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MECAnalysisTool: A method to analyze consumer data
- data.4tu.nl
txtUpdated Jul 6, 2022
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EmailClick to copy linkLink copiedCiteKirstin Foolen-Torgerson; Fleur Kilwinger (2022). MECAnalysisTool: A method to analyze consumer data [Dataset]. http://doi.org/10.4121/19786900.v1txtAvailable download formatsUnique identifierhttps://doi.org/10.4121/19786900.v1Dataset updatedJul 6, 2022Dataset provided by4TU.ResearchDataAuthorsKirstin Foolen-Torgerson; Fleur KilwingerLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).
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MECAnalysisTool: A method to analyze consumer data
- data.4tu.nl
zipUpdated May 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteKirstin Foolen-Torgerson; Fleur Kilwinger (2023). MECAnalysisTool: A method to analyze consumer data [Dataset]. http://doi.org/10.4121/19786900.v2zipAvailable download formatsUnique identifierhttps://doi.org/10.4121/19786900.v2Dataset updatedMay 1, 2023Dataset provided by4TU.ResearchDataAuthorsKirstin Foolen-Torgerson; Fleur KilwingerLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a calculator file for analyzing your data, and instructional videos.
The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview,
(2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).
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FacebookTwitterEmailMECAnalysisTool: A method to analyze consumer data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).