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| Összehasonlító magyarázó kutatás× | Kutatási módszertan× | |
|---|---|---|
| Tudományterület | Kutatástervezés | Kutatástervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1843 (Mill); contemporary social-science formalisation 1971–1987 | Late 19th century; methodologically systematised 1940s–1960s |
| Megalkotó≠ | John Stuart Mill (methods of agreement and difference, 1843); formalised in social science by Arend Lijphart and Charles Ragin | Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s |
| Típus≠ | Observational explanatory research design | Quantitative (and mixed) non-experimental design |
| Alapmű≠ | Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press. ISBN: 978-0520063167 | Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000 |
| Alternatív nevek | comparative explanation, explanatory comparative design, cross-case explanatory research, comparative causal analysis | survey methodology, questionnaire research, survey design, survey study |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | Comparative explanatory research is an observational design that systematically examines two or more groups, nations, organisations, or time points in order to explain why differences in outcomes occur. Rather than merely describing variation, it seeks causal or contributing mechanisms by holding some conditions constant while contrasting others — drawing on Mill's classical methods of agreement and difference. | Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estimating prevalence, mapping attitude distributions, and testing bivariate or multivariate associations across social, behavioural, and health sciences. |
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