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Narine and Harder  Advancements in Agricultural Development 
  Volume 2, Issue 3, 2021 
  agdevresearch.org 

1. Lendel K. Narine, Extension Assistant Professor and Evaluation Specialist, Utah State University, 4900 Old Main Hill, Logan, 

UT 84322, lendel.narine@usu.edu,  https://orcid.org/0000-0001-6962-2770 
2. Amy Harder, Professor, University of Florida, P.O. Box 112060, Gainesville, FL 32611-2060, amharder@ufl.edu, 

 https://orcid.org/0000-0002-7042-2028 
96 

 

Comparing the Borich Model with The Ranked Discrepancy 
Model for Competency Assessment: A Novel Approach 

 
L. K. Narine1, A. Harder2 

 
 

  

Abstract 

In 1980, Borich presented a new model that allowed errors in an individual’s judgment of self-
proficiency to be offset by considering the perception of a group. The model relied upon the 
calculation of means for competency items measured with ordinal scales, an approach subject to 
debate in modern times. The purpose of our study was to explore the use of a novel approach we 
developed, the Ranked Discrepancy Model (RDM), as an alternative method to the Borich model for 
determining training needs. Data obtained from an online survey of extension professionals 
employed by a land-grant university in the United States was used to compare the training needs 
identified by applying the Borich model with those identified by applying the RDM. A very strong and 
statistically significant correlation existed between the scores derived from using both models, 
demonstrating a high level of consistency between models. Researchers conducting competency 
research should consider adopting the RDM given its suitability for delivering results that closely 
resemble findings from the Borich model while providing improved rigor in methods and increased 
detail about training needs. 

Keywords 

Gaps, needs, professional development, extension 

 



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Introduction and Problem Statement 
 
In 1980, Borich introduced a new model for assessing educator training needs. Borich’s (1980) 
use of the group mean to describe the perceived importance of a competency and weight an 
individual’s proficiency gap revolutionized how training needs were identified by accounting for 
errors inherent in an individual’s judgment of what was important to know or do. Agricultural 
and extension education practitioners and scholars alike embraced the Borich model (e.g., 
Elhamoly et al., 2014; Umar et al., 2017; Waters & Haskell, 1988), and we count ourselves 
among its many adopters. However, usage of the Borich model is worth reflecting upon more 
than forty years later to determine its appropriateness for contemporary needs assessment 
research. 
 
One reason to revisit the Borich model is due to an unsettled debate over the use of means to 
describe items measured on ordinal scales. Means derived from individual ordinal items are an 
inherent part of calculating the mean weighted discrepancy scores (MWDS) needed in the 
Borich model. Arguments can be found for and against using means of ordinally-scaled items 
(e.g., Boone & Boone, 2012; Norman, 2010). The controversy over using means for individual 
ordinal items impacts the potential acceptance of research conducted using the Borich model 
within the broader scientific community. A new analytical method is needed to help 
researchers identify competency training needs efficiently and avoid getting caught in the 
ordinal mean debate, while preserving the underlying rationale of the Borich model. 
 

Theoretical and Conceptual Framework 
 
The Borich model is primarily used to determine priority competencies for professional 
development (Borich, 1980) and is appropriate for use when assessing the needs of a sample, 
such as extension professionals or agricultural teachers. The Borich model relies on identifying 
gaps – called discrepancy scores - between a respondent’s perceived ability to perform a 
particular competency and how important that competency is for job success. The discrepancy 
score is the difference between how a respondent rates their ability and importance using 
ordinal scales. In a Borich assessment, a discrepancy suggests an individual does not have 
sufficient ability to perform an important competency, therefore, a gap exists between the 
ideal and current conditions. A deviation between an ideal and current condition, or what 
should be and what is, represents the underlying nature of a need as described by Witkin and 
Altschuld (1995). For example, a respondent may rate a competency as having above average 
importance for their job success, but self-report having below average ability. This calculation is 
analogous to identifying gaps in a quantitative needs assessment process; a gap or discrepancy 
exists when a current condition is less than an ideal condition (Boyle, 1981; English & Kaufman, 
1975; Witkin & Altschuld, 1995).  
 
