Порівняння методів

	Дизайн ex post facto на основі панельних даних	Дизайн «екс пост факто»
Галузь	Дизайн дослідження	Дизайн дослідження
Родина	Process / pipeline	Process / pipeline
Рік появи	1950s–1970s (synthesized from ex post facto tradition and panel survey research)	1960s (systematic codification); concept used in social science from early 20th century
Автор методу	Developed from Kerlinger's ex post facto framework combined with panel survey methodology (Lazarsfeld, Kerlinger)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Тип	Non-experimental longitudinal observational design	Non-experimental quantitative research design
Основоположне джерело	Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417511	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Інші назви	panel ex post facto study, longitudinal causal-comparative design, retrospective panel design, panel causal-comparative study	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Пов'язані	2	3
Підсумок	A panel-based ex post facto design tracks the same group of participants across multiple time points to examine how pre-existing differences in an independent variable — one the researcher did not manipulate — are associated with changes in an outcome over time. It merges the temporal depth of panel methodology with the causal-comparative logic of ex post facto research, enabling stronger causal inference than a single cross-sectional snapshot while remaining fully non-experimental.	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.