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首页> 外文期刊>American journal on intellectual and developmental disabilities: AJIDD >Comparing Single Case Design Overlap-Based Effect Size Metrics From Studies Examining Speech Generating Device Interventions
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Comparing Single Case Design Overlap-Based Effect Size Metrics From Studies Examining Speech Generating Device Interventions

机译：从研究语音生成设备干预的研究中比较基于单例设计的基于重叠的效果大小度量

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摘要

Meaningfully synthesizing single case experimental data from intervention studies comprised of individuals with low incidence conditions and generating effect size estimates remains challenging. Seven effect size metrics were compared for single case design (SCD) data focused on teaching speech generating device use to individuals with intellectual and developmental disabilities (IDD) with moderate to profound levels of impairment. The effect size metrics included percent of data points exceeding the median (PEM), percent of nonoverlapping data (PND), improvement rate difference (IRD), percent of all nonoverlapping data (PAND), Phi, nonoverlap of all pairs (NAP), and Tau(novlap). Results showed that among the seven effect size metrics, PAND, Phi, IRD, and PND were more effective in quantifying intervention effects for the data sample (N = 285 phase or condition contrasts). Results are discussed with respect to issues concerning extracting and calculating effect sizes, visual analysis, and SCD intervention research in IDD.
机译：有意义的是，从干预研究中合成单例实验数据，其中包括低发病率的个体，并产生效应大小估计值仍然具有挑战性。比较了针对单个案例设计（SCD）数据的七个效果大小量度，这些数据侧重于针对中度至重度障碍的智力和发育障碍（IDD）的个人教学语音生成设备的使用。效果大小指标包括超过中位数（PEM）的数据点百分比，不重叠数据（PND）百分比，改善率差异（IRD），所有不重叠数据的百分比（PAND），Phi，所有对的不重叠（NAP），和Tau（novlap）。结果表明，在七个效应大小度量标准中，PAND，Phi，IRD和PND在量化数据样本的干预效应方面更为有效（N = 285相或条件对比）。讨论了有关在IDD中提取和计算效果大小，视觉分析和SCD干预研究的问题的结果。

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