Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Analýza velikosti účinku× | Analýza ROC (Receiver Operating Characteristic)× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Hypothesis test | Hypothesis test |
| Rok vzniku≠ | 1969 (first edition); 1988 (definitive second edition) | 1954 (signal detection); 1982 (AUC formalization) |
| Tvůrce≠ | Jacob Cohen | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Typ≠ | Standardized magnitude estimation | Diagnostic accuracy evaluation |
| Původní zdroj≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. DOI ↗ |
| Další názvy | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations. | ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination). |
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