เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การวิจัยเชิงสังเกตแบบหลายตัวแปรตามช่วงเวลา× | การวิจัยเชิงสำรวจตามช่วงเวลา× | |
|---|---|---|
| สาขาวิชา | การออกแบบการวิจัย | การออกแบบการวิจัย |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1970s–1980s (formalized in behavioral sciences literature) | Late 19th–early 20th century; methodologically codified through the 20th century |
| ผู้ริเริ่ม≠ | Nesselroade, Baltes, and the developmental/behavioral sciences tradition | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| ประเภท≠ | Quantitative observational research design | Quantitative (or mixed) observational research design |
| แหล่งต้นตำรับ≠ | Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press. ISBN: 978-0125154505 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| ชื่อเรียกอื่น | longitudinal multivariate design, MLR, multivariate panel study, multivariate repeated-measures design | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| ที่เกี่ยวข้อง | 4 | 4 |
| สรุป≠ | Multivariate longitudinal research is a quantitative observational design that follows the same units — individuals, groups, or organizations — across two or more time points while measuring several outcome and predictor variables simultaneously. By combining the temporal dimension of longitudinal tracking with multivariate statistical analysis, it allows researchers to examine how a system of variables co-evolves, how early measures predict later outcomes across multiple domains, and whether relationships among variables are stable or change over time. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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