Further Reading — Chapter 8: The Needs Assessment / Significance Section

Data tools and access change; verify currency at the source, and cite every statistic.

Data Sources (Use These)

  • U.S. Census Bureau — data.census.gov. Population, demographic, income, education, and housing data down to the census-tract level. The first stop for the "local specificity" half of your need.
  • CDC — WONDER, BRFSS, and data portals (cdc.gov). Health conditions, mortality, and risk-behavior data from national to county level.
  • Bureau of Labor Statistics (bls.gov). Employment, wages, and occupational projections — essential for workforce and economic-need arguments.
  • PubMed (pubmed.ncbi.nlm.nih.gov) and NIH RePORTER. The peer-reviewed literature and funded-research landscape — the evidence base for a research significance section.
  • Hospital Community Health Needs Assessments (CHNAs). Every nonprofit hospital must publish one every three years — a free goldmine of local health and social data. Search "[your county] community health needs assessment."
  • Reputable research organizations: Pew Research Center, KFF (Kaiser Family Foundation), the Annie E. Casey Foundation's KIDS COUNT, the Urban Institute, and similar. Cite their data carefully, noting methods.

On Building the Argument

  • Karsh & Fox, and O'Neal-McElrath (the nonprofit grant guides). Both have strong chapters on writing a statement of need that argues rather than lists, and on using local data.
  • W.K. Kellogg Foundation and CDC, evaluation and needs-assessment guides. Free resources on conducting and presenting a needs assessment, including how to combine data sources.
  • Robert Porter, "Why Academics Have a Hard Time Writing Good Grant Proposals." On the shift from describing a topic to arguing significance — the core of this chapter for researchers.

On Honest Use of Data

  • Huff, Darrell. How to Lie with Statistics. A classic, readable tour of the misleading framings (percentages without base rates, misleading scales) this chapter warns against — read it to learn what not to do.
  • Wheelan, Charles. Naked Statistics. A clear, modern guide to using statistics honestly and interpreting them correctly; useful for building a credible quantitative need.
  • This book's CONTRIBUTING.md and Chapter 3 — the three-tier citation system (verified / attributed / illustrative) applied throughout. Re-read it before writing any data-heavy section.