Topic 2: Fundamental Statistics. Introductory course designed for students in the fields of education and the social, behavioral, and health sciences who expect to take a second course in statistics. Subjects include descriptive and inferential statistics; sampling distributions; hypothesis testing; correlation; linear prediction; tests of mean differences; tests of frequencies and proportions; and one-way analysis of variance. Educational Psychology 380C (Topic 2) and 180E (Topic 1), 280E (Topic 1), 380E (Topic 1) may not both be counted.

Topic 4: Correlation and Regression Methods. Examines tests of association; multiple regression, including multiple predictors of a single interval-scaled outcome; and related subjects such as regression models for mediation and moderation. Educational Psychology 380C, 480C (Topic 4) and Educational Psychology 182K, 282K, 382K, 482K (Topic 2) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 2), and EDP 380C (Topic 6) or EDP 480C (Topic 6), or consent of instructor.

Topic 6: Statistical Analysis for Experimental Data. Subjects include hypothesis tests and data analysis procedures for a variety of experimental designs, including one-way analysis of variance; factorial analysis of variance; analysis of covariance; and repeated measures designs. Instruction in the use of statistical software is provided. Educational Psychology 380C (Topic 6) and 182K (Topic 1: Experimental Design and Statistical Inference), 282K (Topic 1: Experimental Design and Statistical Inference), 382K (Topic 1: Experimental Design and Statistical Inference), or 482K (Topic 1: Experimental Design and Statistical Inference) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 2), (or 180E (Topic 1), 280E (Topic 1), 380E (Topic 1), or consent of instructor.

Topic 8: Data Analysis Using SAS. Creating and modifying Statistical Analysis Systems (SAS) data sets using Data Step programming; managing a system of SAS data sets; and invoking SAS procedures using the PROC Step. Educational Psychology 380C (Topic 8) and 184 (Topic 6), 284 (Topic 6), or 384 (Topic 6), may not both be counted. Additional prerequisite: Educational Psychology 380C Topic 6 (Statistical Analysis for Experimental Data), (or 182K (Topic 1: Experimental Design and Statistical Inference), 282K (Topic 1: Experimental Design and Statistical Inference), 382K (Topic 1: Experimental Design and Statistical Inference), 482K (Topic 1: Experimental Design and Statistical Inference)), or consent of instructor.

Topic 10: Factor Analysis. Exploratory and confirmatory factor analysis models; elementary matrix algebra; basic formulae for common factor analysis and principal components analysis; factor extraction methods; rotation models; criteria for analytical orthogonal and oblique rotation; interpretation of factors; calculation of factor scores; use of computer programs; and tests of model fit. Educational Psychology 380C (Topic 10) and 182K (Topic 3), 282K (Topic 3), 382K (Topic 3), 482K (Topic 3) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 4) (or 182K (Topic 2), 282K (Topic 2), 382K (Topic 2), 482K (Topic 2)) and 380D (Topic 4) (or 180P (Topic 2), 280P (Topic 2), 380P (Topic 2), 480P (Topic 2)), or consent of instructor.

Topic 12: Survey of Multivariate Methods. Examine fundamentals of vector and matrix algebra; multivariate analysis of variance; principal components analysis and exploratory factor analysis; discriminant analysis; logistic regression; multivariate mixed modeling; cluster analysis; multidimensional scaling; and missing data. Educational Psychology 380C (Topic 12) and Educational Psychology 182K (Topic 4), 282K (Topic 4), 382K (Topic 4), 482K (Topic 4) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 4) (or 182K (Topic 2), 282K (Topic 2), 382K (Topic 2), 482K (Topic 2)) and Educational Psychology 380C (Topic 6) (or 182K (Topic 1: Experimental Design and Statistical Inference), 282K (Topic 1: Experimental Design and Statistical Inference), 382K (Topic 1: Experimental Design and Statistical Inference), 482K (Topic 1: Experimental Design and Statistical Inference)), or consent of instructor.

Topic 14: Structural Equation Modeling. Provides the basic theoretical background necessary for the application of structural equation modeling to research problems including model specification, identification, path analysis, estimation, testing fit, respecification, confirmatory factor analysis, and issues concerning the interpretation of structural equation models. Educational Psychology 380C (Topic 14) and 182K (Topic 6), 282K (Topic 6), 382K (Topic 6), 482K (Topic 6) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 12), (or 182K (Topic 4), 282K (Topic 4), 382K (Topic 4), 482K (Topic 4)), or consent of instructor.

