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

    loading  Checking for direct PDF access through Ovid


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.

    loading  Loading Related Articles