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Needs Assessment:

Retention of Professionals in their 30s at XYZ University.
Conducted by Jill Pearson, Laura Gassaway, Nathan Gomer, Mat Johnston, and Tasha Smagula


XYZ University (XYZU, a pseudonym) is a mid-sized, public university in a mid-sized city. XYZU directly employs approximately 1200 professional, non-teaching staff. Professional staff are full-time, salaried and benefit eligible employees. The university is slightly remote and is the largest employer within the mid-sized city.

XYZU shared that they believe in increasing organizational effectiveness by investing in its faculty and staff and using reliable data to inform decision-making. To that end, the HR Department of XYZU sought the support of the needs assessment team (the NA team) to better understand the root cause of turnover amongst 30-something professionals.


XYZ University’s HR Department started conducting exit interviews in the fall of 2018 to better understand the reason employees leave the university. Surprisingly, the exit interviews found that 82% of the 99 surveyed employees would recommend XYZU as a good place to work, which left the XYZU HR Department stumped as to how it could improve their retention rates. The NA team was engaged by the XYZU HR Department to conduct a thorough needs assessment to make more informed retention decisions. The department wanted to find out the root cause of turnover and better understand the reasons employees stayed.

The HR Department of XYZU identified a gap in the retention of their professional staff, specifically employees in their 30s.


Current performance

  • The organization currently retains professional staff in their 30s at an 83.1% rate.

Desired performance

  • The desired performance is a retention rate of 86.7%. The performance gap is 3.6%.


The purpose of this project was to identify the root causes of this gap in performance and make targeted recommendations that may help close this gap. Increasing the retention rate and closing the performance gap will save the organization money that it currently spends on recruiting, onboarding, and training new hires. It will also save XYZU from losing potential organizational leaders, and it will increase employee satisfaction and engagement.

  • There are currently 1,200 professional staff in total, but between 2018 and 2019, there were roughly 350 professional staff in their 30s (personal communication, XYZU, 2019).

  • 3.6% of this group is approximately 12 employees.

  • According to the Society of Human Resources Management, the cost of losing an employee is approximately one-third of the employee’s salary (Agovino, 2019).

  • According to the HR Department of XYZU, the average salary for those employees is $50,000 (range is $30k-$70k). Therefore, the cost of losing an employee and needing to refill that position is 0.33x the average salary, for a total of $16,500 per employee.

  • By the team’s calculations, the savings that would come from retaining 12 more employees per year would be approximately $198,000.


The NA team conducted a systemic and systematic needs assessment in order to make recommendations that align with XYZU’s strategic goals and objectives. The decisions supported by this needs assessment were to improve the retention rate of XYZU’s professional staff in their 30s, which aligned with the organizational goal of increasing organizational effectiveness and to allocate resources strategically.

The team used four performance improvement models and tools to aid in the assessment as outlined in Table 1.


Data Collection

The NA team chose three data collection methods.


  • An extant data review of data provided by the client. This data helped gave a historical perspective of the problem and helped guide future data gathering. This data included:

    • Exit Interview Survey data

    • Employee Engagement Survey data

    • Retention statistics

  • In-depth semi-structured interviews of three current employees who fall into the target age-range. The interviews provided foundational information that helped the team create the surveys. They also provided qualitative data that could support or refute the survey data.

    • See​​ the complete interview protocol in Appendix B.

  • An online survey administered to all professional employees at XYZU. This was the team’s primary data gathering tool.

    • The initial portion of the survey identified each respondents age range.

    • The survey consisted of 11 dual-response questions​ using a five-point Likert scale, two open-response questions, and one factor-ranking question. 

    • The dual-response questions were created using Marker’s (2007) Synchronized Analysis Model (SAM), to ensure that the questions would investigate all levels and all causes to determine the root cause of the employee retention performance gap. The statements appear in the corresponding box of the SAM model shown in Table 1.

Due to the disruption caused by the COVID-19 pandemic, the university decided to halt data collection, and as a result the project itself was modified mid-semester. The survey was only available to respondents for approximately 24 hours before being pulled offline, and we were not able to conduct focus groups which were originally planned as a fourth data collection method. However, we did receive 99 survey responses from our target audience within the short time, which represented almost 30% of all possible respondents.


The team continued with the project using simulated data and feedback that were provided by the course professor. Despite this fact, the NA team feels that the results of the project and the recommendations offered based on the survey and interview results are still valid and useful, and the team hopes that the client uses the information within as a starting point for future work on this topic.


