SPSS

SPSS

Course Duration: 60 hours

SPSS Statistics is a software package used for statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions (2014) are officially named IBM SPSS Statistics.

Basic 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, variance.
  • Graphs and charts: bar chart, pie chart, histogram, scatter plot.Which of them should be used in different situations?
  • Hypothesis analysis with SPSS. Testing dependence/independence, Pearsons chi-square. Levels of significance.

Charts And Tables

  • Simple charts: bar, pie, histogram
  • Compund charts, chart builder
  • Chart editing, 3D effects, color and fill
  • Addig special effect to charts, jittering, pletora of graphs
  • Saving and manipulating tables

Explanatory Models

  • Using SPSS for Analysis of Variance (ANOVA)
  • Twofold ANOVA, interaction.
  • Linear regression analysis. When should we accept a regression line?
  • Two variable regressions.

Factor Analysis with SPSS

  • 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.
  • When the factors explain more than 100%. A common pitfall.

SPSS in Practise

  • Converting different types of files into ".sav" files.
  • How can one enter raw data into SPSS efficiently, how to label the data.
  • Transforming the data.
  • Multiple independence analysis - a useful way to circumvent "significance level problems".
  • Elementary principal component analysis.

Principle component analysis with SPSS

  • Definition and meaning of the principal component.
  • Communalities, extraction, variance.
  • Usability of the method (the cases of scale and ordinal measures).
  • Information content and the distribution of the principal component.
  • Omission of variables with insufficient communalities.