Courses
| Course title: | Introduction to Statistic in Educational Research |
|---|---|
| Faculty: | Faculty of Education |
| Department: | Department of Preprimary and Primary Education |
| Course code: | KPA / E-ISE |
| Credits: | 6 |
| Semester: | Winter / Summer |
| Level of study: | Mgr. |
| Format of study: | Seminar 2 [Hours/Week] |
| Name of the lecturer: | Mgr. Ondřej Šimik, PhD. (G) |
| Language: | English |
| ISCED F broad: | Education |
| Annotation: | This course introduces students to the fundamental concepts and methods of statistical analysis used in educational research. It covers both descriptive and inferential statistics, focusing on how to collect, analyze, interpret, and present quantitative data in the context of educational research. The course also addresses the application of statistical software (PAST) and explores both univariate and multivariate analysis, hypothesis testing, and regression modeling. By the end of the course, students will be equipped with the skills to conduct independent statistical analysis and interpret results relevant to educational studies. The goal of this course is to present statistics in the context of educational research as a practical and useful tool, not only for the research component of student theses but also for future teaching practice, such as action research. You will learn how to work with various types of data, organize and visualize them, and most importantly, how to apply statistical methods correctly for their evaluation. By using the free PAST software, you will be able to perform a variety of statistical tests to assess differences between groups, analyze relationships between variables, and interpret results in real-world educational contexts, all without the need for complex formulas. The statistical software will "do the work" for you, while you will learn which statistical method to use depending on the research question and how to interpret the results clearly. The course is divided into twelve topics, progressing from basic statistical concepts to more advanced techniques, such as multifactorial analyses and regression models. Each topic includes practical examples and step-by-step guides that demonstrate how to conduct specific tests using the PAST statistical software and how to interpret the outputs so they are clear and applicable to educational research. Additionally, each topic contains practical exercises (assignments) with complete solutions, allowing you to thoroughly practice and reinforce what you have learned. |
