Democratizing Quantitative Data Analysis and Evaluation in Community-Based Research Through a New Automated Tool
Data from community-based research offer crucial insights into community needs, challenges, and strengths, informing effective decision making for development strategies. To ensure efficient analysis, accessible and user-friendly tools are necessary for quick and accurate results. While successful t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Social Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0760/14/6/346 |
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| Summary: | Data from community-based research offer crucial insights into community needs, challenges, and strengths, informing effective decision making for development strategies. To ensure efficient analysis, accessible and user-friendly tools are necessary for quick and accurate results. While successful tools and programming languages exist, many social science researchers struggle with complex analytical tools due to limited exposure during their education, as such tools are often not required. Developing an automated, user-friendly tool for community research can support students, researchers, and data centers by bridging gaps in analysis capabilities and enhancing the accessibility of valuable insights. We developed a new automated tool using the Shiny framework in R designed primarily for analyzing data in community research, which often involves pre- and post-analysis tests. While the tool is specifically tailored for pre- and post-survey data, it can also be easily adapted to provide other statistical information. The findings presented in this paper highlight the efficiency of using this tool for community-based research and emphasize the need for further development to address its shortcomings. Furthermore, this paper is considered the groundwork for developing more accessible, user-friendly, and free tools in the future, especially in an era of advanced and complex technologies. |
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| ISSN: | 2076-0760 |