Nonstandard log-linear models.

This article demonstrates several useful varieties of nonstandard log-linear models. Some can be derived as nonhierarchical models by deleting lower-order effects in hierarchical models, but most often they will arise as the result of special hypotheses that the researcher wants to test. Three approaches to testing nonstandard models, partitioning chi-square, creating homogeneous subgroups, and the model matrix approach, are illustrated on a variety of data sets. It is assumed that the reader has been exposed to the general area of log-linear models, is familiar with dummy and effect coding of categorical variables in the usual regression framework, and knows how to express models in matrix form. A brief summary of the standard approach to log-linear models and of coding methods is provided for readers needing a review. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Linear equality restrictions in regression and loglinear models.

Describes a simple method for implementing general linear equality restrictions on the parameters of linear models. These techniques, unlike those involving Lagrangian multipliers, can be easily implemented on common computer programs such as SPSS, BMDP, and SAS. For multiple regression, both homogenous and nonhomogenous constraints can be made, whereas for loglinear models only homogenous constraints can be made. (16 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mixture or Homogeneous? Comment on Bauer and Curran (2003).

D. J. Bauer and P. J. Curran (see record 2003-09632-007) raised some interesting issues with respect to mixture models of growth curves. Many useful lessons can be learned from their work, and more can be learned by extending the inquiry in related directions. These lessons involve the following issues: (a) what a mixture distribution looks like, (b) the meaning of the term homogeneous distribution, (c) the importance of model checking, (d) advantages and disadvantages of using mixtures and similar procedures to approximate complicated distributions, and (e) intrinsic versus nonintrinsic transformability. (PsycINFO Database Record (c) 2010 APA, all rights reserved)