Next, the Borich model uses the perceptions of the sample to estimate a competency’s actual 
importance (Borich, 1980) by calculating the sample mean for importance. This approach helps 
to overcome individual errors in judgment. Each respondent’s discrepancy score is weighted by 



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multiplying it with the sample mean for importance for a given competency, resulting in an 
individual’s weighted discrepancy score. Finally, a MWDS is calculated by averaging the 
weighted discrepancy scores for the entire sample. The MWDS are then used to determine 
training needs, with positive scores indicating a need for intervention and negative scores 
indicating a need does not exist since ability exceeds importance. 
 
Herein lies two problems with the Borich model. First, the weighted discrepancy score is 
dependent on the use of item means for importance. Recall that a respondent provides a single 
judgment of the importance of a competency based on an ordinal scale, resulting in the use of 
the controversial ordinal scale means (Kuzon et al., 1996). While suitable for reliable 
psychometric constructs consisting of multiple normally distributed ordinal items, assessing the 
mean value of a single ordinal item is generally not recommended by researchers (Dillman et 
al., 2014; Sullivan & Artino, 2013). Second, the MWDS for each competency does not follow an 
immediately clear and interpretable standardized range. A MWDS ranges from -4 to 20 when 
using a 5-point response scale. However, any time a researcher opts to use anything except a 5-
point response scale (e.g., a 7-point importance scale ranging from Not at all important to 
Extremely important), the MWDS range changes, creating difficulties in comparing competency 
needs across different studies. Valid reasons exist for using scales with varying numbers of 
response anchors. Dillman et al. (2014) stated a unipolar scale such as the type used for 
assessing competency training needs would be acceptable with only four scale points and this 
format would decrease the burden on the respondent. Conversely, Preston and Colman (2000) 
found the scores obtained from using 7- to 10-point scales were more reliable, had improved 
criterion validity coefficients, and better discriminating power as compared to scales with fewer 
points. If Borich findings can only be compared across studies when researchers use a 5-point 
response scale, then this is a limitation of the Borich model. 
 
We propose use of the Ranked Discrepancy Model (RDM) as an alternative to the Borich model. 
Application of the RDM is only appropriate when certain conditions exist: (a) cross-sectional 
data (Ary et al., 2014) is gathered from a sample or census of a target population at one point in 
time, (b) data for each variable or item is paired on two ordinal scales with an equivalent 
number of response anchors, and (c) the objective is to assess discrepancies between two 
clearly identified states or conditions for each item. These conditions are also necessary for the 
application of Borich’s (1980) model for determining training needs.  
 
The RDM circumvents the two major drawbacks of the Borich model. As a descriptive approach, 
the RDM avoids the use of means for single items measured with ordinal scales (i.e., individual 
competency items). It also provides an intuitive standardized score that represents the 
discrepancy or gap in ability compared to a known state of equilibrium, which is consistent with 
early needs assessment literature, namely Lewin’s (1939) field theory of motivation.  
There are three steps in the RDM. An illustrated step-by-step example is included after the 
findings. First, calculate the number of occurrences in the sample when respondents’ ability 
ratings are: (a) less than respondents’ importance ratings (Negative Ranks = NR), (b) more than 
respondents’ importance ratings (Positive Ranks = PR), or (c) equal to respondents’ importance 
ratings (Tied Ranks = TR). Second, convert the number of occurrences for NR, PR, and TR into 



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percentages. Third, assign relative weights (W) to NR% (WNR = -1), PR (WPR = 1), and TR (WTR = 
0). The resulting Ranked Discrepancy Score (RDS) is a standardized score ranging between -100 
to 100. The RDS has an equilibrium of 0, with negative scores indicating a priority need or 
discrepancy in ability, and positive scores indicating the absence of a gap or need. 
 
