Linganisha mbinu

	Ubunifu wa Utafiti wa Longitudinal Ex Post Facto	Muundo wa Ex Post Facto
Nyanja	Muundo wa Utafiti	Muundo wa Utafiti
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)	1960s (systematic codification); concept used in social science from early 20th century
Mwanzilishi	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
Aina	Non-experimental quantitative research design	Non-experimental quantitative research design
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	3
Muhtasari	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.