This paper explores multivariate data using principal component analysis (PCA). Traditionally, two different approaches to PCA have been considered, an algebraic descriptive one and a probabilistic one. Here, a third type of PCA approach, lying somewhere between the two traditional approaches, called the fixed effects PCA model, is considered. This model includes mainly geometrical, rather than probabilistic assumptions, such as the optimal choice of dimensionality and metric. The model is designed to account for any possible prior information about the noise in the data to yield better estimates. Parameters are estimated by minimizing a least-squares criterion with respect to a specified metric. A suggestion of how the fixed effects PCA estimates can be improved in a common principal component (CPC) environment is made. If the CPC assumption is fulfilled, then the fixed effects PCA model can consider more information by incorporating common principal component analysis (CPCA) theory into the estimation procedure.