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  5. Predicting High-Stakes Tests of Math Achievement using a Group-Administered RTI Instrument: Validating Skills Measured by the Monitoring Instructional Responsiveness: Math
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Predicting High-Stakes Tests of Math Achievement using a Group-Administered RTI Instrument: Validating Skills Measured by the Monitoring Instructional Responsiveness: Math

Date Issued
August 1, 2014
Author(s)
Coles, Jeremy Thomas  
Advisor(s)
R. Steve McCallum
Additional Advisor(s)
Sherry M. Bell
William L. Seaver
Jennifer A. Morrow
Brian E. Wilhoit
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/23874
Abstract

Three universal screeners and nine progress monitoring probes from the Monitoring Instructional Responsiveness: Math (MIR:M), a silent, group-administered math assessment designed for implementation with an RTI Model, were administered to 223 fifth-grade students. The growth parameters of the overall MIR:M composite and two global composites (math calculation and math reasoning) identified significant variation in student growth, within significant linear and quadratic trajectories. However, there were significant differences in the nature of the growth trajectories that have applied educational implications. In addition, growth parameters across the three composites provided significant predictive potential when using the Tennessee Comprehensive Assessment Program (TCAP) Achievement Test, a high-stakes, end of the year assessment of academic achievement, as the criterion measures (p < .001). Furthermore, these parameters were predictive at the classroom and student level. Differential predictive potential of the parameters and the composites provide additional information about the nature of the MIR:M data. Altogether, the growth modeling and the predictive modeling provide evidence to support two practical uses of the MIR:M.

Subjects

Curriculum-Based Meas...

High-Stakes Assessmen...

Hierarchical Linear M...

Response To Intervent...

Assessment

Disciplines
Applied Statistics
Educational Assessment, Evaluation, and Research
Longitudinal Data Analysis and Time Series
Quantitative Psychology
School Psychology
Degree
Doctor of Philosophy
Major
School Psychology
Embargo Date
January 1, 2011
File(s)
Thumbnail Image
Name

FullDissertation.pdf

Size

2.2 MB

Format

Adobe PDF

Checksum (MD5)

f8d4c79591a4da2151c7018e2c86bdd7

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