Research Methodology
The insights and recommendations we share in this resource are based on trends we identified and findings we culled from three main research activities:
Labor Market Information Analysis
In March 2025, we pulled data from Lightcast that covers both the overall U.S. labor market and four metropolitan statistical areas (MSA): Boston, Massachusetts; Dallas-Fort Worth, Texas; Miami, Florida; and Worcester, Massachusetts.
The primary dataset consisted of job postings from March 2024 through February 2025 for 16 core IT occupations. Within that set, our analysis focused specifically on postings that required low levels of experience (one year or less) and less than a bachelor’s degree. We reviewed these postings to identify the skills and credentials most frequently cited in lists of requirements.
To complement the analysis of job postings, the dataset also included a 10-year (2014–2024) review of trends to understand the overall growth of the 16 core IT occupations. That longer-term perspective provided context for understanding both current demand and historical patterns in the IT labor market.
Credential Landscape Scan
JFF researchers conducted a scan of the IT credential landscape through desk research that focused on academic sources and gray literature. The information gleaned from this landscape scan served as the basis for the fictional learner personas.
Employer Interviews
In March 2025, the JFF team conducted eight structured interviews with IT hiring managers representing organizations in the public administration, health care, and technology sectors. Each interview lasted approximately 60 minutes and provided in-depth insights into employer perspectives on IT talent needs.
Following data collection, the interviews were coded and analyzed for themes using a combination of human-led qualitative analysis and sense-making processes enabled by generative AI. This dual approach allowed for both nuanced interpretation and systematic pattern recognition, strengthening the reliability of the findings.