Education as a Lifelong Process

  • Hans-Peter BlossfeldEmail author
  • Jutta von Maurice
Part of the Edition ZfE book series (EZFE, volume 3)


In modern societies, education has become a lifelong process. This has made the principles of life-course research of utmost significance in empirical education research. As stated by Glen H. Elder, these can be described as: (1) focusing on long-term educational processes over the individual lifespan; (2) considering individual educational pathways within their institutional and social embeddedness (e.g., within not only formal educational institutions but also nonformal/informal contexts such as the family, peer groups, and other social networks); (3) analyzing decision-making processes in education linked to the idea of agency and the idea of plan-making, creative, and self-determining actors; (4) investigating the time structure and timing of educational events and transitions and the consequences they have for the subsequent educational pathways and educational chances; and (5) conceptually differentiating age, cohort, and period effects. This chapter discusses the importance of these five principles for the conception, design, and possibilities for analysis of the German National Educational Panel Study (NEPS). In the context of these principles, we formulate methodological advantages of longitudinal data on educational processes that can be attained within the idea of NEPS. In particular, panel data improve the opportunities to describe trajectories of growth and development over the life course and to study the patterns of causal relationships over longer time spans.


Education Panel study Life-course perspective Empirical education research Longitudinal data 


Bildung ist in modernen Gesellschaften zu einem lebenslangen Prozess geworden. In der empirischen Bildungsforschung sind daher die fünf Prinzipien der Lebensverlaufsforschung, wie sie von Glen H. Elder formuliert wurden, von größter Bedeutung: (1) Die Fokussierung auf langfristige Bildungsprozesse über die individuelle Lebensspanne hinweg, (2) die Betrachtung individueller Bildungsverläufe in ihrer institutionellen und sozialen Einbettung (nicht nur in formalen Bildungsinstitutionen, sondern auch in nonformalen/informellen Kontexten wie der Familie, Peergruppen und anderen sozialen Netzwerken), (3) die Untersuchung von bildungsrelevanten Entscheidungsprozessen und damit verbunden die Idee von aktiv Handelnden und planenden, kreativen und selbstbestimmten Akteuren, (4) die Analyse der Zeitstruktur und des Timings von Bildungsereignissen und -übergängen und ihrer Auswirkungen auf die späteren Bildungsverläufe und Bildungschancen sowie (5) die konzeptionelle Unterscheidung von Alters-, Kohorten- und Periodeneffekten. Das vorliegende Kapitel diskutiert die Bedeutung dieser fünf Prinzipien für die Konzeption, das Design und die Analysepotentiale des Nationalen Bildungspanels. Im Kontext dieser Prinzipien werden die methodologischen Vorteile von Längsschnittdaten im Bereich der Bildungsforschung formuliert, wie sie im Nationalen Bildungspanel gewonnen werden können. Mit Hilfe von Paneldaten lassen sich Wachstum und Entwicklung im Lebenslauf beschreiben und kausale Beziehungsstrukturen über längere Zeitspannen hinweg untersuchen.


Bildung Panelstudie Lebensverlaufsperspektive Empirische Bildungsforschung Längsschnittdaten 


  1. Allison, P. D. (1994). Using panel data to estimate the effects of events. Sociological Methods & Research, 23, 174–199.Google Scholar
  2. Baltes, P. B. (1990). Entwicklungspsychologie der Lebensspanne. Theoretische Leitsätze. Psychologische Rundschau, 41, 1–24.Google Scholar
  3. Baltes, P. B., Reese, H. W., & Lipsitt, L. P. (1980). Life-span developmental psychology. Annual Review of Psychology, 31, 65–110.Google Scholar
  4. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.Google Scholar
  5. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.Google Scholar
  6. Blalock, H. M. (Ed.). (1970). Causal models in the social sciences, Chicago, IL: Aldine.Google Scholar
  7. Blossfeld, H.-P. (1990). Changes in educational careers in the Federal Republic of Germany. Sociology of Education, 63(3), 165–177.Google Scholar
  8. Blossfeld, H.-P. (2009). Comparative life course research: A cross-national and longitudinal perspective. In G. H. Elder, Jr. & J. Z. Giele (Eds.), The craft of life course research (pp. 280–306). New York, NY: The Guilford Press.Google Scholar
  9. Breen, R., & Goldthorpe, J. H. (1997). Explaining educational differentials. Towards a formal rational action theory. Rationality and Society, 9, 275–305.Google Scholar
  10. Breen, R., & Jonsson, J. O. (2000). Analyzing educational careers: A multinomial transition model. American Sociological Review, 65, 754–772.Google Scholar
  11. Bronfenbrenner, U. (1979). The ecology of human development. Experiments by nature and design. Cambridge, MA: Harvard University Press.Google Scholar
  12. Cameron, S. V., & Heckman, J. J. (1998). Life cycle schooling and dynamic selection bias: Models and evidence for five cohorts of American males. Journal of Political Economy, 106, 262–333.Google Scholar
  13. Coleman, J. S. (1981). Longitudinal data analysis. New York, NY: Basic Books.Google Scholar
  14. Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld F. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: U. S. Government Printing Office.Google Scholar
  15. Cox, D. R. (1990). Role of models in statistical analysis. Statistical Science, 5, 169–174.Google Scholar
  16. Cox, D. R. (1992). Causality: Some statistical aspects. Journal of the Royal Statistical Society Series A, 155, 291–301.Google Scholar
  17. Dannefer, D. (1984). Adult development and social theory: A paradigmatic reappraisal. American Sociological Review, 49, 100–116.Google Scholar
  18. Dannefer, D. (1987). Aging as intercohort differentiation: Accentuation, the Matthew Effect, and the life course. Sociological Forum, 2, 211–236.Google Scholar
  19. Earls, E., & Carlson, M. (1995). Promoting human capability as an alternative to early crime prevention. In P.-O. Wikström, R.V. Clarke, & J. McCord (Eds.), Integrating crime prevention strategies: Propensity and opportunity (pp. 141–168). Stockholm, Sweden: National Council for Crime Prevention.Google Scholar
  20. Elder, G. H. Jr., & Giele, J. Z. (2009). Life course studies: An evolving field. In G. H. Elder, Jr. & J. Z. Giele (Eds.), The craft of life course research (pp. 1–24). New York, NY: Guilford Press.Google Scholar
  21. Elder, G. H. Jr., Johnson, M. K., & Crosnoe, R. (2004). The emergence and development of life course theory. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course (pp. 3–19). New York, NY: Springer.Google Scholar
  22. Erikson, R., & Jonsson, J. O. (1996). Explaining class inequality in education: The Swedish case. In R. Erikson & J. O. Jonsson (Eds.), Can education be equalized? The Swedish case in comparative perspective (pp. 1–63). Oxford, England: Westview Press.Google Scholar
  23. Goldstein, H. (1995). Multilevel statistical models. London, England: Edward Arnold.Google Scholar
  24. Goldthorpe, J. H. (2001). Causation, statistics, and sociology. European Sociological Review, 17, 1–20.Google Scholar
  25. Halaby, C. N. (2004). Panel models for the analysis of change and growth in life course studies. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course (pp. 503–528), New York, NY: Springer.Google Scholar
  26. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.Google Scholar
  27. Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449–484.Google Scholar
  28. Hsiao, C. (1986). Analysis of panel data. Cambridge, England: Cambridge University Press.Google Scholar
  29. Kelly, J. R., & McGrath, J. E. (1988). On time and method. Newbury Park, CA: Sage.Google Scholar
  30. Kerckhoff, A. C., Fogelman, K., Crook, D., & Reeder, D. (1996). Going comprehensive in England and Wales: A study of uneven change. London, England: Woburn Press.Google Scholar
  31. Lewontin, R. (2000). The triple helix: Gene, organism, and environment. Cambridge, MA: Harvard University Press.Google Scholar
  32. Macy, M. W. (1991). Chains of cooperation: Threshold effects in collective action. American Sociological Review, 56, 730–747.Google Scholar
  33. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.Google Scholar
