In this study, two available methods of item reduction (factor analysis and clinical impact) are compared to determine the effect of the method of item reduction on the final PTRQoL instrument. OBJECTIVE: To perform a factor analysis of 57-items of the developmental-version of the PTRQoL (d-PTRQoL) and to determine if the results differ from a previous clinical impact method item reduction of the same 57-items. METHODS: Factor analysis (using principal axis factoring and direct oblimin method of rotation with 125 iterations ) was performed on a dataset accumulated from a previous work to compare results of the clinical impact item reduction from a separate dataset.The dataset (n=182) used was obtained earlier by a survey of patients from various community pharmacies using the d-PTRQoL (93-items on a six-point likert scale). Factor analysis was restricted to the same 57 items used in the earlier clinical impact item reduction, where Juniper et al's clinical impact method was adapted on a dataset with a five-point anchor. RESULTS: Factor analysis resulted in 24 items. 21 items with factor loadings above 0.6 were items with high clinical impact scores from the clinical impact approach. 3 items with low to moderate impact scores loaded high in the factor analysis method. 2 factors, each had items loading from 3 similar domains, suggesting they merged into a single factor. This led to 7 factors from an initial 11-factor solution, similar to the clinical impact method and with relatively minor differences in items. CONCLUSIONS: The factor analysis approach to item reduction produced a PTRQoL instrument with domains similar to the clinical impact approach. Measurement properties of the instrument developed using both methods may decide the optimal approach. Results from the 2 separate datasets and methods provide evidence for the robustness of the underlying conceptual framework of the PTRQoL.