השוואת שיטות

	סקר יחסים השוואתי	מחקר מתאמי רב-משתני
תחום	תכנון מחקר	תכנון מחקר
משפחה	Process / pipeline	Process / pipeline
שנת המקור	Mid-20th century onward; systematized in educational research c. 1960s–1990s	1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s
הוגה השיטה	Rooted in survey methodology tradition; formalized by scholars such as Fraenkel, Wallen, and Creswell	Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others
סוג	Quantitative non-experimental survey design	Non-experimental quantitative research design
מקור מכונן	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0073525 670	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541
כינויים	comparative correlational survey, multi-group relational survey, cross-group relational survey design	multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research
קשורות	4	2
תקציר	A comparative relational survey is a quantitative, non-experimental design that examines the relationships among variables within a single study while simultaneously comparing those relationship patterns across two or more distinct groups. It extends a standard relational (correlational) survey by adding a comparative dimension, revealing whether associations observed in one group hold, differ, or even reverse in another. It is widely used in education, psychology, organizational behavior, and health sciences.	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.