This lab is due February 25<\/b>.<\/p>\n
To do this lab you will need access to the CHILDES data and the CLAN analysis program. The analysis programs can be used either online or locally (available on Mac, Windows, and some flavor of Unix). The description here will be primarily for the CHILDES transcript browser<\/a> (\u00e2\u20ac\u0153CTB\u00e2\u20ac\u009d) interface. (Another online option is the more bare-bones WebCLAN<\/a> interface).<\/p>\n On this page, you will find information on:<\/p>\n (The task you\u00e2\u20ac\u2122ll be doing in this lab assignment was originally formulated by Martha McGinnis<\/a>, U. Calgary)<\/em><\/p>\n <\/a>The CLAN program is really a collection of several different commands that you can execute to analyze your data. There are two ways that you can use it. The simplest is to simply use the CHILDES transcript browser<\/a> (\u00e2\u20ac\u0153CTB\u00e2\u20ac\u009d) online, but you can also download and install it on your computer<\/a>, along with the corpus we\u00e2\u20ac\u2122ll be working with. Each has advantages and disadvantages, but my advice is to use the online CTB for this assignment, and to download the program and data if you intend to do your course project using CHILDES.<\/p>\n The instructions here will be tailored to the CTB, but you can refer to the notes at the end of this page about using the CLAN program on your own computer for comments on how things differ if you run the program locally, as well as a bit more elaboration on the advantages and disadvantages.<\/p>\n <\/a>A CLAN instruction comes in three parts. Here is an example of one such instruction. The first part names the sub-program (or, as I\u00e2\u20ac\u2122ll call it, the \u00e2\u20ac\u0153command<\/b>\u00e2\u20ac\u009d). In the example below it is The second part, after the command, contains the parameters<\/b>. These modify the way in which the command operates. Above, there is one parameter given: Important note:<\/b> There must be no spaces in The third part of the instruction, In the CTB, the command is chosen using a popup menu in the lower left corner of the screen. The parameters and files to analyze are typed into the blank space right next to the popup menu. When you are ready to run the command, you press the The result of running a CLAN instruction will be displayed on the right side of the screen. As far as I know, there is no good way to capture this information except to select it, copy it, and paste it into a text editor (Notepad, TextEdit, Word, whatever you prefer).<\/p>\n <\/a>In the upper left corner, click Eng-USA<\/b>, then Suppes<\/b>. You should now see several files like <\/a>The lab assignment comes in several parts.<\/p>\n \n <\/li>\n <\/a>In the first section, we\u00e2\u20ac\u2122ll isolate a common verb in each of a couple of transcripts, and determine how often the subject is omitted, to get some experience with CLAN and to get some baseline information.<\/p>\n <\/a>Use the Hand in<\/b>: A list containing, for each file from nina01.cha to nina19.cha, Nina\u00e2\u20ac\u2122s age from the transcript, and the MLU as computed by the <\/a>We are going to be doing the analysis on two of Nina\u00e2\u20ac\u2122s transcripts, which we will take to be representative. Those two files are The The result should be a list of words along with a count of how many times each word appears in the transcript. To double-check, you should find that there are 21 instances of \u00e2\u20ac\u0153Mommy\u00e2\u20ac\u009d in Hand in:<\/b> For each file ( <\/a>Having picked a common verb from each file, what we\u00e2\u20ac\u2122re going to do is look at each time the verb is used in the transcript and count how often it appears with a subject (the idea being that we can extrapolate this result to estimate overall rates of subject omission).<\/p>\n To perform the search, use the CLAN command The results of these searches will be somewhat large. You will want to copy and paste the results into a text file (using whatever you find convenient, e.g., TextEdit, Microsoft Word, whatever you have on hand).