This course is designed to introduce you to graduate-level behavioral research with a focus on data analysis. We will examine the core concepts in inferential statistics, as well as issues of replication and reproducibility. The course also provides a survey of methods that serves as a foundation for other quantitative courses, both within and outside the department.
Prerequisites: This course assumes that students have taken a previous undergraduate-level research methods course (e.g. PSYC 2100WQ) and have a strong understanding of descriptive statistics, t-tests, and correlations. If you are concerned about your readiness for the course you may want to discuss how to approach the course with Professor Stevenson or your advisor.
Problem sets can use your choice of programming language (e.g. R or SPSS). R is available here (or Rstudio). SPSS is available here (or AnyWare).Topic | Assignments | |
08.29 08.31 | Course Intro Data Analysis |
Problem Set 1 due 9/2 |
09.05 09.07 | Labor Day (no class) Visualization |
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09.12 09.14 | Null Hypothesis Significance Testing ...and its interpretation |
Problem Set 2 due 9/13 |
09.19 09.21 | Validity and Reliability Validity and Reliability |
Problem Set 3 due 9/23 |
09.26 09.28 | Experimental Design Effect size and power |
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10.03 10.05 | One-way ANOVA General Linear Model |
Problem Set 4 due 10/04 |
10.10 10.12 | Contrasts and trend analysis ANOVA in practice |
Problem Set 5 due 10/14 |
10.17 10.19 | Factorial ANOVA Factorial ANOVA con't |
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10.24 10.26 | ANCOVA Multiple comparisons |
Problem Set 6 due 10/25 |
10.31 11.02 | Repeated-measures ANOVA Repeated-measures ANOVA con't |
Problem Set 7 due 11/04 |
11.07 11.09 | Intro to Mixed effects models Outliers and Transformations |
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11.14 11.16 | Tests for distributions Nonparametric tests |
Problem Set 8 due 11/15 |
11.21 11.23 | Fall Break (no class) Fall Break (no class) |
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11.28 11.30 | Intro to Bayesian methods Intro to Factor Analysis |
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12.05 12.07 | Project Presentations Project Presentations |
Project Reports due 12/16 |