方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 纵向确认性研究× | 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≠ | 1970s onward; consolidated in SEM literature from 1990s | Mid-20th century (formalized 1940s–1960s) |
| 提出者≠ | Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969) | Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.) |
| 类型≠ | Quantitative research design | Non-experimental quantitative design |
| 开创性文献≠ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 | 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 | longitudinal confirmatory study, confirmatory longitudinal design, longitudinal hypothesis-testing design, longitudinal CFA design | longitudinal correlational study, prospective correlational design, longitudinal associational research, repeated-measures correlational design |
| 相关≠ | 5 | 3 |
| 摘要≠ | Longitudinal confirmatory research combines the temporal depth of longitudinal design with the hypothesis-driven logic of confirmatory analysis. The researcher specifies a priori hypotheses or structural models about how variables change or remain stable over time, then tests those predictions against data collected at two or more time points. It is the design of choice when theory is mature enough to make specific predictions about developmental, causal, or stability processes. | 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. |
| ScholarGate数据集 ↗ |
|
|