Factor Rotations in Factor Analyses. Herve Abdi1 Summary: Factor Rotations in Factor Analyses. Herv´e Abdi1 The University of Texas at Dallas Introduction The different methods of factor analysis first extract a set a factors from a data set. These factors are almost always orthogonal and are ordered according to the proportion of the variance of the original data that these factors explain. In general, only a (small) subset of factors is kept for further consideration and the remaining factors are considered as either irrelevant or nonexistent (i.e., they are assumed to reflect measurement error or noise). In order to make the interpretation of the factors that are considered rel- evant, the first selection step is generally followed by a rotation of the factors that were retained. Two main types of rotation are used: orthogonal when the new axes are also orthogonal to each other, and oblique when the new axes are not required to be orthogonal to each other. Because the rotations are always performed in a subspace (the so-called factor space), the new axes will always explain less variance than the original factors (which are computed to be opti- mal), but obviously the part of variance explained by the total subspace after rotation is the same as it was before rotation (only the partition of the variance has changed). Because the rotated axes are not defined according to a statistical