**ANTH 595D:** **R programming for data visualization and analysis**

This is a great first or second class. It is unlike a traditional statistics course in that it focuses roughly equally on learning the R programming language, developing data visualization skills, and implementing statistical models in R. The programming part of the course introduces the R environment and skills related to reading/writing data, functions, control structures, data tidying and data manipulation/aggregation. The data visualization part of the course focuses on effective methods for conveying information through statistical graphics. It introduces the primary graphics systems in R, with a focus on the grammar of graphics and the ggplot2 package. The data analysis portion of the course covers a variety of statistical models at a conceptual level (little to no math) and includes: t-tests, ANOVA, bivariate linear regression, multiple regression, mixed models, and a brief overview of dimension reduction and clustering techniques.

**EDP 541: Introductory Statistics in Education**

This is a great first class if you have little or no prior training in quantitative research, or you’ve taken classes before but didn’t feel like you understood them, or you are nervous about statistics. It covers all the basics and moves a little slower than the other introductory classes, but it still provides the foundation you will need to advance to intermediate classes (e.g., sampling distributions, logic behind null hypothesis significance testing, statistical vs. practical significance). The course is taught using the R Statistical Computing platform and you will learn the basics you need to use it for data analysis.

**EDP 558: Tests & Measurements**

Measurement theory. This course is appropriate at an intermediate and/or advanced level, and is best for individuals with heavy measurement interests (e.g., How much error is in scores we get from a test or measure? What sources of measurement error are present and how substantial are they? How well do items match the ability levels of people in the sample? Do Likert categories function the way we expect them to?). The course covers three theories of measurement: Classical Test Theory (heavy focus on reliability), Generalizability Theory, and Item Response Theory. Each theory incudes one or more analyses for test scores as well. Knowledge of ANOVA, correlation, and covariance is assumed.

**EDP 641: Selected Applications of Statistical Methods**

Advanced Linear Models. This is a great second or third class for most people. The class covers multiple regression, logistic regression and introduces multilevel models (a.k.a. hierarchical linear models) with a focus on individuals (e.g., students) nested in groups (e.g., classrooms). The course is taught using the R Statistical Computing platform and you will learn both basic and more advanced skills for data analysis.

**EDP 646A: Applied Multivariate Statistics in Education**

This is a great advanced class if you would like to develop an understanding of matrix algebra and apply it to multivariate analyses. Topics covered include Hotelling’s T-squared, MANOVA, discriminant analysis, canonical correlation, scale development (including PCA, EFA, & CFA), and cluster analysis. The course is taught using R.

**FSHD537A/L: Introduction to Statistical Analysis**

This is a great first or second class if you’ve taken one or two statistics classes and think you understood them. The class covers both traditional Null Hypothesis Significance Testing (NHST) and Bayesian inference, as well as model selection. It emphasizes the general linear model, which includes all the fundamentals (t-test, ANOVA, correlation, multiple regression) and a few more advanced topics (logistic regression, repeated measures and multivariate models). The course is taught using the R Statistical Computing platform and you will learn both basic and more advanced skills for data analysis.

**FSHD 537B: Intermediate Statistics**

This is a great second or third class for most people. The class covers mediation, moderation, missing data handling and introduces person-centered analyses. The course is taught using MPlus and R.

**FSHD 617A: Structural Equation Modeling**

This is a great advanced class for most people, since structural equation models (SEM) are used across many different research domains. The course provides a complete introduction to SEM, including confirmatory factor analysis, mean and covariance models, mediation/moderation and latent growth curves. The course is taught using Mplus, with students having the option of using R instead.

**FSHD 617C: Multilevel Modeling**

This is a great advanced class if your research interests include longitudinal or time-varying processes. The course provides a complete introduction to multilevel modeling (a.k.a. hierarchical linear models), with an emphasis on time nested within individuals, who may also be nested within larger social units such as dyads. Both traditional maximum likelihood and Bayesian approaches are included. The course is taught using R.

**PSYC 507A/597A: Statistical Methods in Psychological Research**

This is a great second or third class if you would like to get a better grasp of the “big picture” and the concepts underlying statistical methods. The class covers the philosophy, history and methodology of science, as well as all the fundamentals, such as correlation, ANOVA and regression models. It also introduces the Continuous Parameter Estimation Method and its use with the UniMult2 software package.

**PSYC 510: Statistics Fundamentals**

This is a great first class if you’ve taken one or two statistics classes and sort of understood them. The class emphasizes working with your own data right from the beginning. It covers the logic of statistical inference and hypothesis testing, as well as all the fundamentals, such as descriptive statistics, data transformation, t-tests, regression, ANOVA, and non-parametric tests, such as chi-square, Wilcoxson and Kruskal Wallis tests. The course is taught using the R Statistical Computing platform, and you will learn both basic and more advanced skills for data analysis and visualization.