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预测唐氏综合征患者语言表达和理解能力的纵向变化:分层线性模型

Predicting longitudinal change in language production and comprehension in individuals with Down syndrome: hierarchical linear modeling.

作者信息

Chapman Robin S, Hesketh Linda J, Kistler Doris J

机构信息

Waisman Center, University of Wisconsin-Madison, 53705, USA.

出版信息

J Speech Lang Hear Res. 2002 Oct;45(5):902-15. doi: 10.1044/1092-4388(2002/073).

Abstract

Longitudinal change in syntax comprehension and production skill, measured four times across a 6-year period, was modeled in 31 individuals with Down syndrome who were between the ages of 5 and 20 years at the start of the study. Hierarchical Linear Modeling was used to fit individual linear growth curves to the measures of syntax comprehension (TACL-R) and mean length of spontaneous utterances obtained in 12-min narrative tasks (MLU-S), yielding two parameters for each participant's comprehension and production: performance at study start and growth trajectory. Predictor variables were obtained by fitting linear growth curves to each individual's concurrent measures of nonverbal visual cognition (Pattern Analysis subtest of the Stanford-Binet), visual short-term memory (Bead Memory subtest), and auditory short-term memory (digit span), yielding two individual predictor parameters for each measure: performance at study start and growth trajectory. Chronological age at study start (grand-mean centered), sex, and hearing status were also taken as predictors. The best-fitting HLM model of the comprehension parameters uses age at study start, visual short-term memory, and auditory short-term memory as predictors of initial status and age at study start as a predictor of growth trajectory. The model accounted for 90% of the variance in intercept parameters, 79% of the variance in slope parameters, and 24% of the variance at level 1. The some predictors were significant predictors of initial status in the best model for production, with no measures predicting slope. The model accounted for 81% of the intercept variance and 43% of the level 1 variance. When comprehension parameters are added to the predictor set, the best model, accounting for 94% of the intercept and 22% of the slope variance, uses only comprehension at study start as a predictor of initial status and comprehension slope as a predictor of production slope. These results reflect the fact that expressive language acquisition continues in adolescence and is predicted by syntax comprehension and its growth trajectory.

摘要

在一项研究开始时年龄在5至20岁之间的31名唐氏综合征患者中,对在6年期间进行4次测量的句法理解和生成技能的纵向变化进行了建模。使用分层线性模型将个体线性生长曲线拟合到句法理解测量值(TACL-R)和在12分钟叙事任务中获得的自发话语平均长度(MLU-S),为每个参与者的理解和生成产生两个参数:研究开始时的表现和生长轨迹。预测变量是通过将线性生长曲线拟合到每个个体的非言语视觉认知(斯坦福-比奈智力量表的图案分析子测试)、视觉短期记忆(珠子记忆子测试)和听觉短期记忆(数字广度)的同步测量值而获得的,为每个测量值产生两个个体预测参数:研究开始时的表现和生长轨迹。研究开始时的实际年龄(以总均值为中心)、性别和听力状况也被用作预测变量。理解参数的最佳拟合HLM模型使用研究开始时的年龄、视觉短期记忆和听觉短期记忆作为初始状态的预测变量,以及研究开始时的年龄作为生长轨迹的预测变量。该模型解释了截距参数中90%的方差、斜率参数中79%的方差以及第1层中24%的方差。在生成的最佳模型中,一些预测变量是初始状态的显著预测变量,没有测量值预测斜率。该模型解释了81%的截距方差和43%的第1层方差。当将理解参数添加到预测变量集时,最佳模型解释了94%的截距和22%的斜率方差,仅使用研究开始时的理解作为初始状态的预测变量,以及理解斜率作为生成斜率的预测变量。这些结果反映了表达性语言习得在青春期仍在继续,并且由句法理解及其生长轨迹预测这一事实。

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