dans R. Lerner, M. H. Bornstein et T. Leventhal (dir.), Handbook of Child Psychology and Developmental Science (p. 6-54). John Wiley & Sons. https://doi.org/10.1002/9781118963418.childpsy402
- Gabadinho, A., Ritschard, G., Müller, N. S. et Studer, M. (2011, 2011-04-07). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4). https://doi.org/10.18637/jss.v040.i04
- Gauthier, J.-A., Widmer, E. D., Bucher, P. et Notredame, C. (2009). How much does it dost?: Optimization of costs in sequence analysis of social science data. Sociological Methods & Research, 38(1), 197-231. https://doi.org/10.1177/0049124109342065
- Gould, M. S., Shaffer, D. et Davies, M. (1990). Truncated pathways from childhood to adulthood: Attrition in follow-up studies due to death. Dans Robins, L. et Rutter, M. (dir.) Straight and devious pathways from childhood to adulthood. (p. 3-9). Cambridge University Press.
- Han, Y., Liefbroer, A. C. et Elzinga, C. H. (2017). Comparing methods of classifying life courses: sequence analysis and latent class analysis. Longitudinal and Life Course Studies; Vol 8, No 4 (2017): Longitudinal and Life Course Studies. https://doi.org/10.14301/llcs.v8i4.409
- Helske, S., Helske, J. et Eerola, M. (2018). Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data. Dans G. Ritschard et M. Studer (dir.), Sequence Analysis and Related Approaches: Innovative Methods and Applications (p. 185-200). Springer International Publishing. https://doi.org/10.1007/978-3-319-95420-2_11
- Johnston, C. A., Crosnoe, R., Mernitz, S. E. et Pollitt, A. M. (2020). Two methods for studying the developmental significance of family structure trajectories. Journal of Marriage and Family 82(3), 1110-1123. https://doi.org/10.1111/jomf.12639
- Kline, R. B. (2016). Principles and practice of structural equation modeling (Fourth edition.e éd.). The Guilford Press.
- Kohler, U., Luniak, M. et Brzinsky-Fay, C. (2020). SQ: Stata module for sequence analysis. Boston College Department of Economics.
- Kruskal, J. B. (1983). An overview of sequence comparison: Time warps, string edits, and macromolecules. 25(2), 201-237. https://doi.org/10.1137/1025045
- Laditka, J. N. et Laditka, S. B. (2015, 2016/12/01). Associations of educational attainment with disability and life expectancy by race and gender in the United States. Journal of Aging and Health, 28(8), 1403-1425. https://doi.org/10.1177/0898264315620590
- Lesnard, L. (2010). Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns. Sociological methods & research 38(3), 389-419. https://doi.org/10.1177/0049124110362526
- Little, T. D. (2013). Longitudinal structural equation modeling. Guilford press.
- Lorentzen, T., Bäckman, O., Ilmakunnas, I. et Kauppinen, T. (2019). Pathways to adulthood: Sequences in the school-to-work transition in Finland, Norway and Sweden. Social Indicators Research, 141(3), 1285-1305. https://doi.org/10.1007/s11205-018-1877-4
- Lu, Y., Zhang, R. et Du, H. (2021). Family structure, family instability, and child psychological well-being in the context of migration: Evidence from sequence analysis in China. Child Development. https://doi.org/https://doi.org/10.1111/cdev.13496
- Maas, M. K., Bray, B. C. et Noll, J. G. (2019). Online sexual experiences predict subsequent sexual health and victimization outcomes among female adolescents: A latent class analysis. Journal of Youth and Adolescence, 48(5), 837-849. https://doi.org/10.1007/s10964-019-00995-3
- Marshall, E. A. et Symonds, J. E. (2021). Young adult development at the school-to-work transition: International pathways and processes. Oxford University Press.
- Mascherini, M. et Ledermaier, S. (2016). Exploring the diversity of NEETs. Publications Office of the European Union Luxembourg.
