Tools for Analyzing Talk
Part 2: The CLAN Program

September 9, 2024
Brian MacWhinney
Carnegie Mellon University
https://doi.org/10.21415/T5G10R

When citing the use of TalkBank facilities, please use this reference to the last printed
version of the CHILDES manual:
MacWhinney, B. (2000). The CHILDES Project: Tools for Analyzing Talk. 3rd Edition.
Mahwah, NJ: Lawrence Erlbaum Associates
This allows us to systematically track usage of the programs and data throughscholar.google.com.

Part 2: CLAN
1

Getting Started........................................................................................................... 8
1.1
1.2
1.3
1.4
1.5

2

Why you want to learn CLAN ........................................................................................8
Learning CLAN....................................................................................................................8
Installing CLAN – MacOS .................................................................................................9
Installation Problems – MacOS ....................................................................................9
Installing CLAN – Windows ...........................................................................................9

Using the Web ......................................................................................................... 10
2.1
2.2
2.3
2.4

3

2

Community Resources ................................................................................................. 10
Downloading Materials ............................................................................................... 10
Using the Browsable Database ................................................................................. 10
Downloading Transcripts and Media ..................................................................... 11

Tutorial ..................................................................................................................... 12
3.1 The Commands Window ............................................................................................. 12
3.1.1 Setting the Working Directory ........................................................................................... 12
3.1.2 Output and Mor Lib Directories ........................................................................................ 13
3.1.3 The Recall Button .................................................................................................................... 13
3.1.4 The Progs Menu........................................................................................................................ 13
3.1.5 The FILE IN Button ................................................................................................................. 13
3.1.6 The TIERS Button .................................................................................................................... 14
3.2 Typing Command Lines ............................................................................................... 14
3.2.1 The Asterisk Wildcard ........................................................................................................... 15
3.2.2 Output Files ................................................................................................................................ 15
3.2.3 Redirection ................................................................................................................................. 16
3.3 Sample Runs..................................................................................................................... 16
3.3.1 Sample KWAL Run .................................................................................................................. 16
3.3.2 Sample FREQ Run .................................................................................................................... 17
3.3.3 Sample MLU Run ...................................................................................................................... 17
3.3.4 Sample COMBO Run................................................................................................................ 18
3.3.5 Sample GEM and GEMFREQ Runs .................................................................................... 18
3.4 Advanced Commands ................................................................................................... 19
3.5 Exercises............................................................................................................................ 22
3.5.1 MLU50 Analysis ........................................................................................................................ 23
3.5.2 MLU5 Analysis .......................................................................................................................... 25
3.5.3 MLT Analysis ............................................................................................................................. 25
3.5.4 TTR Analysis .............................................................................................................................. 26
3.5.5 Generating Language Profiles ............................................................................................ 27
3.6 Further Exercises ........................................................................................................... 28

4

The Editor ................................................................................................................. 30
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10

Screencasts ....................................................................................................................... 30
Text Mode vs. CHAT Mode .......................................................................................... 30
File, Edit, Format, and Font Menus .......................................................................... 31
Mode Menu ....................................................................................................................... 31
Default Window Positioning, Size, and Font Control ........................................ 31
CA Styles ............................................................................................................................ 32
Setting Special Colors ................................................................................................... 33
Searching........................................................................................................................... 33
Send to Sound Analyzer ............................................................................................... 33
Tiers Menu Items ...................................................................................................... 33

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3

4.11
Running CHECK Inside the Editor ....................................................................... 34
4.12
Preferences and Options ........................................................................................ 34
4.13
Coder Mode.................................................................................................................. 35
4.13.1Entering Codes .................................................................................................................... 35
4.13.2
Setting Up Your Codes File ............................................................................................. 36

5Media Linkage......................................................................................................... 38
5.1 Media Formats ................................................................................................................ 38
5.2 Sonic Mode........................................................................................................................ 39
5.3 Transcriber Mode .......................................................................................................... 40
5.3.1 Linking to an already existing transcript ...................................................................... 40
5.3.2 To create a new transcript................................................................................................... 41
5.3.3 Sparse Annotation ................................................................................................................... 42
5.4 Video Linking................................................................................................................... 42
5.5 Walker Controller .......................................................................................................... 43
5.6 Playback Control ............................................................................................................ 44
5.7 Multiple Video Playback.............................................................................................. 44
5.8 Manual Editing ................................................................................................................ 45
5.9 Video Skipping ................................................................................................................ 45
5.10
Audio Anonymization .............................................................................................. 46

6

Other Features ........................................................................................................ 47
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10

7

Supplementary Commands ........................................................................................ 47
Online Help....................................................................................................................... 47
Help for the +sm and +sg switches .......................................................................... 47
Keystroke Listing ........................................................................................................... 47
Macros ................................................................................................................................ 47
Aliases ................................................................................................................................ 47
Testing CLAN.................................................................................................................... 48
Bug Reports ...................................................................................................................... 48
Feature Requests ........................................................................................................... 49
Types of CLAN Commands ..................................................................................... 49

Analysis Commands .............................................................................................. 50
7.1 CHAINS ............................................................................................................................... 51
7.1.1 Sample Runs............................................................................................................................... 51
7.1.2 Unique Options ......................................................................................................................... 53
7.2 Chatter ............................................................................................................................... 54
7.3 CHECK ................................................................................................................................. 54
7.3.1 How CHECK works .................................................................................................................. 54
7.3.2 CHECK in CA Mode .................................................................................................................. 55
7.3.3 Running CHECK ........................................................................................................................ 55
7.3.4 Restrictions on Word Forms............................................................................................... 55
7.3.5 Unique Options ......................................................................................................................... 56
7.4 CHIP..................................................................................................................................... 56
7.4.1 The Tier Creation System..................................................................................................... 56
7.4.2 The CHIP Coding System ...................................................................................................... 58
7.4.3 Word Class Analysis ............................................................................................................... 59
7.4.4 Summary Measures ................................................................................................................ 59
7.4.5 Unique Options ......................................................................................................................... 60
7.5 CHIPUTIL ........................................................................................................................... 61

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4

7.6 COMBO ............................................................................................................................... 62
7.6.1 Composing Search Strings ................................................................................................... 62
7.6.2 Examples of Search Strings ................................................................................................. 63
7.6.3 Referring to Files in Search Strings ................................................................................. 64
7.6.4 COMBO for %mor and %gra sequences ........................................................................ 64
7.6.5 Cross-tier COMBO .................................................................................................................... 65
7.6.6 Cluster Sequences in COMBO ............................................................................................. 65
7.6.7 Tracking Final and Initial Words ...................................................................................... 65
7.6.8 Limiting with COMBO .......................................................................................................... 66
7.6.9 Adding Codes with COMBO ................................................................................................. 66
7.6.10Unique Options .................................................................................................................... 67
7.7 COOCUR ............................................................................................................................. 69
7.7.1 Unique Options ......................................................................................................................... 69
7.8 DIST ..................................................................................................................................... 70
7.8.1 Unique Options ......................................................................................................................... 70
7.9 FREQ .................................................................................................................................... 71
7.9.1 What FREQ ignores ................................................................................................................. 71
7.9.2 Studying Lexical Groups using the +s@file switch ................................................... 71
7.9.3 FREQ for %mor and %gra combinations ...................................................................... 72
7.9.4 Searches in Multilingual Corpora ..................................................................................... 72
7.9.5 Building Concordances with FREQ .................................................................................. 72
7.9.6 Using Wildcards with FREQ ................................................................................................ 73
7.9.7 FREQ for the %mor line ........................................................................................................ 75
7.9.8 Errors for morphological codes ........................................................................................ 76
7.9.9 Directing the Output of FREQ ............................................................................................. 76
7.9.10Limiting in FREQ ................................................................................................................. 77
7.9.11Creating Crosstabulations in FREQ ............................................................................ 78
7.9.12
TTR for Lemmas.................................................................................................................. 78
7.9.13
Studying Unique Words and Shared Words ........................................................... 79
7.9.14
Grammatical Complexity Analysis through FREQ ............................................... 79
7.9.15Unique Options .................................................................................................................... 81
7.9.16Further Illustrations.......................................................................................................... 83
7.10
FREQMERG ................................................................................................................... 85
7.11
FREQPOS ....................................................................................................................... 85
7.11.1Unique Options .................................................................................................................... 85
7.12
GEM ................................................................................................................................. 86
7.12.1
Sample Runs ......................................................................................................................... 86
7.12.2
Limiting with GEM ............................................................................................................. 87
7.12.3
Unique Options .................................................................................................................... 87
7.13
GEMFREQ ...................................................................................................................... 88
7.13.1Unique Options .................................................................................................................... 88
7.14
GEMLIST........................................................................................................................ 89
7.15
KEYMAP ........................................................................................................................ 89
7.15.1
7 Sample Runs ..................................................................................................................... 90
7.15.2
Unique Options.................................................................................................................... 90
7.16
KWAL ............................................................................................................................. 90
7.16.1
Tier Selection in KWAL .................................................................................................... 91
7.16.2KWAL for breaking up files by code type................................................................. 91
7.16.3KWAL for %mor and %gra combinations ............................................................... 92
7.16.4KWAL with signs and speech ........................................................................................ 92

Part 2: CLAN
7.16.57.17
7.17.1
7.17.27.18
7.18.1
7.18.2
7.19
7.19.1
7.19.27.20
7.20.1
7.20.2
7.20.37.20.4
7.21
7.22
7.22.1
7.23
7.23.1
7.23.2
7.23.37.23.47.23.5
7.24
7.25
7.25.1
7.25.2
7.25.37.25.47.25.57.26

8

5Unique Options .................................................................................................................... 93
MAXWD ......................................................................................................................... 94
Unique Options .................................................................................................................... 94
Unique Options .................................................................................................................... 95
MLT ................................................................................................................................. 95
MLT defaults ......................................................................................................................... 96
Unique Options .................................................................................................................... 96
MLU................................................................................................................................. 97
Exclude files for MLU and MLT................................................................................ 100
Unique Options ................................................................................................................. 101
MODREP ..................................................................................................................... 102
Exclusions and Inclusions............................................................................................ 103
Using a %mod Line ......................................................................................................... 104
MODREP for the %mor line ........................................................................................ 104
Unique Options ................................................................................................................. 105
Phon and PhonTalk ............................................................................................... 105
RELY ............................................................................................................................ 106
Unique Options ................................................................................................................. 108
SCRIPT ........................................................................................................................ 108
The Model Script .............................................................................................................. 108
The Participant’s Script ................................................................................................ 109
Running SCRIPT ............................................................................................................... 109
Variations............................................................................................................................ 110
Unique Options ................................................................................................................. 110
TIMEDUR ................................................................................................................... 110
VOCD ........................................................................................................................... 111
Origin of the Measure .................................................................................................... 111
Calculation of D ................................................................................................................ 112
Sample Size ........................................................................................................................ 113
VOCD Running and Output .......................................................................................... 113
Unique Options ................................................................................................................. 115
WDLEN ....................................................................................................................... 115

Profiling Commands ........................................................................................... 117
8.1 C-NNLA ............................................................................................................................ 117
8.1.1 CHAT File Format Requirements ................................................................................... 117
8.1.2 C-NNLA Output ...................................................................................................................... 118
8.2 C-QPA ............................................................................................................................... 120
8.2.1 CHAT File Format Requirements ................................................................................... 120
8.2.2 C-QPA Output ......................................................................................................................... 121
8.3 CORELEX ......................................................................................................................... 124
8.3.1 CHAT File Format Requirements ................................................................................... 124
8.3.2 Unique Options ...................................................................................................................... 125
8.4 DSS .................................................................................................................................... 125
8.4.1 CHAT File Format Requirements ................................................................................. 125
8.4.2 Selection of a 50-sentence Corpus ................................................................................ 126
8.4.3 Automatic Calculation of DSS .......................................................................................... 126
8.4.4 Sentence Points ..................................................................................................................... 127
8.4.5 DSS Output ............................................................................................................................. 127
8.4.6 DSS Summary ........................................................................................................................ 127

Part 2: CLAN

6

8.4.7 DSS for Japanese.................................................................................................................... 129
8.4.8 How DSS works...................................................................................................................... 131
8.4.9 Unique Options ...................................................................................................................... 132
8.5 EVAL and EVAL-D ........................................................................................................ 132
8.5.1 Explanation of EVAL Measures....................................................................................... 133
8.5.2 EVAL Demo.............................................................................................................................. 135
8.5.3 EVAL Output ........................................................................................................................... 136
8.5.4 Comparing Multiple Transcripts .................................................................................... 137
8.5.5 Unique Options ...................................................................................................................... 137
8.6 FLUCALC ......................................................................................................................... 138
8.6.1 FluCalc Measures .................................................................................................................. 139
8.6.2 Unique Options ...................................................................................................................... 141
8.7 IPSYN ............................................................................................................................... 141
8.7.1 Rule Syntax .............................................................................................................................. 142
8.7.2 Cascading Credit.................................................................................................................... 143
8.7.3 IPSYN Rules ............................................................................................................................. 144
8.7.4 Unique Options ...................................................................................................................... 149
8.8 KIDEVAL ......................................................................................................................... 149
8.8.1 KIDEVAL for summaries .................................................................................................... 149
8.8.2 Creating a custom language script ................................................................................ 153
8.8.3 KIDEVAL with a comparison database ........................................................................ 153
8.8.4 Unique Options ...................................................................................................................... 155
8.9 MORTABLE .................................................................................................................... 155
8.10
SUGAR ......................................................................................................................... 156

9Morphological Analysis: UD and MOR ........................................................... 157
9.1
9.2
9.3

10

Alignment ...................................................................................................................... 158%mor item syntax ...................................................................................................... 158%gra syntax and graphs ........................................................................................... 158

Format Conversion Commands ....................................................................... 160

10.1
CHAT2ANVIL ............................................................................................................ 160
10.2
CHAT2CA ................................................................................................................... 160
10.3
CHAT2ELAN .............................................................................................................. 160
10.4
CHAT2PRAAT........................................................................................................... 162
10.5
CHAT2SRT ................................................................................................................. 162
10.6
CHAT2TEXT .............................................................................................................. 162
10.7
CHAT2XMAR............................................................................................................. 162
10.8
ANVIL2CHAT ............................................................................................................ 163
10.9
ELAN2CHAT .............................................................................................................. 163
10.10 LAB2CHAT................................................................................................................. 164
10.11 LENA2CHAT .............................................................................................................. 164
10.12 LIPP2CHAT ............................................................................................................... 164
10.13 PRAAT2CHAT........................................................................................................... 165
10.14 RTF2CHAT................................................................................................................. 165
10.15 SALT2CHAT .............................................................................................................. 165
10.15.1 Unique Options ................................................................................................................. 166
10.16 SRT2CHAT ................................................................................................................. 166
10.17 TEXT2CHAT .............................................................................................................. 166

11

Reformatting Commands .................................................................................. 168

Part 2: CLAN
11.1
11.1.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8
11.9
11.10
11.11
11.12
11.13

12

7

CHSTRING .................................................................................................................. 168
Removing bullets ............................................................................................................. 169
DATES ......................................................................................................................... 170
FLO ............................................................................................................................... 170
INDENT ....................................................................................................................... 171
LINES ........................................................................................................................... 171
LONGTIER .................................................................................................................. 171
MEDIALINE ............................................................................................................... 171
REPEAT ...................................................................................................................... 171
RETRACE .................................................................................................................... 171
ROLES.......................................................................................................................... 171
SEGMENT ................................................................................................................... 172
TIERORDER .............................................................................................................. 172
TRIM ............................................................................................................................ 172

Format Repair Commands ................................................................................ 174

12.1
12.2
12.3
12.4
12.5
12.6
12.7

COMBTIER ................................................................................................................. 174
CP2UTF ....................................................................................................................... 174
DELIM ......................................................................................................................... 174
FIXBULLETS .............................................................................................................. 174
FIXIT ............................................................................................................................ 175
LOWCASE ................................................................................................................... 175
QUOTES ...................................................................................................................... 175

13Supplementary Commands............................................................................... 176

14

Options .................................................................................................................... 178

13.1
13.2
13.3
13.4
13.5
13.6

14.1
14.2
14.3
14.4
14.5
14.6
14.7
14.8
14.9
14.10
14.11
14.12
14.13
14.14

15

batch ........................................................................................................................... 176
cd .................................................................................................................................. 176
dir ................................................................................................................................. 176
info ............................................................................................................................... 176
ren(ame).................................................................................................................... 176
rm ................................................................................................................................. 177+F Option ................................................................................................................... 178+K Option .................................................................................................................. 179+L Option ................................................................................................................... 179+P Option................................................................................................................... 179+R Option .................................................................................................................. 180+S Option ................................................................................................................... 180+T Option................................................................................................................... 182+U Option .................................................................................................................. 183+V Option................................................................................................................... 183+W Option ................................................................................................................. 184+X Option................................................................................................................... 184+Y Option................................................................................................................... 184+Z Option ................................................................................................................... 185
Metacharacters for Searching ........................................................................... 185

References .............................................................................................................. 187

Part 2: CLAN

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8

Getting Started

This manual describes the use of the CLAN program, designed and written by Leonid
Spektor at Carnegie Mellon University. The acronym CLAN stands for Computerized
Language ANalysis. CLAN is designed specifically to analyze data transcribed in the
CHAT format. This is the format used in the various segments of the TalkBank system.
There are three parts to the overall TalkBank manual. Part 1 describes the CHAT
transcription system. Part 2 (this current manual) describes the CLAN analysis programs.
Part 3 describes the segments of the CLAN program that perform automatic
morphosyntactic analysis.

