قارن الطرق

	أبحاث اختبار النماذج متعددة المتغيرات	البحث الارتباطي متعدد المتغيرات
المجال	تصميم البحث	تصميم البحث
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1970s–1980s (multivariate model testing as a distinct approach)	1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s
صاحب الطريقة	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)	Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others
النوع	Quantitative confirmatory research design	Non-experimental quantitative research design
المصدر التأسيسي	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541
الأسماء البديلة	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research	multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research
ذات صلة	5	2
الملخص	Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.	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.