  34. Maddala, G. S. (1987). Limited dependent variable models using panel data. Journal of Human Resources, 22, 307–338.Google Scholar
  35. Magnusson, D., & Törestad, B. (1992). The individual as an interactive agent in the environment. In W. B. Walsh, K. H. Craik, & R. H. Price (Eds.), Person-environment psychology: Models and perspectives (pp. 89–126). Hillsdale, NJ: Erlbaum.Google Scholar
  36. Mare, R. D. (1980). Social background and school continuation decisions. Journal of the American Statistical Association, 75, 295–305.Google Scholar
  37. Marsh, H. W., Hau, K.-T., Artelt, C., Baumert, J., & Peschar, J. L. (2006). OECD’s brief self-report measure of educational psychology’s most useful affective constructs: Cross-cultural, psychometric comparisons across 25 countries. International Journal of Testing, 6, 311–360.Google Scholar
  38. Mayer, K. U., & Müller, W. (1986). The state and the structure of the life course. In A. B. Sørensen, F. E. Weinert, & L. R. Sherrod (Eds.), Human development and the life course. Multidisciplinary perspectives (pp. 217–245). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  39. Mayer, K. U. & Tuma, N. B. (Eds.). (1990). Event history analysis in life course research. Madison, WI: University of Wisconsin Press.Google Scholar
  40. McArdle, J. J., & Epstein, D. (1987). Latent growth curves with developmental structural equation models. Child Development, 58, 110–133.Google Scholar
  41. Moen, P., & Herandez, E. (2009). Social convoys: Studying linked lives in time, context, and motion. In G. H. Elder, Jr. & J. Z. Giele (Eds.), The craft of life course research (pp. 258–279). New York, NY: Guilford Press.Google Scholar
  42. Natriello, G. (1994). Coming together and breaking apart: Unifying and differentiating processes in schools and classrooms. Research in Sociology of Education and Socialisation, 10, 111–145.Google Scholar
  43. OECD (1999). Measuring student knowledge and skills. A new framework for assessment. Paris, France: OECD.Google Scholar
  44. O’Rand, A. M. (2009). Cumulative processes in the life course. In G. H. Elder, Jr. & J. Z. Giele (Eds.), The craft of life course research (pp. 121–140). New York, NY: Guilford Press.Google Scholar
  45. O’Rand, A. M., & Henretta, J. C. (1999). Age and inequality: Diverse pathways through later life. Boulder, CO: Westview Press.Google Scholar
  46. Pallas, A. M. (2002). Educational participation across the life course: Do the rich get richer? In R. A. Settersten, Jr. & T. J. Owens (Eds.), Advances in life course research. New frontiers in socialisation (pp. 327–354). Oxford, England: Elsevier.Google Scholar
  47. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.Google Scholar
  48. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.Google Scholar
  49. Rubin, D. B. (1980). Randomization analysis of experimental data: The Fisher Randomization Test comment. Journal of the American Statistical Association, 75, 591–593.Google Scholar
  50. Sampson, R. J., & Laub, J. H. (2004). Desistance from crime over the life course. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course (pp. 295–310). New York, NY: Springer.Google Scholar
  51. Schaie, K. W. (1996). Intellectual development in adulthood: The Seattle longitudinal study. Cambridge, England: Cambridge University Press.Google Scholar
  52. Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson R. J. (2007). Estimating causal effects: Using experimental and observational designs. Washington DC: American Educational Research Association.Google Scholar
  53. Settersten, R. A. Jr. (2004). Age structuring and the rhythm of the life course. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course (pp. 81–102). New York, NY: Springer.Google Scholar
  54. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.Google Scholar
  55. Shavit, Y., & Blossfeld, H.-P. (1993). Persistent inequality: Changing educational attainment in thirteen countries. Social inequality series. Boulder, CO: Westview Press.Google Scholar
  56. Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London, England: Sage.Google Scholar
  57. Spenner, K. I., Otto, L. B,. & Call, V. R. (1982). Career lines and careers. Lexington, MA: Lexington Heath.Google Scholar
  58. Spilerman, S. (1977). Careers, labor market structure, and socioeconomic achievement. American Journal of Sociology, 83, 551–593.Google Scholar
  59. Tuma, N. B., & Hannan, M. T. (1984). Social dynamics: Models and methods. Orlando, FL: Academic Press.Google Scholar
  60. Wikström, P.-O. H., & Sampson, R. J. (2003). Social mechanisms of community influences on crime and pathways in criminality. In B. B. Lahey, T. E. Moffitt, & A. Caspi (Eds.), Causes of conduct. Disorder and juvenile delinquency (pp. 118–148). New York, NY: Guilford Press.Google Scholar
  61. Willet, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363–381.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.University of BambergBambergGermany
  2. 2.Leibniz Institute for Educational TrajectoriesBambergGermany

Personalised recommendations