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Activity Number:
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443
- Latent Variables, Causal Inference, Machine Learning and Other Topics in Mental Health Statistics
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #318947
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Title:
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A Beta-Binomial Model for Latent Accuracy When Estimating Oral Reading Fluency
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Author(s):
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Cornelis J. Potgieter*
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Companies:
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Texas Christian University
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Keywords:
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Oral Reading Fluency;
Latent Variable Model;
Accuracy;
Speed;
Psychometric Model;
Validation
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Abstract:
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Oral reading fluency (ORF) an important scholastic metric for identifying and monitoring at-risk readers, and is used by teachers and school districts across the country. In traditional ORF administration, students are given one minute to read a grade-level passage, after which the assessor calculates the words correct per minute (WCPM) fluency score based on the raw reading scores. As part of a larger effort to develop an improved ORF assessment system, this study expands on and demonstrates the performance of a new model-based estimate of WCPM using a latent-variable psychometric model of speed and accuracy for ORF data. The proposed method uses a beta-binomial distribution for the reading count data and a log-normal model for reading time. The proposed model-based WCPM approach is illustrated by an application to real data.
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Authors who are presenting talks have a * after their name.
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