Table 2. Optimal data analysis classification performance summary (N = 46).
| Performance index | Performance parameter | |
|
| ||
| Overall classification accuracy | 42/46 (91.30%) | |
| Sensitivity (No Job Change) | 14/16 (87.50%) | |
| Sensitivity (Job Change) | 28/30 (93.33%) | |
| Effect strength for sensitivity | 80.83% | |
| Predictive value (No Job Change) | 14/16 (87.50%) | |
| Predictive value (Job Change) | 28/30 (93.33%) | |
| Effect strength for predictive value | 80.83% | |
| Effect strength overall | 80.83% | |
| Cross-classification table (P= .531330 × 10−7) | ||
|
| ||
| Respondents' predicted status | ||
|
|
||
| Respondents' actual status | No Job Change | Job Change |
|
|
|
|
| No Job Change | 14 | 2 |
| Job Change | 2 | 28 |
Overall classification accuracy is the percentage of the observations classified correctly. Sensitivity is the percentage of how many observations were correctly classified among observations that actually belong to a given category. Predictive value is the percentage of how many observations were correctly classified among observations that were predicted as a given category. Higher percentage indicates greater classification performance. Effect strength overall is the mean of effect strength for sensitivity and effect strength for predictive value. According to Yarnold and Soltysik,29 the effect strength is strong for the present model (75% < ES < 90%).