We know that few SLPs use language sample analysis. And, real talk, we get it—transcribing and analyzing language samples takes forever, and sometimes you end up with a whole bunch of numbers and no idea what they mean. To help with that, this study gives us a little more guidance for analyzing narrative language samples using percent grammatical utterances (PGU).
The authors used data from 4- to 9-year-old children who took the Edmonton Narrative Norms Instrument (ENNI). As part of the ENNI, children generated stories for six picture sequences, which were transcribed and coded. PGU coding is pretty straightforward. You:
Divide the sample into C-units
Decide whether each C-unit has a verb
Mark each C-unit with a verb as grammatical or ungrammatical
Divide the number of grammatical C-units by total eligible C-units (those with a verb) to get PGU
That’s it—no complex coding, no lengthy rubrics, just a yes/no decision for each utterance (see the article and supplemental material for more examples and guidance). And as easy as it is, PGU is also a good measure. The authors found that PGU was reliable, valid, and able to distinguish children with and without developmental language disorder (DLD) with acceptable diagnostic accuracy. Using the data in the article, you can supplement diagnostic decisions (Table 5) or track progress (Supplemental File S4). Note that you should use the same language sample context that they did; luckily, the ENNI pictures are freely available. And for an even faster measure to use with 3-year-olds, check out these researchers’ previous work on percent grammatical responses (PGR).
*Note that we shouldn’t use PGU to score samples from speakers of non-mainstream dialects of American English because the scoring rules don’t (yet) account for dialect differences.
Guo, L., Eisenberg, S., Schneider, P., & Spencer, L. Percent grammatical utterances between 4 and 9 years of age for the Edmonton Narrative Norms Instrument: Reference data and psychometric properties. American Journal of Speech–Language Pathology. doi:10.1044/2019_AJSLP-18-0228