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Guide · Metrics Explained

What Are Q1, Q2, Q3, and Q4 Journals? Quartiles Explained

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Short answer: Evaluating Q1 Q2 Q3 Q4 journals involves understanding quartile rankings: each subject category is divided into four equal 25% tiers based on citation metric percentiles, allowing field-independent quality assessment.

Q1
Top 25%
Percentile: 75–100%
Q2
25% – 50%
Percentile: 50–74.9%

Q3
50% – 75%
Percentile: 25–49.9%

Q4
Bottom 25%
Percentile: 0–24.9%

Figure 1: Visual breakdown of academic journal quartiles (Q1, Q2, Q3, Q4) and category percentiles.

Understanding Q1 Q2 Q3 Q4 Journals and Category Percentiles

Within a given subject category, journals are ranked from highest to lowest by their metric value (JIF in JCR, CiteScore in Scopus). That ranked list is then split into four equal segments:

  • Q1 — top 25% of journals in the category
  • Q2 — 25th–50th percentile
  • Q3 — 50th–75th percentile
  • Q4 — bottom 25%

Because this is a percentile split within a category, the raw JIF that separates Q1 from Q2 in one field can look completely different from the cutoff in another. See our What Is a Good Impact Factor? guide for field-specific benchmarks.

Why the Same Journal Can Have Different Quartiles

A single journal is often indexed under more than one subject category — for example, a journal might be classified in both “Oncology” and “Cell Biology.” Its quartile in Oncology might be Q1 while its quartile in Cell Biology is Q2, because the competitive set of journals differs in each category.

Quartiles also differ by data source:

  • JCR quartiles are based on JIF within Web of Science categories
  • Scopus/CiteScore quartiles are based on CiteScore within Scopus subject areas

A journal can be Q1 in JCR and Q2 in Scopus’s CiteScore ranking simply because the category composition and citation windows differ. See Scopus vs Web of Science for how the underlying databases diverge.

Why Quartile Shifts Happen Without a Real Quality Change

Quartile position can move for reasons unrelated to the journal’s actual output:

  • Other journals in the category improved or declined
  • The category itself gained or lost members
  • The journal was reclassified into a different or additional category
  • The citation window rolled forward, changing which articles count

This is covered in more depth in our JCR 2026 release explainer.

How Institutions and Funders Use Quartiles

Quartile rank is often used as a shorthand filter in:

  • Tenure and promotion reviews, where some institutions set minimum quartile requirements for publications
  • Grant and funding assessments, where a track record of Q1/Q2 publications can signal research impact
  • PhD program requirements, where some universities specify a minimum quartile for thesis-related publications

Because the bar varies so much by field, always check what “Q1” means within the applicant’s specific subject category rather than assuming a fixed threshold.

Practical Tips for Checking a Journal’s Quartile

  1. Confirm which category the quartile is being reported for — a journal indexed in multiple categories may list several
  2. Check both JCR and Scopus rankings if available, since they can diverge
  3. Don’t treat Q2/Q3 as automatically “bad” — in small or specialized fields, respectable journals often sit outside Q1
  4. Combine quartile with other checks in our journal selection checklist before submitting

Key Takeaways

  • Quartiles rank journals relative to others in the same category, not against an absolute scale
  • The same journal can hold different quartiles across categories and across databases
  • Quartile position can shift year to year for reasons unrelated to research quality
  • Always verify the category and database before comparing quartiles across journals

Reviewed by Umair Abbasi

AI Engineer & Data Scientist | PhD Candidate, Artificial Intelligence

Umair Abbasi is an AI Engineer and Data Scientist with a background in machine learning, AI agents, and MLOps, currently pursuing a PhD in Artificial Intelligence and holding a completed M.S. in Data Science. He leads the data architecture and tooling behind the journal metrics database and comparison tools. As an active researcher navigating journal selection and citation metrics in his own academic work, he brings a practitioner's perspective to how impact factor, CiteScore, and related metrics are actually used — not just how they're defined.

Credentials & Expertise:
- M.S., Data Science (completed) - PhD Candidate, Artificial Intelligence (in progress) - Machine Learning Specialization — Stanford University (Coursera) - Google Data Analytics Professional Certificate

This article is reviewed for accuracy on a rolling basis. Data on individual journal metrics changes annually with each Clarivate JCR release (typically June) and Scopus CiteScore update (typically June). See our Data Sources & Methodology page for sourcing details.

Written and reviewed by named authors Every page is bylined with real credentials, linked to ORCID or Google Scholar — no anonymous “Team” posts.
Every figure sourced and dated No metric is published without a visible primary source and a “last reviewed” date.