Sylabus
1. Qualitative and quantitative educational research - their characteristics and comparison and the most common types.
2. Main steps of research process (identifying a research problem, reviewing the literature, selecting participants, collecting data, analyzing and interpreting data, reporting and evaluating research).
3. Characteristics of a good research topic.
4. Questionnaire - construction, examples of questionnaires used in educational research.
5. Observation - example of structured observation a its use in educational research.
6. Types of attitude scales - Likert, semantic differential and rating scales.
7. International comparative studies in education - organizations conducting these studies, studies TIMSS and PISA and comparison of their goals, testing population and test items, results of Czech students in reading, scientific and mathematical literacy.
8. Descriptive statistics - frequencies, measures of central tendency and variability, correlation coefficient, graphing data.
9. Inferential statistics - population, random sample, normal distribution, hypotheses testing, error of the first and second kind, p-value. T-test, F-test, chi-square test and simple linear regression analysis.
10. Analyzing data using the described methods of descriptive and inferential statistics in free statistical softwares Gretl and R.
Anotace
The course prepares students to conducting qualitative and quantitative research in education. Students are taught to work independently with data using statistical software Gretl and R.
The content of the course is divided into two parts - educational research methods and statistical analysis of data. In the first part, we, for example, discuss characteristics of qualitative and quantitative research, main steps in the research process and selected instruments of data collection.
We also focuse on international comparative studies in education (PISA, TIMSS, CIVED etc.). In the second part, students are introduced to the basics of descriptive statistics (frequencies, measures of central tendency and variability, correlation coefficient, graphing data) and inferential statistics (hypotheses testing and linear regression) using selected examples and demonstration of data analyses in statistical software Gretl and R.
Students are also introduced how to develop and analyse achievement tests.