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.

Some syntactic aspects of the oral language of 20 mildly intellectually handicapped, 20 normal seven year old and 20 normal ten year old children were examined in order to determine the comparative development of the mildly intellectually handicapped children and some of the difficulties they might experience. The language was classified into 24 categories for various types of analysis. These types included traditional counts and an examination of the types of subordination as well as of non-conventional usage. In addition, Developmental Sentence Scoring (Lee : 1974) was used to assess the maturity of personal pronoun and main and secondary verb usage. The sentence repetition technique was employed as a means of assessing competence in a variety of later-developing structures. Questions were designed to assess ability in other specific syntactic areas. Analysis of variance was used to compare group scores and determine if any significant differences occurred. Several significant differences did occur. The findings provided strong evidence that the language of mildly intellectually handicapped children is more like that of children of the same chronological age than it is like that of children of the same mental age and that it is less mature than the former. These handicapped children experience considerable delay in the development of pronouns and verbs and have a high incidence of non-conventional usage. This study also provided evidence of the continuing language development of normal primary age children. Some methods of sampling and analysing oral language were found to be of particular value. Of these the sentence repetition technique seems promising both as a research tool and as a classroom instrument for assessing individual children's language competence. The importance of this and similar research lies in its implications for educational programming.

Developmental Sentence Scoring (DSS; Lee 1974) is a well established, structured method for analyzing a child's expressive syntax within the context of a conversational speech sample. Automated DSS programs may increase efficiency of DSS analysis; however the program must be accurate in order to yield valid and reliable results. A recent study by Sagae, Lavie, and MacWhinney (2005) proposed a new method for analyzing the accuracy of automated language analysis programs. This method was used in addition to previously established methods to analyze the accuracy of a new automated DSS program, entitled DSSA (Channell, 2006). Previously collected language samples from 118 children between the ages of 3 and 11 years in age were manually and automatedly coded for DSS. The overall accuracy of DSSA was about 86%, while the mean point difference was approximately .7. DSSA generally scored language samples of children achieving lower manual DSS scores or children with language impairment with less accuracy than those of other children. While some precautions may need to be taken, accuracy levels are sufficiently high to allow the fully automated use of DSSA as an alternative to manual DSS scoring.

Grammatical classification ("tagging") of words in language samples is a component of syntactic analysis for both clinical and research purposes. Previous studies have shown that probability-based software can be used to tag samples from adults and typically-developing children with high (about 95%) accuracy. The present study found that similar accuracy can be obtained in tagging samples from school-aged children with and without language impairment if the software uses tri-gram rather than bi-gram probabilities and large corpora are used to obtain probability information to train the tagging software.

A variety of tools have been developed to assist in the quantification and analysis of naturalistic language samples. In recent years, computer technology has been employed in language sample analysis. This study compares a new automated index, Scoring Sentences Developmentally (SSD), to two existing measures. Eighty samples from three corpora were manually analyzed using DSS and MLU and the processed by the automated software. Results show all three indices to be highly correlated, with correlations ranging from .62 to .98. The high correlations among scores support further investigation of the psychometric characteristics of the SSD software to determine its clinical validity and reliability. Results of this study suggest that SSD has the potential to compliment other analysis procedures in assessing the language development of young children.