Methoden vergleichen
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| Hierarchische relationale Umfrage× | Relational Survey× | |
|---|---|---|
| Fachgebiet | Forschungsdesign | Forschungsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1980s–2002 (modern HLM-based survey tradition) | Mid-20th century onward (systematised ~1960s–1990s) |
| Urheber≠ | Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis) | Established in educational and social science research methodology; systematised by Fraenkel & Wallen and others |
| Typ≠ | Quantitative survey design with multilevel relational analysis | Quantitative non-experimental survey design |
| Wegweisende Quelle≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0073525748 |
| Aliasnamen | nested relational survey, multilevel relational survey, HLM-based relational survey, hierarchical correlational survey | correlational survey, associational survey, relationship survey design, relational descriptive survey |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | A hierarchical relational survey combines the correlational goals of relational survey research with a multilevel data structure in which respondents are nested within higher-level units such as classrooms, schools, hospitals, or organizations. The design acknowledges that observations within the same group are not independent, and uses hierarchical linear modeling (HLM) or equivalent multilevel techniques to examine relationships among variables both within and between levels simultaneously. | Relational survey research is a quantitative, non-experimental design that gathers structured self-report data from a sample and examines the statistical associations among two or more variables. Unlike purely descriptive surveys, which only characterise distributions, relational surveys ask whether and how strongly variables co-vary — providing evidence of relationships without manipulating conditions or establishing causation. |
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