The newest CFI is 0.953, above the required 0.95 fundamental getting a great fit. The newest TLI is 0.945, beneath the required 0.95 practical getting a beneficial match. But not, CFI and TLI usually are sensed appropriate when higher than 0.90, together with TLI property value 0.945 is experienced adequate. Thus, the new hypothesized a couple-basis Peplau model lead a reasonable so you’re able to great fit on study.
IOM model
In contrast to the acceptable fit of the charmdate Peplau model, the nine-factor IOM model performed extremely well. As with the Peplau model, all items loaded onto their anticipated latent factors, and no outliers were identified (Cook’s Ds < 1.00; range = 0.0-0.16). In contrast to the mediocre to good score ranges found in the Peplau model, overall indicators of the nine-factor model fit were excellent. The RMSEA was 0.027, 90% CI (0.024, 0.028), well below the cutoff of 0.05 for a good model fit. The calculated probability that the true RMSEA value was <0.05 was 1.00, confirming the strong fit of the model. The CFI was 0.995, which was above the recommended 0.95 standard for excellent. The TLI was 0.993, also above the recommended 0.95 standard for excellent.
Formal model analysis
The BIC, which accounts for the number of items in a model, can be used to compare the relative fit of two models to the exact same data-as was the case in the current study. The BIC for the Peplau model, 276,596, was slightly larger than the BIC for the IOM-based model, 270,482, suggesting that the IOM-based model fit these data better than the Peplau-based model. The two models were also compared using log likelihood, which further supported the better fit of the IOM-based model (? 2 = , df = 20, p < .0001).
Ancillary Analyses
When you look at the light of these conclusions and results Peplau’s brand-new about three-stage design in mind, amendment indicator (MIs) was examined to understand modifications on one or two-factor Peplau-built model who increase the fit. Particularly, correlations ranging from items’ residual variances was sensed when theoretically related. A relationship between the recurring variances (MI = ) try discovered within remedies for HCAHPS Goods step one (“With this healthcare sit, how often did nurses cure your that have using and you will respect?”) and you may Items dos (“During this medical stay, how often did nurses tune in carefully to you?”). That it relationship try consistent with the orientation phase for the Peplau’s () unique three-phase idea. It actually was ergo considered that brand new originally hypothesized two-grounds design is actually diminished and therefore the latest direction phase was a stand-alone phase and may even not subsumed of the most other a couple of levels.
The two-factor Peplau-based model was therefore modified to include a third latent factor (orientation), and a CFA was run on this new model (see Figure 3 ). The three-factor model resulted in an improved fit (RMSEA = 0.068 [CI 0.066, 0.069; probability of RMSEA ? .05 = 1.00], CFI/TLI 0.958/0.950, ? 2 = 5,, df = 101, p < .0001).
The three-factor model’s MIs were then inspected to identify adjustments to the three-factor model that would improve the fit. Inspection of the MIs revealed relevant relationships between six items’ residual variances: (a) items 13 and 14 (MI = 3,) (pain management), (b) items 16 and 17 (MI = ) (medication teaching), and (c) items 2 and 3 (MI = ) (nurses listening carefully and explaining). The inclusion of these relationships further improved the fit of the three-phase Peplau model (RMSEA = 0.039 [CI 0.038, 0.041; probability of RMSEA ? .05 ? 1.00], CFI/TLI = 0.986/0.983, ? 2 = 1,, df = 98, p < .0001). As noted previously, a RMSEA score of 0.01 is considered excellent, 0.05 good, and 0.08 mediocre. The RMSEA score of 0.039 for the three-factor model is within the excellent to good score range of 0.01 to 0.05.