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The course covers the basic analysis of quantitative data from social surveys. Topics include basic knowledge
about of quantitative research, simple descriptive statistics (central tendency and dispersion, frequency
distributions), and elementary data manipulation; other topics are normal distribution and, transformation of
variables. The Ffocus is on conceptual understanding and practical knowledge. Students will gain experience
practicing their learning through various assignments using the statistical software SPSS. Students will learn how
to (a) create datasets (e.g. from their own survey) and assess the type and quality of data and potential problems
(missing values, polarized responses, outliers, etc.) and transform variables (recoding); (b) use basic descriptive
and explorative statistical methods to answer a simple research question, assess the validity of simple hypotheses
and graphically present the results; (cd) control the basic functions in the statistical software SPSS, e.g. elementary
data transformation, descriptive statistics, and simple graphs. Final credit will be fulfilled with own simple data
analysis.
Last update: Šedivcová Karolína, Mgr. (04.06.2019)
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seminar paper - presentation of primary or secondary quantitative data analysis, mostly descriptive statistics - introducing aims, research questions, methodology, main results, interpretation test - quantitative data analysis of the first degree, working with IBM SPSS Statistics Last update: Jirkovská Blanka, PhDr., Ph.D. (13.06.2019)
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Structure of Lessons:
1. Introduction to Quantitative Data Analysis. 2. Basics of quantitative research - mass data, selection of units, measuring, hypotheses, secondary analysis. 3. Validity and reliability, creation of on-line questionnaire. 4. Introduction to SPSS, basic functions. 5. Basic rules to forming questionnaires, types of questions, their coding in SPSS. 6. Working with mass data before analysis. 7. Basics of one-dimensional analysis. 8. Normal distribution. 9. Standardized normal distribution. 10. Statistical inference a testing of hypotheses. 11. Transformation of variables I (recode). 12. Transformation of variables II. 13. Conclusion. Required reading:
Last update: Šedivcová Karolína, Mgr. (04.06.2019)
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