SPSS Data Analysis provides a practical pathway for developing statistical analysis skills using one of the most widely recognised software environments for academic, social science, healthcare and business research. This course guides learners from essential data preparation tasks to more advanced statistical procedures, helping them understand how to organise data, select appropriate methods and interpret analytical results.

The course begins with the fundamentals of working in SPSS, including defining variables, entering data manually and importing information from external databases. Learners then explore essential data-handling techniques such as recoding, transforming, computing and reversing variables. You will also learn how to generate descriptive statistics, select cases and split datasets for more focused analysis.

As your skills develop, the course introduces comparative statistical methods commonly used to examine differences between groups and measurements. You will explore independent and repeated measures t-tests, one-way ANOVA and repeated measures ANOVA. ROC curve analysis is also introduced, providing insight into evaluating diagnostic or classification accuracy in appropriate research contexts.

The programme then progresses to relationships and prediction. Learners will examine Pearson correlation before working with simple, multiple, hierarchical and stepwise linear regression techniques. Logistic regression is also covered, expanding your ability to investigate outcomes that require a different modelling approach.

More advanced sections explore multifactorial analysis, including independent factorial ANOVA, ANCOVA and mixed factorial ANOVA. These techniques can support the analysis of more complex research designs involving multiple factors, covariates and combinations of independent and repeated measures.

By completing the course, learners can develop a more systematic approach to statistical analysis and greater confidence when working with research data. The skills gained can support academic projects, dissertations, workplace research and analytical tasks, although the appropriate statistical method should always be selected according to the research question, study design and characteristics of the data.