Uhusiano dhidi ya Usababishi
Uhusiano hupima nguvu na mwelekeo wa uhusiano kati ya vigezo viwili; usababishi unamaanisha kuwa mabadiliko katika kigezo kimoja husababisha moja kwa moja mabadiliko katika kingine. Uhusiano mkali (k.m., r = 0.9) hauthibitishi usababishi. Mifano ya kawaida ni mingi: ukubwa wa kiatu na uwezo wa kusoma vinahusiana kwa watoto (vikichanganywa na umri), lakini ukubwa wa kiatu hausababishi uwezo wa kusoma. Kuelewa ni lini uhusiano unamaanisha usababishi kunahitaji kutathmini muundo wa utafiti, vigezo vinavyochanganya, utangulizi wa muda, na utaratibu. Majaribio ya nasibu hutoa ushahidi mkali zaidi wa kisababishi; tafiti za uchunguzi lazima zidhibiti kwa uangalifu vigezo vinavyochanganya.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0-521-89560-6
- Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. DOI: 10.1037/h0037350 ↗
- Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300. DOI: 10.1177/003591576505800503 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Understanding the Distinction Between Correlation and Causation in Research. ScholarGate. https://scholargate.app/sw/research-statistics/correlation-vs-causation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Ukubwa wa Athari (Effect Size)Takwimu za Utafiti↔ compare
- Tatizo la Linganisho NyingiTakwimu za Utafiti↔ compare
- Upimaji wa Hipothesi sifuriTakwimu za Utafiti↔ compare
- P-Value na Umuhimu wa KimahesabuTakwimu za Utafiti↔ compare
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