Salīdzināt metodes

	Kondicionālās procesa analīze (moderētā mediācija)	Hierarhiskā lineārā modelēšana (HLM / daudzlīmeņu modelēšana)	Mediācijas analīze
Nozare	Cēloņsakarību secināšana	Statistika	Statistika
Saime	Regression model	Hypothesis test	Hypothesis test
Izcelsmes gads	2018	1986	1986
Autors	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Baron & Kenny
Tips	Regression-based conditional process model	Parametric nested-data regression	Indirect effects / path test
Pirmavots	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	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.
Citi nosaukumi	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	HLM, MLM, multilevel modeling, multilevel analysis	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
Saistītās	5	4	5
Kopsavilkums	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	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.