So sánh phương pháp

	Khảo sát Quan hệ Mặt cắt Ngang	Nghiên cứu tương quan dọc
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Mid-20th century onward	Mid-20th century (formalized 1940s–1960s)
Người khởi xướng	Rooted in survey methodology traditions; codified by Fraenkel, Wallen, and Creswell among others	Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.)
Loại	Non-experimental quantitative design	Non-experimental quantitative design
Công trình gốc	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0078097706	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0078097898
Tên gọi khác	cross-sectional correlational survey, one-time relational survey, cross-sectional associational survey, single-occasion relational survey	longitudinal correlational study, prospective correlational design, longitudinal associational research, repeated-measures correlational design
Liên quan	4	3
Tóm tắt	A cross-sectional relational survey collects data from a representative sample at a single point in time and examines the statistical relationships (correlations, associations, predictions) among two or more variables. It combines the temporal efficiency of cross-sectional design with the relational focus of correlational survey research, making it one of the most widely used quantitative designs in education, social science, and health research when a quick, population-level picture of variable relationships is needed.	Longitudinal correlational research is a non-experimental quantitative design that examines the strength and direction of relationships among variables by collecting data from the same participants at two or more points in time. Unlike a cross-sectional correlational study, the longitudinal approach captures how associations evolve, persist, or dissolve across time, providing a stronger empirical basis for causal inference without experimental manipulation.