Bayesian Asymmetric Regression as a Means to Estimate and Evaluate Oral Reading Fluency Slopes

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Abstract

Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading fluency (ORF). An overview of Bayesian methods and their application to the problem-solving model is first presented, which is further illustrated by a case example. We conclude the paper with a Monte Carlo simulation study demonstrating the validity of BAR, as compared to the current standard of practice for CBM decision-making, ordinary least squares (OLS) regression. Results suggest that BAR is most advantageous with studies using small-to-moderate sample sizes, and when distributional information (such as the probability of intervention success) is of interest.

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