方法对比
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| 横断面关系调查× | 纵向相关研究× | |
|---|---|---|
| 领域 | 研究设计 | 研究设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Mid-20th century onward | Mid-20th century (formalized 1940s–1960s) |
| 提出者≠ | 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.) |
| 类型 | Non-experimental quantitative design | Non-experimental quantitative design |
| 开创性文献≠ | 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 |
| 别名 | 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 |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. |
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