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| 계층적 선형 모형 (HLM / 다층 모형)× | Mixed Effects Model× | 반복측정 분산분석× | |
|---|---|---|---|
| 분야 | 통계학 | 통계학 | 통계학 |
| 계열≠ | Hypothesis test | Regression model | Hypothesis test |
| 기원 연도≠ | 1986 | 1982 | 1992 |
| 창시자≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Laird & Ware | Girden (textbook treatment); Field (2013) |
| 유형≠ | Parametric nested-data regression | Mixed effects regression | Parametric within-subjects mean comparison |
| 원전≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| 별칭≠ | HLM, MLM, multilevel modeling, multilevel analysis | LME, LMM, mixed model, random effects model | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| 관련 | 4 | 4 | 4 |
| 요약≠ | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. | Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013). |
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