Statistics and methods classes at UO

Contributed by Rose Maier and John Flournoy.

There are stats and research methods classes offered by several departments around campus, but it can be hard to know which classes to take, or who to ask for advice. This is a venue for us to pool our knowledge about stats and methods courses offered around campus.

After the jump, you’ll find an extensive, but likely incomplete, list of statistics and methods courses at UO. If you’re aware of any that are missing, especially 607 or 610 listed courses, please speak up in the comments or email us.

If you have taken any quantitative classes outside of psych, please take a moment to write up a little information about your experiences. In particular, please try to include the following:

  1.  The name and course number of the class, including department code (e.g. “LING610: Empirical Methods II”)
  2.  The instructor’s name
  3. The strengths and weaknesses of the class, from your own experience as a psychology student (e.g. applicability to psych research, level of redundancy with psych stats classes like 611/612/613, etc.). If you have a copy of the syllabus, consider uploading that with your post.

(Also please keep in mind that this is a public blog, not an anonymous eval form.)

Hopefully this will turn out to be a handy little resource! Thanks for your help!

All the Stats & Methods Classes We Could Find:


  • 485/585 Techniques in Computational Neuroscience (4)Introduction to numerical techniques for modeling the nervous system from single neurons to neutral networks. Prereq: BI 360 or 461; MATH 247 or 252 or higher.
  • 486/586 Population Genetics (4)Analysis of the genentic mechanisms of evolutionary change. Study of artificial and natural selection, mutation, migration, population structure, and genetic drift. Prereq: BI 380; MATH 247 or 252.
  • 493/593 Genomic Approaches and Analysis (4) Introduction to experimental methods and analytical techniques for studying biological questions on a genome-wide scale. Prereq: BI 320.

Computer and Information Sciences

  • 445/545 Modeling and Simulation (4)Theoretical foundations and practical problems for the modeling and computer simulation of discrete and continuous systems. Simulation languages, empirical validation, applications in computer science. Prereq: CIS 315, 330.
  • 453/553 Data Mining (4)Databases, machine learning, artificial intelligence, statistics, and data visualization. Examines data warehouses, data preprocessing, association and classification rule mining, and cluster analysis. Prereq: CIS 451/551.
  • 454/554 Bioinformatics (4)Introduction to bioinformatics from a computer science perspective covering algorithms for basic operations such as sequence comparison and phylogenetic inference on existing databases.
  • 471/571 Introduction to Artificial Intelligence (4)Basic themes, issues, and techniques of artificial intelligence, including agent architecture, knowledge representation and reasoning, problem solving and planning, game playing, and learning. Prereq: CIS 315.
  • 472/572 Machine Learning (4) A broad introduction to machine learning and its established algorithms. Topics include concept learning, decision trees, neural network. Prereq: CIS 315.


  • 427/527 Games and Decisions (4)Game-theoretic methods of decision-making. Topics may include extensiveform games, noncredible threats, subgame perfect equilibrium, strategic-form games, undominated strategies, Nash equilibrium, coalitional games, and the core. Prereq: EC 311, 320. Van den Nouweland.
  • 428/528 Behavioral and Experimental Economics (4)Investigates the “rational choice” model and behavioral alternatives, using laboratory experiments. Topics may include altruism, auctions, bargaining, behavioral finance, hyperbolic discounting, and decision-making under uncertainty. Prereq: EC 311, 320.


  • 644 Applied Multivariate Statistics (4) Advanced statistical techniques including covariance analyses, discriminant function analysis, multivariate analysis of variance, principal components analysis, exploratory factor analysis. Prereq: EDUC 640.

Educational Leadership

  • 625 Survey and Questionnaire Design (4) Students gain practical experience in the collection and analysis of social science information through the design of surveys and questionnaires. Scalise.
  • 628 Hierarchical Linear Models I (4)Introduction to multilevel modeling and hierarchical data structures, random and fixed effects, intercepts and slopes as outcomes models, estimation, centering, and two-level models. Sequence with EDLD 629. Prereq: EDUC 642.
  • 629 Hierarchical Linear Models II (4)Advanced topics in multilevel modeling and hierarchical data structures including three-level models with random and fixed effects, longitudinal models, and multilevel models. Sequence with EDLD 628. Prereq: EDLD 628. Offered alternate years. Not offered 2013-14.
  • 633 Structural Equation Modeling I (4) Theory, application, and interpretation of structural equation modeling techniques. Includes covariance structures, path diagrams, path analysis, model identification, estimation, and testing. Sequence with EDLD 634. Prereq: EDUC 642. Offered 2013-14 and alternate years.
  • 634 Structural Equation Modeling II (4)Emphasis on structural and latent variable models, including crossvalidation, mean structures, comparing groups and models, latent growth-curve analyses. Sequence with EDLD 633. Prereq: EDLD 633. Offered 2013-14 and alternate years.
  • 661 Item Response Theory I (3)Theory and application of item response measurement models. Participation outcomes include knowledge of IRT models, terminology, and resources. Emphasis on popular models and underlying assumptions. Offered alternate years. Not offered 2013-14.
  • 663 Measurement in Research (2) Covers applied knowledge in measurement and assessment with an emphasis on use of measures for research purposes. Prereq: EDLD 560.
  • 670 Analysis of Discrete and Categorical Data (4)Advanced methods for analysis of discrete data. Topics include log-linear, logit, probit, latent class, and mixture models, and other generalized linear models. Prereq: EDUC 642. Offered alternate years.
  • 672 Analysis of Large-Scale Databases (4)Introduction to secondary data analysis and the use of data from national and other databases. Prereq: EDUC 642. Offered alternate years. Not offered 2013-14.

