User Guide to Means-End Chain Analysis: The Data Analysis Manual - Foolen-Torgerson, K.L.; Kilwinger, Fleur B.M. (International Potato Center, 2021)
Means-end chain (MEC) analysis originates from the field of marketing and consumer studies. Its attractiveness is the freedom it gives to respondents to describe what they like or dislike about a product or service, in their own words. The means-end chain interviews consist of two parts: 1) attribute elicitation and 2) laddering. The “User Guide to Means-End Chain Analysis” described how to collect means-end chain data (Kilwinger 2020). The analysis of means-end chain data has three parts: 1) coding responses, 2) developing an implication matrix and 3) constructing a hierarchical value map. Analyzing means-end chain data manually is time consuming. To simplify the analysis, several software programs have been developed. Unfortunately, technical support for some of these programs has been discontinued. Therefore, the authors have developed an Excel tool to help analyze means-end chain data. In this user guide, we provide a detailed description of how to use this Excel tool. The file mainly addresses step 2 in the analysis: developing an implication matrix. The analysis can be elaborated by using Atlas.ti to code responses and using Excel add-in NodeXL to construct a hierarchical value map. This manual also provides a description for NodeXL.