Using MLU to study early language development in English

Open access

Using MLU to study early language development in English

The study examines the parameter of Mean Length of Utterance (MLU), measured both in morphemes (MLUm) and words (MLUw), in early language development in the case of two English children matched for age. The MLU scores of a normally developing child were compared to the MLU results of a language-impaired child in a longitudinal study. Moreover, the reliability of the MLU index measured in words was also tested in both children. The MLU analysis was based on the CHILDES database and CLAN programme, where the transcripts of spontaneous speech samples are used to calculate basic language parameters at different age-points. The findings of this study indicate that despite the expected delay, the language-impaired child followed a similar route of language development as the control child. However, significant differences between MLUw and MLUm confirmed that the parameters performed two different linguistic analyses.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Bates E. (1999). Letter to the Info-Childes. Child Language Bulletin 19 (1).

  • Blake J. Quartaro G. & Onorati S. (1993). Evaluating quantitative measures of grammatical complexity in spontaneous speech samples. Journal of Child Language 20 139-152

  • Bol G. W. (1996). Optional subjects in Dutch child language. In Ch. Koster & F. Wijnen (Eds.) Proceedings of the Groningen Assembly on Language Acquisition (pp. 125-133). Groningen: Centre for Language and Cognition.

  • Bol G. W. (2003). MLU-Matching and the Production of Morphosyntax in Dutch Children with Specific Language Impairment (SLI). In Y. Levy & J. Schaeffer (Eds.) Language Competence Across Populations: Toward a Definition of Specific Language Impairment (pp. 259-272). Mahwah NJ: Lawrence Erlbaum.

  • Brown R. (1973). A first language: The early stages. Cambridge MA: Harvard University Press.

  • DeThorne L. S. Johnson B. W. & Loeb J. W. (2005). A closer look at MLU: What does it really measure? Clinical Linguistics and Phonetics 19 (8) 635-648.

  • Eisenberg S. L. Fersko T. M. & Lundgren C. (2001). The Use of MLU for identifying language impairment for preschool children: A review. American Journal of Speech-Language Pathology 10 323-342.

  • Johnston J. R. (2001). An alternate MLU calculation: Magnitude and variability of effects. Journal of Speech Language and Hearing Research 44 156-164.

  • Johnston J. R. & Kamhi A. 1984. Syntactic and semantic aspects of the utterances of language impaired children: The same can be less. Merrill-Palmer Quarterly 30 65-85.

  • Klee Th. (1992). Measuring Children's Conversational Language. In S. F. Warren & J. Reichle (Eds) Causes and effects in communication and language intervention (pp. 315-330). Baltimore: Brookes.

  • Leonard L. B. & Finnerman D. (2003). Grammatical morpheme effects on MLU: "The same can be less" revisited. Journal of Speech Language and Hearing Research 46 878-888.

  • Miller J. (1991). Quantifying Productive Language Disorders. In J. Miller (Ed.) Research on Child Language Disorders: A Decade of Progress (pp. 211-220). Austin TX: Pro Ed.

  • Miller J. F. & Chapman R. S. (1981). The relation between age and mean length of utterance in morphemes. Journal of Speech and Hearing Research 24 154-161.

  • Moore D. S. & McCabe G. P. (2003). Introduction to the Practice of Statistics. New York: Freeman.

  • Parker M. D. & Brorson K. (2005). A comparative study between mean length of utterance in morphemes (MLUm) and mean length of utterance in words (MLUw). First Language 25 (3) 365-376.

  • Paul R. (2000). Language Disorders from infancy through adolescents (2nd ed.). Saint Louis MO: Mosby-Year Book.

  • Peters A. (1999). From Info-Childes. IASCL Child Language Bulletin 19 (1).

  • Plante E. Swisher L. Kiernan B. & Restrepo M. A. (1993). Language Matches: Illuminating or Confounding? Journal of Speech and Hearing Research 36 772-776.

  • Retherford K. S. (1993/2000). Guide to analysis of language transcripts (2nd ed.). Eau Claire Wl: Thinking Publications.

  • SPS Inc. (2001). SPS (Version 11.0). [Computer software]

Search
Journal information
Impact Factor


CiteScore 2018: 0.29

SCImago Journal Rank (SJR) 2018: 0.118
Source Normalized Impact per Paper (SNIP) 2018: 0.410

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1484 1229 82
PDF Downloads 544 487 12