Pesticide applicators questionnaire content validation: A fuzzy delphi method

The most crucial step in forming a set of survey questionnaire is deciding the appropriate items in a construct. Retaining irrelevant items and removing important items will certainly mislead the direction of a particular study. This article demonstrates Fuzzy Delphi method as one of the scientific analysis technique to consolidate consensus agreement within a panel of experts pertaining to each item's appropriateness. This method reduces the ambiguity, diversity, and discrepancy of the opinions among the experts hence enhances the quality of the selected items. The main purpose of this study was to obtain experts' consensus on the suitability of the preselected items on the questionnaire.

The panel consists of sixteen experts from the Occupational and Environmental Health Unit of Ministry of Health, Vector-borne Disease Control Unit of Ministry of Health and Occupational and Safety Health Unit of both public and private universities. A set of questionnaires related to noise and chemical exposure were compiled based on the literature search. There was a total of six constructs with 60 items in which three constructs for knowledge, attitude, and practice of noise exposure and three constructs for knowledge, attitude, and practice of chemical exposure. The validation process replicated recent Fuzzy Delphi method that using a concept of Triangular Fuzzy Numbers and Defuzzification process.

A 100% response rate was obtained from all the sixteen experts with an average Likert scoring of four to five. Post FDM analysis, the first prerequisite was fulfilled with a threshold value (d) ≤ 0.2, hence all the six constructs were accepted. For the second prerequisite, three items (21%) from noise-attitude construct and four items (40%) from chemical-practice construct had expert consensus lesser than 75%, which giving rise to about 12% from the total items in the questionnaire. The third prerequisite was used to rank the items within the constructs by calculating the average fuzzy numbers. The seven items which did not fulfill the second prerequisite similarly had lower ranks during the analysis, therefore those items were discarded from the final draft.

Post FDM analysis, the experts' consensus on the suitability of the pre-selected items on the questionnaire set were obtained, hence it is now ready for further construct validation process.