Statistical Data Analysis with SPSS 2400-ZEWW398
1. SPSS environment, basic objects and data structures, including: data files, commands and reports. Example of statistical analysis.
2. Scales of measurement. Data preparation: rules for coding qualitative variables, creating data description, various data formats.
3. Data file management: selection of observations for analysis, sampling, sorting of observations, weights for observations, data analysis in subgroups.
4. Transformations of quantitative and qualitative variables, recoding, ranks, missing values analysis and outliers.
5. Working with many data sets: methods of combining data sets, data aggregation.
6. Statistical data analysis: analysis of one statistical characteristic (descriptive statistics, data mining, graphs, M-estimators), analysis of interdependencies of two statistical variables (contingency tables, correlation analysis, measure of relationship strength).
7. Parametric statistical hypotheses (Student's t-tests for independent and related samples, one-way analysis of variance - ANOVA).
8. Nonparametric hypotheses for one and many independent or paired variables (chi-square tests, randomness tests, independence tests).
9. Linear regression model, methods of selecting explanatory variables to the model, model quality measures, analysis of the random component, co-linearity of variables.
10. Time series analysis: time series graphs, trend models, autocorrelation and cross-correlation study, time series decomposition, time series smoothing, introduction to ARIMA methods.
11. Working with variables containing many answers: defining variable sets, using multi-answer variables in the survey analysis.
12. Creating presentation tables and reports: text objects, tabular and graphical objects, pivot tables, report management.
13. Review of multidimensional data analysis methods (introduction): factor analysis, discrimination analysis, cluster analysis, correspondence analysis.
14. Programming in SPSS. Examples of creating macro commands.
Type of course
Prerequisites (description)
Learning outcomes
It is expected to acquire the ability to perform statistical analysis in the field of:
- preparation of the source data set for statistical calculations,
- applying appropriate statistical methods and procedures,
- presenting the results of statistical analysis in a form understandable to the recipient,
- interpretation of the results obtained.
Completing the course increases the competences of the participants of the classes on the labor market, giving them theoretical and practical foundations in the field of statistical data analysis.
SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04
Assessment criteria
In addition to class time, participants prepare their own research projects including statistical analysis of data using SPSS: problem formulation with research hypotheses, data description (data source, characteristics of studied units and variables), task solution method in SPSS (applying appropriate procedures and tests with reports including tables and result charts), interpretation of the results obtained, conclusions. It is advisable to present the report during the class. The projects are evaluated in terms of originality, correctness of the applied methods and interpretation of results, as well as the quality and aesthetics of the description. It is required to attend classes and actively participate in them.
Bibliography
1. Jarosław Górniak, Janusz Wachnicki, Pierwsze kroki w analizie danych SPSS for Windows, SPSS Polska, Kraków 2004 i nast.
2. Anna Malarska, Statystyczna analiza danych wspomagana programem SPSS, SPSS Polska, Kraków 2005.
3. Alan Bryman, Duncan Cramer, Quantitative Data Analysis with SPSS 12 and 13. A guide for social scientists, Routledge 2005.
4. Marija J. Norusis, SPSS for Windows. Base System. User's Guide, SPSS Inc. 1993 i nast.
5. Maria Nawojczyk, Przewodnik po statystyce dla socjologów, SPSS Polska, Kraków 2002.
6. Mark Rodeghier, Survey with Confidence. A Practical Guide to Survey Research Using SPSS, SPSS Inc. 1996.
7. Tomasz Żądło, Janusz Wywiał, Prognozowanie szeregów czasowych za pomocą pakietu SPSS, SPSS Polska, Kraków 2008.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: