No abstract

Opportunity to Learn (OTL) stems from the basic premise that there is an important relationship between the quality and intensity of classroom instruction and students’ levels of academic success. For many students with disabilities, an emphasis on OTL has become national priority, yet measuring its impact is a complex challenge. The first purpose of this study was to explore the factorial validity of OTL using indicators found in the 2005 4th grade National Assessment of Educational Progress (NAEP). The study entailed confirmatory factor analyses for potential OTL factors including teacher preparation, professional development, classroom activities, and access to technology. Separate factor analyses were conducted using the reading and mathematics datasets. The authors then looked at the degree to which OTL factors influenced NAEP estimates of ability for both students with disabilities and their non-disabled peers. The following three OTL factors differentially predicted student scores: classroom activities (reading), student constructed projects (reading), and using calculators for instruction (mathematics). For the remaining three reading factors and seven mathematics factors, there were no differences in the relationship between the factors and scores for students with and without disabilities.

This pedagogical article is intended to explain the similarities and differences between the parameterizations of two multilevel measurement model (MMM) frameworks. The conventional two-level MMM that includes item indicators and models item scores (Level 1) clustered within examinees (Level 2) and the two-level crossclassified MMM (in which item scores are cross-classified by two Level 2 classifications including item and examinee) are discussed. A small subset of National Assessment of Educational Progress 4th grade mathematics item scores is used to demonstrate use of the MMMs for assessing facets of the validity of accommodated test scores. The models' similarities and distinctions are emphasized as well as the flexibility of the models' extensions.