Topic 16: Hierarchical Linear Modeling. Introduction to the basic concepts and applications of hierarchical linear (i.e. multilevel) models. Learn strategies for analyzing various nested data structures, such as students within schools, and measurement occasions within subjects. Determine when and how to use hierarchical linear models, with an emphasis placed both on the statistical concepts of the modeling framework and the application of hierarchical linear modeling to data sets via software. Educational Psychology 380C (Topic 16) and 184 (Topic 16), 284 (Topic 16), 384 (Topic 16) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 2), 380C (Topic 6) or 480C (Topic 6), and 380C (Topic 4) or 480C (Topic 4).

Topic 18: Applied Bayesian Analysis. Examine practical introduction to Bayesian statistical inference with an emphasis on applications in behavioral and measurement research. Explore conceptual differences between Bayesian and classical statistical inference and the differences between these approaches in the context of various statistical procedures. Use Bayesian software for analysis. Educational Psychology 380C (Topic 18) and 182K (Topic 10), 282K (Topic 10), 382K (Topic 10), 482K (Topic 10) may not both be counted. Additional prerequisite: Educational Psychology 380C (Topic 4) or 382K (Topic 2), or 482K (Topic 2), or consent of instructor.

Topic 20: Data Analysis and Simulation in R. Introduces the R language and statistical computing environment, with a focus on using R for real-life empirical data analysis projects and simulation studies. Subjects include reading in, cleaning, merging, and re-shaping data; creating information-dense graphics; running and using the results of regressions and other statistical models; reproducible data analysis; the logic and design of statistical simulation. Additional prerequisite: Educational Psychology 380C (Topic 6)(or 182K (Topic 1: Experimental Design and Statistical Inference), 282K (Topic 1: Experimental Design and Statistical Inference), 382K (Topic 1: Experimental Design and Statistical Inference), 482K (Topic 1: Experimental Design and Statistical Inference), or consent of instructor.

Topic 22: Analysis of Categorical Data. Introduces basic concepts, common statistical models, and analysis methods for categorical data. Provides theory and examples of applications using categorical techniques. Subjects include inference methods for contingency tables, generalized linear models for binary and count data, logistic regression models, multicategory logit models, loglinear models for contingency tables, and models for matched pairs. Educational Psychology 380C, 480C (Topic 22) and 182K, 282K, 382K, 482K (Topic 5) may not both be counted. Additional prerequisite: Educational Psychology 380C, 480C (Topic 4) and 380C, 480C (Topic 6); or consent of instructor.

Topic 23: Missing Data Analysis. Analyze data with missing values. Examine missing data theory, estimating statistical models with missing data, and other practical issues related to missing data, including the use of statistical software for handling missing data. Additional prerequisite: Educational Psychology 380C (Topic 4) and 380C (Topic 12).

Topic 26: Data Exploration and Visualization in R. Explore R, an open-source statistical programming platform for data analysis and visualization. Examine the foundational facilities and skills in R programming and how to use the tools to accomplish real-world, reproducible data analysis and visualization. Only one of the following may be counted: Educational Psychology 380C (Topic: Data Analysis/Sim in R), 380C (Topic 20), 380C (Topic 26). Additional prerequisite: Educational Psychology 380C (Topic 2) or consent of instructor.

Topic 28: Statistical Modeling and Simulation in R. Building on the foundational skills in R, examine how to design and program Monte Carlo simulation studies, which are a tool for evaluating the performance of statistical estimation and inference procedures. Explore translating mathematical equations to code and implementing statistical algorithms, building R packages, parallelizing, and integrating high-performance functions into R. Additional prerequisite: Educational Psychology 380C (Topic 12); and 380C (Topic 26) or 380C (Topic 20).

Topic 30: Applied Research Design in Program Evaluation. Discuss in depth the quantitative, qualitative, and mixed methods analyses used to conduct program evaluations. Practice communicating complex evaluative ideas, designs, and results to program leaders and participants. Develop a large scale evaluation proposal that focuses on the research design methods discussed. Additional prerequisite: Educational Psychology 380D (Topic 6).