































   Advancements in Agricultural Development 
  Volume 5, Issue 4, 2024 
  agdevresearch.org 

 

1. Amy Harder, Associate Dean for Extension, University of Connecticut, 1376 Storrs Rd., Unit 4134, W.B. Young Building, 

Room 233, Storrs, CT 06269, amy.harder@uconn.edu,  https://orcid.org/0000-0002-7042-20283 
2. Lendel Kade Narine, Extension Associate Professor, Utah State University, Old Main Hill Logan, Utah 84322, 

lendel.narine@usu.edu,  https://orcid.org/0000-0001-6962-2770  
 

53 

 

Best Practices in the Application of the Ranked Discrepancy 
Model 

 
A. Harder1, L. K. Narine2 

 
 

Article History 
Received: October 18, 2024 
Accepted: November 4, 2024 
Published: November 6, 2024 
 
 
Keywords 
needs assessment; Borich model; 
ranked discrepancy scores;  
SDG 4: Quality Education   

Abstract 
In this brief article, we discuss the rationale for the Ranked Discrepancy 
Model (RDM) and best practices. An overview of the history of the RDM 
and its appropriate usages is offered to create clarity for researchers. We 
then provide an explanation of the role of tied ranks in determining 
ranked discrepancy scores (RDS), guidance on the interpretation of RDS 
for planning, and recommendations for comparing the RDM with the 
Borich model. A summary of prior research comparing the RDM to the 
Borich model is included. We conclude by encouraging researchers to use 
needs assessment models appropriate for the problem, population, and 
context. 

 

mailto:amy.harder@uconn.edu
https://orcid.org/0000-0002-7042-20283
mailto:lendel.narine@usu.edu
https://orcid.org/0000-0001-6962-2770


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Introduction 
 
In 2021, we introduced the Ranked Discrepancy Model (RDM) as an alternative to Borich’s 
(1980) model of needs assessment. We have been encouraged by the interest in the RDM and 
have welcomed feedback from researchers and practitioners around the world which has 
allowed us to revisit and strengthen the original concept to make it a more effective and 
efficient approach (Narine & Harder, 2024). A recent article from Johnson et al. (2024) provided 
another chance to review our work and assess a critique of it. We appreciate the opportunity 
provided by Advancements in Agricultural Development to respond and the willingness of 
Johnson et al. to allow such a response. We offer the following discussion on the rationale for 
the RDM and best practices in its usage in the spirit of collegiality and a desire to promote the 
advancement of agriculture and extension education research methods. 
 

Rationale for RDM Development and Appropriate Usages 
 
The impetus for developing the RDM arose from the longstanding debate regarding the use of 
means for ordinally scaled items, a central component in the use of the Borich model. Rather 
than enter the fray, we advocated for the pursuit of a solution which could avoid the argument 
entirely “while preserving the underlying rationale of the Borich model” (Narine & Harder, 
2021, p. 97). The debate over using means for ordinal and interval variables will continue, so 
the RDM was presented as an alternative that moves away from the controversy. 
 
We initially presented the RDM as a novel approach to analyze professional development 
needs. Like the Borich model, the emphasis was on identifying which needs were most urgent 
by examining gaps between respondents’ perceptions of a competency’s importance and 
perceptions of their knowledge or ability. Unlike the Borich model, the RDM allows researchers 
to make these determinations by comparing positive ranks, tied ranks, and negative ranks 
within pairs of observations, a nonparametric approach to needs identification (for more 
details, see Narine & Harder, 2021). Later, we provided detailed steps as to how to use the 
same underlying logic of the RDM to analyze needs assessments with repeated measures data 
(Narine & Harder, 2024). The RDM has been applied, peer reviewed, and published in the 
professional development literature several times (e.g., Choi & Park, 2022; Flanagan et al., 
2023; Seitz et al., 2022; Zickafoose et al., 2023). 
 
By 2024, we recognized and were forthcoming about a limitation of the RDM. The ranked 
discrepancy scores (RDS) produced by the RDM are sensitive to the size of the sample, as we 
illustrated in Narine and Harder (2024) using three sample sizes of 1,000, 500, and 100 across 
two populations. As a result, we recommended and continue to recommend that “researchers 
should be aware of this consideration if they intend to use the RDM for analysis” (Narine & 
Harder, 2024, p. 115). We note increasing the sample size is always going to affect data 
distribution and subsequent results from that data regardless of the parametric or 
nonparametric procedures adopted in the study (Serdar et al., 2021). To our knowledge, no 
research exists which has examined the validity or reliability of Mean Weighted Discrepancy 

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Score (MWDS) from the Borich model across sample sizes, so caution should also be applied 
before assuming the MWDS of a small sample are stable and representative of the target 
population. 
 

