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| 종단적 사후 검증 설계× | 종단 상관 연구× | |
|---|---|---|
| 분야 | 연구설계 | 연구설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966) | Mid-20th century (formalized 1940s–1960s) |
| 창시자≠ | Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework) | Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.) |
| 유형≠ | Non-experimental quantitative research design | Non-experimental quantitative design |
| 원전≠ | Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417498 | 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 |
| 별칭 | longitudinal causal-comparative design, longitudinal after-the-fact design, longitudinal retrospective design, LEPF design | longitudinal correlational study, prospective correlational design, longitudinal associational research, repeated-measures correlational design |
| 관련≠ | 5 | 3 |
| 요약≠ | A longitudinal ex post facto design combines the time-depth of longitudinal research with the retrospective logic of ex post facto inquiry. Participants are grouped by a naturally occurring characteristic or past event — not randomly assigned — and then observed or measured at multiple points over time. The goal is to trace how pre-existing differences between groups unfold or predict outcomes across an extended period, without the researcher ever manipulating the independent variable. | 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. |
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