A longitudinal factor analysis model that is entirely exploratory is proposed for use with multiple populations. Factorial collapse, period/practice effects, and an invariant and/or stationary factor pattern are allowed. The model is formulated stochastically and implemented via a stage-wise EM algorithm. (TJH)

An extension of component analysis to longitudinal or cross-sectional data is presented. Components are derived under the restriction of invariant and/or stationary compositing weights. Multiple occasion and multiple group analyses, the computing algorithm, component pattern and structure matrices, and an example are discussed. (TJH)

An alternating least squares method for iteratively fitting the longitudinal reduced-rank regression model is proposed. For social and behavioral science applications, the developed algorithm does not rely on the assumption of multinomial errors and allows for scaling of input and output variables. (SLD)