Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Многовариантно напречно изследване× | Напречно-секторно изследване× | |
|---|---|---|
| Област | Дизайн на изследването | Дизайн на изследването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1960s–1970s (formalized with widespread multivariate methods) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Създател≠ | Developed from the convergence of survey methodology (Kerlinger) and multivariate statistics (Tabachnick, Fidell) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Тип≠ | Quantitative observational design | Quantitative (or mixed) observational research design |
| Основополагащ източник≠ | Kerlinger, F. N., & Lee, H. B. (2000). Foundations of Behavioral Research (4th ed.). Harcourt College Publishers. ISBN: 978-0155078970 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Други названия | multivariate survey design, multi-variable cross-sectional study, MXSR, multivariate observational study | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Свързани≠ | 3 | 4 |
| Резюме≠ | Multivariate cross-sectional research collects data on multiple variables from a defined population at a single point in time and uses multivariate statistical techniques — such as multiple regression, MANOVA, factor analysis, or structural equation modeling — to examine simultaneous relationships among those variables. It combines the efficiency of a cross-sectional snapshot with the analytical power to handle complex, multi-variable research questions in a single study. | 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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