Spotfire Statistica® Factor Analysis

The main applications of factor analytic techniques are 1) to reduce the number of variables and 2) to detect structure in the relationships between variables, that is to classify variables. Therefore, factor analysis is applied as a data reduction or structure detection method (classification). It is commonly used in finance, marketing, research and different scientific fields. 

The Factor Analysis module contains a wide range of statistics and options. It provides factor and hierarchical factor analytic techniques with extended diagnostics. It will perform:

• principal components analysis (PCA)
• principal factor analyses: communalities=multiple R², MINRES (minimum residual factor method; Harman & Jones, 1966), maximum likelihood factors, centroid method, and principal axis method. 

Confirmatory factor analysis, as well as path analysis, can also be performed via the Structural Equation Modeling and Path Analysis (SEPATH).

Note: A hands-on how-to approach to factor analysis can be found in Stevens (1986). More detailed technical descriptions are provided in Cooley and Lohnes (1971), Harman (1976), Kim and Mueller, (1978a, 1978b), Lawley and Maxwell (1971), Lindeman, Merenda, and Gold (1980), Morrison (1967) or Mulaik (1972). The interpretation of secondary factors in hierarchical factor analysis, as an alternative to traditional oblique rotational strategies, is explained in detail by Wherry (1984). Fabrigar (1999) addresses the controversy over differences between principal components analysis (PCA) and factor analysis.

Historical note: The term factor analysis was first introduced by Thurstone, 1931, although similar techniques were used by Spearman as early as 1904 in his classic research on the nature of intelligence.