Course: Introduction to Sociology (SOC 1) · Silver Oak University (fictional sample) · Prof. Adeyemi
Module: Week 2 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objective covered: Objective 2 — Describe the major sociological research methods and read social data critically, distinguishing correlation from causation.

This file holds two pieces: (A) the Module 2 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. Dates below assume a Tuesday/Thursday session pattern with Week 2 meeting Tue Sep 8 and Thu Sep 10, and end-of-week work due Sunday Sep 13, 11:59 p.m. Adjust the day-of-week and times to match your section.

(A) Module 2 Overview — Start Here

Welcome to Week 2: Sociological Research Methods

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module.

Last week we made a promise: sociology runs on evidence, not anecdote. This week we make good on it. How do sociologists actually find out what's true about society — and just as important, how do they read a statistic without fooling themselves? You see numbers and "studies" every day — in the news, in ads, from a chatbot. This week you learn the toolkit working sociologists use to gather evidence, and the discipline that keeps them honest: the rule that two things moving together is a clue, not a verdict.

The week's big question

"How do sociologists turn a curiosity about society into trustworthy evidence — and what is the single most expensive mistake in all of social science?"

By Friday you'll be able to walk the research cycle, match a research method to the question it fits, tell reliability from validity and a population from a sample, explain why a representative sample beats a merely big one, and — the load-bearing skill — explain why correlation is not causation.

By the end of this week, you can…

Use this as a checklist. If you can do all five out loud, you're ready for the quiz.

• [ ] Walk the research cycle — question → hypothesis → operationalization → method → data → analysis — and define an operational definition and the independent vs. dependent variable.
• [ ] Match the four major methods to their uses — surveys, experiments (the one method built to establish causation, via control + random assignment), field research/ethnography, and secondary/existing-data analysis (Durkheim's Suicide used existing records).
• [ ] Distinguish reliability from validity (consistent vs. accurate) and population from sample, and explain why representativeness beats size and why a self-selected sample can't be generalized.
• [ ] Explain correlation vs. causation — name the third (confounding) variable and the spurious correlation, and say what it takes to claim a cause.
• [ ] Read a real social statistic critically — name what it measures, over what population, what it shows and what it does not, and whether it's correlation or causation; and state the core research ethics (informed consent, confidentiality, IRB).

What's due this week, and when

Work these in order — each one gets you ready for the next.

Heads-up on the AI tools: you'll use a chatbot to draft and explain, and then you judge its work. This week's content is exactly where chatbots fail loudest — they invent statistics, fabricate plausible-sounding "studies" and citations, generalize from biased samples, and report a correlation as if it proved a cause. Catching the model is the point — and it's the whole skill the Workshops build.

Late policy reminder: 10% off per day late. If life happens, reach out before the deadline — I'd much rather hear from you early.

How to succeed this week

• Lead with the idea, not the jargon. Operationalize just means "define it so two different people would measure it the same way." A sample is just the slice you actually study. Validity is "does it measure the real thing?"; reliability is "does it give the same answer twice?" The vocabulary comes after the idea clicks.
• Memorize two tiny hooks. "Correlation is a clue, not a verdict." And for sampling: "A representative sample beats a big one — size can't fix bias."
• Practice the "how do you know?" move. Whenever you hear "a study says…," ask four things: Who was studied? How were they chosen? What was measured? Cause, or just two things moving together?
• Hunt the third variable. When two things rise together (ice cream & drowning; bookstores & income), don't ask "which causes which?" first — ask "what third thing could be driving both?"
• Treat the chatbot as a smart intern, not an oracle. It drafts; you check every figure and citation at the source. That habit is the whole semester in miniature — and never more than this week.

You don't need any math background for this week — just a willingness to ask "how do you know?" and to slow down before a number. Come to class ready to argue about whether an online poll "of 50,000 readers" tells us anything about the country. See you Tuesday.

(B) Welcome Announcement — Module 2

Release setting: post on the module's start day (offset = 0 days), i.e., Tue Sep 8, 2026 — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Tue Sep 8."

Subject: Welcome to Week 2 — how we actually know anything about society 🔎

Hi everyone — great first week. You learned to see society, not just individuals. Now the obvious next question: how do we actually find out what's true?

Quick warm-up: a news site runs a poll, 50,000 of its own readers click in, and 80% favor a new policy. Does that tell us what the country thinks? (It doesn't — and by Friday you'll be able to say exactly why.) Or this one: cities with more police tend to have more reported crime — so do police cause crime? (No — and you'll name the flaw.) This week is the research toolkit and the single most valuable habit in all of social science: telling a correlation (two things moving together) from a cause.

This week — Sociological Research Methods — we tackle the big question: How do sociologists turn a curiosity about society into trustworthy evidence, and what is the most expensive mistake in social science? By Friday you'll walk the research cycle, match a method to a question, tell reliability from validity and a population from a sample, know why a good sample beats a big one, and explain why correlation is not causation.

Four things not to miss:
1. Lecture Tutorial 2 — work through the methods and correlation-vs-causation with one approved chatbot (Gemini, Claude, or ChatGPT) and submit the share link. This is the week chatbots fabricate the most — you'll catch it. Due Sun Sep 13.
2. Quiz 2, Discussion 2, and Assignment 2 also close Sun Sep 13 — Discussion 2 ("What Does This Statistic Actually Show?") is a quick AI dialogue you summarize and post, so start early and leave time to reply to classmates.
3. Workshop 2 — "Read the Data" — our first data workshop. You'll read a real Pew Research statistic with a four-question scaffold, run a correlation-vs-causation drill, and fact-check an AI. Due Sun Sep 13.
4. Open the Start Here page first — it lays out everything in order with due dates.

One promise: after this week, you'll never read "a study says…" the same way again. The next time a headline jumps from a correlation to a cause, you'll know exactly what's missing.

Bring your skepticism (and maybe a headline that smells fishy) to class on Tuesday.

See you soon,
Prof. Adeyemi