Library Courses


  • 610: Empirical Methods II
  • 621 Empirical Methods in Linguistics (4) Empirical quantified methods of data collection and analysis; statistical evaluation of results. Data derived from discourse, conversation, psycholinguistics, first- and secondlanguage acquisition, speech pathology, speech and writing deficiencies. Prereq: LING 450/550, 452/552. Kapatsinski, Kendall, Redford.


  • 461/561, 462/562 Introduction to Mathematical Methods of Statistics I,II (4,4)Discrete and continuous probability models; useful distributions; applications of moment-generating functions; sample theory with applications to tests of hypotheses, point and confidence interval estimates. Prereq: MATH 253; one from MATH 232, 262, 307.
  • 463/563 Mathematical Methods of Regression Analysis and Analysis of Variance (4) Multinomial distribution and chi-square tests of fit, simple and multiple linear regression, analysis of variance and covariance, methods of model selection and evaluation, use of statistical software. Prereq: MATH 462/562.
  • 467/567 Stochastic Processes (4) Basics of stochastic processes including Markov chains, martingales, Poisson processes, Brownian motion, and their applications. Prereq: MATH 461/561.

Political Science

  • 445/545 Methods for Politics and Policy Analysis I (4)Introduction to quantitative analysis, concepts and methods of empirical research, applied statistical data analysis in political science. Methods include descriptive statistics, bivariate correlation, and regression techniques.
  • 446/546 Methods for Politics and Policy Analysis II (4) Survey of multivariate model building for political analysis. Multiple regression, discrete-variable techniques, recursive systems, and cross-level analysis. Application of these techniques to concrete political problems. Prereq: PS 445/545.


  • 607 Programming in R
  • 607 Bayesian Methods in R
  • 610 fMRI Methods in Psychology
  • 610 Structural Equation Modeling
  • 610 Multilevel Modeling


  • 612 Overview of Sociological Methods (5)Examines the research process-framing research questions, qualitative and quantitative design, relationships between methods and theory, deductive and inductive investigation logic, research ethics, sampling procedures, explanatory power.
  • 613 Advanced Sociological Methods: [Topic] (5R)Major methodological topics such as comparative, demographic, experimental, field, historical, and survey methods. Other possible topics include time-series analysis. Prereq: SOC 612 or equivalent. Rtwice when topic changes for maximum of 15 credits.

2 thoughts on “Statistics and methods classes at UO

  1. LING610: Empirical Methods II (Prof. Volya Kapatsinski).
    This class is the second in a sequence, but I was able to skip Empirical Methods I based on the stats classes I had taken in PSY (611,612,613, and 610:MLM). I found this class incredibly useful! It’s about 50/50 conceptual discussion and hands-on practice, focusing on issues in research design, analysis and interpretation. It’s mostly structured around articles rather than chugging through a stats text, which I enjoyed since it shifted the focus away from abstract theory to the more practical issues that arise in real published studies. Students each take a day to lead discussion of whatever the assigned reading for that day is, and everyone is required to post discussion questions/comments the night before class. Weekly homework assignments mostly come from R. H. Baayen “Analyzing Linguistic Data: A practical introduction to statistics”, which focuses on the nuts and bolts of how to run analyses in R and includes tons of examples of R code.
    I found this class to be an excellent complement to my PSY stats training. There was only a tiny bit of discussion of the underlying math (e.g. how is an ANOVA actually calculated), which is great since we go through all of that pretty well in 611/612/613. Instead, we spent time talking about when and why to use different analysis strategies, and what to do in cases where “typical” tests fail. This included substantial discussion about (and practice with) non-normal distributions, bootstrapping, etc, and lots of work with multilevel/mixed effects models. And, of course, it was fun to work in R after having been trained in SPSS.
    One thing to note is that this class is offered by Linguistics, so of course all of the examples are about language data. That worked out perfectly for me since I study language acquisition, but it may be less useful for someone with another focus. My guess is that most PSY students would get a lot out of this class, though, regardless of research area.

Leave a Reply

Your email address will not be published. Required fields are marked *