Classic Delphi and Fuzzy Delphi methods are used to test content validity of data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solves it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.

翻译：经典德尔菲法与模糊德尔菲法常用于检验问卷等数据收集工具的内容效度。模糊德尔菲法从语言视角处理专家意见，通过模糊数减少意见歧义。我们提出一种扩展方法——2-元组模糊语言德尔菲法，用以处理专家具有不同专业程度的场景：利用语言术语的模糊多粒度语义，并以2-元组语言值表达中间与最终结果。本方法的核心思想是通过评估问卷各组成部分来验证整体问卷，将每个条目的效度判定定义为决策问题。依据专家意见，我们度量每个条目的共识度、一致性与语言评分，从而识别出对工具质量产生正向或负向影响的条目。针对以共识性问卷评估混合式学习体验的实际需求，我们提出一种用于问卷效度验证的决策模型。此外，我们开发了一款基于GPL v3许可证的在线工具以促进共识达成：该软件可可视化每次迭代的集体评估结果，并辅助确定需修改的问卷部分，从而达成共识性解决方案。