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 1 – Closing Equity Gaps: The Perkins V Comprehensive Local Needs Assessment
Author: Brooke Dirtzu, Research Analyst
Perkins V was signed into federal law on July 31, 2018, thereby amending the Perkins IV (2006). This law aims to improve access and programming for high-quality career and technical education (CTE) that meets local needs. Minnesota Perkins consortia are made up of at least one school district and at least one postsecondary institution within the Minnesota State system that work together to deliver quality programs of study. Every two years, these consortia complete a Comprehensive Local Needs Assessment (CLNA) through which they analyze program data, make decisions, and examine their programs for ways to increase equity and access. Note: the data presented below is derived from a random distribution and not representative of any Perkins consortia.
Interpreting CTE program enrollment data with an equity lens
Race and ethnicity matter to the educational outcomes in all elements of the CLNA. Here we focused on Element 2: Program Size, Scope, and Quality as it builds the foundation for evaluating program alignment to the labor market. In addition, consortia members often have the most influence to change structures related to items reported in Element 2.
Purpose
Program enrollment data can be leveraged to complete an equity gap analysis. The National Alliance for Partnerships in Equity (NAPE) describes the equity gap analysis as an evaluation strategy used to compare levels of participation of one group of students to a comparison group (link). Remember, this analysis does not have to be done alone. The first step is to set up the research to identify gaps in participation by student subgroups. Next, present the gaps you identified for feedback from key stakeholder from the subgroups where the gaps exist, such as students or parent groups. Stakeholders from these subgroups are a great source for identifying the root causes of these equity gaps.
Collection
One best data equity practice is to disaggregate data whenever possible. Minnesota consortia receive disaggregated secondary and postsecondary CTE student enrollment data from the Minnesota Department of Education, and Minnesota State Colleges and Universities. CTE course enrollments and level of participation are disaggregated per the grant definitions, including gender (male/female/unknown), race/ethnicity (American Indian, Asian, Black or African American, Native Hawaiian/Pacific Islander, Hispanic, White, Multiple Races, Unknown), and special populations (Individuals with Disabilities, Individuals with Econ. Disadvantaged Families, Individuals Preparing for Non-Traditional Fields by Gender, Single Parents, Out of the Workforce, English Learners, Homeless, Youth in Foster Care, Youth with Parents in Active Military).
Analyzing
Secondary and postsecondary CTE student enrollment data can be used to complete a gap analysis to identify equity gaps. Below is an example of a gap analysis using sample data based on a random distribution and not indicative of any actual student outcomes.
When setting up your analysis, a best practice is not to center the student data on the majority experience. The example analysis below is set up to compare the overall secondary student population to the CTE student population and measure the under- or overrepresentation relative to the student groups’ own population. See the general observations below.
General observations that could be made using this sample data are:
Students identifying as Black account for 33.23% of CTE enrollments and 34.17% of the overall student population, representing a one-percentage-point underrepresentation in CTE programming. Among the students identifying as Black, 78.2% of this student group are enrolled in CTE programs.
Students identifying as Asian account for 34.45% of CTE enrollments and 30.53% of the overall student population, representing a four-percentage point overrepresentation in CTE programming. Among the students identifying as Asian, 90.7% of this student group are enrolled in CTE programs.
Interpreting
The next step is to identify key takeaways from the data analysis and interpretation to evaluate program alignment to the local labor market. Are there student groups that are under- or overrepresented in programs that lead to high wage, high skill, and in-demand occupations? Are these under- or overrepresentations due to accessibility? Who can influence changes that would increase equity and accessibility?
Who gets to interpret these patterns is also essential. The perceived answer to why a particular student group is under- or overrepresented in CTE programs may vary depending on who interprets the data. The next step could be to host an engagement session and ask students, parents, or community members to help analyze key data points.
Click here to read the next blog in this series where we look at 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.
In the final blog in this series guest contributor, Eva Scates-Winston the CTE Equity Specialist at Minnesota State Colleges and Universities, explores strategies for a root cause analysis. These recommendations can be used for CTE consortia to evaluate the results of the local needs assessment. Click here to read the final blog in this series.
