Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model
The Ranked Discrepancy Model was introduced in 2021 as an alternative for analyzing Borich-style competency-based needs assessment data which avoided the pitfalls associated with the original methods for analysis. In this article, we sought to expand upon that work by developing and testing a new fr...
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| Format: | Article |
| Language: | English |
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Advancements in Agricultural Development Inc
2024-01-01
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| Series: | Advancements in Agricultural Development |
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| Online Access: | https://agdevresearch.org/index.php/aad/article/view/321 |
| _version_ | 1797337552229761024 |
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| author | Lendel Narine Amy Harder |
| author_facet | Lendel Narine Amy Harder |
| author_sort | Lendel Narine |
| collection | DOAJ |
| description | The Ranked Discrepancy Model was introduced in 2021 as an alternative for analyzing Borich-style competency-based needs assessment data which avoided the pitfalls associated with the original methods for analysis. In this article, we sought to expand upon that work by developing and testing a new framework to analyze and visualize repeated-measures needs assessment data using the Ranked Discrepancy Model (RDM). Data for the analyses were taken from statewide community needs assessments conducted in Utah and Florida with paid survey panelists recruited by an online survey vendor. We found it was possible to apply the RDM to repeated-measures data using Microsoft Excel. A comparison of results obtained from analyzing data using paired t-tests and the RDM model showed strong positive correlations. Additionally, the transition to a spreadsheet format enabled the expansion of data analysis possibilities to include sorting needs by demographic subgroups. We recommend researchers use Excel for the RDM so they can easily examine subgroup needs and apply data visualization techniques to improve the utility of needs assessments and the decisions made by the individuals who interpret the results.
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| first_indexed | 2024-03-08T09:12:18Z |
| format | Article |
| id | doaj.art-7fd37f26969047638b752ed72bb1a346 |
| institution | Directory Open Access Journal |
| issn | 2690-5078 |
| language | English |
| last_indexed | 2024-03-08T09:12:18Z |
| publishDate | 2024-01-01 |
| publisher | Advancements in Agricultural Development Inc |
| record_format | Article |
| series | Advancements in Agricultural Development |
| spelling | doaj.art-7fd37f26969047638b752ed72bb1a3462024-01-31T21:36:34ZengAdvancements in Agricultural Development IncAdvancements in Agricultural Development2690-50782024-01-015210.37433/aad.v5i2.321Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy modelLendel Narine0Amy Harder1Utah State University, USAUniversity of Connecticut, USAThe Ranked Discrepancy Model was introduced in 2021 as an alternative for analyzing Borich-style competency-based needs assessment data which avoided the pitfalls associated with the original methods for analysis. In this article, we sought to expand upon that work by developing and testing a new framework to analyze and visualize repeated-measures needs assessment data using the Ranked Discrepancy Model (RDM). Data for the analyses were taken from statewide community needs assessments conducted in Utah and Florida with paid survey panelists recruited by an online survey vendor. We found it was possible to apply the RDM to repeated-measures data using Microsoft Excel. A comparison of results obtained from analyzing data using paired t-tests and the RDM model showed strong positive correlations. Additionally, the transition to a spreadsheet format enabled the expansion of data analysis possibilities to include sorting needs by demographic subgroups. We recommend researchers use Excel for the RDM so they can easily examine subgroup needs and apply data visualization techniques to improve the utility of needs assessments and the decisions made by the individuals who interpret the results. https://agdevresearch.org/index.php/aad/article/view/321needs assessmentrankingdiscrepancyordinal dataanalysisBorich |
| spellingShingle | Lendel Narine Amy Harder Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model Advancements in Agricultural Development needs assessment ranking discrepancy ordinal data analysis Borich |
| title | Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model |
| title_full | Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model |
| title_fullStr | Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model |
| title_full_unstemmed | Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model |
| title_short | Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model |
| title_sort | analyzing and visualizing repeated measures needs assessment data using the ranked discrepancy model |
| topic | needs assessment ranking discrepancy ordinal data analysis Borich |
| url | https://agdevresearch.org/index.php/aad/article/view/321 |
| work_keys_str_mv | AT lendelnarine analyzingandvisualizingrepeatedmeasuresneedsassessmentdatausingtherankeddiscrepancymodel AT amyharder analyzingandvisualizingrepeatedmeasuresneedsassessmentdatausingtherankeddiscrepancymodel |