<\/p>\n Hand in:<\/b> The full <\/a>Now, go through each example and decide which of the following categories it falls under. Be sure to read the \u00e2\u20ac\u0153exclusion\u00e2\u20ac\u009d criteria carefully.<\/p>\n X. Excluded.<\/b> The utterance is (a) a repetition of an immediately preceding utterance (either by the child or the adults), (b) incomprehensible, (c) part of a rote-learned expression (e.g., \u00e2\u20ac\u0153\u00e2\u20ac\u00a6how I wonder what you are\u00e2\u20ac\u009d), (d) an imperative or infinitive where a subject is not required in the adult language. We do not want to count these because they are not certain to reflect the child\u00e2\u20ac\u2122s productive grammar, or because no subject is required in the adult language.<\/p>\n O. Overt subject.<\/b> The verb has an overt (pronounced) subject. I include among these cases where there is a modal (like will<\/i>) or auxiliary (like am<\/i> or don\u00e2\u20ac\u2122t<\/i> or go<\/i>) before the verb.<\/p>\n N. Null subject.<\/b> The verb should have had a subject, but the subject is missing.<\/p>\n F. Fragment.<\/b> These look a lot like null subjects, but if a child answers a question like \u00e2\u20ac\u0153what are you doing?\u00e2\u20ac\u009d with \u00e2\u20ac\u0153Eating sandwiches\u00e2\u20ac\u009d, it isn\u00e2\u20ac\u2122t accurate to call that a null subject utterance. However, in response to \u00e2\u20ac\u0153What were the monkeys eating?\u00e2\u20ac\u009d, \u00e2\u20ac\u0153Eating a balloon\u00e2\u20ac\u009d should count as a null subject (not as a fragment), since this is not a well-formed fragment in adult speech. We will exclude these from the analysis, but you might as well differentiate them from the X category.<\/p>\n Hand in:<\/b> Create a 2×3 table of results (2 rows and 3 columns) like the one below. Fill in the overt and null subject numbers for each file. In the third column, compute the percentage of included utterances for each file that have overt subjects (divide the number of overt subjects by the sum of both overt and missing subjects, and then multiply by 100).<\/p>\n Hand in: Write a couple of sentences that describe the results in the table.<\/b><\/p>\n\n
Picking a version of CLAN to use<\/h2>\n
The structure of a CLAN instruction<\/h2>\n
mlu<\/code>. The mlu<\/code> command computes the MLU (mean length of utterance) from the utterances in a transcript file. You will also be making use of the commands freq<\/code> and combo<\/code> before we are finished. For a more complete description of the available commands, you can consult the CLAN manual<\/a>.<\/p>\nmlu +t*CHI nina*<\/code><\/p>\n+t*CHI<\/code>. Transcripts that are in the standard format (called CHAT<\/a>) are organized into \u00e2\u20ac\u0153tiers\u00e2\u20ac\u009d, and the major tiers represent who is speaking at any given point in the transcript. These major tiers are named with an asterisk as the first character. It is common for *CHI to be the tier assigned to the child. What +t*CHI<\/code> will do in the instruction above is tell the mlu<\/code> command to only consider utterances in the *CHI<\/code> tier (\u00e2\u20ac\u0153+t<\/code>\u00e2\u20ac\u009d stands for \u00e2\u20ac\u0153examine tier\u00e2\u20ac\u009d). That is, you only want the MLU to be computed for the child utterances. You can also examine other tiers (like *MOT<\/code> for \u00e2\u20ac\u0153mother\u00e2\u20ac\u009d, if that\u00e2\u20ac\u2122s what it\u00e2\u20ac\u2122s called in the transcript) in the same way.<\/p>\n+t*CHI<\/code>. Parameters are separated by spaces, but +t*CHI<\/code> is a single parameter.<\/p>\nnina*<\/code> above, indicates which transcripts<\/b> you wish to process. You can name a particular file (such as nina09.cha<\/code>), or you can use the asterisk as above as a \u00e2\u20ac\u0153wildcard.