Mastering knowledge of statistical analysis of primary and secondary data. Building the ability to recognise a research problem, correctly select statistical methodology, independently calculate and interpret statistical indicators and parameters using software support (IBM SPSS Statistics). Develop a statistical and analytical way of thinking.
1. Data familiarisation. Orientation in SPSS. Data structure. Basics of statistical research. Research problem. File construction. Descriptive statistics using software support. Standardised variable. Normal distribution. Graphical representation and editing of data. Data adjustment.
Inferential statistics. Sampling method. Concept of a sample with selected sample designs. Sample selection. Sample size. G*power. Sampling distributions. Parameter estimator (concept, selected theoretical properties). Parameter estimation and hypothesis testing. Estimation by number and interval of the arithmetic mean, total, and proportion of a single population. Hypothesis testing. Type I and Type II errors. Power of the test. p-value. Parametric statistical procedures. Testing the hypothesis about the assumed arithmetic mean and proportion of the population. Testing the hypothesis about the difference in arithmetic means and proportions of two populations. ANOVA hypothesis testing about the means of three or more populations. Non-parametric statistical methods. Tests on the shape of distributions. Chi-square test.
After completing the course, the student will be able to:
1. Differentiate between types of variables and their levels of measurement in the analysis of a researched phenomenon
2. Analyse statistical indicators and parameters of descriptive statistical methods
3. Describe the fundamental concepts of inferential statistics
4. Use appropriate statistical methodology in testing posed hypotheses
5. Formulate conclusions about the analyzed phenomenon based on the conducted statistical methods
6. Apply statistical analytical computer software (IBM SPSS Statistics)
