Course objectives: Introduce students to the basic contents of descriptive and inferential statistics and enable understanding of basic statistical concepts and procedures that are most often used in social research.
The acquired knowledge and skills will enable critical reading and understanding of the scientific literature in which the results of statistical analysis are referred to, as well as the implementation of simpler statistical
processing and data analysis.
Enrolment requirements and/or entry competences required for the course: None
Learning outcomes at the level of the programme to which the course contributes:
(1) Evaluate the empirical validity of statements and conclusions in various types of texts
(2) Specify and explain the types of sources and strategies for gathering quantitative data
(3) Recognize, use, integrate, and document data from various sources
Expected learning outcomes at the level of the course (3 to 10 learning outcomes):
(1) Define the basic concepts of descriptive and inferential statistics
(2) Adapt existing databases to statistical processing in an appropriate statistical program
(3) Describe the data with appropriate statistical indicators and summarize them in tabular and graphical form
(4) Conduct simple inferential statistical data analyses
(5) Set valid statistical hypotheses
(6) Select statistical procedures appropriate to different types of data / variables and apply them in statistical analysis
(7) Interpret printouts of statistical analyses generated by common statistical software packages
(8) Draw valid conclusions based on statistical analysis, ie generalize findings from the sample to the population
(9) Evaluate the quality of papers in which the results of statistical analysis are presented
(10) Present the results of statistical analysis according to common standards in the social sciences
Course content (syllabus):
(1) Introduction to the course.
(2) The concept of statistics and its brief history. Statistics and the public: prejudices related to statistics; examples of “bad statistics”. The role of statistics in social research.
(3) The concept of measurement. Concept and types of variables. The notion of a statistical set. Concept and types of statistical series. Forming statistical series and editing statistical data.
(4) Review of statistical data processing programs. Description of IBM SPSS Statistics and JASP. Loading data in other formats; Merge files.
(5) Frequencies and percentages; tabular and graphical presentation of statistical data / series.
(6) Basic notions of descriptive statistics: notion and types of measures of central tendency, dispersion, asymmetry and form of Distribution. Calculation and use of z-values.
(7) Basic concepts of probability theory. Population and sample.
(8) Statistical estimates.
(9) Concept and types of hypotheses. Principles of testing statistical hypotheses. Errors in reasoning based on statistical tests. Assumptions for conducting statistical tests.
(10) T-test and its types.
(11) Analysis of variance and accompanying post-hoc tests.
(12) Chi-square test and other non-parametric tests.
(13) Regression and correlation analysis of quantitative variables.
(14) Pearson’s correlation coefficient, correlation coefficients for ordinal and nominal variables.
(15) Final considerations of the course.
Format of instruction: Lectures, exercises, independent assignments
Student responsibilities: The course is performed by a combination of lectures and practical exercises in the software for statistical data analysis in which the topic of the weekly lecture is elaborated. The
course is based on direct teaching methods. Student progress in the course is evaluated through individual work during the semester on exercises, through homework, two colloquium and through the final written exam.
Monitoring student work: Class attendance, preliminary exam, practical work, written exam