Like the MWDS, the RDS provides a snapshot of the professional capacity of an organization 
with respect to a competency area. Therefore, a negative, equilibrium, or positive RDS does not 
imply every individual of the sample has inadequate or adequate capacity to perform a specific 
competency. It assesses priority professional development needs of the sample as whole by 
accounting for the ability of all individuals within the sample. Like Borich (1980), the RDM does 
not take a deficit approach to identifying needs by only considering those with negative ranks. 
In addition, the RDM does not rely on a sample mean for importance, instead, it capitalizes on 
the frequency distribution of each item. This approach is widely regarded is an appropriate way 
to handle ordinal items (Sullivan & Artino, 2013), even with nonnormally distributed data. The 
frequency distribution of importance and ability ratings directly influences the RDS via NR%, 
PR%, and TR%. The RDM is intended to be an intuitive approach to handling paired needs 
assessment data. The RDS represents the severity of a need and allows for direct comparison 
and priority ranking between competencies. 
 

Purpose 
 

The purpose of the study was to explore the use of the RDM as an alternative method to the 
Borich model for determining training needs. The objectives were to: 
1. Describe the unweighted rank frequencies and RDS for program planning and program 

evaluation competencies. 
2. Compare MWDS and RDS for program planning and program evaluation competencies. 
3. Describe the relationship between scores resulting from the application of the Borich model 

and RDM. 
 

Methods 
 
Our study used Borich-type data from a competency assessment conducted at the University of 
Florida in 2021. A convenience sample of county agents was taken by surveying those who 
were registered (N = 276) for an annual professional development symposium. With a 58.30% 
response rate, the sample consisted of 161 individuals (n = 161). However, an examination of 
the dataset revealed several incomplete responses and/or majority missing values. Partial 
responses were removed from the dataset, and the final usable sample was 122 county agents 
(n = 122, 44.20%). A small number of missing values (< 1% of observations) in the final dataset 
were determined to be missing at random. For each competency item, missing values were 
replaced with the corresponding sample mean for that item to maintain the initial distribution 
properties of the data (Dodeen, 2003). 
 



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Survey data were gathered using a researcher-made questionnaire (Ary et al., 2014). The 
questionnaire consisted of a list of Extension core competency items related to program 
planning and evaluation. Selected items were consistent with previous studies in the subject 
area (e.g., Harder et al., 2010; Lakai et al., 2012; Lindner et al., 2010; Maddy et al., 2002; Narine 
& Ali, 2020; Scheer et al., 2011; Suvedi & Kaplowitz, 2016). There were 17 competencies in 
program planning and 15 competencies in program evaluation. Following Borich’s (1980) 
approach, respondents were first asked to rate their ability to perform each competency using 
a 5-point ordinal scale with the following options: 1 = None, 2 = Below Average, 3 = Average, 4 = 
Above Average, and 5 = Exceptional. Following, respondents were asked to rate the degree to 
which each competency was important to their job success using a 5-point ordinal scale with 
the following options: 1 = None, 2 = Below Average, 3 = Average, 4 = Above Average, and 5 = 
Essential. Data were analyzed using the Borich model and RDM to compare findings.  
Complete details about how to calculate training needs according to Borich (1980) can be found 
by visiting the original reference. 
 
Negative Ranks (NR), Positive Ranks (PR), and Tied Ranks (TR) were calculated using IBM SPSS 
Statistics (Version 27) by performing the Wilcoxon rank test between paired responses (i.e., 
observations for ability and importance) for each competency item. The first variable entered in 
the option window was perceived importance, then paired with self-assessed ability. An 
automatic output table with NR, PR, and TR was generated. Then, rank values (i.e., number of 
occurrences for NR, PR, and TR) from the SPSS output table were exported to Microsoft Excel to 
perform Steps 2 and 3. After finding the number of occurrences of NR, PR, and TR for each 
competency item, the next step was to convert the three rank counts into percentages. The 
final step was to apply weights to NR%, PR%, and TR%. The RDS was calculated as follows: RDS 
= NR% (-1) + PR% (1) + TR% (0). In practice, the last expression in the equation (TR% [0]) 
naturally drops off, leaving RDS = NR% (-1) + PR% (1). However, TR is important to the model as 
it affects percentages for NR and PR.  
 