\u00e2\u20ac\u009d The effect of using nina*<\/code> is that it will process all of the files whose names begin with \u00e2\u20ac\u0153nina<\/code>\u00e2\u20ac\u009d (so, \u00e2\u20ac\u0153nina01.cha<\/code>\u00e2\u20ac\u009d, \u00e2\u20ac\u0153nina02.cha<\/code>\u00e2\u20ac\u009d, and so on).<\/p>\nRun<\/code> button (or just hit return in the text box). In the list of transcripts that appears in the upper left corner of the screen, you will see a [+]<\/code> symbol after each transcript. Clicking on the [+]<\/code> is a shortcut that will put the name of the transcript at the end of the text in the instruction box.<\/p>\nLocating Nina\u00e2\u20ac\u2122s transcripts<\/h2>\n
nina01.cha<\/code>; these are the transcripts we will be analyzing in this exercise.<\/p>\nYour assignment<\/h2>\n
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Section 1: Sampling subject drop rate<\/h2>\n
Part 1: Determine Nina\u00e2\u20ac\u2122s age and MLU for files 01-19.<\/h3>\n
mlu<\/code> command to determine the MLU for these transcripts (note that there is no file nina08.cha<\/code>). The command described above should do it. The MLU is the \u00e2\u20ac\u0153ratio of morphemes over utterances.\u00e2\u20ac\u009d To get Nina\u00e2\u20ac\u2122s age, you can look inside each transcript, it is recorded at the top.<\/p>\nmlu<\/code> command.<\/p>\nPart 2: Determine the word frequencies for two representative files.<\/h3>\n
nina10.cha<\/code> and nina19.cha<\/code>. The goal of this part is to pick a verb in each of the transcripts that occurs often. Later, we will examine all utterances containing these verbs to count things like the number of null subjects. The freq<\/code> command will search a transcript and count the number of times each word appears, then provide you with a list.<\/p>\nfreq<\/code> command works just like mlu<\/code> did. The parameters are the same. We want to restrict the frequency computation to just the child utterances, and we are looking at just the files nina10.cha<\/code> and nina19.cha<\/code>. (You can if you wish put both file names after the parameters, and it will do the computation on both of them in a single Run<\/code>, or you can do them separately.)<\/p>\nnina10.cha<\/code> and 2 in nina19.cha<\/code>. These results are sorted alphabetically by word, rather than by most common. In each file, look for verbs, and look for the verbs that are the most frequent. For these purposes, ignore the following verbs: have<\/b>, go<\/b>, want<\/b> (because they can be used as an auxiliary and might behave differently) and see<\/b> (because it so often occurs as \u00e2\u20ac\u0153See?\u00e2\u20ac\u009d which is correct without a subject). Pick the most frequent verb from each file (excluding those just listed).<\/p>\nnina10.cha<\/code>, nina19.cha<\/code>), the verb you\u00e2\u20ac\u2122ll look at and the number of times it occurs (according to freq<\/code>). Don\u00e2\u20ac\u2122t forget to count all forms (so, not just the 1sg present form, but also the past form and the progressive –ing<\/i> form). As a check, the forms I picked each have 24 occurrences.<\/p>\nPart 3: Search the transcripts for the examples.<\/h3>\n
combo<\/code> (you might want to refer to the notes on combo<\/a> at the bottom of this page). You will want to use the -w2<\/code> parameter in order to display the two lines preceding any matches, so you can get an idea of the context in which the matched utterances occur. The context will allow you to determine whether an utterance should be excluded. Here too, be sure you include all forms of the verb (1st present, past tense, -ing<\/i> form, etc.). You can search for more than one form using something like +s\"(see+seeing+saw+seen)\"<\/code> as a parameter.<\/p>\ncombo<\/code> instruction you used (you can copy this from the top of the search result) and the number of matches it returned (you can copy this from the bottom of the search result).<\/p>\nPart 4: Count up and report on the totals<\/h3>\n