Сравнение на методи

	Напречно сечение на причинно-сравнително изследване	Лонгитюдни причинно-сравнителни изследвания
Област	Дизайн на изследването	Дизайн на изследването
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1960s onward	1970s–1980s (as an established combined design in educational and social research)
Създател	Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists	Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)
Тип	Non-experimental quantitative design	Non-experimental quantitative research design
Основополагащ източник	Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers. ISBN: 978-1429295154	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532
Други названия	cross-sectional ex post facto design, single-wave causal-comparative study, cross-sectional group-comparison design, cross-sectional criterion-group study	longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study
Свързани	3	4
Резюме	Cross-sectional causal-comparative research compares two or more pre-existing groups — defined by a characteristic or experience that has already occurred — on one or more outcome variables, with all data collected at a single point in time. Because the presumed cause (group membership) precedes measurement but cannot be manipulated, the design sits between purely descriptive and truly experimental work. It is widely used in education, psychology, and social sciences when randomization is impossible or unethical.	Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.