SPSS for Beginners (Statistical Package for Social Sciences)

The SPSS for Beginners (Statistical Package for Social Sciences) short course provides you with fundamental training in the use of the the Statistical Package for Social Sciences (SPSS) software widely used in statistical data analysis.

During the course, you will learn basic functionalities of the SPSS software and progress to a number of higher-level techniques that can be specifically used in the social sciences environment.

The course is presented through a combination of presentations and hands-on sessions to help you perform data entry and analysis to create accurate tables and graphs that can be used in the classroom.

Learning Outcomes

The course is designed to fulfill the following objectives:

•     To introduce the basic functionalities of SPSS To coach delegates in using the data editor
•     To coach delegates in modifying data files
•     To coach delegates in data preparation
•     To coach delegates in firstly choosing the correct statistical tests and secondly in running statistical analyses
•     To coach delegates in constructing tables and graphs
•     To coach delegates in interpreting the data and graphs
•     To discuss more advance topics such as nonparametric techniques

After attending the course the delegates will be able to make a decision on which statistical test to use, how these techniques are applied, how to correctly enter the data and execute the tests in SPSS and, finally, how to interpret the findings.

Course Content

Day 1: Questionnaire design:

•     Theoretical aspects of measurement
•     Deciding on a conceptual framework
•     Constructing an assessment framework
•     Item construction

Day 2:

Using the data editor

•     Entering numeric data
•     Entering string data
•     Defining data.

Modifying data files

•     Deleting and inserting a case
•     Deleting and inserting a variable
•     Sorting and splitting data files
•     Selecting cases
•     Computing new variables.

Data preparation

•     Data errors
•     Data transformation.

Running analysis

•     Descriptive statistics
•     Assessing normality
•     Interpreting output generated by assessing normality
•     Interpreting graphs.

Using graphs

•     Histograms
•     Bar graphs
•     Scatterplots Boxplots
•     Line graphs.

Cross tabulations

•     Introduction to Cross tabulations.

Choosing the right procedure

•     Different statistical techniques
•     Questions that need to be addressed

Differences between groups

•     Independent sample t-test
•     Paired sample t-test
•     ANOVA: One-ways analysis of variance

Other analyses

•     Correlation analysis
•     Reliability analysis

Nonparametric methods

•     Introduction
•     Chi-square analysis Mann-Whitney U test / Wilcoxon rank-sum test Wilcoxon matched-pairs signed-rank test
•     Kruskal-Wallis analysis of variance by ranks Spearman rank correlation coefficient