- Morin, A. J. S. et Litalien, D. (2019). Mixture modeling for lifespan developmental research. Oxford University Press. https://doi.org/10.1093/acrefore/9780190236557.013.364
- Navarro, J. F., Frenk, C. S. et White, S. D. M. (1997, 1997/12). A Universal density profile from hierarchical clustering. The Astrophysical Journal, 490(2), 493-508. https://doi.org/10.1086/304888
- Newell, M. A., Cook, D., Hofmann, H. et Jannink, J.-L. (2013). An algorithm for deciding the number of clusters and validation using simulated data with application to exploring crop population structure The Annals of Applied Statistics, 7(4), 1898-1916. http://www.jstor.org/stable/23566447
- Ng-Knight, T. et Schoon, I. (2017). Can cocus of control compensate for socioeconomic adversity in the transition from school to work? A Multidisciplinary Research Publication, 46(10), 2114-2128. https://doi.org/10.1007/s10964-017-0720-6
- Nicholson, J. S., Deboeck, P. R. et Howard, W. (2017). Attrition in developmental psychology: A review of modern missing data reporting and practices. International Journal of Behavioral Development 41(1), 143-153. https://doi.org/10.1177/0165025415618275
- 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: Series A (Statistics in Society), 170(1), 167-183. https://doi.org/10.1111/j.1467-985X.2006.00450.x
- Ranasinghe, R., Chew, E., Knight, G. et Siekmann, G. (2019). School-to-Work Pathways. National Centre for Vocational Education Research, Adelaide, SA.
- Richmond-Rakerd, L. S., Slutske, W. S. et Wood, P. K. (2017). Age of initiation and substance use progression: A multivariate latent growth analysis. Psychology of Addictive Behaviors, 31(6), 664-675. https://doi.org/10.1037/adb0000304
- Ritschard, G. et Studer, M. (2018). Sequence Analysis and Related Approaches: Innovative Methods and Applications. Springer. https://doi.org/10.1007/978-3-319-95420-2
- Rossignon, F., Studer, M., Gauthier, J.-A. et Goff, J.-M. L. (2018). Sequence history analysis (SHA): Estimating the effect of past trajectories on an upcoming event. Dans G. Ritschard et M. Studer (dir.), Sequence Analysis and Related Approaches: Innovative Methods and Applications (p. 83-100). Springer International Publishing. https://doi.org/10.1007/978-3-319-95420-2_6
- Schoon, I. (2015). Diverse pathways: Rethinking the transition to adulthood. Dans Amato, P.R, Booth, A., McHale, S. et Van Hook, J. Families in an Era of Increasing Inequality (p. 115-136). Springer.
- Schoon, I. et Lyons-Amos, M. (2017). A socio-ecological model of agency: The role of structure and agency in shaping education and employment transitions in England. Journal of Longitudinal and Lifecourse Studies, 8(1), 35-56.
- Struffolino, E., Studer, M. et Fasang, A. E. (2016). Gender, education, and family life courses in East and West Germany: Insights from new sequence analysis techniques. Advances in Life Course Research, 29, 66-79. https://doi.org/10.1016/j.alcr.2015.12.001
- Studer, M. (2013). WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R. LIVES Working Papers, 2013(24). https://doi.org/10.12682/lives.2296-1658.2013.24
- Studer, M. (2019). A general introduction to sequence analysis and its use in the social sciences [présentation d’un conférencier invitié]. Institut de démographie et socioéconomie, Université de Genève.
- Studer, M. et 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 (Statistics in Society) 179(2), 481-511. https://doi.org/10.1111/rssa.12125
- Studer, M., Struffolino, E. et Fasang, A. E. (2018). Estimating the relationship between time-varying covariates and trajectories: The sequence analysis multistate model procedure. (Revue) 48(1), 103-135. https://doi.org/10.1177/0081175017747122
- Thouin, É. (2022). La transition de l’école au travail chez les jeunes en situation de vulnérabilité scolaire ou sociale : examen des déterminants, des conséquences et des processus explicatifs [thèse de doctorat, Université de Montréal]. Papyrus
- Thouin, É., Dupéré, V., Denault, A. S. et Schoon, I. (en révision). Beyond college for all: Portrait of rapid and successful school-to-work Transitions among vulnerable youth.
- Van de Velde, C. (2016). Les “NEETs”: une déconstruction sociologique. Bulletin d’information Observatoire jeunes et société, (2), 18-19.
- Wu, L. L. (2000). Some comments on “Sequence analysis and optimal matching methods in sociology: Review and prospect”. Sociological methods & research 29(1), 41-64. https://doi.org/10.1177/0049124100029001003