Vertaile menetelmiä

	Monitasomallinnus	Varianssianalyysi (ANOVA)	Rakenteellinen yhtälömallinnus
Tieteenala	Tutkimuksen tilastomenetelmät	Tutkimuksen tilastomenetelmät	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Process / pipeline	Process / pipeline	Process / pipeline
Syntyvuosi	1992	1925	1921
Kehittäjä	Anthony Bryk and Stephen Raudenbush	Ronald A. Fisher	Sewall Wright
Tyyppi	Method	Method	Method
Alkuperäislähde	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Rinnakkaisnimet	HLM, mixed-effects models, random effects models, MLM	ANOVA, F-test	SEM, path analysis, latent variable modeling, causal modeling
Liittyvät	3	4	3
Tiivistelmä	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.	ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.