Original Research Checklist: The Essential Best Practices
An original research content checklist covers four phases: planning the study, collecting the data cleanly, presenting the findings credibly, and promoting the piece so it actually earns links. Missing a step in any phase tends to surface later as a credibility gap — a challenged statistic, an unlinked publication, or a piece nobody trusts enough to cite.
We keep a version of this checklist pinned in every research project we run for clients at Salterra, because the failure modes repeat across niches: skipped documentation, sample sizes too small for the claim being made, or findings buried where nobody — human or AI system — can easily quote them.
Planning Phase Checklist
- The research question is specific and testable — not a vague topic area but an answerable question.
- The data source is realistically accessible given your budget, audience size, and timeline.
- Sample size and selection method are defined in advance, before any data is collected.
- You’ve checked whether this exact question has already been answered by someone else — if so, either skip it or design a version that adds something new (larger sample, different population, updated timeframe).
- You have a realistic collection timeline that doesn’t force rushed or inconsistent data gathering.
Skipping the duplicate-check step is a common early mistake. Publishing a study that simply re-confirms an already well-known finding, with no new angle, earns far fewer citations than one that adds genuinely new information to the public record.
Data Collection Checklist
- Collection criteria are applied consistently across every entry in the sample — no tightening or loosening standards partway through.
- Every data point is logged with source and date, not just the final tally.
- Outliers are flagged and individually verified before inclusion, not silently dropped or silently kept.
- Raw data is saved and retrievable in case a claim is later challenged and you need to show your work.
- Any exclusions are documented along with the reason for excluding them.
The Documentation Habit That Pays Off Later
Keep a running collection log — a simple dated note of what was gathered, from where, and any judgment calls made along the way. This turns into your methodology section almost automatically, and it’s the single fastest way to answer a skeptical reader or journalist who asks “how exactly did you get this number?”
Analysis Checklist
- Basic descriptive numbers are calculated first — averages, ranges, frequencies — before any narrative is drafted.
- The finding wasn’t cherry-picked to match a pre-decided headline; the data was reviewed openly before the story was chosen.
- Limitations and possible biases are identified honestly, including sample size, selection bias, and generalizability.
- Statistical claims match what the data can actually support — correlation isn’t presented as causation, and small samples aren’t presented as universally representative.
A good habit here is running the analysis twice, on two different days, before locking in the final numbers. Fatigue and confirmation bias both creep into late-night spreadsheet work, and a fresh second pass catches transposed digits, double-counted rows, and miscategorized outliers that a tired first pass will miss. This is a five-minute check that prevents an embarrassing correction after publication.
Presentation and Credibility Checklist
- A clear methodology section is included in the published piece, not just kept in an internal file.
- Findings are stated as clean, quotable, standalone sentences that a journalist or another writer could lift directly.
- Numbers, dates, and sample sizes are consistent throughout the piece — no contradicting figures in different sections.
- Visualizations (if used) accurately represent the data without distorting scale or omitting context.
- The piece distinguishes your own findings from any external data you reference for comparison, so readers know exactly what’s original.
E-E-A-T and Trust Signals Checklist
- A named author with relevant credibility is attached to the piece — anonymous research reads as less trustworthy.
- The organization behind the research is identifiable and has a legitimate connection to the topic studied.
- Any conflicts of interest are disclosed — for example, a company studying its own product category should say so plainly.
- The data collection date or period is stated, so readers and AI systems can judge how current the finding is.
This phase is where original research content earns or loses long-term trust. A study with excellent data but no visible authorship or disclosure reads as less credible than a smaller study that’s fully transparent about who did the work and why.
AI Search Readiness Checklist
- Key findings are phrased as self-contained facts — understandable without needing the surrounding paragraph for context.
- The most citable statistic appears early, not buried at the end of a long section.
- Structured data or clear headers mark distinct findings, making them easier for both users and AI crawlers to isolate and cite.
- The page is technically crawlable and indexable — no accidental noindex tags or blocked resources on your most valuable asset.
AI Overviews and answer engines favor content that offers unambiguous, attributable facts. A checklist item worth treating as non-negotiable: never let your most citation-worthy statistic exist only inside an image or a chart with no accompanying text version, since that makes it invisible to both text-based crawlers and most AI extraction systems.
Promotion Checklist
- A clear, quotable headline stat is ready for outreach emails and social posts.
- Relevant journalists, newsletters, and communities are identified before publication, not scrambled together afterward.
- A refresh or follow-up cycle is planned if the topic supports repeat studies (annual re-survey, updated audit).
- Internal links point to the study from other relevant pages on your site, reinforcing its authority within your own content structure.
Don’t underestimate the internal-linking item. A proprietary study buried three clicks deep with no links pointing to it from your other high-traffic pages sends a weak signal to search engines and AI crawlers about its importance, even if the data itself is excellent. Link to it from every page on your site where it’s genuinely relevant, using descriptive anchor text that names the actual finding rather than a generic “see our research” link.
Final Review Checklist Before Publishing
- Every number in the piece has been cross-checked against the raw data at least once more before going live.
- A second team member has reviewed the methodology for gaps a close-to-the-data author might miss.
- The headline and any pull quotes accurately reflect the finding, without overstating it for effect.
- A plan exists for handling questions or pushback from readers or journalists who want more detail on the methodology.
This final pass matters because original research invites more scrutiny than typical content — precisely because it’s making a claim to new information rather than restating something already accepted. Treating the pre-publish review with the same rigor as the collection phase closes the loop on the whole process.
Frequently Asked Questions
What's the single most commonly skipped item on this checklist?
The methodology section. Many teams collect solid data but publish it without a visible, honest description of how it was gathered, which undercuts the credibility the research effort actually earned.
Is a checklist like this overkill for a small business with limited resources?
No — it scales down. A small survey of 50 customers still benefits from consistent collection criteria, an honest limitations note, and a clear methodology section; the checklist isn't about scale, it's about rigor at whatever scale you're operating.
How often should an original research piece be refreshed?
It depends on how fast the underlying subject changes. Fast-moving topics (technology adoption, pricing trends) benefit from annual refreshes; slower-moving topics might hold their value for several years before a refresh is warranted.
Should I include negative or unflattering findings if my business is the subject?
Yes. Omitting unflattering findings damages credibility more than the finding itself would, and it undermines the entire premise of original research, which is trustworthiness through transparency.
Does this checklist apply to qualitative research, not just numeric data?
Yes, with adjustments. Qualitative findings (interview themes, observational patterns) still need documented methodology, honest limitations, and clear, quotable presentation — the core discipline is the same even though there's no statistical analysis step.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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