1.1 Why you want to learn CLAN
If you are a researcher studying conversational interaction, language learning, or
language disorders, you will want to learn to use CLAN, because it will help you address
basic research questions and explore many different language types. If you are a clinician,
CLAN can help you analyze data from individual clients and compare them against a large
database of similar transcripts. For both these purposes, CLAN emphasizes the automaticcomputation of indices such as MLU, TTR, DSS, and IPSyn. It also provides powerful
methods for speeding transcription, linking transcripts to media, sending data to automaticacoustic analysis, and automatic computation of a wide range of morphosyntactic features.
For conversation analysts, CLAN provides the full range of Jeffersonian markings within
a computationally clear framework. For all these purposes, CLAN is available free, as is
the huge TalkBank database of transcripts compatible with CLAN analyses.

1.2 Learning CLAN
The first six chapters of this manual provide a basic introduction to CLAN. However,most users will find that it is best to begin learning about CLAN, CHILDES, and TalkBankby working through the screen tutorials to be found at https://talkbank.org/screencasts.
Those screencasts will give you a clearer view of the actual process of data entry andanalysis and then you can refer to the following six chapters for explicit descriptions of themethods.
1. Chapter 1 explains how to install and configure CLAN. This process has different steps,
depending on whether you are using Windows or MacOS.
2. Chapter 2 explains how to access and use materials from the CHILDES and TalkBank
homepages on the web.
3. Chapter 3 provides a tutorial on how to begin using CLAN commands.
4. Chapter 4 explains how to use the editor.
5. Chapter 5 explains how to link transcripts to media.
6. Chapter 6 provides advanced exercises for learning CLAN.
Ideally, you should work through all six chapters in that order. However, some users
may wish to skip some sections. If you are not interested in transcribing new data, you canskip chapters 4 and 5 on the editor and linkage. People working with CA (Conversation
Analysis) will probably not need to read chapter 3 on CLAN commands. The examples
and analyses all focus on child language data. People working with other language types

Part 2: CLAN

9such as aphasia, adult conversation, or second language may wish to use practice theexercises with CHAT files and media appropriate to those areas.

1.3 Installing CLAN – MacOS
Here is how to install and configure CLAN for MacOS:
1. Download the Mac version of CLAN from the link labeled “CLAN” at
https://talkbank.org .
2. Click to open clan.dmg and then click to start the installer.
3. The system will complain about something from an unidentified developer and then
you will have to open the Security and Privacy Panel in System Preferences, selectGeneral, open the lock, and click on "Anywhere".
4. CLAN will install in your Applications folder and your working directory will be:
Applications/CLAN/work. For shared computers, there is also an option to installin ~/Applications.
5. Drag the CLAN file icon into the dock to create a link for easy access.
6. Go to System Preferences and select Keyboard. Check the two boxes there to use
standard function keys and to show Keyboard Viewers in the menu bar.
7. Once CLAN is installed, you will probably also want to install the MOR grammar
for your language. You can do this by selecting “Install MOR grammar” from theFile menu, as described at https://talkbank.org/screencasts/mor-download.mp4.
This will also set your path for MORLIB to the correct location.

1.4 Installation Problems – MacOS
If you are not able to start CLAN after installation or at some later point, the problem
may be related to window positioning. Do you see on the left of menu bar next to Apple
Icon the "CLANc" word? If you do, then CLANc did start. Then select "Window>Commands" menu or press command-D keys on keyboard. If you still do not see
Commands window, then try menu "Edit->Reset options" and follow instructions in the
pop-up dialog window. If it doesn't work, then quit CLANc, start it again and select "File>New" menu, next select "Edit->Reset options" menu again. If you do not see on the leftof menu bar next to Apple Icon the "CLANc" word, then try deleting CLANc from
Applications folder, empty trash and reboot your Mac. Next reinstall CLANc and try again.

1.5 Installing CLAN – Windows
Here is how to install and configure CLAN for Windows:
1. Download the Windows version of CLAN from the link labeled “CLAN” at
https://talkbank.org .
2. CLAN will automatically install in c:/TalkBank and your working directory will be
c:\TalkBank\CLAN\work. The installer will create shortcuts for CLAN and the\work folder.
3. Once CLAN is installed, you will probably also want to install the MOR grammar
for your language. You can do this by selecting “Install MOR grammar” from theFile menu, as described at https://talkbank.org/screencasts/mor-download.mp4
This will also set your path for MORLIB to the correct location.

Part 2: CLAN

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10

Using the Web

In this chapter, we will first survey some of the community resources available at the
TalkBank homepages. Then we will learn about how to download and play transcripts andlinked media, and how to use the Browsable Database.

2.1 Community Resources
From the TalkBank homepage at https://talkbank.org, look at the community resource
information at these links under System:
1. Ground rules. Whenever using TalkBank data, remember to cite the sources provided.
2. Contributing New Data. How to configure new research projects for eventual
inclusion in TalkBank.
3. IRB Principles. We explain how to configure consent forms to specify the levels ofconfidentiality protection appropriate for your project.
4. Programs. Manuals and programs.
Then take a quick look at the homepages for AphasiaBank, BilingBank, CHILDES,SLABank, PhonBank, CABank, and other banks. Finally, look at the information about
Google Group mailing lists and membership.

2.2 Downloading Materials
By default, CLAN materials will download to your desktop. You can then download
additional materials, such as the manuals. Rather than printing out the long manuals, it isbest to keep them in your /work folder and access them through Adobe Reader.

2.3 Using the Browsable Database
The Browsable Database facility allows you to playback transcripts with linked media
directly from your browser. Here are the steps to follow:
1. Click on the Browsable Database link on the homepage for the TalkBank database you
are using. When the new page opens, glance over the instructions. You can always
come back to read these in detail later.
2. For an example in the CHILDES database, you can select EngUK/Forrester/biggirl.cha in the navigation area to the left. The screencase athttps://talkbank.org/screencasts/browsable-watch.mp4 illustrates this further.
3. Study the display to get a sense of what a CHAT file looks like. There are headers forthe first 11 lines and then the dialog begins on line 12. *E: is the child Ella and *F: isher father.
4. Each line is linked to the corresponding segment of the video, and both will play backover the web. Place your cursor on the right arrow on line 11 and either click or press“s”. Usually, it takes a few seconds to establish the initial web connection, but playbackis smooth after that.
5. If the video is stopped, pressing the “s” key starts it. If the video is playing, pressing
the “s” key stops it. You can just follow along with continuous playback or you canPart 2: CLAN

11

select certain segments to play.

2.4 Downloading Transcripts and MediaIf you want to study transcripts more closely, you will probably want to download
them, rather than playing through the Browsable Database. Using the Forrester transcripts
as an example, here is how you do this:
1. At https://childes.talkbank.org click on Index to Corpora, then Eng-UK and
Forrester.
2. Click on the link called download transcripts and the .zip file will download to yourcomputer.
3. If it is not automatically unzipped, you should unzip it.
4. To download the media, click on Link to Media Folder and you can then download
individual videos one by one. Downloading media takes a lot more time thandownloading transcripts.

Part 2: CLAN

312

TutorialOnce you have installed CLAN, you start it by double-clicking on its icon or its
shortcut.

3.1 The Commands Window
After this, a window titled Commands opens and you can type commands into thiswindow. If the window does not open automatically, then type Control-d (Windows) or⌘-d (Macintosh). This window controls many of the functions of CLAN. It remains active
until the program is terminated. The main components of the Commands window are thecommand box in the center and the several buttons. There is also some text in the bottom
line giving you the data when your version of CLAN was compiled.
Sometimes the Commands window may disappear into the areas off the screen andthen it can be difficult to drag it back. When this happens, you can go to the top menu Editand pull down to Reset Options and the window should reappear.

3.1.1 Setting the Working Directory
The first thing you need to do when running CLAN is to set the working directory.
The working directory is the place where the files you would like to work with are located.
For the tutorial examples, we use the CLAN /examples directory as our working directory.
To set the working directory, press the working button in the Command window and use
the navigation facility to locate the examples directory inside the CLAN directory andpress the Select Current Directory button.
After selecting your working directory, you will return to the Commands window.
The directory you selected will be listed to the right of the working button. This is useful
because you will always know what directory you are working in without having to leave
the Commands window. You can also double-click on the actual name of the working
directory to see and go back to other directories you have recently visited.
To test your installation, type the command “freq sample.cha” into the Commands
window. Then either hit the return key or press the Run button. You should get thefollowing output in the CLAN Output window.
> freq sample.cha
freq sample.cha
Tue Aug 7 15:51:12 2007
freq (03-Aug-2007) is conducting analyses on:
ALL speaker tiers****************************************
From file <sample.cha>
1 a
1 any
1 are
5 chalk
1 delicious
1 don't---- (more lines here)
2 you------------------------------

Part 2: CLAN
32
51
0.627

13

Total number of different item types used
Total number of items (tokens)
Type/Token ratio

The output continues down the page. The exact shape of this window will depend on
how you have sized it.

3.1.2 Output and Mor Lib Directories
You do not need to worry about setting the LIB directory, because CLAN sets this to
the /lib folder in the CLAN distribution by default. You typically also do not have to worry
about setting your output directory, because it will be the same as your working directory.
However, if you want output files to go to some other directory, you can use this button tonavigate to some other output folder. If you want to undo that action, you can use the button
to navigate back to your working directory and it will again become the default.
If you are using the Get MOR Grammar function in the CLAN File menu, it will
download MOR to your desktop and set the path automatically. Alternatively, you may
wish to control where to put your MOR grammars and then you need to set the mor lib
button on your own for this.

3.1.3 The Recall Button
If you want to see some of your old commands, you can use the recall function. Just hit
the Recall button and you will get a window of old commands. The Recall window contains a list of the last 20 commands entered in the Commands window. These commands
can be automatically entered in the Commands window by double-clicking on the line.
This is particularly useful for repetitive tasks and tracking command strings. Another way
to access previously used commands is by using the  arrow on the keyboard. This willenter the previous command into the Commands window each time the key is pressed.

3.1.4 The Progs Menu
The Progs menu gives you a list of CLAN commands you can run. Try clicking thisbutton and then selecting the FREQ command. The name of the command will then be
inserted into the Commands window.

3.1.5 The FILE IN Button
Once you have selected the FREQ command, you now see that the File In button will
be available. Click on this button and you will get a dialog that asks you to locate some
input files in your working directory. The files on the left are the items in your working
directory. The files on the right will be the ones used for analysis. The Remove button thatappears under the Files for Analysis scrolling list is used to eliminate files from theselected data set. The Clear button removes all the files you have added. The radio button
at the bottom right allows you to see only *.cha and *.cex files if you wish. When you arefinished adding files for analysis, hit Done. After the files are selected and you havereturned to the Commands window, an @ is appended onto the command string. Thissymbol represents the set of files listed. In this case, the @ represents the single filePart 2: CLAN

14“sample.cha”.

3.1.6 The TIERS Button
This button will allow you to restrict your analysis to a certain participant. For thisexample, we will restrict our analysis to the child, who is coded as *CHI in the transcript,
so we type “CHI” into the Tier Option dialog, leaving the button for “main tier” selected.

At this point, the command being constructed in the Commands window should look
like this: freq @ +t*CHI If you hit the RUN button at the bottom right of the Commands
window, or if you just hit a carriage return, the FREQ program will run and will display
the frequencies of the six words the child is using in this sample transcript.

3.2 Typing Command Lines
There are two ways to build up commands. You can build commands using buttons andmenus. However, this method only provides access to the most basic options, but you willfind it useful when you are beginning. Alternatively, you can just type in commands
directly to the Commands window. Let us try entering a command just by typing. Suppose
we want to run an MLU analysis on the sample.cha file. Let us say that we also want to
restrict the MLU analysis so that it looks only at the child’s utterances. To do this, we enter
the following command into the window:
mlu +t*CHI sample.cha

In this command line, there are three parts. The first part gives the name of thecommand; the second part tells the program to look at only the *CHI lines; and the thirdpart tells the program which file to analyze as input.
If you press the return key after entering this command, you should see a CLAN Output window that gives you the result of this MLU analysis. This analysis is conducted, by
default, on the %mor line which was generated by the MOR program. If a file does nothave this %mor line, then you will need to use other forms of the MLU command that only
count utterances in words. Also, you will need to learn how to use the various options,Part 2: CLAN

15such as +t or +f. One way to learn the options is to use the various buttons in the graphic
user interface as a way of learning what CLAN can do. Once you have learned these
options, it is often easier to just type in this command directly. However, in other cases, it
may be easier to use buttons to locate rare options that are hard to remember. The decisionof whether to type directly or to rely on buttons is one that is left to each user.
What if you want to send the output to a permanent file and not just to the temporary
CLAN Output window? To do this you add the +f switch:
mlu +t*CHI +f sample.cha

Try entering this command, ending with a carriage return. You should see a message
in the CLAN Output window telling you that a new file called sample.mlu.cex has beencreated. If you want to look at that file, type Control-O (Windows) or ⌘-o (Mac) for Open
File and you can use the standard navigation window to locate the sample.mlu.cex file. It
should be in the same directory as your sample.cha file.
You do not need to worry about the order in which the options appear. In fact, the only
order rule that is used for CLAN commands is that the command name must come first.
After that, you can put the switches and the file name in any order you wish.

3.2.1 The Asterisk Wildcard
CLAN uses several metacharacters or wildcards in commands. A very common wildcard is the asterisk symbol (*) which is used to mark the presence of any string. For example, if you want to run this command across a group of ten files all ending with the extension .cha, you can enter the command in this form:
mlu +tCHI +f *.cha

The asterisk wildcard can be used to refer to a group of files (*.cha), a group of speakers(CH*), or a group of words with a common form (*ing). To see how these could work
together, try out this command:
freq *.cha +s”*ing”

This command runs the FREQ program on all the .cha files in the LIB directory andlooks for all words ending in “-ing.” The output is sent to the CLAN Output window andyou can set your cursor there and scroll back and forth to see the output. You can print thiswindow or you can save it to a file.

3.2.2 Output Files
When you run a command with the +f option, the program will create an output filewith the .cex extension. It drops the .cha extension from the input file and then adds a twopart extension to indicate which command has run and the fact that this is CLAN output
file (.cex). If you run this command repeatedly, it will create additional files such as
sample.ml0.cex, sample.ml1.cex, sample.ml2.cex, and the like. You can add up to threeletters after the +f switch, as in:
mlu +fmot sample.cha

If you do this, the output file will have the name “sample.mot.cex.” As an example ofa case where this would be helpful, consider how you might want to have a group of output

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files for the speech of the mother and another group for the speech of the father. The
mother’s files would be named *.mot.cex and the father’s files would be named *.fat.cex.

3.2.3 Redirection
Instead of using the +f switch for output, you may sometimes want to use the redirect
symbol (>). This symbol sends all of the outputs to a single file. The individual analysis ofeach file is preserved and grouped into one output file that is named in the command string.
There are three forms of redirection, as illustrated in the following examples:
freq sample.cha > myanalyses
freq sample.cha >> myanalyses
freq sample.cha >& myanalyses

These three forms have slightly different results.
1. The single arrow overwrites material already in the file.
2. The double arrow appends new material to the file, placing it at the end of material
already in the file.
3. The single arrow with the ampersand writes both the analyses of the program andvarious system messages to the file.
If you want to analyze a whole collection of files and send the output from each to a
separate file, use the +f switch instead.

3.3 Sample Runs
Now we are ready to try out a few sample runs with the five most basic CLAN commands: KWAL, FREQ, MLU, COMBO, and GEM.

3.3.1 Sample KWAL Run
KWAL searches data for user-specified words and outputs those keywords in context.
The +s option is used to specify the words to be searched. The context or cluster is a combination of main tier and the selected dependent tiers in relation to that line. The following
command searches for the keyword “bunny” and shows both the two sentences preceding
it, and the two sentences following it in the output. To access the 0042.cha file, you needto change your working directory to the /transcripts folder inside the examples folder.
kwal +sbunny -w2 +w2 0042.cha

The -w and +w options indicate how many lines of text should be included before andafter the search words. A segment of the output looks as follows:---------------------------------------*** File "0042.cha": line 2724. Keyword: bunny
*CHI:
0 .
*MOT:
see ?
*MOT:
is the bunny rabbit jumping ?
*MOT:okay .
*MOT:
wanna [: want to] open the book ?
----------------------------------------

If you triple-click on the line with the three asterisks, the whole orginal transcript will

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open with that line highlighted. Repetitions and retracing will be excluded by default
unless you add the +r6 switch to the command.

3.3.2 Sample FREQ Run
FREQ counts the frequencies of words used in selected files. It also calculates the type–token ratio typically used as a measure of lexical diversity. In its simplest mode, it generates
an alphabetical list of all the words used by all speakers in a transcript along with the frequency with which these words occur. The following example looks specifically at thechild’s tier. The output will be printed in the CLAN window in alphabetical order:
freq +t*CHI 0042.cha

In this file, the child uses the filler “uh” a lot, but that is ignored in the analysis. The
output for this command is:> freq +t*CHI 0042.cha
freq +t*CHI 0042.cha
Sat Jun 14 14:38:12 2014
freq (13-Jun-2014) is conducting analyses on:
ONLY speaker main tiers matching: *CHI;****************************************
From file <0042.cha>
Speaker: *CHI:
1 ah
2 bow+wow
1 vroom@o-----------------------------3 Total number of different item types used
4 Total number of items (tokens)
0.750 Type/Token ratio

A statistical summary is provided at the end. In the above example, there were a totalof 4 words or tokens used with only 3 different word types. The type–token ratio is foundby dividing the total of unique words by the total of words spoken. For our example, thetype–token ratio would be 3 divided by 4 or 0.750.
The +f option can be used to save the results to a file. CLAN will automatically add the.frq.cex extension to the new file it creates. By default, FREQ excludes the strings xxx,
yyy, www, as well as any string immediately preceded by one of the following symbols:
0, &, +, -, #. However, FREQ cludes all retraced material unless otherwise commanded.
For example, given this utterance:*CHI: the dog [/] dog barked.

