Guo Ling-Yu, Eisenberg Sarita, Ratner Nan Bernstein, MacWhinney Brian
Department of Communicative Disorders and Sciences, University at Buffalo, NY.
Department of Audiology and Speech-Language Pathology, Asia University, Taichung, Taiwan.
Lang Speech Hear Serv Sch. 2018 Jul 5;49(3):622-627. doi: 10.1044/2018_LSHSS-17-0084.
In this letter, the authors respond to Pavelko and Owens' (2017) newly advanced set of procedures for language sample analysis: Sampling Utterances and Grammatical Analysis Revised (SUGAR).
The authors contrast some of the new guidelines for transcription, morpheme segmentation, and language sample elicitation in SUGAR with traditional conventions for language sample analysis (LSA). They address the potential impact of the new guidelines on some of the target measures in SUGAR-mean length of utterances in morphemes (MLUm), words per sentence (WPS), and clauses per sentence (CPS)-and provide their suggestions.
Inclusion of partially intelligible utterances in SUGAR may over- or underestimate children's MLUm and reduce the reliability of computing WPS. Counting derivational morphemes and the component morphemes of catenatives (e.g., gonna) may result in overestimation of children's morphosyntactic skills.
Further data are needed to determine whether MLUm including derivational morphemes and the component morphemes of catenatives is a better measure of children's morphosyntactic skills than MLUm excluding those morphemes. Pending such data, the authors recommend maintaining traditional LSA conventions and measures. Furthermore, free, fast automated utilities already exist that reduce barriers for clinicians to conduct informative, in-depth LSA.
在这封信中,作者回应了帕维尔科和欧文斯(2017年)最新提出的一套语言样本分析程序:《话语抽样与语法分析修订版》(SUGAR)。
作者将SUGAR中一些关于转录、语素切分和语言样本引出的新指南与传统的语言样本分析(LSA)惯例进行了对比。他们探讨了新指南对SUGAR中的一些目标测量指标——语素平均语句长度(MLUm)、每句单词数(WPS)和每句从句数(CPS)——的潜在影响,并给出了自己的建议。
在SUGAR中纳入部分可理解的话语可能会高估或低估儿童的MLUm,并降低计算WPS的可靠性。计算派生词素和连环结构(如gonna)的组成语素可能会导致对儿童形态句法技能的高估。
需要进一步的数据来确定包含派生词素和连环结构组成语素的MLUm是否比不包含这些语素的MLUm更能衡量儿童的形态句法技能。在获得此类数据之前,作者建议维持传统的LSA惯例和测量方法。此外,已经有免费、快速的自动化工具,可减少临床医生进行信息丰富、深入的LSA的障碍。