The data analysis process began as soon as the NA team received the extant data from the client. Exit interviews, engagement survey data, and retention statistics were reviewed and used to begin the development of a bottom-up codebook (LeCompte& Schensul, 2013), using the data itself to generate new analytic categories. Then the data and the initial coding categories were compared to Marker’s (2007) SAM. This helped the team develop the next level of coding from the top-down, using definitions for each of the 12 SAM categories (Table 2) to guide our analysis. These definitions formed the root of the team’s analysis process for interview and survey data.

Table 2.png

The team coded all qualitative data statements as either a barrier (why people leave) or a facilitator (why people stay).​ Using barriers or facilitators on every piece of data allowed the NA team to determine not only reasons why professionals in their 30s may choose to leave their employment, but also things that could encourage them to stay. Considering ways to both remove barriers but also increase the facilitators provided XYZU with twice as many intervention options as is noted in Appendix B.

Finally, data was triangulated (Table 3). The goal of triangulation was to find patterns and trends across all data sources to corroborate the themes that emerged (LeCompte & Schensul, 2013). To create this table the team looked at the most common responses from each data method and cross referenced them with responses from other methods. The importance rankings were identified from the ranking question in the survey. The data in the qualitative columns is derived from the interviews and the open-ended survey questions. The data in the dual-response columns is derived from the Likert-scale survey questions.

Table 3.png

Findings & Interventions Considered

Five areas were repeated throughout the data as if indicated within the triangulation table: salary, opportunities for growth, lack of training for advancement, meaningful work, and work-life balance. Opportunities for growth, and lack of training for advancement are similar and were grouped together as “opportunities for advancement/lack of training” in order to simplify the findings. Although many other needs were identified during the needs assessment, as Watkins (2012) suggests, needs must be prioritized when selecting interventions and the importance ranking question from the survey allowed us to do that.

Salary and lack of opportunities for growth/lack of training were identified as needs discrepancies (barriers to employee retention). Meaningful work and work-life balance were identified as appreciative areas (facilitators to employee retention). Each of the four areas of opportunity was reviewed to determine possible interventions within each area, including the benefits and limitations to those interventions.


To determine the interventions that would have the best return on investment, the team presented the complete list of suggestions to the client. The client then ranked each of the interventions based on their impact and ease of implementation using a scale of 1-5.

impact vs invasiveness.png

Using these scores, each intervention was plotted on a graph using Siko’s (2013) Intervention Matrix. The matrix is divided into four quadrants. Interventions that have a high impact and low difficulty to implement (quadrant II in Figure 1) would likely deliver the highest return on investment, while those that have a low impact and high difficulty level (quadrant IV in Figure 1), would likely deliver the lowest return on investment. 


The NA team recommended that the following four interventions should be given the highest priority:

  • Use social media to show employees their positive impact on students;

  • Promote social connections amongst employees;

  • Market the academic perks of working at XYZU, and

  • Create a “total rewards package”.

Based on the numerical results derived from the Siko (2013) Intervention Matrix, each of these four interventions scored as having a high impact and low difficulty and therefore they create a high potential return on investment. Appendix B contains a table that ranks each of the nine interventions considered and describes the reasoning for each recommendation as well as how that intervention aligns with the stated business objectives of the organization.


Agovino, T. (2019). To have and to hold. Society for Human Resources Management.

College and University Professional Association for Human Resources (CUPA). (2019). 2019 HR benchmarking stats. CUPA HR.

Gilbert, T. (2007). The behavior engineering model. Human competence: Engineering worthy performance (pp. 73-107). Pfeiffer.

Harless, J. (1973). An analysis of front-end analysis. Improving Human Performance: A Research Quarterly, 4, 229-244.

Kaufman, R. (September 01, 1988). Preparing useful performance indicators. Training & Development Journal, 80-83.

LeCompte, M., & Schensul, J. (2013). Analysis and interpretation of ethnographic data: A mixed methods approach (2nd ed.). AltaMira Press.

Marker, A. (2007). Synchronized analysis model (SAM): Linking Gilbert’s behavior engineering model with environmental analysis

models. Performance Improvement, 46(1), 26-32.

Schensul, J., & LeCompte, M. (2013). Essential ethnographic methods: A mixed methods approach (2nd edition). AltaMira Press.

Siko, J. P. (2013). Using the I2 intervention matrix to select the best courses of action. Performance Improvement, 52(10), 23-26.

Watkins, R., Meiers, M. W., & Visser, Y. (2012). A guide to assessing needs: Essential tools for collecting information, making decisions and

achieving development results. Washington, D.C.: The World Bank.


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