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| Ricerca comparata sulle tendenze× | Ricerca di Tendenza× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1970s–1990s (formalized alongside longitudinal and trend designs) | Mid-20th century (formalised in social science methodology ~1950s–1960s) |
| Ideatore≠ | Developed within the survey research tradition; comparative extension attributed broadly to Babbie, Creswell, and related methodologists | Earl Babbie and survey research tradition |
| Tipo≠ | Quantitative non-experimental design | Quantitative longitudinal research design |
| Fonte seminale≠ | Creswell, J. W. (2002). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage Publications. ISBN: 978-0761924425 | Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage. ISBN: 978-1452226101 |
| Alias | comparative trend study, multi-group trend study, cross-group trend analysis, comparative longitudinal survey | trend study, trend survey, longitudinal trend study, time-series survey |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | Comparative trend research is a quantitative non-experimental design that tracks changes in one or more variables over time within two or more distinct groups or populations. By drawing independent cross-sectional samples from each group at multiple time points, it reveals whether trends diverge, converge, or differ in magnitude across groups — answering not just 'is this changing?' but 'is it changing differently for different populations?' | Trend research is a longitudinal quantitative design that tracks changes in a characteristic of a general population over time by surveying different, independently drawn samples at two or more time points. Unlike panel studies, the same individuals are not followed; rather, each wave draws a fresh sample from the same population, allowing researchers to detect population-level shifts in attitudes, behaviours, or conditions while avoiding the attrition and panel conditioning problems of repeated-measures designs. |
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