FAVz092 Categorical data analysis (from the elementary procedures to correspondence analysis)

Faculty of Arts
Autumn 2021
Extent and Intensity
0/2/0. 8 credit(s). Type of Completion: zk (examination).
Teacher(s)
PhDr. Petr Soukup, Ph.D. (seminar tutor)
Ewa Ciszewska (assistant)
doc. Mgr. Pavel Skopal, Ph.D. (assistant)
Mgr. Michal Večeřa, Ph.D. (assistant)
Guaranteed by
doc. Mgr. Pavel Skopal, Ph.D.
Department of Film Studies and Audiovisual Culture – Faculty of Arts
Supplier department: Department of Film Studies and Audiovisual Culture – Faculty of Arts
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 10 student(s).
Current registration and enrolment status: enrolled: 1/10, only registered: 0/10
fields of study / plans the course is directly associated with
there are 14 fields of study the course is directly associated with, display
Syllabus
  • 1. Introduction to SPSS environment (SPSS windows&menus).
  • 2. Introduction to statistics - types of variables, elementary description of data
  • 3. Data matrix preparation in SPSS. Rows (cases) and columns (variables). Data view and Variable view. Properties of individual variables.
  • 4. Data cleaning. Data transformations – recode, autorecode, compute and count.
  • 5. Description of categorical data – charts, frequency tables, custom tables and descriptive statistics.
  • 6. Contingency table as descriptive tool for categorical data. Observed counts, row, column and total percentages. 2x2 table – odd and odds ratio. General intro to statistical testing. Case of two related categorical variables.
  • 7. Detailed analysis of contingency table, chi-square test, contingency coefficients, sign scheme.
  • 8. Intro to advanced techniques for categorical data_ logistic regression, loglinear models and correspondence analysis. Correspondence analysis as an alternative to analysis of contingency tables. Pros and cons of correspondence analysis. Examples of usage. Intro to correspondence analysis in SPSS.
  • 9. Assumptions for usage of correspodence analysis (CA). Basic logic behind CA. Row and column profiles. Number of dimensions. Output tables and charts in CA. Different types of normalization in CA. Inertia explained by individual categories and dimensions. Interpretation of dimensions.
  • 10. Complex example of CA. Permutation of categories. Supplemental categories. Special data inputs for CA: contingency table as input. More than 2 variables as input for basic CA.
  • 11. Multiple CA: correspondence analysis for 3 and more variables. Outputs for multiple CA.
Literature
  • Doey L., J. Kurta. 2011. Correspondence Analysis applied to psychological research. Tutorials in Quantitative Methods for Psychology Vol. 7(1), p. 5-14.
  • Meulman, J. J., W. J. Heiser. SPSS Categories® 11.0. SPSS Inc. 2001, ch. 5, 11
  • Greenacre, M. J. 2007. Correspondence analysis in practice (2nd ed.). Chapman&Hall, ch. 1-19
  • AGRESTI, Alan. An introduction to categorical data analysis. 2nd ed. Hoboken, NJ: Wiley-Interscience, 2007, xvii, 372. ISBN 9780471226185. info
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught: every week.
  • Enrolment Statistics (recent)
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