FREQ would give a count of one for the word dog. If you wish to include retraced material,
use the +r6 option. To learn more about the many variations in FREQ, read the section
devoted specifically to this useful command.

3.3.3 Sample MLU Run
The MLU command is used primarily to determine the mean length of utterance of a
specified speaker. It also provides the total number of utterances and of morphemes in a
file. The ratio of morphemes over utterances (MLU) is derived from those two totals. The
following command would perform an MLU analysis on the mother’s tier (+t*MOT) from

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18

the file 0042.cha:
mlu +t*MOT 0042.cha

The output from this command looks like this:> mlu +t*MOT 0042.cha
mlu +t*MOT 0042.cha
Sat Jun 14 14:41:48 2014
mlu (13-Jun-2014) is conducting analyses on:
ONLY dependent tiers matching: %MOR;****************************************
From file <0042.cha>
MLU for Speaker: *MOT:
MLU (xxx, yyy and www are EXCLUDED from the utterance and morphemecounts):Number of: utterances = 511, morphemes = 1588
Ratio of morphemes over utterances = 3.108
Standard deviation = 2.214

Thus, we have the mother’s MLU or ratio of morphemes over utterances (3.108) andher total number of utterances (511).

3.3.4 Sample COMBO Run
COMBO is a powerful program that searches the data for specified combinations ofwords or character strings. For example, COMBO will find instances where a speaker says
kitty twice in a row within a single utterance. The following command would search themother’s tiers (+t*MOT) of the specified file 0042.cha:
combo +tMOT +s"kitty^kitty" 0042.cha

Here, the string +tMOT selects the mother’s speaker tier only for analysis. Whensearching for a combination of words with COMBO, it is necessary to precede the combination with +s (e.g., +s"kitty^kitty") in the command line. The symbol ^ specifies thatthe word kitty is immediately followed by the word kitty. The output of the command used
above is as follows:> combo +tMOT +s"kitty^kitty" 0042.chakitty^kitty
combo +tMOT +skitty^kitty 0042.cha
Sat Jun 14 14:44:21 2014
combo (13-Jun-2014) is conducting analyses on:
ONLY speaker main tiers matching: *MOT;****************************************
From file <0042.cha>---------------------------------------*** File "0042.cha": line 3034.
*MOT:(1)kitty (1)kitty kitty .
---------------------------------------*** File "0042.cha": line 3111.
*MOT:
and (1)kitty (1)kitty .
Strings matched 2 times

3.3.5 Sample GEM and GEMFREQ Runs
GEM and GEMFREQ look at previously tagged selections or “gems” within larger

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19transcripts for further analyses. For example, we might want to divide the transcript bydifferent social situations or activities. In the 0012.cha file, there are gem markers
delineating the segment of the transcript that involves book reading, using the code word“book”. By dividing the transcripts in this manner, separate analyses can be conducted oneach situation type. Once this is done, you can use this command to compute a frequency
analysis for material in these segments:
gemfreq +t*CHI +sbook 0012.cha

The output is as follows:> gemfreq +sbook +t*CHI 0012.cha
gemfreq +sbook +t*CHI 0012.cha
Sat Jun 14 14:54:19 2014
gemfreq (13-Jun-2014) is conducting analyses on:
ONLY speaker main tiers matching: *CHI;
and ONLY header tiers matching: @Bg:; @Eg:;****************************************
From file <0012.cha>
24 tiers in gem "book":
2 kitty
2 no+no2 oh
2 this

GEM and GEMFREQ are particularly useful in corpora such as the AphasiaBank
transcripts. In these, each participant does a retell of the Cinderella story that is marked
with @G: Cinderella. Using the three Kempler files, the following command will create
three new files with only the Cinderella segment:
gem +sCinderella +n +d1 +t*PAR +t%mor +f *.cha

You can then run further programs such as MLU or FREQ on these shorter files.

3.4 Advanced Commands
This section provides a series of CLAN commands designed to illustrate a fuller range
of options available in some of the most popular CLAN commands. With a few exceptions,the commands are designed to run on the Adler directory included in the /examples folder.
So, you should begin by opening CLAN and setting your working directory to the Adler
folder. Each command is followed by an English-language explanation of the meaning ofeach of the terms in the command, translating in order from left to right. You should testout each command and study its results. To save typing, you can cut cut each commandfrom this document and paste it into the CLAN Commands window and then hit a carriage
return.
Run KWAL on the Participant looking for "slipper" in all the files:
kwal +t*PAR +s"slipper*" *.cha

Run KWAL on the Participant looking for "because" in adler23a.cha:kwal +t*PAR +s”because” adler23a.cha

Run KWAL on the Participant looking specifically for "because" transcribed as(be)cause (produced as "cause") in adler 23a.cha:kwal +t*PAR +s”(be)cause” adler23a.cha +r2

Part 2: CLAN

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Run KWAL on the Participant on the list of words in the whwords.cut file in the/examples/pos folder. You will need to copy that file into the /Adler folder.
kwal +t*PAR +s@whwords.cut adler23a.cha

Run KWAL on the Participant to exclude utterances coded with the post-code [+ exc]
and create new files in legal CHAT format for all the files:
kwal -s"[+ exc]" +d+t*PAR +t%mor +t@ +f *.cha

Run COMBO on the Participant to find all sequences of "fairy” followed immediately
by “godmother" and combine the results from all the files into a single file:
combo +t*PAR +sfairy^godmother +u *.cha

Run COMBO on the Participant's %mor tier to find all combinations of infinitive andverb in adler01a.cha:
combo +s"inf|*^v|*" +t*PAR +t%mor adler01a.cha

Run MAXWD on the Participant to get the longest utterance in words in all files:
maxwd +g2 +t*PAR *.cha

Run EVAL on the Participant to get a spreadsheet with summary data (duration, MLU,
TTR, % word errors, # utterance errors, % various parts of speech, # repetitions, and #revisions) in all the files. Add +o4 to get output in raw numbers instead of percentages.
eval +t*PAR +u *.cha

This program is similar to EVAL, but tailored for child data:
kideval +t*PAR +leng *.cha

Run MLU on the Participant, creating one spreadsheet for all files. Add -b to get mlu
in words:
mlu +t*PAR +d +u *.cha

Run MLT on the Participant, creating one spreadsheet for all files. MLT counts
utterances and words on a line that may include xxx (unlike MLU):
mlt +t*PAR +d *.cha

Run TIMEDUR on the Participant, creating a spreadsheet with ratio of words andutterances over time duration for all files:
timedur +t*PAR +d10 *.cha

Run GEM on the Participant, including the %mor line, using the “Sandwich” gem withlazy gem marking, outputting legal CHAT format for adler07.cha:
gem +t*PAR +t%mor +sSandwich +n +d1

adler07a.cha

Same thing, excluding irrelevant lines:
gem +t*PAR +t%mor +sSandwich +n +d1-s"[+ exc]" adler07a.cha

Run GEM on the Participant main tier and %mor tier for the Sandwich “gem”, using
lazy gem marking, create a new file in legal CHAT format called "Sand" for all Adler files
gem +t*PAR +t%mor +sSandwich +n +d1 +fSand *.cha

Run VOCD on the Participant, output to spreadsheet only, and include repetitions andrevisions in all the files:

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vocd +t*PAR +d3 +r6 *.cha

Run CHIP to compare the Mother and the Child in terms of utterance overlaps withboth the previous speaker (%chi and %adu, echoes) and their own previous utterances(%csr and %asr, self-repetitions) in chip.cha:chip +bMOT +cCHI chip.cha(the chip.cha file is in examples/progs)

Same thing, but excluding printing of the results for the self-repetitions:chip +tMOT +cCHI –ns chip.cha

The next commands all use the FREQ program to illustrate various options.
Run FREQ on the Participant tier and get output in order of descending frequency foradler01a.cha:
freq +t*PAR +o adler01a.cha

Run FREQ on the Participant tier and send output to a spreadsheet for adler01a.cha. To
open the spreadsheet, triple-click on stat.frq.xls:
freq +t*PAR +d2 adler01a.cha

Same, on all the files in Adler:
freq +t*PAR +d2 *.cha

Same, but only include Anomics:
freq +t@"ID=*|Anomic|*" +d2 *.cha

Run FREQ on the Participant tier and get type token ratio only in a spreadsheet foradler01a.cha:
freq +t*PAR +d3 adler01a.cha

Run FREQ on the Participant %mor tier and not the Participant speaker tier and get
output in order of descending frequency for adler01a.cha:
freq +t%mor +t*PAR -t* +o adler01a.cha

Run FREQ on the Participant %mor tier for stems only (happily and happier = happy)
and get output in order of descending frequency for adler01a.cha:
freq +t*PAR +t%mor -t* +s"mr-*,o-%” +o adler01a.cha

Learn how to use the +s switch for analysis of the %mor line
freq +sm

Learn how to use the +s switch for analysis of the %gra line
freq +sg

Run FREQ on the Participant tier, include fillers "uh" and "um", and get output in orderof descending frequency for adler01a.cha:
freq +t*PAR +s+&uh +s+&um +o adler01a.cha

Run FREQ on the Participant tier and count instances of unintelligible jargon foradler01a.cha:
freq +t*PAR +s"xxx" adler01a.cha

Same, but adding +d to see the actual place of occurrence, then triple-click on any linethat has a file name to open the original:Part 2: CLAN

22

freq +t*PAR +s"xxx" +d adler01a.cha

Run FREQ on the Participant tier, counting instances of gestures for adler01a.cha:
freq +t*PAR +s&=ges* adler01a.cha

Run FREQ on the Participant tier, including repetitions and revisions, excluding
neologisms (nonword:unknown target), and getting output in order of descending
frequency for adler01a.cha. Add +d6 to include error production info. Add +d4 for typetoken info only.
freq +t*PAR +r6 -s"<*\* n:uk*>" +o adler01a.cha

Run FREQ on the Participant, searching for a list of words in a 0list.cut file you havecreated with multiple words searched per line, where multiple words do not have to befound in consecutive alignment, but must be in the same utterance, and merging output
across all files:
freq +t*PAR +s@0list.cut +c3 +u *.cha

Same with +d added for outputting the original utterance:
freq +t*PAR +s@0list.cut +c3 +u +d *.cha

Here are some additional switches for making specific exclusions:•add -s*\** if you want to exclude words that were produced in error (coded withany of the [* errorcodes] on the main tier)• add +r5 if you want to exclude any text replacements (horse [: dog], beds [: breads])• add +r6 if you want to include repetitions and revisions
The final section in the description of the FREQ command gives many further detailed
examples of how to use FREQ with the %mor and %gra tier.

3.5 Exercises
This section presents exercises designed to help you think about the application of
CLAN for specific aspects of language analysis. The illustrations in the section below are
based on materials developed by Barbara Pan originally published in Chapter 2 of Sokolov
and Snow (1994).They are included in the /examples/transcripts/ne20 folder. The originaltext has been edited to reflect subsequent changes in the programs and the database.
Barbara Pan devised the initial form of this extremely useful set of exercises and kindlyconsented to their inclusion here.
One approach to transcript analysis focuses on the computation of certain measures orscores that characterize the stage of language development in the children or adults in thesample.
1. One popular measure (Brown, 1973) is the MLU or mean length of utterance, which
can be computed by the MLU program.
2. A second measure is the MLU of the five longest utterances in a sample, or MLU5.
Wells (1981) found that increases in MLU of the five longest utterances tend to parallel
those in MLU, with both levelling off after about 42 months of age. Brown suggested
that MLU of the longest utterance tends, in children developing normally, to beapproximately three times greater than MLU.

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3. A third measure is MLT or Mean Length of Turn which can be computed the the MLT
program.
4. A fourth popular measure of lexical diversity is the type–token ratio of Templin (1957)
In these exercises, we will use CLAN to generate these four measures of spontaneous
language production for a group of normally developing children at 20 months. The goals
are to use data from a sizeable sample of normally developing children to inform us as tothe average (mean) performance and degree of variation (standard deviation) among children at this age on each measure; and to explore whether individual children's performance
relative to their peers was constant across domains. That is, were children whose MLU was
low relative to their peers also low in terms of lexical diversity and conversational participation? Conversely, were children with relatively advanced syntactic skills as measured by
MLU also relatively advanced in terms of lexical diversity and the share of the conversational load they assumed?
The speech samples analyzed here are taken from the New England corpus of the
CHILDES database, which includes longitudinal data on 52 normally developing children.
Spontaneous speech of the children interacting with their mothers was collected in a playsetting when the children were 14, 20, and 32 months of age. Transcripts were preparedaccording to the CHAT conventions of the Child Language Data Exchange System,
including conventions for morphemicizing speech, such that MLU could be computed interms of morphemes rather than words. Data were available for 48 of the 52 children at 20
months. The means and standard deviations for MLU5, TTR, and MLT reported below are
based on these 48 children. Because only 33 of the 48 children produced 50 or moreutterances during the observation session at 20 months, the mean and standard deviation
for MLU50 is based on 33 subjects.
For illustrative purposes, we will discuss five children: the child whose MLU was thehighest for the group (68.cha), the child whose MLU was the lowest (98.cha), and one childeach at the first (66.cha), second (55.cha), and third (14.cha) quartiles. Transcripts for these
five children at 20 months can be found in the /examples/transcripts/ne20 directory.
Our goal is to compile the following basic measures for each of the five target children:
MLU on 50 utterances, MLU of the five longest utterances, TTR, and MLT. We then compare these five children to their peers by generating z-scores based on the means and standard deviations for the available sample for each measure at 20 months. In this way, wewere will generate language profiles for each of our five target children.

3.5.1 MLU50 Analysis
The first CLAN analysis we will perform involves calculating MLU for each child ona sample of 50 utterances. By default, the MLU program runs on the %mor line that isalready present in these files. This means that it computes the mean length of utterance interms of morphemes, not words. Also by default, the MLU program excludes the strings
xxx, yyy, www, as well as any string immediately preceded by one of the following
symbols: 0, &, +, -, #, $, or : (see the CHAT manual for a description of transcription
conventions). The MLU program also excludes from all counts material in angle brackets
followed by [/], [//], or [% bch] (see the CLAN manual for list of symbols CLAN considers
to be word, morpheme, or utterance delimiters). Remember that to perform any CLAN

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analysis, you need to be in the directory where your data is when you issue the appropriate
CLAN command. In this case, we want to be in the /examples/transcripts/ne20 folder.
The command string we used to compute MLU for all five children is:
mlu +t*CHI +z50u +f *.cha+t*CHI
Analyze the child speaker tier only
+z50u
Analyze the first 50 utterances only+f
Save the results in a file*.chaAnalyze all files ending with the extension .cha

The only constraint on the order of elements in a CLAN command is that the name ofthe program (here, MLU) must come first. Many users find it good practice to put the nameof the file on which the analysis is to be performed last, so that they can tell at a glanceboth what program was used and what file(s) were analyzed. Other elements may come inany order.
The option +t*CHI tells CLAN that we want only CHI speaker tiers considered in theanalysis. Were we to omit this string, a composite MLU would be computed for all speakers
in the file.
The option + z50u tells CLAN to compute MLU on only the first 50 utterances. We
could, of course, have specified the child’s first 100 utterances (+z100u) or utterances fromthe 51st through the 100th (+z51u-100u). With no +z option specified, MLU is computedon the entire file.
The option +f tells CLAN that we want the output recorded in output files, rather thansimply displayed onscreen. CLAN will create a separate output file for each file on whichit computes MLU. If we wish, we may specify a three-letter file extension for the output
files immediately following the +f option in the command line. If a specific file extension
is not specified, CLAN will assign one automatically. In the case of MLU, the default
extension is .mlu.cex. The .cex at the end is mostly important for Windows, since it allows
the Windows operating system to know that this is a CLAN output file.
Finally, the string *.cha tells CLAN to perform the analysis specified on each file ending in the extension .cha found in the current directory. To perform the analysis on a single
file, we would specify the entire file name (e.g., 68.cha). It was possible to use the wildcard* in this and following analyses, rather than specifying each file separately, because all thefiles to be analyzed ended with the same file extensions and were in the same directory;
and in each file, the target child was identified by the same speaker code (i.e., CHI), thus
allowing us to specify the child’s tier by means of +t*CHI.
Utilization of wildcards whenever possible is more efficient than repeatedly typing insimilar commands. It also cuts down on typing errors. For illustrative purposes, let us
suppose that we ran the above analysis on only a single child (68.cha), rather than for all
five children at once (by specifying *.cha). We would use the following command:
mlu +t*CHI +z50u 68.cha

The output for this command would be as follows:> mlu +t*CHI +z50u 68.cha
mlu +t*CHI +z50u 68.cha
Tue Jun 24 17:15:38 2014
mlu (24-Jun-2014) is conducting analyses on:
ONLY dependent tiers matching: %MOR;

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From file <68.cha>
MLU for Speaker: *CHI:MLU (xxx, yyy and www are EXCLUDED from the utterance and morphemecounts):Number of: utterances = 50, morphemes = 133
Ratio of morphemes over utterances = 2.660
Standard deviation = 1.595

MLU reports the number of utterances (in this case, the 50 utterances we specified),the number of morphemes that occurred in those 50 utterances, the ratio of morphemes
over utterances (MLU in morphemes), and the standard deviation of utterance length inmorphemes. The standard deviation statistic gives some indication of how variable thechild’s utterance length is. This child’s average utterance is 2.660 morphemes long, with astandard deviation of 1.595 morphemes.
Check line 1 of the output for typing errors in entering the command string. Check lines
3 and possibly 4 of the output to be sure the proper speaker tier and input file(s) were
specified. Also, check to be sure that the number of utterances or words reported is what
was specified in the command line. If CLAN finds that the transcript contains fewer
utterances or words than the number specified with the +z option, it will still run theanalysis but will report the actual number of utterances or words analyzed.

