השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מחקר השוואתי-הסברי× | מבנה חקירת מקרי-מבחן מרובים× | |
|---|---|---|
| תחום≠ | תכנון מחקר | איכותני |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 1843 (Mill); contemporary social-science formalisation 1971–1987 | 1980s–1990s (Yin's first edition 1984; Stake's collective case study concept 1995) |
| הוגה השיטה≠ | John Stuart Mill (methods of agreement and difference, 1843); formalised in social science by Arend Lijphart and Charles Ragin | Robert K. Yin (systematic replication logic); Robert E. Stake (naturalistic/collective case tradition) |
| סוג≠ | Observational explanatory research design | Qualitative research method |
| מקור מכונן≠ | Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press. ISBN: 978-0520063167 | Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage. ISBN: 978-1506336169 |
| כינויים | comparative explanation, explanatory comparative design, cross-case explanatory research, comparative causal analysis | comparative case study, multi-site case study, collective case study, cross-case analysis |
| קשורות≠ | 4 | 6 |
| תקציר≠ | 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. | Multiple-case study design investigates two or more bounded real-world cases using the same research protocol, then compares findings across cases to identify patterns, contrasts, and explanatory insights that a single case could not produce. Developed primarily through Robert Yin's replication logic and Robert Stake's collective case tradition, the approach is particularly powerful when a researcher needs to determine whether a phenomenon occurs under varied conditions or to test an emerging theoretical explanation against rival contexts. |
| ScholarGateמערך נתונים ↗ |
|
|