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Иерархическое причинно-сравнительное исследование×Лонгитюдное каузально-сравнительное исследование×
ОбластьДизайн исследованияДизайн исследования
СемействоProcess / pipelineProcess / pipeline
Год появления1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)1970s–1980s (as an established combined design in educational and social research)
Автор методаKerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)
ТипNon-experimental quantitative research designNon-experimental quantitative research design
Основополагающий источникRaudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532
Другие названияmultilevel causal-comparative design, nested causal-comparative research, HLM causal-comparative study, hierarchical ex post facto comparisonlongitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study
Связанные44
СводкаHierarchical causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on an outcome variable while explicitly modeling the nested structure of the data. Participants are clustered within higher-level units — students within classrooms, employees within organizations — and the design uses multilevel analytical techniques to distinguish group differences at each level. The cause-and-effect inference is strengthened by accounting for variance attributable to the hierarchy rather than misattributing it to individual-level group membership.Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.
ScholarGateНабор данных
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ScholarGateСравнение методов: Hierarchical Causal-Comparative Research · Longitudinal Causal-Comparative Research. Получено 2026-06-19 из https://scholargate.app/ru/compare