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Educating Children with Autism
skills, IEPs and measuring progress, and promoting social interaction (LEAP Preschool and Outreach Project, 1999). These protocols could be used in a research capacity to document the level of implementation of the comprehensive program. Also, as Strain (2000) indicated, they were used in the LEAP program to provide feedback to staff on their level of implementation in order to maintain treatment fidelity. Some researchers use hours of service provided as a measure of the intensiveness of intervention (Smith et al., 2000). Although it provides important information, hours of service is not an adequate measure of treatment fidelity, because it does not describe the procedures used during the service hours. Assessment of treatment fidelity has a long history in general education (see Leinhardt, 1980) and has been proposed as a standard for high quality intervention research in early intervention for children with disabilities (LeLaurin and Wolery, 1992). However, one review of early intervention programs for children with autism (Wolery and Garfinkle, 2000) found that only 4 out of 15 programs provided any evidence of implementation of program components. In future research on educational intervention for young children with autistic spectrum disorders and their families, measurement of the fidelity of treatment should be a standard feature of the program of research and publication of findings.
Modeling Growth and Intervention Effects
In most experimental group studies, as noted above, the developmental growth of children with autistic spectrum disorders is measured through the collection of pretest and posttest outcome measures, followed by analyses of differences between groups. More sophisticated procedures for examining the growth and development of children are available (Dunst and Trivette, 1994), but they have not been used in analyses of intervention outcomes for young children with autistic spectrum disorders. Growth curve analysis (Burchinal and Appelbaum, 1991) and the related techniques of hierarchical linear regression modeling (Bryk and Raudenbush, 1987) and structural equation modeling (Willet and Sayer, 1994) have been used to model the growth of groups of children for whom longitudinal data are available. These techniques may also be used to examine patterns of growth for children with different types of characteristics or children involved in different types of treatment conditions or programs (e.g., Burchinal, 1999; Burchinal, Bailey and Synder, 1994; Hatton et al., 1997). Natural history studies of development in children with autistic spectrum disorders are critical using these methods to provide both theoretically based insight and empirical “baselines.”
The advantage of growth curve analysis and related regression models is that they allow researchers to control for nested variables (e.g., children participating in the same intervention but in different class-