This course introduces students to techniques of data analysis and statistical inference based on the General Linear Model (GLM). The major topics covered in this class include simple/multiple regression, one- and two-way Analysis of Variance (ANOVA) followed by multiple comparisons, Analysis of Covariance (ANCOVA), and Repeated Measures ANOVA. This course aims to provide a solid conceptual background of these topics, as well as the analytic skills for conducting educational and psychological research in practice. Knowledge of basic algebra and SPSS is required, as is an understanding of the fundamental principles of descriptive statistics and hypothesis testing. Knowledge of calculus is not required. Students will conduct statistical analyses using real datasets.

This course will provide: (1) a conceptually-oriented introduction to regression methods and (2) opportunities to learn related data-analytic techniques.

This course introduces the use of statistical software packages R for acquiring, managing, and preparing datasets that are required to produce reliable and valid statistical inferences. With a special focus on R, the course will cover a broad range of hands-on activities in the data analytic process including data coding, file manipulation tasks, data screening, statistical analysis, and also provide a brief introduction to R programming.

Review of emerging quantitative methodological advances relevant to educational research for which formal course title and syllabus have not been developed and formalized in the UM Bulletin. Allow for experimental instructional formats. See Course Notes for specific topic.