Best Practices in RDM Application 
 
Tied Ranks 
As mentioned, the RDM relies upon the frequency with which positive ranks, tied ranks, and 
negative ranks occur within paired data for a single item, such as a competency. The use of 
ranks is common in nonparametric procedures such Wilcoxon rank-sum and Mann-Whitney U 
(Krzywinski & Altman, 2014; Nahm, 2016; Schober & Vetter, 2020). Tied ranks are particularly 
important in the RDM because they represent equilibrium between two conditions, which is 
theoretically consistent with Lewin (1939). In the RDM, tied ranks substantially influence the 
RDS. When fewer negative or positive ranks are observed within pairs, and more tied ranks 
exist, the RDS moves closer to zero (or equilibrium) because of tied ranks. Note that assigning a 
score of zero does not cause tied ranks to be ignored. Their existence decreases the available 
percentages which can be assigned to positive or negative ranks when the rank counts are 
converted for the calculation of the RDS (Narine & Harder, 2021). 
 
Interpreting the RDS for Planning  
Johnson et al. (2024) rightly expressed concerns about investing organizational resources wisely 
and the need for clear data to drive those decisions. We agree. We offer these 
recommendations for interpreting RDS: 
1. Any negative RDS indicates there is a negative discrepancy between the two variables (or 

conditions) being measured, meaning the situation is below a desired state of equilibrium. 
2. RDS represent higher priorities for intervention as they trend towards -100. A negative 

score represents a deficit in an ideal condition.  
3. The RDS should be used to determine what (if any) resources should be applied to 

addressing a gap.  
4. Relative rankings (e.g., 1st, tied for 3rd, 16th) should not be exclusively relied upon when 

interpreting RDS. The use of relative rankings neglects the actual magnitude of the need 
based on contextual factors and only provides an ordinal interpretation as one source of 
evidence. Two items with remarkably similar scores may be many rankings apart when 
there is limited variation in the results across the group, which could lead to errors in 
interpretation. 

5. Examining the distribution of positive ranks, tied ranks, and negative ranks can provide 
additional insight for decision-making when items share the same RDS. While this feature is 
not built into the formulas provided in the spreadsheet linked to in the appendix in Narine 
and Harder (2024), using the COUNTIF option in Excel allows such exploration. 

6. Use good judgment. This is a practical solution which requires the individuals conducting 
needs assessments to themselves have a sufficient level of professional competency to 
interpret the results. In the absence of that, an alternative could be to triangulate the 
results with qualitative methods. 

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Comparing RDS to MWDS 
Researchers may wish to continue their exploration of RDM and how it performs when 
compared to the Borich model. We recommend a standard procedure for doing so to ensure 
transparency in such analyses. First, researchers should include frequency tables in their 
findings as a precursor to calculating MWDS or RDS, so that readers can better understand the 
data distribution and how that led to the reported MWDS or RDS. Second, we encourage 
researchers to use z-scores when examining relationships between MWDS and RDS as they do 
not share the same properties. Standardization is recommended to reduce error when 
comparing raw scores with different ranges (Hopkins & Rowlands, 2024). 
 
What Others Are Saying 
To our knowledge, the RDM has been examined twice in comparison to the Borich model in 
addition to Johnson et al. (2024). The first and most comprehensive examination was published 
in 2023 by Choi and Park. They assessed the needs of 75 school guidance teachers in South 
Korea using four analysis methods: (a) descriptive statistics, (b) the RDM, (c) the weighted total 
index (WTI), and (d) the Borich model. Following analysis, Choi and Park (2023) noted the 
priorities identified through the RDM and WTI approaches had few differences and concluded: 
“This indicates that the RDM has achieved concurrent validity, as the RDM results were almost 
the same as those from the WTI, which has already been tested and validated” (p. 11). Further, 
they found similarities between the RDM and Borich results, recommending: “it might be more 
appropriate to adopt the RDM as an alternative quantitative method for needs assessment 
relative to the Borich model in cases involving ordinal items, cross-sectional data, or non-
normally distributed data” (Choi & Park, 2023, p. 12). 
 
Another comparison of RDM and the Borich model was presented by Eze and Siegmund (2024) 
in their study of the competency gaps of 141 UNESCO site actors. They did not explicitly seek to 
test the relationships between RDS and MWDS but presented both in their findings and noted 
the tendency for the two scores to provide similar results for the highest priority needs. Some 
variation existed when comparing relative rankings for the less pressing needs. Eze and 
Siegmund (2024) noted “expanding the sample sizes in future research facilitates more rigorous 
and robust statistical analyses, increased reliability of outcomes, higher accuracy in findings and 
generalizability” (p. 7). 
 

Conclusions 
 
Regardless of the model selected to analyze needs, researchers must be cognizant of its 
limitations and do their best to mitigate those limitations through informed methodological 
decisions. We encourage researchers to use needs assessment models appropriate for the 
problem, population, and context. We appreciate the contributions of Johnson et al. (2024) in 
advancing the discussion about how best to identify professional development needs given the 
importance of this task to advancing agricultural development and look forward to continuing 
to refine the RDM as an option to serve the needs of our discipline (and beyond). 
 

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Acknowledgments 
 
Author Contributions: A. Harder – conceptualization, writing – original draft, writing – review & 
editing; L. Narine – conceptualization, writing – review & editing 
 

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© 2024 by authors. This article is an open access article distributed under the terms and conditions of 
the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). 
 

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