Factor analysis
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Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Factor analysis
- 2. FACTOR ANALYSIS A data reduction technique designed to represent a wide range of attributes on a smaller number of dimensions. DEFINITION “ A statistical approach that can be used to analyze interrelationship among a large number of variables and a explain these variables in terms of their common underlying dimension(factor)”
- 3. What is factor analysis ? Factor analysis is a general name denoting a class of Procedures primarily used for data reduction and summarization. Variables are not classified as either dependent or independent. Instead, the whole set of interdependent relationships among variables is examined in order to define a set of common dimensions called Factors.
- 4. Purpose of Factor Analysis To identify underlying dimensions called Factors, that explain the correlations among a set of variables. -- lifestyle statements may be used to measure the psychographic profile of consumers. To identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables for subsequent analysis such as Regression or Discriminant Analysis. -- psychographic factors may be used as independent variables to explain the difference between loyal and non loyal customers.
- 5. Types of FA Exploratory FA Summarizing data by grouping correlated variables Investigating sets of measured variables related to theoretical constructs Usually done near the onset of research
- 6. Types of FA Confirmatory FA More advanced technique When factor structure is known or at least theorized Testing generalization of factor structure to new data, etc.
- 7. Assumptions Models are usually based on linear relationships Models assume that the data collected are interval scaled Multicollinearity in the data is desirable because the objective is to identify interrelated set of variables. The data should be amenable for factor analysis. It should not be such that a variable is only correlated with itself and no correlation exists with any other variables. This is like an Identity Matrix. Factor analysis cannot be done on such data.
- 8. SAMPLE SIZE Minimum numbers of variable for FA is 5 cases per variable e.g. 20 variables should have>100 cases(1:5) IDEAL CONDITION
- 9. A few examples We can now take few examples with hypothetical data and run factor analysis using SPSS package.
- 10. How to RuN on spss