With weights applied, the RDS equals -100 if all individuals have a negative discrepancy in 
ability relative to a competency’s importance (i.e., 100% NR). A negative RDS trending to -100 
reflects the magnitude of the discrepancy for one competency item and is directly comparable 
to the RDS for other competencies. In contrast, the RDS will equal (+) 100 when all individuals 
have a positive discrepancy in ability relative to a competency’s importance. As mentioned, the 
RDS should be interpreted as representing the overall capacity of the sample to perform a 
competency; it indicates the needs of the sample as a whole.  
 

Findings 
 
Table 1 provides the unweighted rank frequencies used to calculate the RDS for program 
planning competency items. After applying weights to NR (-1), PR (1), and TR (0), the RDS shows 
discrepancies in each item from the point of equilibrium (0). All items in Table 1 had a negative 
RDS, indicating a gap in ability to perform all program planning competencies. The RDS also 
shows the magnitude of the gap since all items are directly comparable on a standardized score 



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between -100 and 100. Based on the results, the top three priority competency items for 
attention are to (a) develop long-term program objectives (RDS = -61), (b) conduct a needs 
assessment for your program (RDS = -60), and (c) use the results of a needs assessment for 
planning (RDS = -57). 
 
Table 1 
 
Ranks and Ranked Discrepancy Scores for Program Planning 

Program planning Ranks (%) RDS 
NR PR TR 

Develop long-term (social, economic, environmental) program 
objectives 

63 2 35 -61 

Conduct a needs assessment for your program 69 9 22 -60 
Use the results of a needs assessment for planning 61 4 34 -57 
Develop medium-term (behavior change) program objectives 56 5 39 -51 
Translate needs assessment information into a situation statement 54 7 39 -48 
Develop long-term Extension program plans (extending beyond 2-3 

years) 
52 7 41 -46 

Establish programming priorities 48 6 46 -43 
Align program priorities at the local level with the Extension 

Roadmap 
48 8 43 -40 

Organize an effective program advisory committee 46 7 48 -39 
Assess available local/community resources 44 7 49 -38 
Conduct interviews to obtain information for planning 45 8 47 -37 
Develop short-term (knowledge, attitude, skill, aspiration) program 

objectives 
44 8 48 -36 

Develop an annual plan of work 44 9 47 -35 
Develop a logic model for a planned program 44 11 44 -33 
Develop monthly work schedule 35 10 55 -25 
Consult professionals with knowledge and experience about 

planning educational activities 
34 12 53 -22 

Develop weekly work schedule 33 13 54 -20 
 
Table 2 shows the unweighted rank frequencies and RDS for program evaluation competency 
items. Based on the RDS, there was a gap in ability to perform all competency items related to 
evaluation. The largest discrepancy identified by the RDS was to “conduct follow-up surveys to 
measure behavior change” (RDS = -58). 
 
 
 
 
 
 



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Table 2 
 
Ranks and Ranked Discrepancy Scores for Program Evaluation 

Program evaluation 
Ranks (%) 

RDS 
NR PR TR 

Conduct follow-up surveys to measure behavior change (e.g., 
practices adopted) 

60 2 39 -58 

Write interview and/or focus group questions 59 4 37 -55 
Establish measurable objectives for evaluating the success or failure 

of a program 
59 5 36 -54 

Communicate evaluation information to stakeholders 60 6 34 -54 
Use evaluation results to improve your program 57 4 39 -52 
Clearly distinguish between program outputs and outcomes 54 2 44 -52 
Develop intended outcomes that relate to the measurable 

objectives 
57 7 37 -50 

Analyze findings from evaluation activities 52 2 45 -50 
Write survey questions 57 7 35 -50 
Interpret findings from evaluation activities 52 4 43 -48 
Prepare reports on program outcomes using evaluation findings 53 5 42 -48 
Design valid pre- and post-tests 54 8 38 -46 
Align local impact data with UF/IFAS Extension Roadmap 51 7 42 -43 
Use online survey tools such as Qualtrics to collect data 50 8 42 -42 
Monitor Extension program activities 36 7 57 -29 

