Course transforms anthropology, archaeology, and other social science students with little-to-no background in statistics into competent quantitative reasoners who understand the philosophical bases of quantification, know how to interpret analyses and graphs, and know enough basic statistics to discover the analyses they need to conduct independent research.
Athena Title
Quant Analysis Anth
Equivalent Courses
Not open to students with credit in SOCI 3610
Prerequisite
ANTH 1102 or ANTH 1102E or ANTH 2120H
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will learn to create quantitative datasets and analyses to address a range of research questions.
Students will learn to critically evaluate quantitative arguments in published peer-reviewed scholarship.
Students will learn to create intelligible, legible, convincing graphs for science communication.
Students will learn to confidently perform basic data management and analysis tasks using STATA or R.
Students will gain intuition about the shape of frequency distributions and functions for different kinds of social measures and interactions.
Students will understand the possibilities and limits of using artificial intelligence as a tool for learning about statistical analyses.
Students will learn to apply quantitative vocabulary with sufficient fluency that they may collaborate with statisticians and continue their independent learning of statistics.
Topical Outline
In anthropology, who quantifies what, and why? (Empiricism's assumptions, possibilities, limits in comparison to other anthropological epistemologies)
What do quantitative anthropological datasets look like, and why? (Numbers as quantities, measures, frequencies, ranks, or codes; variables; cases)
What is Excel useful for? What is Stata useful for? What is R useful for? (Why Excel is error-prone; basics of commands, modeling, and coding)
When might generative AI tools be helpful in learning and doing quantitative methods? What are the dangers?
What are frequencies? Why do frequency distributions have different shapes? (What kinds of variables tend to be normal vs skewed in distribution)
What anthropological questions can I answer by comparing the shapes of frequency distributions?
What does it mean to model Y as a function of X? (Correlation, regression, imputation, prediction)
Why do functions different shapes? (What kinds of variable interactions tend to be linear, quadratic, or exponential)
What anthropological questions can I answer by modeling Y as a function of X?
How do I understand the results of regression analyses? (The limits of p-values and R2 values; how to read regression coefficients, and what to do with them)
Why is correlation different from causation, and how can I tell them apart? (Directed Acyclic Graphs, Path Analysis)
Why are there so many different kinds of regression models? What are they for?
What is model fitting, and why should I care? (Because it is more informative than null-hypothesis testing)
How do I know whether I can trust my data and my analyses? (Face validity, internal validity, external validity, etc)
How do I report the results of data analyses? How do I use my analyses to interpret human culture and behavior?
How do I learn more about statistics as I encounter new data and new questions?
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
Communication
The ability to effectively develop, express, and exchange ideas in written, oral, interpersonal, or visual form.
Critical Thinking
The ability to pursue and comprehensively evaluate information before accepting or establishing a conclusion, decision, or action.
