Ukubwa wa Athari (Effect Size)
Ukubwa wa athari hupima kiwango cha matokeo ya utafiti bila kujali ukubwa wa sampuli. Wakati thamani ya p (p-value) inakuambia kama matokeo yana umuhimu wa takwimu, ukubwa wa athari unakuambia jinsi matokeo yalivyo makubwa. Jacob Cohen aliweka rasmi kipimo cha ukubwa wa athari katika sayansi ya tabia (1988), akianzisha vipimo sanifu (vidogo = 0.2, vya kati = 0.5, vikubwa = 0.8 kwa Cohen's d). Ukubwa wa athari ni muhimu kwa uchambuzi meta (meta-analysis), uchambuzi wa nguvu (power analysis), na kuwasilisha umuhimu wa kivitendo wa matokeo ya utafiti.
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
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
- Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge. ISBN: 0-415-87968-8
- Lakens, D. (2013). Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and ANOVAs. Frontiers in Psychology, 4, 863. DOI: 10.3389/fpsyg.2013.00863 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Effect Size: Quantifying the Magnitude of Research Findings. ScholarGate. https://scholargate.app/sw/research-statistics/effect-size
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.
- Kipindi cha KujiaminiTakwimu za Utafiti↔ compare
- P-Value na Umuhimu wa KimahesabuTakwimu za Utafiti↔ compare
- Nguvu ya Takwimu na Ukubwa wa SampuliTakwimu za Utafiti↔ compare
- Makosa ya Aina ya I na Aina ya IITakwimu za Utafiti↔ compare
Imerejelewa na
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