a) Methods of quantitative sociological research 1) Science, scientific knowledge, goals of social research and factors influencing it (theory and research, induction and deduction; epistemological and ontological bases; ethical and practical consequences of social research) 2) Quantitative and qualitative research strategy (basic distinction, role and place of theory; epistemological and ontological bases and their relation to the chosen research strategy; reduction of information) 3) Measurement and quality criteria in social research (concepts, indicators, measurements; reliability, validity, replicability), survey errors 4) Research design I (causality, respectively causal relationship, conditions necessary for confirmation; experiment - basic scheme and types; social sciences and experiment) 5) Research design II (cross-sectional, longitudinal and comparative research; case studies) 6) Research questions and hypotheses (from theory to hypotheses, concepts and their measurement, literature search; formulation of research questions; operationalization; indicator) 7) Main steps of quantitative research (research project; quantitative research phase; strengths and weaknesses of quantitative research strategy) 8) Selection procedures in quantitative research 9) Questionnaire and / versus standardized interview (administration, online questionnaire; advantages, disadvantages) 10) Asking questions (types of questions in questionnaire surveys; rules for formulating questions; mistakes when creating them) 11) Standardized observation (Why observation? Unobtrusive techniques; non / participating observation; observation plan; observation record) 12) Content analysis (Two different examples of research questions for content analysis. What are they calculated and how are they coded? Advantages and disadvantages of content analysis. Note: Auxiliary texts can be used.) 13) Secondary analysis and official statistics (advantages and disadvantages; limits of secondary analysis; social science data archives; criticism of official statistics; big data) 14) What precedes, accompanies and follows data collection? (piloting or pre-research - testing of questions; codebook; field entry; team preparation; research tool administration; field entry; data recording and data preparation for analysis) b) Quantitative data analysis 1) Basic statistical concepts and types of statistical features (descriptive x inference statistics; statistical set, units, features; specific examples) 2) Basic statistical characteristics (classification, explanation of the role of individual groups of characteristics, definition of basic statistics including examples of their use) 3) Basic graphical means of expression 4) Introduction to testing statistical hypotheses (explanation of the principle, definition of basic concepts and methods of testing) 5) Testing of statistical hypotheses (distribution of tests, their assumptions and outline of use on specific examples) 6) Theoretical distributions (explanation of the principle of how they arise, normal and normalized normal distribution - transformation principle, binomial distribution) 7) Analysis of variance, incl. K-W test (principle of method, definition of factor, assumptions of use, explanation of its use on a specific task) 8) Pivot Table (Pivot Table Reading - Percentage in Pivot Table, Dependency / Context Analysis of Two Variables in Pivot Table, Example of Use) 9) The principle of generalization of the relationship of two variables in the pivot table (how, by, conditions of use, explanation of the difference between theoretical and empirical frequencies) 10) Analysis of dependency / correlation of two at least ordinal variables (coefficients showing the strength of dependence of two variables, concrete examples, correlation fields, application example) 11) Regression analysis (principles)
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