Σύγκριση μεθόδων

	Έρευνα Συγκριτικής Δοκιμής Μοντέλων	Έρευνα Ελέγχου Μοντέλων
Πεδίο	Ερευνητικός Σχεδιασμός	Ερευνητικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1969–2000s	1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s
Δημιουργός	Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000)	Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition
Τύπος	Quantitative confirmatory-comparative research design	Confirmatory quantitative research design
Θεμελιώδης πηγή	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344	Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344
Εναλλακτικές ονομασίες	comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation	model-based research, structural model testing, theory-testing research, MTR
Συναφείς	4	5
Σύνοψη	Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups.	Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence.