So sánh phương pháp

	Thiết kế Nghiên cứu Dọc Hồi Cứu (Longitudinal Ex Post Facto Design)	Thiết kế Ex Post Facto
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)	1960s (systematic codification); concept used in social science from early 20th century
Người khởi xướng	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
Loại	Non-experimental quantitative research design	Non-experimental quantitative research design
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	3
Tóm tắt	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.