مقاله انگلیسی رایگان در مورد مهارتهای واژگانی در کودکان با لکنت زبان – الزویر ۲۰۲۰

مقاله انگلیسی رایگان در مورد مهارتهای واژگانی در کودکان با لکنت زبان – الزویر ۲۰۲۰

 

مشخصات مقاله
ترجمه عنوان مقاله تنوع لغوی و مهارتهای واژگانی در کودکانی که لکنت زبان دارند
عنوان انگلیسی مقاله Lexical diversity and lexical skills in children who stutter
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۱۴ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR – MedLine
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۲٫۰۲۳ در سال ۲۰۱۹
شاخص H_index ۴۷ در سال ۲۰۲۰
شاخص SJR ۰٫۸۷۰ در سال ۲۰۱۹
شناسه ISSN ۰۰۹۴-۷۳۰X
شاخص Quartile (چارک) Q1 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط روانشناسی
گرایش های مرتبط روانشناسی بالینی کودک و نوجوان، روانشناسی رشد، روانسنجی
نوع ارائه مقاله
ژورنال
مجله  مجله اختلالات سلاست – Journal Of Fluency Disorders
دانشگاه Department of Hearing and Speech Sciences, University of Maryland, College Park, MD, 20742, United States
کلمات کلیدی سلاست، تنوع واژگانی، زبان، لکنت زبان
کلمات کلیدی انگلیسی Fluency، Lexical diversity، Language، Stuttering
شناسه دیجیتال – doi
https://doi.org/10.1016/j.jfludis.2020.105747
کد محصول E14672
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract

۱- Introduction

۲- Methods

۳- Results

۴- Discussion

References

بخشی از متن مقاله:

Abstract

Purpose: Numerous “small N” studies of language ability in children who stutter have produced differing conclusions. We combined test and spontaneous language data from a large cohort of children who stutter (CWS) and typically fluent peers, gathered from independent laboratories across the US, to appraise a variety of lexical measures. Method: Standardized receptive and expressive vocabulary test data and spontaneous language samples from 99 pairs of CWS (ages 25–۱۰۰ months), and age-, gender-, and SES-matched children who do not stutter (CWNS) were compared. Language sample transcripts were analyzed with four measures of lexical diversity. Correlations between lexical diversity measures and expressive vocabulary scores were also calculated. Results: On standardized tests of both receptive and expressive vocabulary, there were significant differences between CWS and CWNS. In contrast, on spontaneous language measures of lexical diversity, CWS did not differ in their lexical diversity, across analyses, compared to CWNS. Three of the four lexical diversity analyses, MATTR, VocD, and NDW, were significantly correlated with each other. Conclusions: We were able to confirm prior findings of relative disadvantage on standardized vocabulary tests for a very large sample of well-matched CWS. However, spontaneous language measures of lexical diversity did not distinguish the groups. This relative weakness in CWS may emerge from task differences: CWS are free to encode their own spontaneous utterances but must comply with explicit lexical prompts in standardized testing situations.

Summary

The primary goal of this study was to examine lexical diversity in spontaneous conversational language, as well as to examine performance on standardized vocabulary testing, by pooling CWS and CWNS across seven sites/research labs within the United States. A second goal was to assess the relations among the different lexical diversity measures to explore questions of concurrent validity of MATTR. Finally, we examined the relations between lexical diversity measures and expressive vocabulary knowledge. A highly significant difference was found between CWS and CWNS on standard expressive and receptive vocabulary scores across studies that used different vocabulary measures. The CWS performed, on average, almost one standard deviation below the CWNS on the expressive vocabulary tests. Both groups still fell within the normal (nonclinical) range, however. In contrast, CWS demonstrated no significant differences in lexical diversity compared to CWNS on several lexical diversity measures in spontaneous language samples: overall TTR, TTR50, NDW, VocD, and a new measure, MATTR. Among spontaneous lexical diversity measures, there were significant correlations among NDW, VocD, and MATTR, providing some evidence of MATTR’s concurrent validity with similar measures of lexical diversity, and suggesting the importance of using the entire transcript for language sample analysis. However, none of the lexical diversity analyses were significantly correlated with children’s expressive vocabulary test score performance. We wish to point out that behavioral measures are at best a crude indicator of skill areas that may be more difficult for CWS than their typical peers. Many of the children we profiled were participants in neuroimaging studies that found atypical profiles of activation under linguistic task demand, including lexical processing (Hartfield & Conture, 2006; Kreidler, Hampton Wray, Usler, & Weber, 2017; Weber-Fox, Wray, & Arnold, 2013). Experimental tasks, such as naming following priming conditions, have also been shown to distinguish CWS from typical peers (e.g., Pellowski & Conture, 2005). Thus, work to examine either standardized test performance or language sample analysis profiles in CWS need to be part of a larger unified, multifactorial approach to understanding the nature of stuttering in early childhood. Though stuttering is, at its core, a disorder of speech motor coordination, at this point, there is a variety of converging results indicating that CWS often score below fluent, well-matched peers on tests of language skill, demonstrate neuroimaging profiles that suggest that CWS process language tasks atypically, or show exacerbation of disfluency under linguistic demand. Such findings clearly have ramifications for a full understanding of how stuttering arises, its precipitators in the speech of young children, and potential ramifications for assessment and intervention. We chose to explore these concerns by combining data to permit more robust statistical analysis of past work, and hope that others will follow in our lead.

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