In classical statistics, linear regression is regarded as the ‘gateway to predictive modelling’. For decades students have been taught about regression from theory to practice simply because it still one of the most versatile and simple ways to understand and predict the effect of key factors on critical outcomes.
Core statistical techniques in SPSS
This video series provides a guide to some of the most commonly used statistical and analytical procedures, showing how to execute them correctly in SPSS Statistics.
This form of analysis enables analysts to identify interaction effects that alter the relationship between a dependent and independent variable. For example, the relationship between salary and employment tenure might be different for men and women. In such a situation, employee gender could be specified as a moderator variable and researchers could test to see if it did indeed change the relationship.
This is an analytical approach used to test if a third factor could represent the underlying cause of a relationship between an independent and dependent variable. For example, the relationship between wealth and educational success might be explained by the amount spent on private tuition. In such a situation, educational expenditure could act as a mediator variable and researchers could test to see if it did indeed explain the relationship.
This short video shows how you can perform a simple affinity analysis using IBM SPSS Modeler. Affinity analysis can be used to understand interconnected relationships between key factors. For example, in retail it can be used to perform basket analysis, whereby retailers can identify which products are most commonly purchased together by customers in a single transaction or over a given period time.