 
Table 3 provides a comparison between the Borich model and RDM for program planning 
competency items. Both models confirmed a discrepancy in ability for all program planning 
competencies. In Table 3, competency items were ranked based on the discrepancy in ability, 
which is translated as priorities for professional development; a positive MWDS and negative 
RDS indicate a need for professional development. In Table 3, the top three priority 
competencies were the same for the Borich model and RDM. Further, nine of the top 10 items 
were equivalent across models, with the only exception being “align program priorities at the 
local level with the Extension Roadmap.” This item was ranked 11th in the Borich model and 
8th in the RDM. Meanwhile, “develop short-term program objectives” was ranked 10th in the 
Borich model and 12th in the RDM. Lastly, the five items of lowest priority were equivalent 
across both models. 
 
 
 
 
 
 
 
 



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Table 3 
 
MWDS Compared to RDS for Program Planning 

Planning 
Scores Rank by model 

MWDS RDS Borich RDM 
Develop long-term (social, economic, 

environmental) program objectives 
4.04 -61 1 1 

Conduct a needs assessment for your program 3.77 -60 2 2 
Use the results of a needs assessment for planning 3.34 -57 3 3 
Develop medium-term (behavior change) program 

objectives 
2.88 -51 4 4 

Translate needs assessment information into a 
situation statement 

2.64 -48 6 5 

Develop long-term Extension program plans 
(extending beyond 2-3 years) 

2.77 -46 5 6 

Establish programming priorities 2.42 -43 8 7 
Align program priorities at the local level with the 

Extension Roadmap 
2.08 -40 11 8 

Organize an effective program advisory committee 2.48 -39 7 9 
Assess available local/community resources 2.21 -38 9 10 
Conduct interviews to obtain information for 

planning 
1.88 -37 12 11 

Develop short-term (knowledge, attitude, skill, 
aspiration) program objectives 

2.09 -36 10 12 

Develop an annual plan of work 1.86 -35 13 13 
Develop a logic model for a planned program 1.75 -33 14 14 
Develop monthly work schedule 1.27 -25 15 15 
Consult professionals with knowledge and 

experience about planning educational activities 
1.17 -22 16 16 

Develop weekly work schedule 0.97 -20 17 17 
 
Table 4 compares rankings of program evaluation competency items between the Borich model 
and RDM. Like program planning, both models confirmed there were discrepancies in all 
competency items relating to evaluation. The top three priority items were similar across 
models, albeit with differed ordering. While “communicate evaluation information to 
stakeholders” was ranked as the highest priority in the Borich model, “conduct follow-up 
surveys to measure behavior change” was ranked highest in the RDM. Similar to program 
planning items, nine of top ten evaluation competency items were consistent across models. 
While “analyze findings from evaluation activities” was ranked 12th in the Borich model, it was 
ranked 9th in the RDM. Also, “design valid pre- and post-tests” was ranked 9th in the Borich 
model, but ranked 12th in the RDM. 
 
 



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Table 4 
 
MWDS Compared to RDS for Program Evaluation 

Evaluation 
Scores Rank by model 

MWDS RDS Borich RDM 
Conduct follow-up surveys to measure behavior 

change (e.g., practices adopted) 
3.73 -58 2 1 

Write interview and/or focus group questions 3.48 -55 3 2 
Communicate evaluation information to 

stakeholders 
3.75 -54 1 3 

Establish measurable objectives for evaluating the 
success or failure of a program 

3.40 -54 4 4 

Clearly distinguish between program outputs and 
outcomes 

3.27 -52 7 5 

Use evaluation results to improve your program 3.28 -52 6 6 
Write survey questions 3.37 -50 5 7 
Develop intended outcomes that relate to the 

measurable objectives 
2.96 -50 10 8 

Analyze findings from evaluation activities 2.89 -50 12 9 
Prepare reports on program outcomes using 

evaluation findings 
3.03 -48 8 10 

Interpret findings from evaluation activities 2.92 -48 11 11 
Design valid pre- and post-tests 2.97 -46 9 12 
Align local impact data with UF/IFAS Extension 

Roadmap 
2.60 -43 14 13 

Use online survey tools such as Qualtrics to collect 
data 

2.63 -42 13 14 

Monitor Extension program activities 1.65 -29 15 15 
 
Figure 1 illustrates the observed distances between MWDS and RDS. Absolute z-scores were 
used for comparison due to the inverted interpretation of scores between models; a positive 
MWDS in the Borich model and a negative RDS in the RDM represent a need or discrepancy. 
The figure shows a clear relationship between MWDS and RDS; scores followed a similar 
pattern across all 32 competency items. A Pearson’s test revealed a very strong correlation 
between scores (r = 0.98), demonstrating a high level of consistency between models.  
 
 
 
 
 
 
 
 



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Figure 1 
 
Relationship Between Scores in the Borich Model and Ranked Discrepancy Model 

 
 

Analytical Steps in the RDM 
 

Getting Started: Gather Borich-type competency data. Figure 2 provides a sample item from a 
competency assessment questionnaire. 
 
Figure 2 
 
Program Planning Item in a Competency Assessment 

 
 
Figure 3 shows sample raw data in SPSS for five respondents. On the left shows the value labels 
for each respondent (i.e., row), and on the right shows the coded value. The sample data view 
is the first place to observe the number of occurrences in the sample when respondents’ ability 
ratings are: (a) less than respondents’ importance ratings (Negative Ranks = NR), (b) more than 
respondents’ importance ratings (Positive Ranks = PR), or (c) equal to respondents’ importance 
ratings (Tied Ranks = TR). For example, Respondent 1 has a positive rank, Respondent 2 has a 
negative rank, and Respondent 4 has a tied rank. 

0.0

0.5

1.0

1.5

2.0

2.5

Competency Items (i = 32)

z-MWDS (Abs) z-RDS (Abs)



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Figure 3 
 
Sample Competency Data Viewed in SPSS 

  
 
Step 1: Calculate the number of occurrences for negative ranks (NR), positive ranks (PR), and 
tied ranks (TR) in SPSS (see Figure 4). In SPSS, run the test as follows:  

• Analyze → Nonparametric Tests → Legacy Dialogs → 2 Related Samples.  
• For each competency item, enter responses for Importance (Variable 1) and Ability 

(Variable 2).  
• Repeat entries in Pairs for each competency item.  
 

Figure 4 
 

The Wilcoxon Test Window in SPSS (v. 27) 

 
 

The resulting SPSS Output will provide Negative Ranks, Positive Ranks, and Tied Ranks as shown 
in Figure 5. 
 
 
 
 
 
 



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Figure 5 
 
Ranks Generated in the Output Window for the Wilcoxon Test in SPSS (v. 27) 

 
 
Step 2: Convert the number of occurrences of NR, PR, and TR into percentages in Excel.  
Copy output from SPSS to Excel and calculate NR%, PR%, and TR%. Figure 6 shows the data 
structure in Excel. From Figure 5: 

• NR% = (NR/Sample Size) x 100  → (3/5) x 100 = 60 
• PR% = (PR/Sample Size) x 100  → (1/5) x 100 = 20 
• TR% = (TR/Sample Size) x 100  → (1/5) x 100 = 20 

 
Figure 6 
 
Basic Data Structure of RDM Data in Excel 

 
 
Step 3: Assign relative weights to NR% (WNR = -1), PR% (WPR = 1), and TR% (WTR = 0). From 
Figure 6, weights were assigned by multiplying the percentage for each rank by the 
corresponding weight as follows: 

• NR% x (-1)  → 60 x (-1) = -60 
• PR% x (1)  → 20 x (1) = 20 
• TR% x (0)  → 20 x (0) = 0  

 
The final Ranked Discrepancy Score (RDS) is calculated by summing the weighted score for each 
rank as follows: 

• NR% (-1) + PR% (1) + TR% (0) = RDS  → (-60) + 20 + 20 = -40 
 
 



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Conclusions, Discussion, and Recommendations 
 
We sought to determine if the RDM could serve as a suitable alternative to the Borich model. 
Our goal was to retain Borich’s (1980) emphasis on using group perception to determine when 
or if gaps in competency should be considered priorities for training. Our findings support the 
utility of the RDM approach as a compatible alternative to the Borich model. 
 
