This paper compares the weighting schemes in the traditional, principal component and dynamic factor approaches to summarizing information from a number of component variables. To facilitate the comparison, we propose a framework to discuss the approaches with respect to their implied loadings in a latent variable model. We also propose a way to transform the dynamic factor index into an analogous index which is a weighted average of the components. The framework shows the strengths and weakness of the alternative weighting schemes and the sense in which the dynamic factor approach has the advantage of capturing both the significance and the variability of the components.