Course Outline

Module 1: Introduction to Education Statistics

• Overview of statistical concepts and their relevance in education.
• Types of data in education: Quantitative vs. Qualitative.
• Descriptive statistics: Measures of central tendency and variability.
• Introduction to statistical software used in education.
• Case Study: Analyzing Student Performance Data in a School District.

Module 2: Data Collection and Sampling Techniques

• Methods of data collection in educational research.
• Designing surveys and questionnaires for educational studies.
• Sampling methods and their implications in education.
• Dealing with missing data and data quality issues.
• Case Study: Designing a Survey to Assess Student Engagement in a University.

Module 3: Inferential Statistics in Education

• Hypothesis testing and confidence intervals.
• Comparing means using t-tests and ANOVA.
• Correlation and regression analysis in educational research.
• Practical application of inferential statistics using educational datasets.
• Case Study: Examining the Relationship between Teacher Quality and Student Achievement.

Module 4: Advanced Statistical Techniques for Educational Research

• Multivariate analysis: Principal Component Analysis (PCA) and Factor Analysis.
• Structural Equation Modeling (SEM) in education.
• Longitudinal data analysis in educational settings.
• Applying advanced techniques to complex educational data.
• Case Study: Analyzing the Impact of Socioeconomic Status on Educational Attainment Over Time.

Module 5: Reporting and Visualizing Educational Data

• Best practices for presenting statistical findings.
• Creating effective visualizations for educational data.
• Writing comprehensive reports for educational stakeholders.
• Communicating statistical results to non-technical audiences.
• Case Study: Developing a Report on the Effectiveness of a New Curriculum Program.