Bibliographic Details
| Title: |
Examination of an Automated Procedure for Calculating Morphological Complexity. |
| Authors: |
Wood, Carla1 carla.wood@cci.fsu.edu, Garcia-Salas, Miguel1, Schatschneider, Christopher1 |
| Source: |
American Journal of Speech-Language Pathology. Sep2023, Vol. 32 Issue 5, p2322-2330. 9p. 2 Charts. |
| Subject Terms: |
*Research, *Automation, *Teachers, *Learning disabilities, *Vocabulary, *Written communication, *School children, *Statistical correlation, *Predictive validity, *Elementary schools, *Children, Linguistics, Pearson correlation (Statistics), Research funding, Descriptive statistics, Statistical sampling, Data analysis software |
| Abstract: |
Purpose: The aim of this study was to advance the analysis of written language transcripts by validating an automated scoring procedure using an automated open-access tool for calculating morphological complexity (MC) from written transcripts. Method: The MC of words in 146 written responses of students in fifth grade was assessed using two procedures: (a) hand-coding of words containing derivational morphemes by trained scorers and (b) an automated analysis of MC using Morpholex, a newly developed web-based tool. Correlational analysis between the different MC calculations was examined to consider the relation between hand-coded derivational morpheme counts and the automated measures. Additionally, all MC measures were compared to a previously gathered rating of writing quality to consider predictive validity between the automated Morpholex score and teachers' ratings of writing quality. Results: Automated measures of MC had a strong relation (r = .63) with handcoding of the number of words with derivational morphemes. Additionally, the number of derivational and inflectional and derivational morphemes accounted for a significant amount of the variation in teachers' overall ratings of writing quality. Conclusion: Automated scoring of MC has potential utility as a valid alternative to hand-coding language samples, which may be valuable for progress monitoring of growth in complexity across repeated samples and measuring components that influence perceived quality of academic writing. [ABSTRACT FROM AUTHOR] |
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| Database: |
Education Research Complete |
