In this blog series we examine how to center equity and access at each step of the Perkins V Comprehensive Local Needs Assessment (CLNA) process. This series is a guide for Perkins consortia, stakeholders, and community members who are interested in better understanding how to advance equity of access to quality programs of study and in-demand careers.
Part 3 – Root Cause Analysis for the Perkins V Comprehensive Local Needs Assessment
Author: Eva Scates-Winston, CTE Equity Specialist, Minnesota State
Even if the data is disaggregated, student or participant’s identities are flattened. To be responsive and inclusive, we need to pair data with knowledge and perspectives. In other words, the context surrounding the data –and the views of those persons examined in the data– matters.
The learners’ viewpoint and perspective matter. That is, data is not just about numbers:
- It’s about humanizing the data in that we are focused on the people, which the data is about;
- How we use data and communicate it speaks volumes about our mindset, which can be harmful or transformational;
- Data collection and action steps are part of (what should be) a collective process in those efforts we wish to impact; it should tell a complete story about the learner.
What is meant by ‘humanizing?
The term ‘humanizing’ considers the intentional actions that include the student experience –their historical participation, trauma, marginalization, etc. –that will ensure student success.
Consider what processes are necessary to make meaningful change in a program that positively impacts student participation. For example, a program may be designed to grant equal access at one level. Still, the program does not provide a supportive and cultural environment where all students feel welcome and valued for their identities and support their unique circumstances; it is certainly not fair (equity) or representative of their perspective. Consequently, the program participation may not show the successful outcomes desired or expected.
Perspective on Data Conversations:
In humanizing the CLNA process, reflect on what has been the dominant culture and practices, what and how data has been used, and how has it impacted learner success or lack of success of learners.
- Identify where there is an absence of critical data that gives a complete perspective of a learner’s story;
- Ensure objectivity in reviewing the data that is void of a deficit-thinking where there is a bias in the mindset how we normally view and use data and especially how it’s communicated
- Be willing to be challenge assumptions and learn from them, disrupting the same old organizational culture and dominating [deficit] mindset
Data Collection, Analysis, and Identifying Root Causes
When collecting and analyzing data, those learners’ perspectives are essential to understand their educational journey based on their experiences. Example questions might include:
- What attracts economically disadvantaged students to be represented more in certain programs than others?
- Are there potential successful practices, student stories, etc., to share with other programs?
- What is one or two focus areas within my control that I can start with? Start small!
When conducting a root cause analysis, it is important to include perspectives that share more than just operational elements for program evaluation and design. Consider reframing your analysis to include these perspectives:
- In what ways might students be missing or not engaged in participation? How might their identities be tied to that exclusion?
- Under what conditions could eligibility, budget priorities, or programmatic decisions impact participation or successful completion?
- In what ways are cultural and gender perspectives missing from the conversations?
In closing, keep the end-user in mind! It is important to build trust through transparency and actionable steps that are responsive and inclusive of those served. The needs assessment process and root cause analysis are tools to improve how the institution serves learners, NOT to solely satisfy compliance.
Interested in learning more about applying a root cause analysis to your consortium’s CLNA? Reach out to Eva Scates-Winston by email (Eva.Scates-Winston@minnstate.edu).
In our previous two blog posts, we discussed using an equity gap analysis in the Perkins V Comprehensive Local Needs Assessment and taking labor market indicator analysis further with an equity lens based on Erin Olson’s Metro Workforce Trends & Careers of Tomorrow webinar and the discussion questions posted by Eva Scates-Winston, Minnesota State Colleges and Universities’ CTE Equity Specialist.
