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Methods for characterizing participants' nonmainstream dialect use in child language research.

Janna B Oetting1, Janet L McDonald

  • 1Department of Communication Sciences & Disorders, Louisiana State University, Baton Rouge 70803-2606, USA. cdjanna@lsu.edu

Journal of Speech, Language, and Hearing Research : JSLHR
|June 19, 2002
PubMed
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This study compared methods for identifying nonmainstream dialects in children, finding that token-based counts were best for rate characterization. Accurate dialect classification (Southern White English or Southern African American English) was achieved for most participants.

Area of Science:

  • Linguistics
  • Developmental Psychology
  • Sociolinguistics

Background:

  • Characterizing nonmainstream dialect use in research is crucial for accurate child language studies.
  • Existing literature includes listener judgment ratings, type-based counts, and token-based counts for dialect analysis.

Purpose of the Study:

  • To compare three distinct methods for characterizing nonmainstream dialect use in children.
  • To evaluate the efficacy of shortcuts within these methods.
  • To assess implications for dialect diversity research.

Main Methods:

  • Analysis of language samples from 93 children speaking rural Louisiana Southern White English (SWE) and Southern African American English (SAAE).
  • Comparison of listener judgment ratings, type-based counts, and token-based counts.

Related Experiment Videos

  • Application of regression algorithms to type- and token-based results.

    • Main Results:

    • Correct dialect classification (SWE or SAAE) ranged from 88% to 97% depending on the method and shortcut used.
    • Token-based methods demonstrated superiority in characterizing the rate of nonmainstream pattern production.
    • Estimates from all approaches showed moderate to high correlations.
    • Substantial reduction in the number of patterns analyzed was possible without compromising validity for type- and/or token-based methods.

    Conclusions:

    • The findings offer valuable insights for future child language research involving dialect diversity.
    • Token-based methods are recommended for accurately characterizing dialectal pattern rates.
    • Methodological choices significantly impact the accuracy and efficiency of dialect characterization in child language studies.