Stability of Needs Identified using the Borich Model and Ranked Discrepancy Model: Influence of Sample Size and Data Distribution

Amy Harder; Lendel K. Narine; Rebecca Syme

doi:10.5032/jae.v67i2.3305

Authors

Amy Harder University of Connecticut https://orcid.org/0000-0002-7042-2028
Lendel K. Narine Utah State University https://orcid.org/0000-0001-6962-2770
Rebecca Syme University of Connecticut https://orcid.org/0009-0004-8407-5452

DOI:

https://doi.org/10.5032/jae.v67i2.3305

Keywords:

data distribution, SDG 4: Quality Education, needs assessment methodology, agricultural education, sample size effects

Abstract

The Borich model has been widely used for the past 40 years to assess professional development needs. The more recent creation of the Ranked Discrepancy Model (RDM) introduced another option for researchers and therefore the need to investigate the appropriateness of the approaches for different research scenarios. In this study, we have sought to answer questions of how sample size and different data distributions affect the stability of results. Using a pre-existing dataset, we analyzed the data using the Borich model and RDM with samples of 25, 50, 75, 100, and 150, and compared the results between a semi-normal distribution and a heavily skewed distribution (simulated from the real data set). We found that the Borich model was more stable compared to the RDM when there was a small data set that was semi-normally distributed. The RDM was more stable with heavily skewed data, regardless of sample size. Both models showed greater stability with larger sample sizes. For future research conducted with either model, we recommend researchers carefully consider the purpose for their needs assessment, evaluate factors which may influence the accomplishment of that purpose, and provide full transparency when reporting their methodological decision-making and actions.

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