Training on Statistical Data Analysis Using SPSS

Venue: Strathmore University
Duration: 4 days

About the course:

This course provides introduction to applied statistics and SPSS for analyzing quantitative data. Participants who have an interest in learning and picking up quantitative data analysis methods and concepts will find this course useful and applicable. Practical data analysis sessions will be based on publicly available data from Kenya National Bureau of Statistics.

Why the Course

Evidence based programs within National and County government circles, Non-profit making organizations, corporate organizations requires concrete knowledge in data analysis and results interpretation. It is therefore imperative that technocrats, programmers and policy makers are adequately equipped with relevant data analysis skills. This course is tailored to introduce statistical knowledge and prepare participants for data analysis tasks.


This course is designed with beginners in mind and assumes no prior knowledge of statistics or quantitative research background. Participants are expected to come with their own laptops.

Course Outline

Each session will include a lecture followed by a practicals and participants will get a chance to see how data analysis are being applied in real-life scenario.

  • Introduction to Statistics: Types of statistics, types of variables, summarizing and presenting data.
  • Introduction to SPSS: Data entry, data manipulation
  • Statistical Inferences: Hypothesis testing
  • Test statistics: parametric and non-parametric tests
  • Correlation and regression: Simple and multiple linear regression, logistic regression


The course will;

  • Introduce participants to SPSS as a data analysis tool.
  • Introduce survey data as a key quantitative resource for social science research;
  • Introduce basic statistics for exploratory data analysis including methods for describing and summarizing variable distributions;
  • Provide essential skills of data manipulation including selecting sub-sets and recoding;
  • Introduce the visual representation of variables in scatter graphs, bar charts and histograms.
  • Examine relationships between variables.

For any enquiries, kindly email Linda –