A number of analyses have been conducted on the ENNI normative data set in order to derive norms. Stories should be evaluated using both macrostructural and microstructural analyses in order to capture the full range of narrative skills. Macrostructural analyses focus on the overall content and organization of stories. Story Grammar is a way to evaluate the macrostructure of stories. In contrast, microstructure approaches focus on relationships among parts of stories (Hughes, McGillivray & Schmidek, 1997). These types of measures thus each contribute to our overall analysis of story quality; no one measure appears to capture everything that contributes to perceived story quality (McCabe & Peterson, 1984). In order to evaluate story quality, attention must be given to both the macrostructure and microstructure of stories.
Besides being examples of stories and thus extended language use, the ENNI samples are also language samples and thus can be analysed as any other language samples. Thus we also include commonly used language sampling measures and norms for these measures. It must be remembered when using these norms that such measures are affected by the genre of language; thus narrative samples are likely to be different in some ways from conversational samples, for example (MacLachlan & Chapman, 1988).
Norms are available for the following
(Referring expressions/referential cohesion)
Language Sampling analyses
(MLCU, Complexity Index)