Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)× | Mediācijas analīze× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Hypothesis test | Hypothesis test |
| Izcelsmes gads | 1986 | 1986 |
| Autors≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Baron & Kenny |
| Tips≠ | Parametric nested-data regression | Indirect effects / path test |
| Pirmavots≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182. link ↗ |
| Citi nosaukumi≠ | HLM, MLM, multilevel modeling, multilevel analysis | indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS) |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism. |
| ScholarGateDatu kopa ↗ |
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