Factor Analysis

Q-Mode Factor Analysis

  • In Q-mode technique, the main objective is analyzing the inter-object relationships. Accordingly, the first step in Q-mode analysis is to create an n x n matrix of similarities. For this, Aabel uses cosine theta coefficient of proportional similarity, which is the most widely used in Q-mode factor analysis.
The Analysis Output

The analysis output can include:

  • Factor loadings from the Q-mode analysis of cosine theta
  • Eigenvalues
  • Factor scores
  • Cosine theta similarity matrix
  • The first factor usually represents the combined magnitudes of the variables measured on each objects, i.e., it has no information about the structure of data.
  • The second factor provides information about the structure of data; e.g., in the following graphs it differentiates the silica-undersaturated samples from silica-rich rocks, and the intermediate samples.

Factor Loadings From Q-Mode Analysis of a Published Dataset*

Q-Mode Factor Loadings

Graph of Q-Mode Factor Loadings

* Source of data: Naldrett, A.J., Hewins, R.H., Dressler, B.O., and Rao, B.V. (1984). The Contact Sublayer of the Sudbury Igneous Complex, in the Geology and Ore Deposits of the Sudbury Structure (Edited Pye, E.G., Naldrett, A.J., and Gilblin, P.E.), pp. 253-274, Ministry of Natural Resources, Ontario.