Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Badanie porównawcze w formie ankiety× | Badania podłużne× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | Mid-20th century onward | Mid-20th century (formalized ~1950s–1970s) |
| Twórca≠ | Rooted in survey methodology traditions (Gallup, Likert, Lazarsfeld mid-20th century); comparative extension codified in social science research methods literature | Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard |
| Typ≠ | Quantitative non-experimental research design | Quantitative observational research design |
| Źródło pierwotne≠ | Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922452 |
| Inne nazwy | comparative survey design, cross-group survey, multi-group survey research, comparative questionnaire study | longitudinal survey study, repeated-measures survey, prospective survey design, panel survey |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | Comparative survey research is a quantitative non-experimental design that systematically collects structured survey data from two or more clearly defined groups, populations, or contexts in order to identify, describe, and analyze similarities and differences among them. It extends basic survey research by making comparison the explicit organizing logic: rather than characterizing a single population, the goal is to detect how attitudes, behaviors, or outcomes vary across groups defined by nationality, culture, profession, demographic category, or time period. | Longitudinal survey research collects structured questionnaire data from the same individuals (or units) at two or more points in time. Unlike a one-shot cross-sectional survey, this design captures change, stability, and temporal ordering of variables — enabling researchers to track trajectories, test causal sequences, and distinguish cohort effects from aging effects within a quantitative framework. |
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