Módszerek összehasonlítása

	Longitudinal Ex Post Facto Design	Ex Post Facto Design
Tudományterület	Kutatástervezés	Kutatástervezés
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)	1960s (systematic codification); concept used in social science from early 20th century
Megalkotó	Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Típus	Non-experimental quantitative research design	Non-experimental quantitative research design
Alapmű	Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417498	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Alternatív nevek	longitudinal causal-comparative design, longitudinal after-the-fact design, longitudinal retrospective design, LEPF design	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Kapcsolódó	5	3
Összefoglaló	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.	Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.