Part 3 — Reading for Information

What you'll learn: how Part 3's paragraph-labelled article works, the paragraph-matching question type, and a keyword-mapping technique that avoids re-reading the whole article per question.

What Part 3 gives you

Part 3 is an informational article — often about a general-interest topic (health, technology, history, workplace trends) — split into paragraphs labelled A through E (sometimes more). Two question styles dominate: standard multiple-choice on content, and paragraph matching, where you're given a statement or detail and asked which lettered paragraph it belongs to. Paragraph matching is the signature format of this part and the main reason it needs a different approach than Parts 1 or 4.

Why re-reading the whole article per question fails

With five or six paragraphs and around nine to ten questions, a habit of re-reading from paragraph A every time you hit a new question can cost several minutes you don't have. The fix is to build a lightweight index during your first skim, so each question becomes a lookup instead of a search.

The paragraph-index technique

1. Skim each paragraph (10–15 seconds each) and jot (mentally or literally, if scratch space is available) a 2–4 word topic tag per letter — e.g., "A: history," "B: current stats," "C: causes," "D: expert quote," "E: future outlook."
2. Read each question/statement and predict which tag it's most likely to belong to before scanning.
3. Go directly to that paragraph and verify — don't re-scan the others unless your prediction turns out wrong.
4. For statements that don't obviously match a topic tag, look for a specific keyword instead (a name, a number, a defined term) — these are usually confined to one paragraph even when the topic tags are similar.

Worked example

Paragraph tags from a skim: A: what the trend is / B: why it started / C: statistics on adoption / D: criticism from experts / E: what happens next.

Statement: "Some researchers argue the trend has been adopted faster than the supporting evidence justifies."

This maps clearly to the "criticism from experts" tag — paragraph D — without needing to open A, B, C, or E again. Compare that to a test-taker without an index, who reads all five paragraphs from scratch looking for the word "criticism," burning 60–90 seconds on a question the index would answer in 10.

A harder case: "The idea was first proposed at a conference in 2019." This doesn't obviously match any of the five tags above — it's a specific fact, not a broad topic. Here you scan for the keyword "2019" or "conference" specifically, most likely inside paragraph B ("why it started"), since origin stories usually carry founding dates.

Part 3 checklist

• Built a one-line topic tag for every paragraph before answering questions
• Predicted a paragraph before scanning, then verified rather than searching blind
• For fact-specific statements (dates, names, numbers), scanned by keyword instead of topic
• Confirmed the whole statement matches the paragraph, not just one word in it
• Didn't assume a statement matches the first paragraph that "sounds close"

Common mistakes

• Matching a paragraph because it shares one keyword with the statement, without checking that the full claim is actually supported there.
• Skipping the indexing step under time pressure and reverting to a slow top-to-bottom re-read for every question.
• Confusing two paragraphs with similar topics (e.g., "causes" vs "criticism") because the tags weren't specific enough during the skim.

Recap

Part 3's paragraph-matching format punishes re-reading and rewards a fast, upfront index of what each lettered paragraph covers. Tag first, predict before scanning, and verify the full claim — not just a shared keyword — before locking in a paragraph letter.