Salīdzināt metodes

	Daudzvariablā skaidrojošā izpēte	Cēloņu salīdzinošā izpēte
Nozare	Pētījuma dizains	Pētījuma dizains
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	Mid-to-late 20th century (consolidated ~1960s–1980s)	1964
Autors	Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others	Fred N. Kerlinger
Tips	Quantitative research design	Non-experimental quantitative research design
Pirmavots	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Citi nosaukumi	multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER	ex post facto research, causal-comparative design, retrospective causal study, CCR
Saistītās	4	3
Kopsavilkums	Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data.	Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical.