Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Pesquisa Comparativa de Tendências× | Pesquisa de Painel× | |
|---|---|---|
| Área | Delineamento de pesquisa | Delineamento de pesquisa |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1970s–1990s (formalized alongside longitudinal and trend designs) | 1970s-1980s (econometric formalization); earlier social survey use from 1940s |
| Autor original≠ | Developed within the survey research tradition; comparative extension attributed broadly to Babbie, Creswell, and related methodologists | Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s |
| Tipo≠ | Quantitative non-experimental design | Quantitative longitudinal observational design |
| Fonte seminal≠ | Creswell, J. W. (2002). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage Publications. ISBN: 978-0761924425 | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 |
| Outros nomes | comparative trend study, multi-group trend study, cross-group trend analysis, comparative longitudinal survey | panel study, panel survey, longitudinal panel, repeated-measures panel |
| Relacionados | 3 | 3 |
| Resumo≠ | 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?' | Panel research is a quantitative longitudinal design in which the same individuals, organizations, or other units are measured repeatedly across two or more time points. Unlike cross-sectional surveys that capture a single snapshot, a panel tracks change within units, enabling researchers to separate genuine within-unit change from between-unit differences and to model causal dynamics over time. |
| ScholarGateConjunto de dados ↗ |
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