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| Keresztmetszeti ok-összehasonlító kutatás× | Longitudinális ok-összehasonlító kutatás× | |
|---|---|---|
| Tudományterület | Kutatástervezés | Kutatástervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1960s onward | 1970s–1980s (as an established combined design in educational and social research) |
| Megalkotó≠ | Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists | Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979) |
| Típus≠ | Non-experimental quantitative design | Non-experimental quantitative research design |
| Alapmű≠ | Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers. ISBN: 978-1429295154 | 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 |
| Alternatív nevek | cross-sectional ex post facto design, single-wave causal-comparative study, cross-sectional group-comparison design, cross-sectional criterion-group study | longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study |
| Kapcsolódó≠ | 3 | 4 |
| Összefoglaló≠ | Cross-sectional causal-comparative research compares two or more pre-existing groups — defined by a characteristic or experience that has already occurred — on one or more outcome variables, with all data collected at a single point in time. Because the presumed cause (group membership) precedes measurement but cannot be manipulated, the design sits between purely descriptive and truly experimental work. It is widely used in education, psychology, and social sciences when randomization is impossible or unethical. | 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. |
| ScholarGateAdatkészlet ↗ |
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