Patient Satisfaction with Navigator Interpersonal Relationship (PSN-I): item-level psychometrics using IRT analysis

Background: Patient navigation (PN) is a promising intervention to eliminate cancer health inequities. Patient navigators play a critical role in the navigation process. Patients' satisfaction with navigators is important in determining the effectiveness of PN programs. We applied item response theory (IRT) analysis to establish item-level psychometric properties for the Patient Satisfaction with Interpersonal Relationship with Navigators (PSN-I).

Methods: We conducted a confirmatory factor analysis (CFA) to establish unidimensionality of the 9-item PSN-I in 751 cancer patients (68% female) between 18 and 86 years old. We fitted unidimensional IRT models-unconstrained graded response model (GRM) and Rasch model-to PSN-I data, and compared model fit using likelihood ratio (LR) test and information criteria. We obtained item parameter estimates (IPEs), item category/operating characteristic curves, and item/test information curves for the better fitting model.

Results: CFA with diagonally weighted least squares confirmed that the one-factor model fit the data (RMSEA = 0.047, 95% CI = 0.033-0.060, and CFI ≈ 1). Responses to PSN-I items clustered into the 4th and 5th categories. We aggregated the first three response categories to provide stable parameter estimates for both IRT models. The GRM fit the data significantly better than the Rasch model (LR = 80.659, df = 8, p < 0.001). Akaike's information coefficient (6384.978 vs. 6320.319) and Bayesian information coefficient (6471.851 vs. 6443.771) were lower for the GRM. IPEs showed substantial variation in items' discriminating power (1.80-3.35) for GRM.

Conclusions: This IRT analysis confirms the latent structure of the PSN-I and supports its use as a valid and reliable measure of latent satisfaction with PN.