Salīdzināt metodes

	Daudzvariēblu korelācijas pētījumi	Ceļu analīze
Nozare	Pētījuma dizains	Statistika
Saime	Process / pipeline	Latent structure
Izcelsmes gads	1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s	1921
Autors	Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others	Sewall Wright
Tips	Non-experimental quantitative research design	Causal / mediation model
Pirmavots	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Citi nosaukumi	multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Saistītās	2	5
Kopsavilkums	Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.