Access this on-demand session for a practical and structured introduction to factor and cluster analysis using IBM SPSS Statistics. These two techniques are widely used across research, commercial and not-for-profit sectors to simplify complex data, identify underlying patterns and build meaningful segments for analysis and decision making.

The session explains how factor analysis reduces large sets of variables into underlying themes, with examples ranging from customer satisfaction drivers to composite variables for predictive models and personality scoring. You will also learn how cluster analysis groups people, outlets or transactions based on shared characteristics, supporting applications such as consumer segmentation, retail profiling, donor grouping and anomaly detection.

Across 90 minutes, the session covers correlations, principal components analysis, interpreting rotated factor solutions, analysing component scores and comparing cluster methods. You will see how to run factor and cluster analyses in SPSS, interpret the outputs and explore the resulting groups or dimensions. A full training pack with data files and example outputs is included to support ongoing practice.

Designed for students, academics, analysts and marketers, this on-demand training provides a clear, example-led introduction for anyone who needs to apply factor or cluster analysis in SPSS Statistics.

Factor analysis is a data reduction technique used to identify underlying themes (“factors”) among a range of attributes/variables:

• Using factor analysis, an automotive company with 20 drivers of customer satisfaction can reduce them down to 3 underlying key themes/pillars: product, service and brand
• Variants of factor analysis, such as principal components analysis (PCA) can be used to reduce the number of input fields in predictive models by generating composite variables
• Factor analysis can be used to generate a score for individual people completing personality tests showing where they lie on behavioural dimensions such as introversion-extroversion

Cluster analysis is also used in a wide range of applications for the commercial and not-for-profit organisations, such as:

• Segmenting consumers according to their purchase behaviours
• Clustering similar retail outlets based on their sales patterns
• Creating charity supporter segments based on their donations
• Identifying suspicious or anomalous financial transactions

The 90 minute training session will include:

• Introducing factor analysis
• Exploring correlations
• Performing principal component analysis
• Interpreting output
• Rotated solutions
• Performing factor analysis
• Analysing component scores
• Introducing cluster analysis
• Comparing cluster methods
• Performing cluster analysis
• Interpreting output
• Exploring cluster groupings