Sylabus
* Structure of Lessons:
1. Repetition of basic environment of SPSS.
2. Summary of basic analysis of first order.
3. Comparison of means, T-test, ANOVA.
4. Basics of second order analysis in categorical variables - chi-square.
5. Working with Crosstabs.
6. Deeper analysis of Crosstabs.
7. Rules of sociological interpretation in categorical variables.
8. Correlation.
9. Elaboration.
10. Simple linear regression.
11. Multiple linear regression.
12. Factor analysis. * Required reading: - BRYMAN, A. Social research methods. Oxford: Oxford University Press,
2008. ISBN
0199202958. * Recommended reading: - BABBIE, E. Elementary analyses. In The Practice of social Research. 7th Edition. Belmont: Wadsworth,
1995. Pp. 375-394. ISBN 0-534-18744-7. - De VAUS, D. A. Surveys in social research. Fifth edition. London: Routledge.
2002. (chapters 10, 12 to
16) - GARSON, G. D. Quantitative Research in Public Administration (PA 765 -
766). - IBM SPSS Statistics 20 Brief Guide. [online]. IBM Corporation 1989,
2011. Available at: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Brief_Guide.pdf - IBM SPSS Statistics Base
20. [online]. IBM Corporation 1989,
2011. Available at: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Base.pdf. (chapters 2, 3, 4, 5, 6,
7) - MILLER, J. E. The Chicago guide to writing about numbers. Chicago: University of Chicago Press,
2004. (selected chapters) - STATSOFT, Inc. Electronic Statistics Textbook. Tulsa, OK: StatSoft,
2010. - TREIMAN, D. J. Quantitative data analysis: doing social research to test ideas. San Francisco: Jossey-Bass,
2009. ISBN
780470380031.
Anotace
This course follows Quantitative Data Analysis I. The course covers the basic analysis of quantitative data from social surveys. Topics include simple descriptive statistics (central tendency and dispersion, frequency distributions, cross-tabulation, association/correlation), and elementary data manipulation; other topics are the logic of elaboration, data standardization, introduction to inferential statistics, basic principles of inferential statistics, i.e. statistical testing of hypotheses and multivariate analysis. Focus is on conceptual understanding and practical knowledge. Students will gain experience practicing their learning through various assignments using the statistical software SPSS. The fFinal exam will be fulfilled with own data analysis and a test (analysis of contingency tables).
Distance education will be provided through the provision of learning materials, consultations in MS Teams and practical training of examples in SPSS.