Step 2. Q-Set Development and Validation

A Q-set is a selection of statements deducted from a concourse (step 1).^44,51,53 Forty statements were selected from the concourse in step 1 to make up the Q-set⁵³ (Supplement 2 Table 1). A Q-set of 40 opinion statements provided good coverage of the study, was sufficient to elicit existing viewpoints, and fell within the sample range (i.e., 30–50) that is generally accepted in Q-methodology.^43,51,54 The statements were selected based on 4 considerations as proposed by Uniting to Combat NTDs target product profiles, which provided a theoretical framework and ensured that the Q-set covered the essential diagnostic product requirements for diagnosing urinary schistosomiasis. Specifically, these requirements included (1) context use case, (2) infrastructure, (3) product requirements (design and performance), and (4) rollout strategy.^7,36,55 Infrastructure describes the facility, location, or setting, and product requirement describes the specifications for device design and performance. Rollout strategy describes strategies to introduce, integrate, and commercialize a new product to users. Four domain experts validated the 40 selected statements. Three experts in the domain of parasitology and schistosomiasis diagnostics provided validation regarding urinary schistosomiasis diagnosis within the Nigerian context, and 1 Q-methodology expert provided validation on the construction of the Q-set. The domain expert validations were aimed at measuring the internal consistency and content validity of the Q-set. The internal consistency of the Q-set was measured using Cronbach’s alpha (α) reliability coefficient and the content validity was measured using the Item Content Validity Index (I-CVI).⁵⁶

In measuring the content reliability and validity, the experts rated each of the Q-statements for readability, clarity of statement, and heterogeneity (breadth and depth)⁵³ (Supplement 2 Table 2.) Every statement was clear and made its own original contribution to the Q-set, without overlaps.⁵¹ The domain expert rating (Supplement 3) was used to compute a statistical analysis of Cronbach’s alpha reliability coefficient using Windows SPSS 26. The resulting Cronbach’s alpha reliability coefficient was 0.99, which is acceptable and higher than the Nunnally norm of 0.70⁵⁷ (Supplement 4). Similarly, the expert ratings were used to compute an I-CVI value for each of the 40 statements (Supplement 3): 32 statements scored an I-CVI value of 1.00, 8 statements had an I-CVI value of 0.88, and 1 statement had an I-CVI score of less than 0.80. Experts suggested minor changes in statement wording to improve clarity and readability, especially for the Q-statements with I-CVI values of less than 0.80.⁵⁶ As a result of expert feedback, minor edits were made to 2 statements (including statements with I-CVI values greater than 0.80) and 38 statements remained unchanged (Supplement 2 Table 1).