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| Penelitian Potongan Lintang Multivariat× | Penelitian Korelasional Multivariat× | |
|---|---|---|
| Bidang | Desain Penelitian | Desain Penelitian |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1960s–1970s (formalized with widespread multivariate methods) | 1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s |
| Pencetus≠ | Developed from the convergence of survey methodology (Kerlinger) and multivariate statistics (Tabachnick, Fidell) | Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others |
| Tipe≠ | Quantitative observational design | Non-experimental quantitative research design |
| Sumber perintis≠ | Kerlinger, F. N., & Lee, H. B. (2000). Foundations of Behavioral Research (4th ed.). Harcourt College Publishers. ISBN: 978-0155078970 | Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 |
| Alias | multivariate survey design, multi-variable cross-sectional study, MXSR, multivariate observational study | multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research |
| Terkait≠ | 3 | 2 |
| Ringkasan≠ | Multivariate cross-sectional research collects data on multiple variables from a defined population at a single point in time and uses multivariate statistical techniques — such as multiple regression, MANOVA, factor analysis, or structural equation modeling — to examine simultaneous relationships among those variables. It combines the efficiency of a cross-sectional snapshot with the analytical power to handle complex, multi-variable research questions in a single study. | 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. |
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