1. FACTOR ANALYSIS CLUSTER ANALYSIS Analyzing complex multidimensional patterns.

2. Factor analysis Investigate underlying relationships and patterns for a large number of variables. Decide if the infomation in the data can be compressed, summarized in a smaller numbers of factors. Analyze correlations between a large number of variables by defining underlying factors.

3. Factor analysis is used for Datareduction, i.e. reduce the number of variables to a smaller dimension. Explorative analysis: Generate hypotheses Generate hypotheses Confirmative analysis: Test Hypotheses Test Hypotheses

4. Base for factor analysis All variables are considered independent. The assumption is that it is possible to explain the correlation between variables in the data in terms of underlying factors. The factors are latent variables, hypthetical constructions i.e. intelligens, ability, anxiety etc.

5. Steps in Factor analysis A large numberof people (subjects) is measured on a set of variables. Calculate correlations between variables. The idea is to investigate if some of the variables measure the same underlying component. High correlation between two variables may indicate that they measure the same component.

6. Produce linear combinations of the variables i.e. factors. Typically, a small number of factors account for enough of the variation between the subjects. Determine hoe many factors are needed and how they should be interpreted.

7. Cluster analysis Aims to allocate a set of subjects to a set of mutually exclusive groups such that subjects within a group are similar to one another while subjects in different groups are dissimilar. Each subject belongs only to one cluster The categories are determined from the data.

8. Cluster analysis is used for Determine a classification schemes that accounts for variance among the subjects. Generating ideas regarding the structure of the population. Data reduction. Formalize the hierarcial stucture of the data i.e the hierarchial tree.