Bayesian regression has emerged as a viable alternative for the estimation of curriculum-based measurement (CBM) growth slopes. Preliminary findings suggest such methods may yield improved efficiency relative to other linear estimators and can be embedded into data management programs for high-frequency use. However, additional research is needed, as Bayesian estimators require multiple specifications of the prior distributions. The current study evaluates the accuracy of several combinations of prior values, including three distributions of the residuals, two values of the expected growth rate, and three possible values for the precision of slope when using Bayesian simple linear regression to estimate fluency growth slopes for reading CBM. We also included traditional ordinary least squares (OLS) as a baseline contrast. Findings suggest that the prior specification for the residual distribution had, on average, a trivial effect on the accuracy of the slope. However, specifications for growth rate and precision of slope were influential, and virtually all variants of Bayesian regression evaluated were superior to OLS. Converging evidence from both simulated and observed data now suggests Bayesian methods outperform OLS for estimating CBM growth slopes and should be strongly considered in research and practice.

贝叶斯回归已成为估计基于课程的测量 (CBM) 增长斜率的可行替代方案。初步发现表明，相对于其他线性估计器，此类方法可能会提高效率，并且可以嵌入到数据管理程序中以供高频使用。然而，需要额外的研究，因为贝叶斯估计需要先验分布的多个规范。当前研究评估了几种先验值组合的准确性，包括使用贝叶斯简单线性回归估计阅读流畅性增长斜率时的三个残差分布、两个预期增长率值和斜率精度的三个可能值煤层气。我们还包括传统的普通最小二乘法 (OLS) 作为基线对比。结果表明，平均而言，残差分布的先验规范对斜率的准确性影响微不足道。然而，增长率和斜率精度的规范是有影响的，并且几乎所有评估的贝叶斯回归变体都优于 OLS。来自模拟和观察数据的融合证据表明，贝叶斯方法在估计 CBM 增长斜率方面优于 OLS，应在研究和实践中予以强烈考虑。