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Last update: PhDr. Jiří Šafr, Ph.D. (09.02.2024)
This course is the first step for sociology students into statistics and quantitative data analysis. It pursues two main objectives. The first is to build and strengthen the foundation of data or statistical literacy in students so that they can be critical users of statistical outputs in the media and eventually in academic papers. The second goal is to familiarize them with the process of quantitative data analysis and to teach them how to practically use simple statistical methods of exploration and description. The basics of inferential statistics will also be introduced. The course graduate should be able to use basic descriptive statistical methods to answer simple research questions (classification of level 1 and 2 data) with a focus on categorical data (tables), test the validity of a simple hypothesis (confidence intervals and basic statistical tests of bivariate analysis, e.g. goodness-of-fit test), present results graphically and interpret them substantively. In this way, learners should embark on the path to becoming proficient producers of quantitative sociological knowledge themselves. One repeated enrollment is possible. [end machine translation] |
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Last update: PhDr. Jiří Šafr, Ph.D. (09.02.2024)
The aim of the course is to introduce the basics of statistics and its application in the analysis of quantitative sociological data, and to learn how to use simple statistical methods of exploration and description. |
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Last update: PhDr. Jiří Šafr, Ph.D. (09.02.2024)
The course is completed with an exam. The grade is awarded on the basis of the points gained: 1) for a seminar paper in which the student demonstrates independent analytical work, 2) for the result in the final test, which verifies the basic knowledge and competences acquired in the course. In order to acquire practical skills for the preparation of the seminar paper (analysis in the SPSS statistical program), it is necessary to simultaneously take the course Sociological Data Processing - ASG100116 or its equivalent. If the processing of the submitted seminar paper does not match the quality, the student will have the opportunity to correct or rework and eliminate the shortcomings. The condition of registration for the exam (passing the test) is the submission of the seminar paper no later than 7 days before the exam. [END MACHINE TRANSLATION] |
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Last update: PhDr. Jiří Šafr, Ph.D. (09.02.2024)
Babbie, E. 1995. The Practice of Social Research. Wadsworth Publishing; 7 edition. (chapters: Elementary Analyses. Pp. 375-394, The Elaboration Model. Pp. 395-412) Blalock, H. M. 1960. Social Statistics. New York, Toronto, London: McGraw-Hill Book Company, Inc. (chapters 1-8) Gelman, Andrew, and Jeronimo Cortina, eds. 2009. A Quantitative Tour of the Social Sciences. 1st edition. Cambridge University Press. Miller, J. E. 2004. The Chicago Guide to Writing about Numbers. The University of Chicago Press. (selected chapters) Treiman, D. J. Quantitative Data Analysis: Doing Social Research to Test Ideas. San Francisco: Jossey-Bass/Wiley, 2009. (chapters 1 a 2) de Vaus, D. A. 2002. Surveys in Social Research. Fifth Edition. London: George Allen & Unwin (Publishers) Ltd. (chapters 10-16)
presentations and scanned literature STAT1 on onedrive (access will be granted at the first lecture): |
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Last update: Mgr. Jaromír Mazák, Ph.D. (17.02.2022)
[MACHINE TRANSLATION] Implementation of the course in case of Distance teaching The course will be conducted online according to the schedule published on the stands of the Department of Sociology [END MACHINE TRANSLATION] |
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Last update: PhDr. Jiří Šafr, Ph.D. (03.02.2023)
[MACHINE TRANSLATION] 1 Introduction and motivation 2 Basic concepts of descriptive and exploratory analysis 3 Levels of measurement 4 Data distribution, measures of centrality, variability 5 Categorical data, frequency distribution 6 Classification of statistical units and description of relationships between variables 7 Graphical representation of data distribution and empirical relationships 8 Probability, conditional probability 9 Basics of inferential reasoning (population selection, point and interval estimation, goodness-of-fit tests) 10 Data transformation and standardisation 11 Direct data standardisation, contingency table weighting and interpretation 12 How to unravel relationships between traits and interpret results: interaction (moderation) and logic of elaboration [END MACHINE TRANSLATION] |