Statistical Inference in Redundancy Analysis: A Direct Covariance Structure Modeling Approach.

Redundancy analysis (RA) is a multivariate method that maximizes the mean variance of a set of criterion variables explained by a small number of redundancy variates (i.e., linear combinations of a set of predictor variables).

Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach.

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed.

Pancultural nostalgia: prototypical conceptions across cultures.

Nostalgia is a frequently experienced complex emotion, understood by laypersons in the United Kingdom and United States of America to (a) refer prototypically to fond, self-relevant, social memories and (b) be more pleasant (e.g., happy, warm) than unpleasant (e.

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models.

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated.

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure.

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach.

A SAS/IML program using the Kalman filter for estimating state space models.

To help disseminate the knowledge and software implementation of a state space model (SSM), this article provides a SAS/IML (SAS Institute, 2010) program for estimating the parameters of general linear Gaussian SSMs using the Kalman filter algorithm. In order to use this program, the user should have SAS installed on a computer and have a valid license for SAS/IML. Since the code is completely open, it is expected that this program can be used not only by applied researchers, but also by quantitative methodologists who are interested in improving their methods and promoting SSM as a research instrument.