rooms), nonrandom missing data (i.e., an assessment that occurred at the wrong time or that is missing), and extreme scores of students (Burchinal et al., 1994). Also, hierarchical linear regression modeling and structural equation modeling allow researchers to determine the relationships of variables, in addition to assignment to an early intervention and contrast group conditions, that are associated with development of children (e.g., family characteristics, degree of implementation of the program).

One difficulty in using these techniques in studies of children with autistic spectrum disorders is that many of these techniques require large sample sizes, but most studies of young children with autistic spectrum disorders have small numbers. Nevertheless, to the extent possible, researchers of educational intervention programs for young children with autistic spectrum disorders should consider adopting these or similar models for analyzing variables affecting children’s development and learning. This may require that program developers include sufficient sample sizes in their programs over several years; multiple data points per participant are also required.

Group Size and Experimental Group Design

A clear problem mentioned at several points in the preceding discussion is that methodological tools available to researchers, such as studies of individual differences in response to treatments and sophisticated regression-based techniques, such as hierarchical linear regression modeling, are limited by the number of children with autistic spectrum disorders in intervention programs and the number of data points collected. Implementing an early intervention program for children and families is a labor-intensive and expensive endeavor. Because of the expense, length of treatment, and heterogeneous nature of autistic spectrum disorders, the number of young children in an individual treatment program is usually small. As noted, one solution for program developers is to collect data for multiple cohorts, building their numbers across years. However, this approach requires multiple years of funding and long-term commitments from investigators.

One solution of the sample size problem is the development of a multi-site study of treatment effectiveness. Such a study could be based on a treatment comparison model and could perhaps (because of its potential magnitude) be funded by multiple coordinating agencies (e.g., National Institute of Child Health and Human Development, Office of Special Education Programs, National Institute of Mental Health, Center for Disease Control, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke). There is a precedent for federal funding for large initiatives such as this in other areas (e.g., Fast Track project for aggressive children, Infant Health

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