Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model

Abstract

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.

Keywords

• needs assessment
• ranking
• discrepancy
• ordinal data
• analysis
• Borich