The comparison of RDS to MWDS showed a great deal of consistency in rankings, despite the 
RDM discarding the use of the group mean in calculations seen in Borich’s (1980) model. Nine 
of the 17 program planning competencies had the same ranking when calculated using the 
RDM and Borich model approaches. No ranking was more than three places apart and five 
rankings were within one place of each other. For program evaluation, the same trend was 
observed. Four of fifteen competencies had the same ranking when calculated with either 
model while another four competencies were within one place apart. The remaining 
competencies were no more than three places apart. The visual comparison of absolute z-
scores and the results of the correlational analysis further confirm that a strong relationship 
exists between competency gaps identified by the RDM and Borich model. An implication of our 
finding is that calculating the group mean is not required for determining competency gaps. 
Researchers conducting competency research should consider adopting the RDM given its 
suitability for delivering results that closely resemble findings from the Borich model. Adopting 
the RDM allows researchers to avoid their work being scrutinized for the use of means for 
individual ordinal items (e.g., Boone & Boone, 2012; Norman, 2010). The standardization of 
RDS, regardless of how many scale items are used to measure importance and ability, offers 
researchers the improved ability to compare their work with prior studies of the same 
competencies to determine how closely their findings match others.  
 
Another advantage of the RDM is that it decreases the complexity of interpreting results. The 
Borich model yields positive MWDS when training is needed. In our opinion, this is not intuitive 
given that many readers will have matriculated through a school system in which the goal was 
to score as close to 100 as possible to demonstrate mastery of a subject. We borrowed the 
same logic for the RDM. Instead of positive scores indicating a lack of competence, the RDM 
provides a negative RDS when training needs are greater (i.e., there are many individuals 
lacking sufficient ability and few individuals with an abundance of ability), which more clearly 
conveys that a problem exists that should be corrected. Therefore, the RDS demonstrates the 
magnitude of a discrepancy and maintains the underpinnings of a need as described by Witkin 
and Altschuld (1995), or a motivational force as discussed by Lewin (1939).  
 
The use of a standardized range between -100 and 100 with the RDM is cognitively easier to 
interpret than a range that varies based on the number of response anchors, but often runs 
from -4 to 20 with the Borich model using a 5-point semantic scale. However, as seen in our 
findings, MWDS often range between 2 and 4, making it seem like the magnitude of a training 
need is quite small even when a score of 4 indicates a serious gap in proficiency. The example 
of developing long-term program objectives illustrates the difference; compare the MWDS of 



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4.04 (maximum possible score = 20) versus the RDS of -61 (minimum possible score = -100). The 
RDM does a superior job at showing the magnitude of the gap.  
 
We want to be clear that the proper use of RDM approach requires the consideration of all 
three rank categories: PR, NR, and TR. The reason is that the RDS scores represent the capacity 
of the sample, inclusive of individuals who are excellent at a given competency and those who 
lack the necessary ability. For example, developing long-term objectives has a slightly higher 
RDS (-61) than conducting a needs assessment (-60), despite a greater percentage of NR for the 
latter competency. However, there is 9% of the sample that reported greater ability than 
necessary for conducting needs assessments while only 2% of the sample said the same for 
developing long-term objectives. Cumulatively, this sums up to a greater amount of capacity for 
conducting needs assessments in the overall sample. In practice, knowing what percentage of 
the sample has more ability than needed is helpful for assessing whether professional 
development strategies based on peer-to-peer learning, mentoring, or coaching may be 
effective. A skilled staff development professional should develop interventions that build upon 
existing assets, including human capacity. 
 
Quantitative research in extension often relies on ordinal data; we commonly operationalize 
constructs to test theories and develop ordinal rating scales to measure psychological variables. 
As such, the Borich model has been widely applied in extension over the past forty years due to 
its ability to provide meaningful insights on the professional development needs of 
professionals. However, with ongoing philosophical and statistical debates in academia, we 
must be able to justify our analytical approaches to the wider scientific community. The RDM 
provides an innovative and defensible approach for researchers and practitioners interested in 
using needs assessment data to determine competency gaps when planning their professional 
development interventions. 
 

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