Factor Analysis

anaChapter 7 – Factor Analysis – SPSS

Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis (for example, to identify collinearity prior to performing a linear regression analysis).

Data. The variables should be quantitative at the interval or ratio level. Categorical data (such as religion or country of origin) are not suitable for factor analysis. Data for which Pearson correlation coefficients can sensibly be calculated should be suitable for factor analysis. Assumptions. The data should have a bivariate normal distribution for each pair of variables, and observations should be independent. Extraction method. Allows you to specify the method of factor extraction.

Analyze. Allows you to specify either a correlation matrix or a covariance matrix.

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Extract. You can either retain all factors whose eigenvalues exceed a specified value or retain a specific number of factors. Display. Allows you to request the unrotated factor solution and a scree plot of the eigenvalues. Maximum Iterations for Convergence. Allows you to specify the maximum number of steps the algorithm can take to estimate the solution. Rotation method. Allows you to select the method of factor rotation.

Display. Allows you to include output on the rotated solution, as well as loading plots for the first two or three factors. Maximum Iterations for Convergence. Allows you to specify the maximum number of steps the algorithm can take to perform the rotation. Save scores as variables. Creates one new variable for each factor in the final solution. Select one of the following alternative...