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| Longitudinale Korrelationsforschung× | Longitudinale Kausale-Komparative Forschung× | |
|---|---|---|
| Fachgebiet | Forschungsdesign | Forschungsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | Mid-20th century (formalized 1940s–1960s) | 1970s–1980s (as an established combined design in educational and social research) |
| Urheber≠ | Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.) | Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979) |
| Typ≠ | Non-experimental quantitative design | Non-experimental quantitative research design |
| Wegweisende Quelle≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0078097898 | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532 |
| Aliasnamen | longitudinal correlational study, prospective correlational design, longitudinal associational research, repeated-measures correlational design | longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study |
| Verwandt≠ | 3 | 4 |
| Zusammenfassung≠ | Longitudinal correlational research is a non-experimental quantitative design that examines the strength and direction of relationships among variables by collecting data from the same participants at two or more points in time. Unlike a cross-sectional correlational study, the longitudinal approach captures how associations evolve, persist, or dissolve across time, providing a stronger empirical basis for causal inference without experimental manipulation. | Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally. |
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