Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Mediācijas analīze× | Daudzlīmeņu modelēšana× | |
|---|---|---|
| Nozare≠ | Statistika | Pētniecības statistika |
| Saime≠ | Hypothesis test | Process / pipeline |
| Izcelsmes gads≠ | 1986 | 1992 |
| Autors≠ | Baron & Kenny | Anthony Bryk and Stephen Raudenbush |
| Tips≠ | Indirect effects / path test | Method |
| Pirmavots≠ | 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 ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Citi nosaukumi | indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS) | HLM, mixed-effects models, random effects models, MLM |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
| ScholarGateDatu kopa ↗ |
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