Schedule (topic flow)

A 15-week pacing plan — Blackboard is authoritative for dates and deadlines

Important

This is a pacing plan, not a calendar. It shows the order in which design topics unfold and roughly when, so you can read ahead and see where you are headed. It is not the authoritative calendar: exact dates, deadlines, and any mid-term adjustments live in Blackboard (the LMS), which is authoritative. A few fixed semester dates are noted for orientation only — confirm everything against Blackboard.

The course runs fifteen weeks in four parts. Each part below lists its weeks, the theme for each week, and the throughline — the design question that carries the week. The theme links to that week’s note.

Part I — Questions, measurement & validity (Weeks 1–4)

We set up the frame: what a statistical question is, how we measure, the signature distinction between sampling and assignment, and the threats a claim must survive.

Week	Theme	The throughline
1	Statistical questions & units of analysis	What is the question, what is the unit, and what claim is being made?
2	Measurement & operational definitions	How do we turn an idea into observable data without pretending measurement is automatic?
3	Random sampling vs random assignment	Which mechanism is present, and does it earn a population claim or a causal one?
4	Bias, confounding & validity	What threatens a claim — and is the design internally or externally valid?

Note: Labor Day (Mon, Sep 7) falls in Week 3 — no class that Monday, so the week runs Wednesday and Friday.

Part II — Designed experiments (Weeks 5–8)

We build the experimental machinery: randomized comparisons, then the refinements that sharpen and structure them.

Week	Theme	The throughline
5	Completely randomized experiments	Why does random assignment license a causal claim, and how do we read the comparison?
6	Blocking & paired designs	How do we sharpen the same effect by removing nuisance variation?
7	Factorial experiments (midterm)	How do two factors at once give us main effects efficiently?
8	Interactions in designed studies	When does the effect of one factor depend on another?

Note: the midterm is Friday, Oct 9 (in class), during Week 7. It covers statistical questions through factorial designs. No graded content appears on this public site — see Blackboard.

Part III — Observational & causal evidence (Weeks 9–10)

We move to studies without random assignment, where confounding lurks and causal diagrams keep the reasoning honest.

Week	Theme	The throughline
9	Observational studies	When there’s no assignment, when can adjustment turn association toward causation?
10	Causal diagrams & backdoor reasoning	What should we adjust for — and what is a “bad control”?

Part IV — Surveys, sampling & synthesis (Weeks 11–15)

We add survey sampling and missing data, then critique and synthesize the whole arc.

Week	Theme	The throughline
11	Surveys & sampling frames	What does the sampling frame, coverage, and nonresponse let a survey claim?
12	Stratified & cluster sampling	How do sampling designs change the precision of a population estimate?
13	Missing data & nonresponse	How sensitive is a conclusion to who is missing, and why?
14	Study critique & design-memo workshop	How do you critique a claim and write a memo bounded by what the design supports?
15	Final design memo & review	How do experiments, observational studies, and surveys fit as one practice of evidence?

Note: fall break is Nov 22–28 (no classes), between Weeks 13 and 14. The last class is Mon, Dec 7, the consultation day is Dec 8, and the final-exam window is Dec 9–15 (exact block via Blackboard).

Note

Again: this page is a pacing plan to help you read ahead. For the real calendar — every date, every deadline, and any adjustment during the term — Blackboard (the LMS) is authoritative.