| Abstract: | Examines methods for analyzing complex data (i.e., data that do not conform to the assumptions of independence and homoscedasticity on which many classical procedures are based). Primary attention is given to regression analysis, with ANOVA as a special case, though reference to related work on loglinear models and logit analysis is also made. The problems associated with using standard methods and software on complex data are discussed. Much of the work on alternative strategies for complex data analysis is based on an inferential framework that is fundamentally different from the model-based inference familiar to most psychologists. Though model-based inference is the most popular approach to analyzing experiments in psychology, the randomization approach is increasingly being advocated as an alternative. (PsycINFO Database Record (c) 2010 APA, all rights reserved) |
