Utterance Manual for CHAT/CLAN
Utterances in CHAT/CLAN are separated using a T-unit classification.
A T-unit consists of an independent clause and its corresponding depending clauses.
An independent clause includes a subject and a verb.
A dependent clause provides additional information to an independent clause, but it cannot stand by itself.
For example, if you were transcribing “I went to the store, but I didn’t find anything to buy,” you would separate this into two utterances in CHAT/CLAN.
*PAR: I went to the store.
*PAR: but I didn’t find anything to buy.
Even though these two parts go together in a sentence, they would be separated into two utterances per the T-unit classification.
An example of a dependent and independent clause would be as follows:
*PAR: If I show up late the teacher will give me a tardy.
In CHAT/CLAN, you also need to make a few judgement calls.
For example, in speech, we often start sentences with “because.” “Because” is typically at the beginning of a dependent clause in written communication; however, we use this to start sentences when speaking and thus a transcriber needs to decide whether the “because” is actually starting the sentence in a spoken utterance.
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Dear Kim, in our spoken data of elderly L1 attriters and L2 learners, we hand-coded a specific syntactic complexity tier for information such as clause type, number of finite and non-finite verbs, and noun phrase length tier to measure syntactic complexity because the measures we were interested in were not directly supported by CLAN. We based our choice of measures on Bulté and Housen's and Norris and Ortega's work; you might want to look into their findings. Regarding your data, a relatively simple way to get at two widely-used syntactic complexity data would be to proceed the way described in the material you cited and additionally introduce a tier where you code for every clause whether it is a independent or dependent clause. Using CLAN to calculate the number of dependent and independent clauses in each transcript as well as the total number of words in each transcript would then allow you to calculate the average length of a T-unit in words (total words/number of indep. clauses) and a kind of subordination ratio (dependent/independent clauses). However, I don't know if these measures are appropriate for relating them to the risk of AD. Also, since you are dealing with spoken data, I'd recommend looking into AS- instead of T-units as your unit of analysis (see Foster's work). Regards, Rasmus Steinkrauss
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