Methoden vergleichen

	Bedingte Prozessanalyse (moderierte Mediation)	Methode der kleinsten Quadrate (OLS)	Regression Discontinuity Design (RDD)
Fachgebiet	Kausale Inferenz	Ökonometrie	Kausale Inferenz
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	2018	2019	2008
Urheber	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Wooldridge (textbook treatment); classical least squares	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Typ	Regression-based conditional process model	Linear regression	Quasi-experimental causal design
Wegweisende Quelle	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Aliasnamen	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Verwandt	5	5	5
Zusammenfassung	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.