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Multivariat korrelasjonsforskning×Stianalyse×
FagfeltForskningsdesignStatistikk
FamilieProcess / pipelineLatent structure
Opprinnelsesår1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s1921
OpphavspersonDeveloped from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and othersSewall Wright
TypeNon-experimental quantitative research designCausal / mediation model
Opprinnelig kildeTabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585. link ↗
Aliasmultivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational researchPA, path coefficient analysis, observed-variable SEM, causal path modeling
Relaterte25
SammendragMultivariate 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.
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ScholarGateSammenlign metoder: Multivariate Correlational Research · Path Analysis. Hentet 2026-06-17 fra https://scholargate.app/no/compare