Model | Cronbach’s alpha | AIC | BIC | -2log likelihood | Δ in -2log likelihood | RMSEA | M2 (df) |
---|
51 items | 0.983 | 65,230.18 | 66,432.60 | 64,720.18 | – | 0.43 | 163,378.86 (1071)*** |
Short Form 1 Prioritizing Content (10 items) | 0.946 | 13,762.99 | 14,234.40 | 13,562.99 | 51,157.19 | 0.01 | 1469.53 (1420) |
Short Form 2 Prioritizing Precision (10 items) | 0.945 | 13,825.12 | 14,296.54 | 13,625.12 | 51,095.06 | 0.01 | 1513.90 (1420) |
- Cronbach’s alpha = measure of internal consistency/reliability from Classical Test Theory (criterion: ≥.90).
- AIC Akaike information criterion (criterion: the lower the number, the better the fit)
- BIC Bayesian information criterion (criterion: the lower the number, the better the fit)
- -2log likelihood = if models are nested, subtract at each step to see if step is significant
- RMSEA Root mean square error of approximation (criterion: ≤ .05).
- M2 = model fit.
- ***p < .001 (Note: a significant value for model fit indicates that the model does NOT fit well)
- df Degrees of freedom.