Developmental Sentence Scoring (DSS) is a standardized language sample analysis procedure that evaluates and scores a child's use of standard American-English grammatical rules within complete sentences. Automated DSS programs have the potential to increase the efficiency and reduce the amount of time required for DSS analysis. The present study examines the accuracy of one automated DSS software program, DSSA 2.0, compared to manual DSS scoring on previously collected language samples from 30 children between the ages of 2-5 and 7-11. Additionally, this study seeks to determine the source of error in the automated score by comparing DSSA 2.0 analysis given manually versus automatedly assigned grammatical tag input. The overall accuracy of DSSA 2.0 was 86%; the accuracy of individual grammatical category-point value scores varied greatly. No statistically significant difference was found between the two DSSA 2.0 input conditions (manual vs. automated tags) suggesting that the underlying grammatical tagging is not the primary source of error in DSSA 2.0 analysis.

Developmental Sentence Scoring (DSS) is a standardized language sample analysis procedure that uses complete sentences to evaluate and score a child’s use of standard American-English grammatical rules. Automated DSS software can potentially increase efficiency and decrease the time needed for DSS analysis. This study examines the accuracy of one automated DSS software program, DSSA Version 2.0, compared to manual DSS scoring on previously collected language samples from 30 children between the ages of 2;5 and 7;11 (years;months). The overall accuracy of DSSA 2.0 was 86%. Additionally, the present study sought to determine the relationship between DSS, DSSA Version 2.0, the mean length of utterance (MLU), and age. MLU is a measure of linguistic ability in children, and is a widely used indicator of language impairment. This study found that MLU and DSS are both strongly correlated with age and these correlations are statistically significant, r = .605, p < .001 and r = .723, p < .001, respectively. In addition, MLU and DSSA were also strongly correlated with age and these correlations were statistically significant, r = .605, p < .001 and r = .669, p < .001, respectively. The correlation between MLU and DSS was high and statistically significant r = .873, p < .001, indicating that the correlation between MLU and DSS is not simply an artifact of both measures being correlated with age. Furthermore, the correlation between MLU and DSSA was high, r = .794, suggesting that the correlation between MLU and DSSA is not simply an artifact of both variables being correlated with age. Lastly, the relationship between DSS and age while controlling for MLU was moderate, but still statistically significant r = .501, p = .006. Therefore, DSS appears to add information beyond MLU.