Courses | MS in Data Analytics and Program Evaluation | UOnline

An overview of the history, nature, characteristics, strategies, and ethics of qualitative research methods. Critical analysis and evaluation of various types of qualitative studies, including design, sampling, processes of data collection and analysis, and reporting results.

Focus on standards and practical strategies for designing different types of survey instruments and conducting survey research. Students are required to develop a proposal for survey research, develop a survey instrument, and conduct a small pilot study by collecting, analyzing, and reporting survey data.

Terminology, models, standards, practices, and common problems associated with program evaluation in Educational and Social Service settings.

Review of emerging quantitative methodological advances relevant to educational research. The course will be taken twice, one of the course topics will cover text and sentiment analysis, and the second course topic will be an introduction to machine learning using Python.

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 the statistical software package 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, and statistical analysis, and also provide a brief introduction to R programming.

This course will be taken twice to complete the capstone project. The first course will cover data collection, the second course will cover data analysis and presentation.