当前位置: X-MOL 学术Commun. Disord. Q. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Visual Analysis Plus Hierarchical Linear Model Regressions: Morphosyntax Intervention with Deaf-and-Hard-of-Hearing Students
Communication Disorders Quarterly ( IF 0.764 ) Pub Date : 2021-07-08 , DOI: 10.1177/15257401211026206
Janna Hasko 1 , M. Christina Rivera 1 , Monica K. Erbacher 1 , Shirin D. Antia 1
Affiliation  

We conducted a pilot study using intentional teaching strategies with specially designed materials to improve accuracy and production of targeted English morphosyntax structures with six deaf and hard-of-hearing students (kindergarten to first grade). A multiple baseline single-case research design (SCRD) consisting of 20-minute sessions four times per week for the duration of a school year was implemented to determine the effect of the supplemental syntax curriculum. The data were inconsistent and highly variable. Visual analyses were problematic; therefore, hierarchical linear model (HLM) regression analyses were conducted with the time series SCRD data as an additional analysis. HLM regression analyses were used to interpret data that might otherwise be overlooked in SCRDs to provide specific values for the rate students were learning during the intervention phase of the study. This pilot study demonstrates that the syntax intervention produces promising results when data that are too messy for visual analysis are analyzed with HLM.



中文翻译:

视觉分析加上分层线性模型回归:对聋哑学生的形态句法干预

我们使用有意的教学策略和专门设计的材料进行了一项试点研究,以提高六名聋哑和听力障碍学生(幼儿园至一年级)的针对性英语形态句法结构的准确性和产量。实施了一个多基线单案例研究设计 (SCRD),包括在一个学年期间每周四次 20 分钟的课程,以确定补充句法课程的效果。数据不一致且高度可变。视觉分析有问题;因此,使用时间序列 SCRD 数据进行分层线性模型 (HLM) 回归分析作为附加分析。HLM 回归分析用于解释在 SCRD 中可能会被忽略的数据,以提供学生在研究干预阶段学习的比率的具体值。这项试点研究表明,当使用 HLM 分析对于视觉分析来说过于混乱的数据时,语法干预会产生有希望的结果。

更新日期:2021-07-09
down
wechat
bug