Week 17 · Knowledge, Science & Capstone
How concepts become measures, how causal claims are supported, and where uncertainty enters an explanation.
Your AI study mentor
Abstract ideas enter evidence through measures, indicators, comparisons, and causal stories. Every such choice clarifies something and loses something. I pair measurement and causal audits here to turn the capstone's concepts into observable claims, test the path from association to explanation, and expose uncertainty before it becomes hidden inside the final argument.
Do not let an available indicator become the definition of the concept. A measure can be useful while still excluding important dimensions.
What does your chosen indicator systematically fail to see?
Start with the aims and questions so the readings have a purpose.
Use the prompt under each source to produce notes, not highlights alone.
Test the ideas through observation, analysis, or a small intervention.
Save the deliverables and check your work against the completion criteria.
Define what you are trying to understand before deciding that you have understood it.
As you read, keep a measurement log: concept, possible indicator, what the indicator reveals, and what it leaves out.
Core material
4 guided readings, in the order shown.
Rose M. Spielman and contributors · OpenStax
Research questions, scientific literacy, ethics, descriptive methods, correlation, and experiments in psychology.
Read the sections on variables, correlation, and experiments. Draw one causal diagram for your capstone and mark the variables you cannot observe directly.
Peter Godfrey-Smith · Stanford Encyclopedia of Philosophy
Questions about hypothesis, evidence, experiment, explanation, and the logic of scientific inquiry.
Return to confirmation and explanation. Identify what would make your preferred explanation more or less likely.
Nathan Keirns and contributors · OpenStax
An accessible introduction to social stratification, class, status, mobility, and the institutional reproduction of inequality.
Use one empirical example from the chapter to practice asking whether a measured gap is a cause, an outcome, or both.
CORE Econ · CORE Econ
Hands-on projects using public data to develop basic data handling, statistical reasoning, and empirical economic analysis.
Use one public-data example to audit the path from operational definition to comparison, estimate, and conclusion. Record what the data cannot establish.
7 study actions turn the reading into observable work.
Optional resources for going further after the reading plan.
An expert-written introduction to judging the quality of psychological claims, evidence, explanations, and scientific conclusions.
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