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Exploring Temporal Pattern of Intergenerational Educational Mobility in Germany: An Application of Configural Frequency Analysis Using Weighted Prediction

In: Dependent Data in Social Sciences Research

Author

Listed:
  • Jörg-Henrik Heine

    (Ludwig-Maximilians-Universität & Gesellschaft für Weiterbildung und sozialwissenschaftliche Forschung (GWSF) e.V.)

  • Florian G. Hartmann

    (Paris Lodron University of Salzburg, Department of Psychology)

  • Christian Tarnai

    (Universität der Bundeswehr, Department of Human Sciences)

Abstract

This chapter addresses a substantive and methodological question. From a substantive perspective, typical and atypical patterns of family educational triads from formal school-leaving qualifications are examined. In addition, the central question is addressed to what extent these patterns can be modeled in the longitudinal course of the birth cohorts of the respondents. The data basis is the German General Social Survey (ALLBUS). From a methodological perspective, the influence of the design weights provided in the data, which compensate for the disproportionalities of the two subsamples and varying drawing probabilities of respondents, on the statistical inference to the frequencies of family educational patterns is investigated. To answer these research questions, a series of analyses are carried out using different models of configuration frequency analysis. The complete R-syntax file and the analyzed data are available at the OSF repository (see Heine et al., Exploring Tem poral Pattern - Educational Mobility. Accompanying R syntax. Retrieved from https://osf.io/7ykzv , 2023). The findings are discussed against the theoretical background of the concepts of educational expansion and educational mobility in Germany.

Suggested Citation

  • Jörg-Henrik Heine & Florian G. Hartmann & Christian Tarnai, 2024. "Exploring Temporal Pattern of Intergenerational Educational Mobility in Germany: An Application of Configural Frequency Analysis Using Weighted Prediction," Springer Books, in: Mark Stemmler & Wolfgang Wiedermann & Francis L. Huang (ed.), Dependent Data in Social Sciences Research, edition 2, chapter 0, pages 443-473, Springer.
  • Handle: RePEc:spr:sprchp:978-3-031-56318-8_17
    DOI: 10.1007/978-3-031-56318-8_17
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