Сравнение на методи
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| Лонгитюдни причинно-сравнителни изследвания× | Напречно-секторно изследване× | |
|---|---|---|
| Област | Дизайн на изследването | Дизайн на изследването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1970s–1980s (as an established combined design in educational and social research) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Създател≠ | Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Тип≠ | Non-experimental quantitative research design | Quantitative (or mixed) observational research design |
| Основополагащ източник≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Други названия | longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Свързани | 4 | 4 |
| Резюме≠ | Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally. | 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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