Introduction to SPSS
The course is aimed to cover some of the most
frequent applications of SPSS, such as displaying statistical data
graphically, principal component analysis, factor analysis,hypothesis
analysis, analysis of variance, linear and multilinear regressions
In the end of this course students will be able
to handle statistical data analysis on their own. During the course
both pedagogically prepared "artificial" data and data
from real surveys are used. No advanced prior knowledge on statistics
is required, all the needed concepts will be explained.
SPSS is regarded to be the most widely used statistical
software in social sciences,and it has become a common tool also
in other sciences (economics, biology etc.).
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Basics of SPSS. Descriptive statistics, charts
and graphs, hypothesis analysis, testing dependence/independence.
- Different levels of measure: scale, ordinal, nominal.
- Basic descriptive statistics. Measures of central tendency:
mean, median, mode. Measures of dispersion: range, standard deviation,
- Graphs and charts: bar chart, pie chart, histogram, scatter
plot.Which of them should be used in different situations?
SPSS in practice, some useful tips. The "tricks" of dependence/independence
- Converting different types of files into ".sav" files.
How can one enter raw data into SPSS efficiently, how to label
- Transforming the data.
- Hypothesis analysis with SPSS. Testing dependence/independence,
Pearsons chi-square. Levels of significance.
- Multiple independence analysis - a useful way to circumvent
"significance level problems".
Principal Component Analysis with SPSS.
- Definition and meaning of the principal component.
- Elementary principal component analysis.
- Communalities, extraction, variance.
- Usability of the method (the cases of scale and ordinal measures).
- Information content and the distribution of the principal component.
Charts, Tables, presenting your findings.
- Different type of charts: Bar, pie, histogram.
- Representing mulit-dimensional data.
- Panels, controlling variables.
- The Chart Editor.
- Legacy methods, jittering, population pyramid.
Factor Analysis with SPSS using a "real example".
- The factor matrix and its interpretation.
- The Maximum Likelihood method. Reparing the model.
- Factor rotation and the varimax method.
- Omission of variables belonging to more than one factor, the
appearence of latent variables.
- Establishing factor scores. Statistics of factor scores.Scree
plot, KMO and Bartlett's test in SPSS.
- When the factors explain more than 100%. A common pitfall.
Explanatory models. Analysis of Variance, Regression Analysis.
- Using SPSS for Analysis of Variance (ANOVA)
- Twofold ANOWA, interaction.
- Linear regression analysis. When should we accept a regression
- Two variable regressions.
Useful online resources