3.5.2 MLU5 Analysis
The second CLAN analysis we will perform computes the mean length in morphemesof each child’s five longest utterances. To do this, we will run MAXWD on the five files
in the ne20 folder and then MLU on the output of MAXWD. By default, MAXWD runson the %mor line, rather than the main line.
maxwd +t*CHI +g1 +c5 +d1 *.cha+gl Identify the longest utterances in terms of morphemes+c5 Identify the five longest utterances+d1 Output the data in CHAT format*.cha
The child language transcripts to be analyzed
We then run MLU on the *.cex files that were the output of the previous command.
mlu *.cex

3.5.3 MLT Analysis
The third analysis we will perform is to compute MLT (Mean Length of Turn) for bothchild and mother. Note that, unlike the MLU program, the CLAN program MLT includes
the symbols xxx and yyy in all counts. Thus, utterances that consist of only unintelligible
vocal material still constitute turns, as do nonverbal turns with only “0” on the main line.
We can use a single command to run our complete analysis and put all the results into asingle file.
mlt *.cha > allmlt.cex

In this output file, the results for the mother in 68.cha are:

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From file <68.cha>
MLT for Speaker: *MOT:
MLT (xxx, yyy and www are EXCLUDED from the word counts, but are
INCLUDED in utterance counts):
Number of: utterances = 356, turns = 227, words = 1360
Ratio of words over turns = 5.991
Ratio of utterances over turns = 1.568
Ratio of words over utterances = 3.820

There is similar output data for the child. This output allows us to consider Mean
Length of Turn either in terms of words per turn or utterances per turn. We chose to use
words per turn in calculating the ratio of child MLT to mother MLT, reasoning that wordsper-turn is likely to be sensitive for a somewhat longer developmental period. MLT ratio,
then, was calculated as the ratio of child words/turn over mother words/turn. As the child
begins to assume a more equal share of the conversational load, the MLT ratio should
approach 1.00. For file 68.cha, this ratio is: 2.184 ÷ 5.991 = 0.365.

3.5.4 TTR Analysis
The fourth CLAN analysis we will perform for each child is to compute the TTR ortype–token ratio. For this we will use the FREQ command. By default, FREQ ignores thestrings xxx (unintelligible speech) and www (irrelevant speech researcher chose not totranscribe). It also ignores words beginning with the symbols 0, &, +, -, or #. If you wantto create a TTR based just on the full words on the main line, you will get this automaticallywhen you run just freq *.cha. However, for TTR and other lexical diversity measures, weare interested not in whether the child uses plurals or past tenses, but how many different
dictionary words (lemmas) she uses. Therefore, we wanted to count cats and cat as two
tokens (i.e., instances) of the lemma cat. Similarly, we wanted to count play and played as
two tokens of the lemma play. To make these distinctions correctly, we need to track thestem forms that are given on the %mor line. This means that we need to first use MOR and
POST to create a %mor line for our transcript. The process of doing this is described in
the MOR manual. For now, we will assume that the transcripts already have this %mor
line. In that case, the command we use is:freq +t*CHI +sm;*,o% +f *.cha+t*CHI
Analyze the child speaker only
+sm;*,o% Search for roots or lemmas and ignore the rest+f
Save output in a file*.chaAnalyze all files ending with the extension .cha
The only new element in this command is +sm;*o%. The +s option tells FREQ tosearch for and count certain strings. The m tells the program to look at the %mor line. The
semicolon says to look at the stem and then the *,o% says to ignore everything else. If you
type freq +sm you will see explanations of the use of the +sm switch. The output generated
from this analysis goes into five files. For the ne20/68.cha input file, the output is
68.frq.cex. At the end of this file, we find this summary analysis:
81
242
0.335

Total number of different item types used
Total number of items (tokens)
Type/Token ratio

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We can look at each of the five output files to get this summary TTR information foreach child.

3.5.5 Generating Language Profiles
Once we have computed these basic measures of utterance length, lexical diversity, andconversational participation for our five target children, we need to see how each childcompares to his or her peers in each of these domains. To do this, we use the means andstandard deviations for each measure for the whole New England sample at 20 months, as
given in the following table.
Measure

Mean

SD

Range

MLU50

1.406

0.360

1.00-2.66

MLU5 longest

2.936

1.271

1.00-6.40

TTR

0.433

0.108

0.255-0.611

MLT Ratio

0.189

0.089

0.034-0.438

The distribution of MLU50 scores was quite skewed, with most children who produced
at least 50 utterances falling in the MLU range of 1.00-1.30. As noted earlier, 17 of the 48
children failed to produce even 50 utterances. At this age most children in the sample are
essentially still at the one-word stage, producing few utterances of more than one word ormorpheme. Like MLU50, the shape of the distributions for MLU5 and for the MLT ratio
were somewhat skewed toward the lower end, though not as severely as was MLU50.
Z-scores, or standard scores, are computed by subtracting the sample mean score fromthe child’s score on a particular measure and then dividing the result by the overall standard
deviation: (child's score - group mean) / standard deviation. The results of this computation
are given in the following table.
Child

MLU50

MLU5

TTR

MLT Ratio

14

0.26

0.21

1.65

-0.16

55

-0.30

-0.15

-0.36

-0.53

66

-0.16

-0.11

-0.64

-0.84

68

2.30

2.72

-0.86

1.98

98

-0.96

-0.74

-0.63

-0.08

We would not expect to see radical departures from the group means on any of themeasures. For the most part, this expectation is borne out: we do not see departures greaterthan 2 standard deviations from the mean on any measure for any of the five children,
except for the particularly high MLU50 and MLU5 observed for Subject 068.
It is not the case, however, that all five of our target children have flat profiles. Somechildren show marked strengths or weaknesses relative to their peers in certain domains.
For example, Subject 14, although very close to the mean in terms of utterance length

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(MLU5O and MLU5), shows marked strength in lexical diversity (TTR), even though sheshoulders relatively little of the conversational burden (as measured by MLT ratio).
Overall, Subject 68 seems advanced on all measures except TTR. The subjects at thesecond and third quartile in terms of MLU (Subject 055 and Subject 066) have profiles thatare relatively flat: Their z-scores on each measure fall between -1 and 0. However, the childwith the lowest MLU50 (Subject 098) again shows an uneven profile. Despite her limited
production, she manages to bear her portion of the conversational load. You will recall thatunintelligible vocalizations transcribed as xxx or yyy, as well as nonverbal turns indicatedby the postcode [+ trn], are all counted in computing MLT. Therefore, it is possible thatmany of this child’s turns consisted of unintelligible vocalizations or nonverbal gestures.
What we have seen in examining the profiles for these five children is that, even amongnormally developing children, different children may have strengths in different domains,relative to their age mates. For illustrative purposes, we have considered only three domains, as measured by four indices. To get a more detailed picture of a child’s language
production, we might choose to include other indices, or to further refine the measures weuse. For example, we might compute TTR based on the number of words, or we mighttime-sample by examining the number of word types and word tokens the child produced
in a certain number of minutes of mother–child interaction. We might also consider othermeasures of conversational competence, such as number of child initiations and responses;
fluency measures, such as number of retraces or hesitations; or pragmatic measures, suchas variety of speech acts produced. Computation of some of these measures would requirethat codes be entered in the transcript prior to analysis; however, the CLAN analyses
themselves would, for the most part, simply be variations on the techniques discussed inthis chapter. In the exercises that follow, you will have an opportunity to use thesetechniques to perform analyses on these five children at both 20 months and 32 months.

3.6 Further Exercises
The files needed for the following exercises are in the /examples/transcripts/ne20 and/examples/transcripts/ne32 folders. No data are available for Subject 14 at 32 months.
1. Compute the length in morphemes of each target child’s single longest utterance at 20
months. Compare with the MLU of the five longest utterances. Consider why aresearcher might want to use MLU of the five longest rather than MLU of the single
longest utterance.
2. Use the +z option to compute TTR on each child’s first 50 words at 32 months. Thendo the same for each successive 50-word band up to 300. Check the output each time
to be sure that 50 words were in fact found. If you specify a range of 50 words where
there are fewer than 50 words available in the file, FREQ still performs the analysis,
but the output will show the actual number of tokens found. What do you observe about
the stability of TTR across different samples of 50 words?
3. Use the MLU and FREQ programs to examine the mother’s (*MOT) language to herchild at 20 months and at 32 months. What do you observe about the length/complexityand lexical diversity of the mother’s speech to her child? Do they remain generally thesame across time or change as the child’s language develops? If you observe change,
how can it be characterized?

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4. Perform the same analyses for the four target children for whom data are available atage 32 months. Use the data given earlier to compute z-scores for each target child oneach measure (MLU 50 utterances, MLU of five longest utterances, TTR, MLT ratio).
Then plot profiles for each of the target children at 32 months. What consistencies andinconsistencies do you see from 20 to 32 months? Which children, if any, have similar
profiles at both ages? Which children's profiles change markedly from 20 to 32 months?
5. Conduct a case study of a child you know to explore whether type of activity and/orinterlocutor affect mean length of turn (MLT). Videotape the child and mother engaged
in two different activities (e.g., bookreading, having a snack together, playing with afavorite toy). On another occasion, videotape the child engaged in the same activities
with an unfamiliar adult. Compare the MLT ratio for each activity and adult–child pair.
Describe any differences you observe.

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The Editor

CLAN includes an editor that is specifically designed to work cooperatively withCHAT files. To open up an editor window, either type ⌘-n (Control-n on Windows) for a
new file or ⌘-o to open an old file (Control-o on Windows). This is what a new text
window looks like on the Macintosh:

You can type into this editor window just as you would in any full-screen text editor,
such as MS-Word. In fact, the basic functions of the CLAN editor and MS-Word are all
the same. Some users say that they find the CLAN editor difficult to learn. However, on
the basic level it is no harder than MS-Word. What makes the CLAN editor difficult is thefact that it is used to transcribe the difficult material of child language data with all its
special forms, overlaps, and precise timings. These functions are outside of the scope of
editors, such as MS-Word or Pages.

4.1 Screencasts
Use of the tutorial can be supplemented through the online screencasts for specific
CLAN features found at https://talkbank.org/screencasts/ and on YouTube. These movies,
created by Davida Fromm and Brian MacWhinney, show the use of specific CLAN
functions in real time with real transcripts.

4.2 Text Mode vs. CHAT Mode
The editor works in two basic modes: Text Mode and CHAT Mode. In Text Mode, theeditor functions as a basic text editor. To indicate that you are in Text Mode, the bar at thebottom of the editor window displays [E][Text]. To enter Text Mode, you must uncheck
the CHAT Mode button on the Mode pulldown menu. In CHAT Mode, the editor facilitates
the typing of new CHAT files and the editing of existing CHAT files. If your file has the.cha extension, you will automatically be placed into CHAT Mode when you open it. To
indicate that you are in CHAT Mode, the bar at the bottom of the editor window displays[E][CHAT].
When you are first learning to use the editor, it is best to begin in CHAT mode. Whenyou start CLAN, it automatically opens a new window for text editing. By default, this filePart 2: CLAN

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will be opened using CHAT mode. You can use this editor window to start learning theeditor or you can open an existing CHAT file using the option in the File menu. It is probably easiest to start work with an existing file. To open a file, type Command-o (Macintosh)or Control-o (Windows). You will be asked to locate a file. Try to open the sample.cha filethat you will find in the Lib directory inside the CLAN directory or folder. This is just asample file, so you do not need to worry about accidentally saving changes.
You should stay in CHAT mode until you have learned the basic editing commands.
You can insert characters by typing in the usual way. Movement of the cursor with themouse and arrow keys works the same way as in Word or Pages. Functions like scrolling,highlighting, cutting, and pasting also work in the standard way. You should try out thesefunctions right away. Use keys and the scroll bar to move around in the sample.cha file.
Cut and paste sections and type a few sentences, just to convince yourself that you arealready familiar with the basic editor functions.

4.3 File, Edit, Format, and Font Menus
The functions of opening file, printing, cutting, undoing, and font changing are thesame as in Pages or Word. These commands can be found under the File, Edit, and Font
menus in the menu bar. The keyboard shortcuts for pulling down these menu items arelisted next to the menu options. Note that there is also a File function called “Save Last
Clip As ...” which you can use to save a time-delimited sound segment as a separate file.
This function works on Mac and older Windows systems. However, this function will notwork on versions of Windows after about 2016, because it relies on QuickTime, which is
no longer supported. In the newer version of CLAN for OSX, there is no longer a separate
Font menu and fonts are set through an option in the Format menu.

4.4 Mode Menu
The top section of this menu provides access to three important editor modes described
elsewhere in this manual: entering Coder Mode, setting CHAT mode, and showing line
numbers. The bottom section of the menu allows you to start up these editor functions,which are also described elsewhere in this manual: continuous playback, play bullet media,
transcribe sound or movie, check open file, disambiguate tier, expand bullets, and send tosound analyzer.

4.5 Default Window Positioning, Size, and Font Control
When CLAN starts up it will open a new Commands window and a new Text window
in the same position it used when you last ran CLAN. If you want to change the positionor size of a window, you can move it and resize it. The information regarding the size andposition of a given window is stored in a non-visible @Window line at the beginning of
the file. If you move and/or resize the window and save it, that information will be updated
so that the next time you open the file, it will be in the new size and position.
If you open a new text window, you can position and size it and close it. When you
then open another new window again, it will go to that position and size. However, thiswill not impact already existing files, because they use the information in their @Windowline. Repositioning also works in the same way for the Commands window, but you cannot

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resize the Commands window.
If the Settings option Use Newfile's windows Position and Size for all
files is selected, then the file is saved only if it has been edited
and changes need to be save. Otherwise, the file on the hard disk is notchanged at all. If the Settings option Use Newfile's windows Position and Size for all
files is not selected, then when the file is saved an @Window header will be added, even
if there are no changes in the file.
If you accidentally align the bottom of a window with the bottom of your screen, thelast lines of the window will be cut off. This can make it difficult to use functions like esc2 for disambiguation or Coder's Editor. To solve this problem, just open a new window ina higher position, close your earlier window, and then reopen.
You may also find it useful to have bottom and side scroll bars always visible. On
MacOS, you can set this option to "always visible" in System Preferences/General.
When starting up video playback, it can be the case that the movie window occupies
too much of the screen. To size it properly, you can click on the green button in the top of
the QuickTime video window and the window will be resized to the smallest dimension.
Then you drag on the botton right corner to expand it to the size you wish.
The system for controlling the default Font depends on your operating system. On
Windows (PC) there is a Font menu under View. You use the Set Font option to set the
text window font, and the Set Commands Font to set the Commands window font. The
option to Set Default Font is only needed in rare cases when no default font had yet been
selected. When using the View pulldown to change font or size, both the font and the sizemust be selected. If you select only one, no change will be made. On Macintosh, you can
use the Size/Style menu to control the font. To save your font selection, you must not only
save and quit the file, but also quit and then restart CLAN, because information about fontsize is stored not in the file, but in CLAN preferences.

4.6 CA Styles
CHAT supports many of the CA (Conversation Analysis) codes as developed by Sacks,
Schegloff, Jefferson (1974) and their students. The implementation of CA inside CLAN
was guided by suggestions from Johannes Wagner, Chris Ramsden, Michael Forrester, Tim
Koschmann, Charles Goodwin, and Curt LeBaron. Files that use CA styles should declarethis fact by including CA in the @Options line, as in this example:@Options:CA

By default, CA files will use the CAfont, because the characters in this font have afixed width, allowing the INDENT program to make sure that CA overlap markers areclearly aligned. When doing CA transcription, you can also select underlining and italics,
although bold is not allowed, because it is too difficult to recognize. Special CA characters
can be inserted by typing the F1 function key followed by some letter or number, as
indicated in a list that you can find by selecting Special Characters under CLAN’s
Windows menu. The full list is at https://ca.talkbank.org/codes.html .
The F1 and F2 keys are also used to facilitate the entry of special characters for Hebrew,
Arabic, and other systems. These uses are also listed in the Special Characters window.

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The raised h diacritic is bound to F1-shift-h and the subscript dot is bound to F1-comma.

4.7 Setting Special Colors
In CLAN for Windows, but not for macOS, you can set the color of certain tiers to
improve the readability of your files. To do this, select the Color Keywords option in theSize/Style pulldown menu. In the dialog that appears, type the tier that you want to color
in the upper box. For example, you may want to have %mor or *CHI in a special color.
Then click on “add to list” and edit the color to the type you wish. The easiest way to do
this is to use the crayon selector. Then make sure you select “color entire tier.” To learn
the various uses of this dialog, try selecting and applying different options.

4.8 Searching
In the middle of the Edit pulldown menu, you will find a series of commands forsearching. The Find command brings up a dialog that allows you to enter a search string
and to perform a reverse search. The Find Same command allows you to repeat that samesearch multiple times. The Go To Line command allows you to move to a particular line
number. The Replace command brings up a dialog like the Find dialog. However, this
dialog allows you to find a certain string and replace it with another one. You can replacesome strings and not others by skipping over the ones you do not want to replace with theFind-Next function. When you need to perform a large series of different replacements,
you can set up a file of replacement forms and use it by pressing the from file button. You
then are led through the words in this replacement file one by one. The form of that file islike this:“String_A”“String_B”“Replacement_A”“Replacement_B”

4.9 Send to Sound Analyzer
This option under the Mode menu allows you to send a bulleted sound segment to Praat
or Pitchworks. You choose which analyzer you want to use by an option under the Editmenu. The default analyzer is Praat. The bullets must be formatted in the current format(post 2006). If you have a file using the old format, you can use the FIXBULLETS
program to fix them. If you are using Praat, you must first start up the Praat window(download Praat from https://fon.hum.uva.nl/praat) and place your cursor in front of abullet for a sound segment. You must first play the selected bullet at least once to open the
media window. Then, selecting Send to Sound Analyzer sends the bullet information of
the clip to the Praat window for further analysis. After the media window is open, you justneed to place your text cursor after the bullet when sending another clip's bullet information
to Praat. To run Praat in the background without a GUI, you can also send this commandfrom a Perl or Tcl script:
system (“\”C:\\Program Files\\Praatcon.exe\” myPraatScript.txt

4.10 Tiers Menu Items
When you open a CHAT file with an @Participants line, the editor looks at each of theparticipants in that line and inserts their codes into the Tiers menu. You can then enter thePart 2: CLAN

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name quickly, using the commands listed in that menu. If you make changes to the@Participants line, you can press the Update button at the bottom of the menu to reload
new speaker names. As an alternative to manual typing of information on the @ID lines,
you can enter information for each participant separately using the dialog system that you
start up using the ID Headers option in the Tiers menu.

4.11 Running CHECK Inside the Editor
You can run CHECK from inside the editor. You do this by typing esc-L or selecting
Check Opened File from the Mode menu. If you are in CHAT Mode, CHECK will look
for the correct use of CHAT. Make sure that you have set your Lib directory to the place
where the depfile.cut file is located. On Windows, this should be c:\TalkBank\CLAN\lib.
CHECK can also be run from the command line using a command such as: check *.cha.
See the section on CHECK in the command descriptions of this manual for more details.
The command line version of CHECK can spot a few additional problems that cannot be
detected by the version that operates inside the editor.

4.12 Preferences and Options
You can set various Editor preferences by pulling down the Edit menu and selecting
Options. The following dialog box will pop up:

These options control the following features:
1. Checkpoint frequency. This controls how often your file will be saved. If you set
the frequency to 50, it will save after each group of 50 characters that you enter.
2. Limit of lines in CLAN output. This determines how many output lines will go toyour CLAN output screen. It is good to use a large number, since this will allow
you to scroll backwards through large output results.
3. Tier for disambiguation. This is the default tier for the Disambiguator Modefunction.
4. Open Commands window at startup. Selecting this option makes it so that theCommands window comes up automatically whenever you open CLAN.
5. No backup file. By default, the editor creates a backup file, in case the programPart 2: CLAN

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hangs. If you check this, CLAN will not create a backup file.
6. Start in CHAT Coder mode. Checking this will also start you in Text Mode when
you open a new text window.
7. Auto-wrap in Text Mode. This will wrap long lines when you type.
8. Auto-wrap CLAN output. This will wrap long lines in the output.
9. Show mixed stereo sound wave. CLAN can only display a single sound wave when
editing. If you are using a stereo sound, you may want to choose this option.
10. Output Unix CRs. This is for people who use CLAN on Unix.

4.13 Coder Mode
Coder Mode is an advanced editor feature that is useful for researchers who havedefined a fully structured coding scheme that they wish to apply to all the utterances in atranscript. To begin Coder Mode, you need to shift out of Editor Mode. To verify yourcurrent mode, just double-click on a file. Near the bottom of the text window is a line like
this:
CLAN [E] [chat] barry.cha 1
The [E] entry indicates that you are in editor mode and the [chat] entry indicates thatyou are in CHAT Mode. To begin coding, you first want to set your cursor on the firstutterance you want to code. If the file already has %spa lines coded, you will be adding
additional codes. If none are present yet, Coder’s Editor will be adding new %spa line.
You can use the barry.cha file in the /examples/transcripts folder. Once you have placed
the cursor anywhere on the first line you want to code, you are ready to leave CHAT Modeand start using Coder Mode. To go into Coder Mode, type esc-e (always release the escapekey before entering the next key). You will be asked to load a codes file. Just navigate to
the /examples/coder/ directory and select one of the demo codes files beginning with theword “code.” We will use codes1.cut for our example.

4.13.1 Entering Codes
The coding tier that appears at the top line of the codes1.cut file is shown at the bottomof the screen. In this case it is %spa:. You can either double-click this symbol or just hit
the carriage return and the editor will insert the appropriate coding tier header (e.g. %spa),a colon and a tab on the line following the main line. Next it will display the codes at the
top level of your coding scheme. In this case, they are $POS and $NEG. You can selectone of these codes by using either the cursor keys, the plus and minus keys or a mouseclick. If a code is selected, it will be highlighted. You can enter it by hitting the carriage
return or double-clicking it. Next, we see the second level of the coding scheme.
To get a quick overview of your coding choices, type esc-s several times in succession
and you will see the various levels of your coding hierarchy. Then return to the top levelto make your first selection. When you are ready to select a top-level code, double-clickon it with your mouse. Once you have selected a code on the top level of the hierarchy, thecoder moves down to the next level, and you repeat the process until that complete code isconstructed. To test this out, try to construct the code $POS:COM:VE.
The coding scheme entered in codes1.cut is hierarchical, and you are expected to go
through all the decisions in the hierarchy. However, if you do not wish to code lower levels,

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type esc-c to signal that you have completed the current code. You may then enter any
subsequent codes for the current tier.
Once you have entered all the codes for a tier, type esc-c to signal that you are finishedcoding the current tier. You may then either highlight a different coding tier relevant to thesame main line, or move on to code another main line. To move on to another main line,
you may use the arrow keys to move the cursor, or you may automatically proceed to next
main speaker tier by typing Control-t. Typing Control-t will move the cursor to the next
main line, insert the highlighted dependent coding tier, and position you to select a codefrom the list of codes given. If you want to move to yet another line, skipping over a line,
type Control-t again. Try out these various commands to see how they work.
If you want to code data for only one speaker, you can restrict the way in which theControl-t feature works by using esc-t to reset the set-next-tier-name function. For
example, you confine the operation of the coder to only the *CHI lines, by typing esc-t andthen entering CHI. You can only do this when you are ready to move on to the next line.
If you receive the message “Finish coding current tier” in response to a command (as,for example, when trying to change to editor mode), use esc-c to extricate yourself fromthe coding process. At that point, you can reissue your original command. Here is a summary of the commands for controlling the coding window. On Macintosh, use thecommand key instead of the control key. Remember to release the esc key before the next
character.
Command Function
esc-c
finish current code
esc-c (again) finish current tiercontrol-z
undocontrol-t or F1 finish current tier and go to next
esc-t
restrict coding to a particular speaker
esc-esc
go to the next speakeresc-s
show subcodes under cursor

4.13.2

Setting Up Your Codes File

When you are ready to begin serious coding, you will want to create your own codes
file to replace our sample. To do this, open up a new file using command-N. When editing
this new codes file, make sure that you are in Text Mode and not CHAT Mode. You selectText Mode from the menu by deselecting (unchecking) CHAT Mode in the Mode menu.
To make sure you are in Text Mode, look for [E][TEXT] in the bottom line of the Editor
window. If you decide to use another editor or if you do not use Text Mode in CLAN, youwill probably have problems.
You will probably find it useful to refer to the sample codes files in the /examples/coder
folder. In the next paragraphs, we will explain the construction of the codes-basic.cut filein that folder. The first line of your codes-basic.cut file is:\ +b50 +d +l1 +s1

In this example, the +b option sets the checkpoint buffer (that is, the interval at which
the program will automatically back up the work you have done so far in that session). If
you find the interval is too long or too short, you can adjust it by changing the value of b.
The +d option tells the editor to keep a “.bak” backup of your original CHAT file. To turnPart 2: CLAN37

off the backup option, use –d. The +l option reorders the presentation of the codes basedon their frequency of occurrence. There are three values of the +l option:
0
1
2

leave codes without frequency ordering
move most frequent code to the top
move codes up one level by frequency

If you use the +s option, the program assumes that all the codes at a particular level
have the same codes symmetrically nested within them. For example, consider the codesbasic.cut file:\ +b50 +l1 +s1%spa:" $MOT
:POS
:Que
:Res
:NEG
" $CHIThe spaces in this file must be spaces and not tabs. The line with $MOT begins witha space. Then there is the quote sign, followed by one more space. There are two spaces
before :POS, because that code appears in the second field. There are three spaces before:Que, because that code appears in the third field. There must be a tab following the colonon the %spa: tier, because that code needs to be inserted in the actual output in the CHAT
file. The above file is a shorthand for the following complete listing of code types:$MOT:POS:Que$MOT:POS:Res$MOT:NEG:Que$MOT:NEG:Res$CHI:POS:Que$CHI:POS:Res$CHI:NEG:Que$CHI:NEG:Res

It is not necessary to explicitly type out each of the eight combinations of codes. With
the +s1 switch turned on, each code at a level is copied across the branches so that all of
the siblings on a given level have the same set of offspring. A more extensive example ofa file that uses this type of inheritance is the system for error coding given in the/coder/codeserr.cut file.
If not all codes at a given level occur within each of the codes at the next highest
level, each individual combination must be spelled out explicitly and the +s option should
not be used. The second line in the file should declare the name for your dependent tier. It
should end with a tab, so that the tab is inserted automatically in the line you are constructing. A single codes.cut file can include coding systems for many different dependent
tiers with each system in order in the file and beginning with an identifier such as $spa:.
Setting up the codes.cut file properly is the trickiest part of Coder Mode. Once properly
specified, however, it rarely requires modification. If you have problems getting the editor
to work, chances are the problem is with your codes.cut file.

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38Media Linkage

In the old days, transcribers would use a foot pedal to control the rewinding andreplaying of tapes. With the advent of digitized audio and video, it is now possible to use
the computer to control the replay of sound during transcription. Moreover, it is possible
to link specific segments of the digitized audio or video to segments of the computerizedtranscript. This linkage is achieved by inserting a header tier of this shape@Media: clip, audio

The first field in the @Media line is the name of the media file. You do not need toinclude the extension of the media file name. The second field in the @Media header tells
whether the media is audio, video, or missing. Each transcript should be associated with
one and only one media file. For media linkage to work, it is crucial to include the @Medialine before you begin linking, and the media file name must exactly match the transcript
file name.
Once this header tier is entered, you can use various methods to insert sound markers
that appear initially to the user as bullets. When these bullets are opened, they look like
this:
*ROS:

alert [!] alert !

.

1927_4086.

When then are closed then look like this:*ROS:

alert [!] alert ! .

The size and shape of the bullet character varies across different fonts, but it will usually
be a bit darker than what you see above. The information in the bullet provides clearertranscription and immediate playback directly from the transcript. The first number in thebullet indicates the beginning of the segment in milliseconds and the second number
indicates the end in milliseconds.
Once a CHAT file has been linked to audio or video, it is easy to playback theinteraction from the transcript using “Continuous Playback” mode (esc-8, remember toalways release the escape key before typing the next key). In this mode, the waveform
display is turned off and the computer plays back the entire transcript, one utterance after
another, while moving the cursor and adjusting the screen to continually display the current
utterances. This has the effect of “following the bouncing ball” as in the old sing-along
cartoons or karaoke video. In Continuous Movie Playback Mode, the video is played as
the cursor highlights utterances in the text.
To create a text that can be played back and studied in this way, however, the user canmake use of any combination of six separate methods: sonic mode, transcriber mode, video
mode, sound walker, time mark editing, and exporting to partitur editors. This chapter
describes each of these six methods and leaves it up to the individual researcher which ofthese methods is best for his or her project.

5.1 Media Formats
To use any of these methods, you need to have a digitized audio or video file. Audio
files can be in either WAV (.wav) or MP3 (.mp3) format. For TalkBank, we recommend
the CD quality WAV format which is 16-bit, stereo, with sampling at 44.1 Khz. This rateshould be adquate for human speech. When compressing WAV files to MP3 for TalkBank

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in Amadeus Pro, you should use CBR (Constant Bit Rate), Best quality, 320 kbps bitrate,
Id3v2.4 tags, automatic sample rate, and no joint stereo.
For use in TalkBank browser web display and editing with CLAN, video files should
be in MP4 (.mp4) format. Resolution can be 720x480 or 960x640. Very high resolution is
not good for web delivery and lower resolution can be too grainy.

5.2 Sonic Mode
Sonic Mode involves transcribing from a sound waveform. This mode can also be usedwith a video file. In that case, before beginning, Sonic Mode extracts the audio track fromthe video file. If you do not specifically select the Sonic Mode option, you can still edit
video directly without reference to the audio, as described in the section on Video Linking
below.
To begin Sonic transcription, you should launch CLAN and open a new file. Type inyour basic header tiers first, along with the @Media header discussed above. Then, go to
the Mode pulldown menu and select “Sonic Mode” and you will be asked to locate thedigitized sound file. Once you have selected your file, the waveform comes up, starting at
the beginning of the file. Several functions are available at this point:
1. Sound playing from the waveform. You can drag your cursor over a segment of the
waveform to highlight it. When you release your mouse, the segment will play. If it
stays highlighted, you can replay it by holding down the command key and clicking
the mouse. On Windows, you use the control key, instead of the command key. At
this point, it does not matter where in the bottom waveform window your cursor ispositioned. But, if you click on the bullet in the text window, then you will play thesound linked to the bullet on which you click in that window.
2. Waveform demarcation. You can move the borders of a highlighted region byholding down the shift key and clicking your mouse to place the cursor at the place
to which you wish the region to move. You can use this method to either expand or
contract the highlighted region. As a side effect, once you expand or contract an area,
the sound will also play.
3. Transcription. While you are working with the waveform, you can repeatedly play
the sound by using command-click. This will help you recognize the utterance you
are trying to transcribe. You then go back to the editor window and type out theutterance that corresponds to the highlighted segment.
4. Expanding and hiding the bullets. If you want to see the exact temporal referencesthat are hiding inside the bullet symbols, you can type esc-A to expand them. Typing
esc-A again will hide them again.
5. Adjusting start and stop times. You can use four keyboard shortcuts to adjust thestart and stop time of a segment in 25 msec intervals. To move the start time earlier,use control-leftarrow. To move the start time later, use control-rightarrow. To movethe end time earlier, use shift-leftarrow. To move the end time later, use shiftrightarrow.
6. Linking. When you believe that the highlighted waveform corresponds correctly tothe utterance you have transcribed, you can click on the “s” button to the left of the
waveform display and a bullet will be inserted. This bullet contains information
regarding the exact onset and offset of the highlighted segment. You can achieve thePart 2: CLAN

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same effect using command-I (insert time code). If you want to change the value ofa bullet already in the transcript, you do the same thing while your cursor is inside or
right next to the bullet in the transcript window.
7. Changing the waveform window. The +H and -H buttons on the left allow you toincrease or decrease the amount of time displayed in the window. For highly accurate
border placement, use a very wide horizontal display. The +V and -V buttons allow
you to control the amplitude of the waveform.
8. Scrolling. At the bottom of the sound window is a scrollbar that allows you to move
forward or backward in the sound file (please note that scrolling in the sound file cantake some time as the sound files for long recordings are very large and take up
processing capacity).
9. Utterance -> Waveform Display. To highlight the section of the waveform associated with an utterance, you need to triple-click on the bullet following the utterance
you want to replay. You must triple-click at a point just before the bullet to get reliable
movement of the waveform. If you do this correctly, the waveform will redisplay.
Then you can replay it by using command-click.
10. Waveform -> Utterance Display. Correspondingly, you can double-click an area of
the waveform and, if there is a corresponding bullet in the transcript, then the linewith that bullet will be highlighted.
11. Undo. If you make a mistake in linking or selecting an area, you can use the Undo
function with command-Z to undo that mistake.
12. Time duration information. Just above the waveform, you will see the editor modeline. This is the black line that begins with the date. If you click on this line, you will
see three additional numbers. The first is the beginning and end time of the current
bullets-1 and F5bullets-2window in seconds. The second is the duration of theselected part of the waveform in hours:minutes:seconds.milliseconds. The third is the
position of the cursor in seconds.milliseconds. If you click once again on the modeline, you will see sampling rate information for the audio file.

5.3 Transcriber Mode
This mode is faster than Sonic or Video Mode, but often less precise. However, unlike
Sonic Mode, it can also be used for video transcription. Transcriber Mode is intended for
two uses. The first is for transcribers who wish to link a digitized file to an already existing
CHAT transcript. The second is for transcribers who wish to produce a new transcriptfrom a digitized file.

5.3.1 Linking to an already existing transcript
1.
2.
3.
4.
5.

To link a video or audio file to an already existing transcript, please follow these steps:
Place your CLAN transcript and video file in the same directory.
Set your working directory as the location of the video and transcript.
Open the CLAN transcript.
Type esc-L to make sure that your transcript passes CHECK. If not, fix any problems.
Insert an @Media header after the last @ID line, as described at the beginning of thischapter.

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6. Place your cursor somewhere within the first utterance.
7. Click on Mode, Transcribe Sound or Movie or just type F5.
8. When CLAN asks you for the media, click on the audio or video file you want totranscribe.
9. The movie or audio will automatically start playing in Quicktime (Mac) or Windows
Media Player (PC). When it does, listen for the different utterances. At the end of eachutterance, press the spacebar. This will automatically record a bullet at the end of theline that “connects” the video to the transcript.
10. If you get off at any point in time, click on the video window and the video will stoprunning.
11. Once playback is stopped, reposition your cursor at the last correct bullet and again
click on “Transcribe sound or movie.” The movie will automatically begin at the bullet
where your cursor is. As you press the spacebar, the new bullets will overwrite the old
ones.
12. After you have spent some time inserting bullets, click file, save. The bullets will besaved into your transcript.
13. After you are done adding bullets, click in the video window to stop the process. Then
go to the top of the file and insert @Begin and @Participants lines. Use the @Participants to generate key shortcuts under the View menu. Then replay the first bullet,transcribe it, and use the appropriate command-1 or command-2 key to enter the
speaker ID. Then go on to the next utterance and repeat the process. The result will
be a full transcription that is roughly linked to the audio.

5.3.2 To create a new transcript
You can use the F5 insertion mode to create a new transcript in which the links havealready been inserted. Here are the steps:
1. Open a blank file.
2. Make sure that your media file is in your working directory along with your new blankfile.
3. Enter the @Begin, @Languages, and @Media headers.
4. Go to "Mode," and select "Transcribe Sound or Movie F5."
5. Find the sound or movie clip you want to transcribe.
6. When you click on it, the sound or movie will open in another window.
7. Immediately, start pressing the spacebar at the end of each utterance. This will insert
a bullet into the blank transcript.
8. When you are finished inserting the bullets, save the file.
9. Then, one at a time, Crtl-click on the bullets and transcribe them.
10. If the bullets need some adjusting, you may do this while you are transcribing by
manipulating the numbers in the movie window and clicking on the save button in thelower righthand corner. You can also expand the bullets using esc-A and type it inmanually.

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11. Save frequently.
12. When you are done transcribing, it is a good idea to look at the transcript in continuous
playback mode to make sure everything is transcribed correctly. To do this go to"Mode," and then "Continuous Playback esc-8."

5.3.3 Sparse Annotation
For some applications, it is not necessary to produce a complete transcription of aninteraction. Instead, it is sufficient to link a few comments to just a few importantsegments. We can refer to this as sparse annotation of “naked” video. For example, during
a one-hour classroom Mathematics lesson, it might be sufficient to point out just a few"teachable moments." To do this, you can follow these steps:
1. Open a new file.
2. Insert a few basic headers, along with the @Media header discussed at the beginningof this chapter. Make sure the media is in your working directory.
3. Select the "Edit" menu and pulldown to "Select F5 option".
4. In segment length type 3000 for 3 second bullet length press OK button.
5. Start F5 mode by pressing the F5 key.
6. CLAN will start playing.
7. When you hear what you want to comment on click F1 or F2 or F5.
8. The bullet will be inserted into text and playback will stop.
9. Click on transcript text window and add comment to that bullet.
10. Move text cursor to the last bullet less tier, i.e. "*: ".
11. Press F5 again and the process will start again from last stop.

5.4 Video Linking
To achieve maximum compatibility with CLAN and web-based video playback, we
recommend using the H.264 format, which is now the default for high-definition. This is
the format used by all new recording devices. Older video can be converted to H.264
format using programs like iSKySoft Video Converter or Handbrake. Additional
information about video recording can be found at https://www.talkbank.org/info/dv.html.
When starting up video playback, it can be the case that the movie window occupies
too much of the screen. In order to size it properly, you can click on the green button in
the top of the QuickTime video window and the window will be resized to the smallest
dimension. Then you drag on the botton right corner to expand it to the size you wish.
To learn how to do video linking, you should refer to the screencasts at
https://talkbank.org/screencasts. If you want to link your transcript to a movie or create a
new transcript that is linked to a movie, you can use one of two methods – Transcriber
Mode or Manual Linking Mode. Transcriber Mode was described in the previous section.
It is a quick and easy method that will prove useful for beginning linking to a transcript.
Using this method, however, sacrifices precision. It is then necessary to go back and tighten
up the links using the Manual Linking method. The Help screen on the video window gives

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you the functions you will need for this. Many of these functions apply to both video andaudio. Their use is summarized here:
1. <- will set back the current time. This function makes small changes at first and thenlarger ones if you keep it pressed down.
2. -> will advance the current time. This function makes small changes at first and thenlarger ones if you keep it pressed down.
3. control <- will decrease the beginning value for the segment in the text window as wellas the beginning value for the media in the video window. This function makes smallchanges at first and then larger ones if you keep it pressed down.
4. control -> will increase the beginning value for the segment in the text window as wellas the beginning value for the media in the video window. This function makes smallchanges at first and then larger ones if you keep it pressed down.
5. command <- will decrease the beginning value for the segment in the text window aswell as the beginning value for the media in the video window. This function makes
small changes at first and then larger ones if you keep it pressed down.
6. command -> will increase the beginning value for the segment in the text window aswell as the beginning value for the media in the video window. This function makes
small changes at first and then larger ones if you keep it pressed down.
7. / pressing the button with the right slash with the start time active moves the start time
to current time. If the current time is active, it moves the current time to the start time.
8. \ pressing the button with the left slash with the end time active moves the end time tocurrent time. If the current time is active, it moves the current time to the end time.
9. Triple-clicking on the relevant cell has the same effect as the above two functions.
10. You can play the current segment either by pressing the repeat button or the spacebutton when the video window is active. The behavior of the repeat play function canbe altered by inserting various values in the box to the right of “repeat”. These areillustrated in this way:-400
add 400 milliseconds to the beginning of the segment to be repeated+400
add 400 milliseconds to the end of the segment to be repeatedb400
play the first 400 milliseconds of the segment
e400
play the last 400 milliseconds of the segment

5.5 Walker Controller
The Walker Controller facility is based on a design from Jack DuBois at UC Santa
Barbara. This controller allows you to step forward and backwards through a digitized file,using a few function keys. It attempts to imitate the old transcriber foot pedal, but withsome additional functionality. The options you can set are:
1. walk length: This sets how long a segment you want to have repeated.
2. loop number: If you set 3 loops, for example, the program plays each step three times
before moving on.
3. backspace: The amount of rewinding in milliseconds at the end of each loop.

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4. walk pause length: The duration of the pause between loops.
5. playback speed: This setting allows you to speed up or slow down your playback rate.
The basic contrast here is between "stepping" which means moving one step forward
or back and "walking" which just keeps on stepping one step after another in the manner
you have specified with the above option. The keys you use are:
F6
walk
F7
step backward
F8
play current step
F9
step forward
shift F7 play to the end
F1-F12stop playing
You will find all these options in the "Walker Controller" dialog that you open under
the Window menu. To establish the correct link between your transcript and the media,
you need to place the @Media line into your transcript, as described at the beginning of
this chapter. Once you open the Walker Controller and thereby enable SoundWalker, thefunctions described above become enabled.

If you would like to use a real foot pedal with SoundWalker, you can order one(Windows only) from www.xkeys.com. This foot pedal installs along with the keyboard
and allows you to bind F6, F7, and F8 to the left, middle, and right pedals for the functionsof rewind, play, and forward.

5.6 Playback Control
Once a transcript has been linked, you will want to study it through playback. Basic
playback uses the esc-8 command for continuous playback and command-click forplayback of a single utterance. You can also use esc-9 to playback only the segments
marked with time codes and ignore the silence in between.

5.7 Multiple Video Playback
If you have recorded an interaction using several cameras, and if the videos you havecreated are all synchronized in time, you can switch playback back and forth from thesePart 2: CLAN

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multiple video files using a single CHAT transcript. To do this, you should name yourvideos with a constant first part of the name and then add an additional variable part todistinguish each video. For example, you could have “scale01” as your constant name andthen the three videos would have the names scale01-1.mov, scale01-2.mov, and scale013.mov. If this is your naming convention for the three videos, then you would have these
two lines in your *.cha transcript file:@Media: scale01, video@Videos:
1, 2, 3

If you want somewhat more mnemonic names, you could name the files as scale051center, scale05-2left, and scale05-3right and then the @Videos line would be:@Videos:

1center, 2left, 3 right

When you open the transcript and start playback using continuous playback with esc-8
or some other method, the default video will be the first one in the list. To switch to
playback from another video clip, stop the playback and type F3 followed by the numberof the video you want to play. For example, if you want to shift playback from scale05-1
to scale05-3, you should type F3-3. A sample file with three videos for testing out thisprocess can be downloaded from https://talkbank.org/resources/F3.

5.8 Manual Editing
You can use manual editing of the bullets to modify playback in three ways. The firstway, which is very often quite important and practical, is to simply open the bullets using
esc-A and modify the numbers using your own impressions of the length of the sound. You
can modify either number and then check your work by playing the new alignment usingcommand-click.
The second method uses the four key combinations described in the point on adjustingtime values in the section on the Sonic Mode. To make this work, place your cursor rightnext to a bullet, and then these same four functions will also work even if the waveform
window is not open. If you open a bullet and hit one of these key combinations, you willhear the audio and see the time values changing inside the bullet. Currently (August 2021)
this method is not yet implemented for CLANc.

5.9 Video Skipping
The third method for manual editing applies in fewer cases. It is useful if you have an
audio recording with a large segment that you do not wish to transcribe, you may also wish
to exempt this segment from continuous playback. If you do not apply forced skipping todo this, you will need to listen through this untranscribed material during continuous
playback. To implement forced skipping, you should open your bullets using esc-A. First,
you need to trim the end of the utterance before the region you want to skip, so that it ends
right before the skipped part. Then go to the end of the bullet after the skip and adjust the
beginning of that bullet to begin just after the region to be skipped. Finally, insert a dashafter the second time value in that bullet. For example, you might change 4035_5230 to4035_5230-. Then close the bullets and save the file. You can test the marking by going to
the utterance before the skip and playing through with esc-8. The playback should skip
from the first utterance to the next one without playing the intervening skipped material.

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5.10 Audio Anonymization
To deidentify the segments of the audio track in a recording, you can use Audacity or
Amadeus Pro. In Amadeus Pro, this function is called Generate Silence and it is foundunder the Effects menu. You highlight the area to be silenced and then select that option.
In this case, you would still want to replace the last name with the Lastname in thetranscript. Moreover, if you have been linking the transcript to the media, you can use thetime values in milliseconds in the linkage bullets to locate the stretch to be silenced in the
media. To find the right place in Amadeus, you will want to change the time display at thebottom of the waveform window to milliseconds. You do this by going into Preferences
and selecting Units and entering this string in the Format box: |1/1000:3|
If you need to silence audio within a video recording, you can download the Subler app
for Mac from https://bitbucket.org/galad87/subler/downloads/. You should make a copy ofyour movie to use later. Then you drag your movie onto the Subler icon, delete the video
track, and save. Then drop this version onto Amadeus Pro, silence the stretches of theaudio that you need to deidentify, and save. Then you open the original video in Subler,
delete its audio and drag the output from Amadeus into the Subler window. You can finda screencast illustrating this at https://talkbank.org/share/irb/subler.mov. An advantage ofthis method is that saving the result is quite quick.
Alternatively, you can silence directly in iMovie for Mac. Instructions for this can befound on YouTube at https://www.youtube.com/watch?v=ShrDFjdvB1w. You can holddown the "R" key as an easier way of selecting an area to silence. An advantage of thismethod is that you can watch the video as you silence the audio. A disadvantage is thatsaving large video files takes several minutes.

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47

Other Features

6.1 Supplementary Commands
The chapter on Supplementary Commands lists several basic commands for working
with files. They are batch for creating batch files, cd for changing directory, dir for listing
the files in a directory, info for getting a list of possible commands, ren for renaming files,
and rm for removing or deleting files. See the descriptions in that chapter for details.

6.2 Online Help
CLAN has a limited form of online help. To use this help, you simply type the name of
the command without any further options and without a file name. The computer will thenprovide you with a brief description of the command and a list of its available options. To
see how this works, just type freq and a carriage return and observe what happens. To see
a list of all the possible commands in CLAN, type info. If there is something that you do
not understand about CLAN, the best thing you can do is to try to find the answer to yourproblem in this manual.

6.3 Help for the +sm and +sg switches
There is a complex system for creating searches of the %mor and %gra lines. You canbest understand the patterns used in this system by typing:freq +sm

The patterns for the +sg switch are much less complicated, but you can see these by typing:freq +sg

These patterns for +sm and +sg can also be used with KWAL and COMBO.

6.4 Keystroke Listing
It is possible to get a complete list of keystroke bindings for CLAN commands bytyping esc-h. This creates a file called keys_list.cut that you can then read, save, or printout.

6.5 Macros
CLAN can store up to 10 Macros. Typing esc-n will open a dialog that lets you assign
strings to numbers. For example, you might want to assign the string %spa: as Macro #1.
You would type %spa: into the box next to Macro String: and then enter the number “1”above. Then you could insert this string by typing control-w-1. You will also see that justtyping control-w pops up a list of all the macros you have assigned.

6.6 Aliases
If you make frequent use of a CLAN command with specific switches, you can savetime and memory and increase reliability by creating an alias. For example, this alias
trimkwal +t* +t@ +t% +d +f

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will save you from having to remember all the details of how to configure KWAL toremove a given dependent tier. So, if you then type:
trim –t%mor *.cha

This will operate as if the command were really:kwal +t* +t@ +t% -t%mor +d +f *.cha

Note that it saves you from having to type the first 7 arguments, but you still have totype the last *.cha
If you want to remove both %mor and %gra lines, you can then just type:
trim –t%mor –t%gra *.cha

This trim alias is already included in the 0aliases.cut file in CLAN/lib/fixes. If you typethe word “trim” by itself, CLAN will give you the usage message. The other standard aliasischat2text flo +cr +t*

We will add additional aliases from time to time. However, if you want to create you
own aliases, then you should create a file called aliases.cut that you put into the CLAN/lib
folder. The format of that file is to have one command per line with the alias at the
beginning of the line without spaces, then a space, and then the full CLAN command.

6.7 Testing CLAN
It is a good idea to make sure that CLAN is conducting analyses correctly. In somecases you may think that the program is doing something different from what it is designed
to do. To prevent misunderstandings and misinterpretations, you should set up a small test
file that contains the various features you want CLAN to analyze. For example, if you arerunning a FREQ analysis, you can set a file with several instances of the words or codes
for which you are searching. Be sure to include items that should be “misses” along withthose that should be “hits.” For example, if you do not want CLAN to count items on a tier,make sure you put some unique word on that tier. If the output of FREQ includes that word,
you know that something is wrong. In general, you should be testing not for correct
performance but for possible incorrect performance. To make sure that you are using the+t and +s switches correctly, make up a small file and then run KWAL over it without
specifying any +s switch. This should output exactly the parts of the file that you intend toinclude or exclude.

6.8 Bug Reports
Although CLAN has been extensively tested for years, it is possible that some analyses
will provide incorrect results. When this occurs, the first thing to do is to reread the relevant
sections of the manual to be sure that you have entered all your commands correctly. If a
rereading of the manual does not solve the problem, then you can send e-mail to
macw@cmu.edu to try to get further assistance. In some cases, there may be true “bugs”
or program errors that are making correct analyses impossible. Should the program notoperate properly, please send e-mail to macw@cmu.edu with the following information:
1. a description of the machine you are using and the operating system you are running,Part 2: CLAN

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2.
3.
4.
5.

a copy of the file that the program was being run on,
the complete command line used when the malfunction occurred,
all the results obtained by use of that command, andthe date of compilation of your CLAN program, which you can find by clicking on“About CLAN” at the top left of the menu bar on Macintosh or the “Help CLAN”
option at the top right of the menu bar for Windows.
Use WinZip or Stuffit to save the input and output files and include them as an e-mailattachment. Please try to create the smallest possible file you can that will still illustrate thebug.

6.9 Feature Requests
CLAN has been designed in response to information we have received from users about
the kinds of programs they need for furthering their research. Your input is important, because we are continually designing new commands and improving existing programs. If
you find that these programs are not capable of producing the specific type of analysis thatyou are trying to achieve, contact us and we will do our best to help. Sometimes we can
explain ways of using CLAN to achieve your goals. In other cases, it may be necessary tomodify the program. Each request must include a simple example of an input file and theoutput you would like, given this input. Also, please explain how this output will help you
in your research. You can address inquiries by email to macw@cmu.edu.

6.10 Types of CLAN Commands
1.
2.
3.
4.
5.
6.
7.

There are seven types of CLAN commands:
Analysis commands are the basic commands for searching and corpus analysis.
Profiling commands put a large number of analysis and profiling commands into asingle command package, often comparing a file against a database standard.
Format conversion commands convert from other formats to CHAT and from CHAT
to other formats.
Reformatting commands are used to add features to transcripts which have passed
CHECK and are in good CHAT format.
Format repair commands are used to rework files into CHAT format.
Supplementary commands are for file operations such as renaming or deleting files.
Morphosyntactic analysis commands serve to create a %mor tier for part-of-speechtagging and a %gra tier for grammatical relations tagging.

The first six of these command types are described in detail in the next six chapters.
The morphosyntactic commands are described in the separate MOR manual which is
downloadable from the talkbank.org website.

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Analysis Commands

The analytic work of CLAN is performed by a series of commands that search forstrings and compute a variety of indices. These commands are all run from the Commands
window. In this section, we will examine each of the commands and the various options
that they take. The commands are listed alphabetically. The following table provides an
overview of the various CLAN commands. The CHECK program is included here,
because it is so important for all aspects of use of CLAN. To go directly to any commandin this click, click on the page number.
CLAN also includes two other major groups of commands. The first group is used toperform morphosyntactic analysis on files by tagging words for their part of speech anddetecting grammatical relations. These programs are discussed in in the MOR manual. In
addition, CLAN includes a large group of Utility commands that are described in thechapter on Utility Commands.
The best way to see a complete list of options for a command is to type the names of
the command followed by a carriage return in the Commands window. For example, if
you type just the word chip, you will see a list of all the available options for CHIP. You
can see a list of all available commands by typing "info".
CommandPage

Function

CHAINS

51

Tracks sequences of interactional codes across speakers.

CHECK

54

Verifies the correct use of CHAT format.

CHIP

56

Examines parent-child repetition and expansion.

COMBO

62

Searches for complex string patterns.

COOCUR

69

Examines patterns of co-occurence between words.

DIST

70

Examines patterns of separation between speech act codes.

FREQ

71

Computes the frequencies of the words in a file or files.

FREQMERG

85

Combines the outputs of various runs of FREQ.

FREQPOS85

A variant of FREQ for part of speech.

GEM

86

Finds areas of text that were marked with GEM markers.

GEMFREQ

88

Computes frequencies for words inside GEM markers.

GEMLIST

89

Lists the pattern of GEM markers in a file or files.

KEYMAP

89

Lists the frequencies of codes that follow a target code.

KWAL

90

Searches for word patterns and prints the line.

MAXWD

94

Finds the longest words in a file.

MLT

95

Computes the mean length of turn.

MLU

97

Computes the mean length of utterance.

MODREP

102

Matches the child’s phonology to the parental model.

Phon and PhonTalk

105

Analysis of phonological development.

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RELY

106

Measures reliability across two transcriptions.

SCRIPT

108

Compares transcripts to target scripts

TIMEDUR108

Computes time durations of utterances and pauses

VOCD

111

Computes the VOCD lexical diversity measure.

WDLEN

115

Computes the length of utterances in words.

7.1 CHAINSCHAINS is used to track sequences of interactional codes. These codes must be enteredby hand on a single specified coding tier. To test out CHAINS, you may wish to try the filechains.cha that contains the following sample data.
@Begin@Participants:
CHI Sarah Target_child, MOT Carol Mother*MOT:
sure go ahead [^c].
%cod:
$A
%spa:
$nia:gi*CHI:
can I [^c] can I really [^c].
%cod:$A $D. $B.
%spa:$nia:fp $npp:yq.
%sit:$ext $why. $mor
*MOT:
you do [^c] or you don't [^c].
%cod:$B $C.
%spa:$npp:pa*MOT:
that's it [^c].
%cod:
$C
%spa:
$nia:pa@End

The symbol [^c] in this file is used to delimit clauses. Currently, its only role is within
the context of CHAINS. The %cod coding tier is a project-specific tier used to code possible worlds, as defined by narrative theory. The %cod, %sit, and %spa tiers have periods
inserted to indicate the correspondence between [^c] clausal units on the main line andsequences of codes on the dependent tier.
To change the order in which codes are displayed in the output, create a file called
codes.ord. This file could be in either your working directory or in the \childes\clan\lib
directory. CHAINS will automatically find this file. If the file is not found then the codes
are displayed in alphabetical order, as before. In the codes.ord file, list all codes in any
order you like, one code per line. You can list more codes than could be found in any one
file. But if you do not list all the codes, the missing codes will be inserted in alphabeticalorder. All codes must begin with the $ symbol.

7.1.1 Sample Runs
For our first CHAINS analysis of this sample file, let us look at the %spa tier. If you
run the command:
chains +t%spa chains.cha

you will get a complete analysis of all chains of individual speech acts for all speakers,
as in the following output:

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52

> chains +t%spa chains.cha
CHAINS +t%spa chains.cha
Mon May 17 13:09:34 1999
CHAINS (04-May-99) is conducting analyses on:
ALL speaker tiersand those speakers' ONLY dependent tiers matching: %SPA;****************************************
From file <chains.cha>
Speaker markers: 1=*MOT, 2=*CHI$nia:fp$nia:gi$nia:pa $npp:pa$npp:yqline #
0
1
0
0
0
3
2
0
0
0
2
6
0
0
0
1
0
10
0
0
1
0
0
13
ALL speakers:$nia:fp$nia:gi$nia:pa$npp:pa$npp:yq# chains
1
1
1
1
1
Avg leng
1.00 1.00
1.00
1.00
1.00
Std dev 0.00 0.00
0.00
0.00
0.00
Min leng
1
1
1
1
1
Max leng
11
1
1
1
Speakers *MOT:$nia:fp$nia:gi$nia:pa$npp:pa$npp:yq# chains
01
1
1
0
Avg leng
0.00 1.00
1.00
1.00
0.00
Std dev 0.00
0.00
0.00
0.00
0.00
Min leng01
1
1
0
Max leng
01
1
1
0
SP Part.
0
1
1
1
0
SP/Total
0.00
1.00
1.00
1.00
0.00
Speakers *CHI:$nia:fp$nia:gi$nia:pa$npp:pa$npp:yq# chains 1
0001
Avg leng 1.00
0.00
0.00
0.00
1.00
Std dev
0.00
0.00
0.00
0.00
0.00
Min leng 100
0
1
Max leng 1
0
0
0
1
SP Part. 1
0
0
0
1
SP/Total 1.00
0.00
0.00
0.00
1.00

It is also possible to use the +s switch to merge the analysis across the various speech
act codes. If you do this, alternative instances will still be reported, separated by commas.
Here is an example:
chains +d +t%spa chains.cha +s$nia:%

This command should produce the following output:
Speaker markers:
$nia:
1 gi
2 fp
1 pa
ALL speakers:$nia:# chains 2
Avg leng 1.50

1=*MOT, 2=*CHI
line #
3
6
6
13

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Std dev
0.50
Min leng 1
Max leng 2
Speakers *MOT:$nia:# chains 2
Avg leng 1.00
Std dev-0.00
Min leng 1
Max leng 1
SP Part. 2
SP/Total 0.67
Speakers *CHI:$nia:# chains 1
Avg leng 1.00
Std dev
0.00
Min leng 1
Max leng 1
SP Part. 1
SP/Total 0.33

You can use CHAINS to track two coding tiers at a time. For example, one can look at
chains across both the %cod and the %sit tiers by using the following command. This command also illustrates the use of the +c switch, which allows the user to define units ofanalysis lower than the utterance. In the example file, the [^c] symbol is used to delimit
clauses. The following command makes use of this marking:chains +c"[^c]" +d +t%cod chains.cha +t%sit

7.1.2 Unique Options
CHAINS has the following unique options:+c
The default unit for a CHAINS analysis is the utterance. You can use the +c option
to track some unit type other than utterances. The other unit type must be delimited
in your files with some other punctuation symbol that you specify after the +c, as in+c"[^c]" which uses the symbol [^c] as a unit delimiter. If you have a large set ofdelimiters you can put them in a file and use the form +c@filename. To see how thisswitch operates try out this command:
chains +c"[^c]" +d +t%cod chains.cha+d

Use this switch to change zeroes to spaces in the output. The following command
illustrates this option:
chains +d +t%spa chains.cha +s$nia:%+sS

The +d1 value of this option works the same as +d, while also displaying every input
line in the output.
This option is used to specify codes to track. For example, +s$b will track only the$b code. A set of codes to be tracked can be placed in a file and tracked using theform +s@filename. In the examples given earlier, the following command was used
to illustrate this feature:
chains +d +t%spa chains.cha +s$nia:%

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+wN Sets the width between columns to N characters.

7.2 Chatter
The Chatter program is not included in CLAN. However, for full validation of CHAT
transcripts and inclusion in TalkBank, all transcripts must be validated by Chatter. Whencreating a new CHAT transcript, it is easiest to use CHECK on individual files forvalidation, as described in the next section. Often users submit corpora that have only beenvalidated by CHECK, and then the TalkBank staff will run Chatter for final validation.
However, it is most helpful for users to run Chatter themselves. Chatter can be downloaded
from https://talkbank.org/software/chatter.html. That page also provides additional
instructions for running Chatter. Chatter only runs on complete folders, not on individual
files, although it analyzes each file in the specified folder or folder hierarchy.
After running, Chatter creates a file called 0errors.cut in each folder. It is possible todouble-click on errors reported in that file to open the site of the error in the original filefor correction. If necessary, you can use the column number given for the error to find its
precise location. After correcting errors, you can run Chatter again and there will hopefully
be fewer or no errors. Once all errors detected by Chatter are corrected, a corpus is readyfor inclusion in one of the TalkBank databases.

7.3 CHECKChecking the syntactic accuracy of a file can be done in two ways. One method is towork within the editor. In the editor, you can start up the CHECK program by just typing
esc-L. Alternatively, you can run CHECK as a separate program. The CHECK program
checks the syntax of the specified CHAT files. If errors are found, the offending line isprinted, followed by a description of the problem.

7.3.1 How CHECK works
CHECK makes two passes through each CHAT file. On the first pass, it checks theoverall structure of the file. It makes sure that the file begins with @Begin and ends with
@End, that each line starts with either *, @, %, or a tab and that colons are used properlywith main lines, dependent tiers, and headers that require entries. If errors are found at thislevel, CHECK reports the problem and stops, because further processing would be
misleading. If there are problems on this level, you will need to fix them before continuingwith CHECK. Errors on the first level can mask the detection of further errors on the second
level. It is important not to think that a file has passed CHECK until all errors have been
removed.
The second pass checks the detailed structure of the file. To do this, it relies heavily on
depfile.cut, which we call the “depfile.” The depfile distributed with CLAN inside the /lib
folder lists the legitimate CHAT headers and dependent tier names as well as many of thestrings allowed within the main line and the various dependent tiers. When running
CHECK, you should have the file called depfile.cut located in your LIB directory, which
you set from the Commands window. If the programs cannot find the depfile, they willquery you for its location.
If you find that the depfile is not permitting things that are important to your research,

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please contact macw@cmu.edu to discuss ways in which we can extend the CHAT system
and its reflection in the XML Schema.

7.3.2 CHECK in CA ModeCHECK can also be used with files that have been produced using CA mode. The
features that CHECK is looking for in CA Mode are:
1. Each utterance should begin with a number and a speaker code in the form#:speaker:<whitespace>.
2. There should be paired parentheses around pause numbers.
3. Numbers marking pause duration are allowed on their own line.
4. Latching should be paired.
5. The double parentheses marking comments should be paired.
6. Overlap markers should be paired.
7. Superscript zeros should be paired.
8. The up-arrow, down-arrow, and zeros are allowed inside words.

7.3.3 Running CHECK
There are two ways to run CHECK. If you are working on new data, it is easiest to run
CHECK from inside the editor. To do this, you type esc-L and check runs through the filelooking for errors. It highlights the point of the error and tells you what the nature of theerror is. Then you need to fix the error to allow CHECK to move on through the file.
The other way of running CHECK is to issue the command from the commands window. This is the best method to use when you want to check a large collection of files. If
you want to examine several directories, you can use the +re option to make check work
recursively across directories. If you send the output of check to the CLAN Output
window, you can locate errors in that window and then triple-click on the file name and
CLAN will take you right to the problem that needs to be fixed. This is an excellent way
of working when you have many files and only a few errors.

7.3.4 Restrictions on Word Forms
To guarantee consistent transcription of word forms and to facilitate the building of
MOR grammars for various languages, CHAT has adopted a set of tight restrictions onword forms. Earlier versions of CLAN and CHAT were considerably less restrictive.
However, this absence of tight rules led to many inaccuracies in transcription and analysis.
Beginning in 1998, the rules were significantly tightened. In addition, an earlier system ofmarking morphemes on the main line was dropped in favor of automatic analysis of words
through MOR. The various options for word level transcription are summarized in thechapter of the CHAT manual on Words. However, it is useful here to provide some
additional detail regarding specific CHECK features.
One major restriction on words forms is that they cannot include numbers. Earlier
versions of CHAT allowed for numbers inside UNIBET representations. However, since
we now use IPA instead of UNIBET for phonological coding, numbers are no longer
needed in this context. Also, actual numbers such as “79” are written out in theircomponent words as “seventy nine” without dashes. Therefore, numbers are not needed inPart 2: CLAN

56

this context either.
We also do not allow capital letters inside words. This is done to avoid errors andforms that cannot be recognized by MOR. The exceptions to this principle are for words
with underlining, as in Santa_Claus or F_B_I. CHECK also prohibits dashes within words
in many contexts.
Use CHECK early and often, particularly when you are learning to code in CHAT.
When you begin transcribing, check your file inside the editor using esc-L, even before it
is complete. When CHECK complains about something, you can learn right away how to
fix it before continuing with the same error. If you are being overwhelmed by CHECK
errors, you can use the +d1 switch to limit error reports to one of each type. Or you canfocus your work first on eliminating main line errors by using the -t% switch. You will also
want to learn how to use the query-replace function in your text editor to make generalchanges and CHSTRING to make changes across sets of files.

7.3.5 Unique Options
CHECK has the following options:+cN: check bullets consistency (1- check for missing bullets only)+eN: report only error(s) number N+e : list numbers for every error-eN: report all error(s) except number N+g1: recognize prosodic delimiter+g2: check for "CHI Target_Child"+g3: check words for upper case letters, numbers and illegal apostrophes+g4: check file(s) for missing @ID tiers+g5: check for Speakers specified on @Participants tier, but not used in the file+d : attempts to suppress repeated warnings of the same type+d1: suppress ALL repeated warnings of the same type

7.4 CHIPCHIP was designed and written by Jeffrey Sokolov. The program analyzes specified
pairs of utterances. CHIP has been used to explore parental input, the relation between
speech acts and imitation, and individual differences in imitativeness in both normal andlanguage-impaired children. Researchers who publish work based on the use of this program should cite Sokolov and MacWhinney (1990). CHIP now works on theh %mor tierby default. To run CHIP on the main speaker tier, as illustrated in the following examples,
please add the -t%mor option.
There are four major aspects of CHIP: (1) the tier creation system, (2) the coding
system, (3) the technique for defining substitution classes, and (4) the nature of thesummary statistics.

7.4.1 The Tier Creation System
CHIP compares two specified utterances and produces an analysis that it then inserts
onto a new coding tier. The first utterance in the designated utterance pair is the “source”

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utterance and the second is the “response” utterance. The response is compared to thesource. Speakers are designated by the +b and +c codes. An example of a minimal CHIP
command is as follows:
chip +bMOT +cCHI chip.cha

We can run this command runs on the following seven-utterance chip.cha file that is
distributed with CLAN, which is given here without the %mor and %gra lines:@Begin@Participants:
MOT Mother, CHI Child*MOT: what’s that?
*CHI: hat.
*MOT: a hat!
*CHI: a hat.
*MOT: and what’s this?
*CHI: a hat !
*MOT: yes that’s the hat .
@End

The output from running this simple CHIP command on this short file is as follows:
chip (02-Apr-2024) is conducting analyses on:
ALL speaker tiersand those speakers' ONLY dependent tiers matching: %MOR;****************************************
From file <chip.cha>*MOT:what's that ?
%mor:
pro:int|what~cop|be&3S pro:dem|that ?
*CHI:
hat .
%mor:
n|hat .
%chi:
$NO_REP $REP = 0.00
*MOT:
a hat !
%mor:
det:art|a n|hat !
%asr:$NO_REP $REP = 0.00
%adu:
$EXA:hat $ADD:a $EXPAN $DIST = 1 $REP = 0.50
*CHI:
a hat .
%mor:
det:art|a n|hat .
%csr:$EXA:hat $ADD:a $EXPAN $DIST = 2 $REP = 0.50
%chi:
$EXA:a-hat $EXACT $DIST = 1 $REP = 1.00
*MOT:
and what's this ?
%mor:coord|and pro:int|what~cop|be&3S pro:dem|this ?
%asr:$NO_REP $REP = 0.00
%adu:
$NO_REP $REP = 0.00
*CHI:
that a hat !
%mor:
pro:rel|that det:art|a n|hat !
%csr:$EXA:a-hat $ADD:that $EXPAN $DIST = 2 $REP = 0.67
%chi:
$NO_REP $REP = 0.00
*MOT:
yes that's the hat .
%mor:
co|yes pro:dem|that~cop|be&3S det:art|the n|hat .
%asr:$NO_REP $REP = 0.00
%adu:
$EXA:hat $ADD:yes-that's-the $DEL:that-a $DIST = 1 $REP =0.25

The output also includes a long set of summary statistics which are discussed later. In
the first part of this output, CHIP has introduced four different dependent tiers:%chi: This tier is an analysis of the child’s response to an adult’s utterance, so the adult’sutterance is the source and the child’s utterance is the response.

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%adu: This tier is an analysis of the adult’s response to a child’s utterance, so the child isthe source and the adult is the response.
%csr: This tier is an analysis of the child’s self repetitions. Here the child is both the source
and the response.
%asr: This tier is an analysis of the adult’s self repetitions. Here the adult is both the source
and the response.
By default, CHIP produces all four of these tiers. However, by using the -n option, theuser can limit the tiers that are produced. Three combinations are possible:
1. You can use both -ns and -nb. The -ns switch excludes both the %csr tier and the %asr
tier. The -nb switch excludes the %adu tier. Use of both switches results in an analysis
that computes only the %chi tier.
2. You can use both -ns and -nc. The -ns switch excludes both the %csr tier and the %asr
tier. The -nc switch excludes the %chi tier. Use of both these switches results in ananalysis that computes only the %adu tier.
3. You can use both -nb and -nc. This results in an analysis that produces only the %csr
and the %asr tiers.
It is not possible to use all three of these switches at once.

7.4.2 The CHIP Coding System
The CHIP coding system includes aspects of several earlier systems (Bohannon &Stanowicz, 1988; Demetras, Post, & Snow, 1986; Hirsh-Pasek, Trieman, & Schneiderman,
1984; Hoff-Ginsberg, 1985; Moerk, 1983; Nelson, Denninger, Bonvilian, Kaplan, &Baker, 1984). It differs from earlier systems in that it computes codes automatically. This
leads to increases in speed and reliability, but certain decreases in flexibility and coverage.
The codes produced by CHIP indicate lexical and morphological additions, deletions,exact matches and substitutions. The codes are as follows:$ADD
additions of N continuous words$DEL
deletions of N continuous words$EXA
exact matches of N continuous words$SUB
substitutions of N continuous words from within a word list
$MADD
morphological addition based on matching word stem
$MDEL
morphological deletion based on matching word stem
$MEXA
morphological exact match based on matching word stem
$MSUB
morphological substitution based on matching word stem
$DIST
the distance the response utterance is from the source$NO_REPthe source and response do not overlap$LO_REP
the overlap is below a user-specified minimum$EXACTsource-response pairs with no changes$SUBST
pairs with substitutions, but no additions or deletions$EXPANpairs with additions, but no deletions or substitutions

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$EXP_SUB
pairs with additions and substitutions, but no deletions$REDUC
pairs with deletions, but no additions or substitutions$RED_SUB
pairs with deletions and substitutions, but no additions$FRO
an item from the word list has been fronted$REP
the percentage of repetition between source and response
Let us take the last line of the chip.cha file as an example:
*CHI:
*MOT:
%asr:
%adu:

that a hat!
yes that's the hat .
$NO_REP $REP = 0.00
$EXA:hat $ADD:yes-that's-the $DEL:that-a $DIST=1 $REP=0.25

The %adu dependent tier indicates that the adult’s response contained an EXAct match of
the string “hat,” the ADDition of the string “yes-that’s-the” and the DELetion of “a.” The
DIST=1 indicates that the adult’s response was “one” utterance from the child’s, and therepetition index for this comparison was 0.25 (1 matching stem divided by 4 total stems in
the adult’s response). The maxium value for DIST is 7.
CHIP also takes advantage of CHAT-style morphological coding. Upon encountering
a word, the program determines the word’s stem and then stores any associated prefixes or
suffixes along with the stem. During the coding process, if lexical stems match exactly, theprogram then also looks for additions, deletions, repetitions, or substitutions of attachedmorphemes.

7.4.3 Word Class Analysis
In the standard analysis of the last line of the chip.cha file, the fact that the adult andthe child use a different article before the noun hat. However, if you add the +g option,then CHIP will use the part-of-speech (POS) information on the %mor tier to guess that
there is a within-POS substitution.
The output of this command will add a $SUB field to the %adu tier:
*CHI:
*MOT:
%adu:
=0.50

that a hat!
yes that’s the hat.
$EXA:that $EXA:hat $ADD:yes $SUB:the $MADD:'s $DIST = 1 $REP

The %adu line now indicates that there was an EXAct repetition of hat, an ADDitionof the string yes that’s and a within-class substitution of the for a. If the substitution option
is used, EXPANsions and REDUCtions are tracked for the substituted words only. In
addition to modifying the dependent tier, using the substitution option also affects thesummary statistics that are produced. With the substitution option, the summary statistics
will be calculated relative only to the words that are substituted. In many cases, you will
want to run CHIP analyses both with and without the substitution option and compare thecontrasting analyses.

7.4.4 Summary Measures
In addition to analyzing utterances and creating separate dependent tiers, CHIP also
produces a set of summary measures. These measures include absolute and proportional

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values for each of the coding categories for each speaker type that are outlined below. The
definition of each of these measures is as follows. In these codes, the asterisk stands forany one of the four basic operations of ADD, DEL, EXA, and SUB.
Total # of Utterances The number of utterances for all speakers regardless of the numberof intervening utterances and speaker identification.
Total Responses
The total number of responses for each speaker type regardless ofamount of overlap.
Overlap The number of responses in which there is an overlap of at least one word stemin the source and response utterances.
No Overlap The number of responses in which there is NO overlap between the source
and response utterances.
Avg_Dist
The sum of the DIST values divided by the total number of overlapping
utterances.
%_Overlap The percentage of overlapping responses over the total number of
responses.
Rep_Index
Average proportion of repetition between the source and response utterance
across all the overlapping responses in the data.
*_OPS The total (absolute) number of add, delete, exact, or substitution operations forall overlapping utterance pairs in the data.
%_*_OPSThe numerator in these percentages is the operator being tracked and thedenominator is the sum of all four operator types.
*_WORD
The total (absolute) number of add, delete, exact, or substitution words forall overlapping utterance pairs in the data.
%_*_WORDS
The numerator in these percentages is the word operator being
tracked and the denominator is the sum of all four-word operator types.
MORPH_* The total number of morphological changes on exactly matching stems.
%_MORPH_* The total number of morphological changes divided by the number ofexactly matching stems.
AV_WORD_*The average number of words per operation across all the overlapping
utterance pairs in the data.
FRONTED The number of lexical items from the word list that have been fronted.
EXACT The number of exactly matching responses.
EXPAN The number of responses containing only exact matches and additions.
REDUC The number of responses containing only exact-matches and deletions.
SUBST The number of responses containing only exact matches and substitutions.
IMITAT Index of total imitativeness, using 4 indices above. It is this sum:
EXACT+EXPAN+REDUC+SUBST

7.4.5 Unique Options
CHIP has the following unique options:Part 2: CLAN

61+bSpecify that speaker ID S is an “adult.” The speaker does not actually have to be an
adult. The “b” simply indicates which speaker is taken to be the source. If you want
to study the “child” as respondent, you will focus on the %chi line. If you want tosee focus on the “adult” as respondent, you will focus on the %adu line.
+cSpecify that speaker ID S is a “child.” The speaker does not actually have to be achild. The “c” simply indicates which speaker is taken to be the “response”. If you
want to study the “child” as respondent, you should focus on the %chi line. If you
want to see focus on the “adult” as respondent, you should focus on the %adu line.
+dUsing +d with no further number outputs only coding tiers, which are useful for
iterative analyses. Using +d1 outputs only summary statistics, which can then be
sent to a statistical program.
+gEnable the substitution option.
-h
Use a word list file. The target file is specified after the letter “h.” Words to be
excluded are searched for in the target file. The use of an exclude file enables CHIP
to filter out variations in words that are less important for the message, includingwords such as okay or yeah. Standard CLAN wildcards or metacharacters may be
used anywhere in the word list.
+nThis switch has three values: +nb, +nc, and +ns. See the examples given earlier fora discussion of the use of these switches in combination.
+qN Set the utterance window to N utterances. The default window is seven utterances.
CHIP identifies the source-response utterances pairs to code. When a response isencountered, the program works backwards (through a window determined by the+q option) until it identifies the most recent potential source utterance. Only one
source utterance is coded for each response utterance. Once the source-response pairhas been identified, a simple matching procedure is performed.
+x
Set the minimum repetition index for coding.
CHIP also uses several options that are shared with other commands. For a complete
list of options for a command, type the name of the command followed by a carriage returnin the Commands window. Information regarding the additional options shared acrosscommands can be found in the chapter on Options.

7.5 CHIPUTIL
This program is designed to identify the utterance which is the source of a repetition.
It does this by outputting a pair of utterances, i.e. both the source and the target. By using
the +sS switch you can limit tracking to sources at a particular distance from the target. If
you run this command:chip +bMOT +cCHI chip.cha +fon the chip.cha example used in the previous section, and then run this command on thechip.chip.cex output:
chiputil chip.chip.cex +f +s

you will get this output, where the +sswitch limits output to imitations:
*MOT:
%chU:
%mor:
%asr:

a hat !
$SOURCE=1
det:art|a n|hat !
$NO_REP $REP = 0.00

Part 2: CLAN
%adu:
*CHI:
%chU:
%mor:
%csr:
%chi:

62

$EXA:hat $ADD:a $EXPAN $DIST = 1 $REP = 0.50
a hat .
$IMITATION=1
det:art|a n|hat .
$EXA:hat $ADD:a $EXPAN $DIST = 2 $REP = 0.50
$EXA:a-hat $EXACT $DIST = 1 $REP = 1.00

7.6 COMBO
COMBO provides the user with ways of composing Boolean search strings to match
patterns of letters, words, or groups of words in the data files. This program is particularly
important for researchers who are interested in syntactic analysis. The search strings are
specified with either the +s/-s option or in a separate file. Use of the +s switch is obligatory
in COMBO. When learning to use COMBO, what is most tricky is learning how to specify
the correct search strings.

7.6.1 Composing Search Strings
Boolean searching uses algebraic symbols to better define words or combinations ofwords to be searched for in data. COMBO uses regular expressions to define the searchpattern. These six special symbols are listed in the following table:
MeaningType

Symbolimmediately FOLLOWED by

Boolean^inclusive OR

Boolean+logical NOT

Boolean!

repeated character

metacharacter*

single character

metacharacter_

quoting

metacharacter\

Inserting the ^ operator between two strings causes the program to search for the firststring followed by the second string. The + operator inserted between two strings causes
the program to search for either of the two strings. In this case, it is not necessary for bothto match the text to have a successful match of the whole expression. Any one match is
sufficient. The ! operated inserted before a string causes the program to match a string oftext that does not contain that string.
The items of the regular expression will be matched to the items in the text only if they
directly follow one another. For example, the expression big^cat will match only the word
big directly followed by the word cat as in big cat. To find the word big followed by theword cat immediately or otherwise, use the metacharacter * between the items big and cat,as in big^*^cat. This expression will match, for example, big black cat. Notice that, in thisexample, * ends up matching not just any string of characters, but any string of words orcharacters up to the point where cat is matched. Inside a word, such as go*, the asterisk
stands for any number of characters. In the form ^*^, it stands for any number of words.
The * alone cannot be used in conjunction with the +g or +x option.

Part 2: CLAN

63

The underscore is used to “stand in for” for any single character. If you want to match
any single word, you can use the underscore with the asterisk as in +s"_*." which will
match any single word followed by a period. For example, in the string cat., the underscore
would match c, the asterisk would match at and the period would match the period.
The backslash (\) is used to quote either the asterisk or the underline. When you want
to search for the actual characters * and _, rather than using them as metacharacters, youinsert the \ character before them.
Using metacharacters can be quite helpful in defining search strings. Suppose you want
to search for the words weight, weighs, weighing, weighed, and weigh. You could use thestring weigh* to find all the previously mentioned forms. Metacharacters may be usedanywhere in the search string.
When COMBO finds a match to a search string, it prints out the entire utterance inwhich the search string matched, along with any previous context or following context that
had been included with the +w or -w switches. This whole area printed out is what we willcall the “window.”

7.6.2 Examples of Search Strings
The following command searches the sample.cha file and prints out the window whichcontains the word “want” when it is directly followed by the word “to.”combo +swant^to sample.cha

If you are interested not just in cases where “to” immediately follows “want,” but also cases
where it eventually follows, you can use the following command syntax:
combo +s"want^*^to" sample.cha

The next command searches the file and prints out any window that contains both “want”
and “to” in any order:
combo +s"want^to" +x sample.cha

The next command searches sample.cha and sample2.cha for the words “wonderful” or“chalk” and prints the window that contains either word:
combo +s"wonderful+chalk" sample*.cha

The next command searches sample.cha for the word “neat” when it is not directly followedby the words “toy” or “toy-s.” Note that you need the ^ in addition to the ! to clearly specify
the exact nature of the search you wish to be performed.
combo +s"neat^!toy*" sample.cha

In this next example, the COMBO program will search the text for either the word “see”
directly followed by the word “what” or all the words matching “toy*.”
combo +s"see^(what+toy*)" sample.cha

You can use parentheses to group the search strings unambiguously as in the next example:
combo +s"what*^(other+that*)" sample.cha

This command causes the program to search for words matching “what” followed by either
the word “that” or the word “other.” An example of the types of strings that would be foundare: “what that,” “what’s that,” and “what other.” It will not match “what is that” or “whatPart 2: CLAN64

do you want.” Parentheses are necessary in the command line because the program reads
the string from left to right. Parentheses are also important in the next example.
combo +s"the^*^!grey^*^(dog+cat)" sample2.chaThis command causes the program to search the file sample2.cha for the followed, immediately or eventually, by any word or words except grey. This combination is then to befollowed by either dog or cat. The intention of this search is to find strings like the big dog
or the boy with a cat, and not to match strings like the big grey cat. Note the use of theparentheses in the example. Without parentheses around dog+cat, the program would
match simply cat. In this example, the sequence ^*^ is used to indicate “immediately or
later.” If we had used only the symbol ^ instead of the ^*^, we would have matched only
strings in which the word immediately following the was not grey.

7.6.3 Referring to Files in Search Strings
Inside the +s switch, one can include reference to one, two, or even more groups ofwords that are listed in separate files. For example, you can look for combinations of prepositions followed by articles by using this switch:+s@preps.cut^@arts.cut

To use this form, you first need to create a file of prepositions called “preps.cut” with
one preposition on each line and a file of articles called “arts.cut” with one article on eachline. By maintaining files of words for different parts of speech or different semantic fields,
you can use COMBO to achieve a wide variety of syntactic and semantic analyses. Somesuggestions for words to be grouped into files are given in the chapter of the CHAT manual
on word lists. Some particularly easy lists to create would be those including all the modal
verbs, all the articles, or all the prepositions. When building these lists, remember the possible existence of dialect and spelling variations such as dat for that.
Here is another example of how to refer to files in search strings. In this case, we arelooking in Spanish files for words that follow the definite articles la and el and begin witheither vowels or the silent “h” followed by a vowel. So, we can have one file, called
arts.cut, with the words el and la each on their own line. Then, we can have another file,called vowels.cut, that looks like this:
hu*
u*
ha*
a*

etc.

In this case, the command we use looks like this:combo +s@arts.cut^@vowels.cut test.cha

7.6.4 COMBO for %mor and %gra sequences
You can use the +d7 switch in COMBO, FREQ, and KWAL to search for matches
between the %gra and %mor lines, because these two lines are in one-to-one
correspondence. The following command would search for cases in which a SUBJ on the%gra line is followed by a verb on the %mor line:
combo +d7 +sg|SUBJ^m|v filename.cha

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65

7.6.5 Cross-tier COMBO
By default, COMBO excludes the header and dependent code tiers from the search andoutput. However, when the +t switch is used to include specified dependent tiers, they are
combined with their speaker tiers into clusters. For example, if the search expression isthe^*^kitten, the match would be found even if the is on the speaker tier and kitten is on oneof the speaker’s associated dependent tiers. This feature is useful if one wants to select foranalyses only speaker tiers that contain specific word(s) on the main tier and some specificcodes on the dependent code tier. For example, if one wants to produce a frequency countof the words want and to when either one of them is coded as $INI on the %spa line, orneat when it is $CON on the %spa line, the following two commands could be used:
combo +s(want^to^$INI*)+(neat^$CON*) +g5 +t%spa +f +d sample.cha
freq +swant +sto +sneat sample.cmb

In this example, the +g5 option specifies that the words want, to, as well as the $INI on the%spa line may occur in any order. The +t%spa option must be added to allow the program
to look at the %spa tier when searching for a match. The main tier is always searched, but
dependent tiers are only searched if they are specifically included with the +t switch. The+d option is used to specify that the information produced by the program, such as file
name, line number and exact position of words on the tier, should be excluded from theoutput. This way the output is in a legal CHAT format and can be used as an input toanother CLAN program, FREQ in this case.

7.6.6 Cluster Sequences in COMBO
Most computer search programs work on a single line at a time. If these programs finda match on the line, they print it out and then move on. Because of the structure of CHAT
and the relation between the main line and the dependent tiers, it is more useful to have the
CLAN commands work on “clusters” instead of lines. The notion of a cluster is particularly
important for search programs, such as COMBO and KWAL. A cluster can be defined asa single utterance by a single speaker, along with all its dependent tiers. By default, CLAN
commands work on a single cluster at a time. For COMBO, one can extend this searchscope to a pair of contiguous clusters or even a sequence of several clusters by using the+b switch. However, this switch should only be used when cross-cluster matches areimportant, because addition of the switch tends to slow down the running of the program.
To illustrate the use of the +b switch, consider how you might want to perform a FREQ
analysis on sentences that the mother directs to the younger child, as opposed to sentences
directed to the older child or other adults. To find the sentences directed to the younger
child, one can imagine that sentences from the mother that are followed by sentences fromthe younger child are most likely directed to the younger child. To find these, you can use
this command:
combo +b2 +t*MOT +t*CHI +s\*MOT:^*^\*CHI: eve01.cha

7.6.7 Tracking Final and Initial Words
To find the final words of utterances, you need to use the complete delimiter set in yourCOMBO search string. You can do this with this syntax (\!+?+.) where the parentheses
enclose a set of alternative delimiters. To specify the single word that appears before thesePart 2: CLAN

66

delimiters, you can use the asterisk wildcard preceded by an underline. Note that this useof the asterisk treats it as referring to any number of letters, rather than any number ofwords. By itself, the asterisk in COMBO search strings usually means any number ofwords, but when preceded by the underline, it means any number of characters. Here is thefull command:
combo +s"_*^(\!+?+.)" +f sample.cha

Then you can run FREQ on the output file.
You can use COMBO to track initial words by including the line begin %mor:, as inthis example, which searches for an initial wh-word in a question.
combo +re +s"\%mor:^pro:wh|*^?" +t%mor +t*CHI *.cha +u

7.6.8 Limiting with COMBO
Often researchers want to limit their analysis to some group of utterances. CLAN
provides the user with a series of switches within each program for doing the simplest typesof limiting. For example, the +t/-t switch allows the user to include or exclude whole tiers.
However, sometimes these simple mechanisms are not suficient and the user must use
COMBO or KWAL for more detailed control of limiting. COMBO is the most powerfulprogram for limiting, because it has the most versatile methods for string search using the+s switch. Here is an illustration. Suppose that, in sample.cha, you want to find thefrequency count of all the speech act codes associated with the speaker *MOT when thisspeaker used the phrase “want to” in an utterance. To accomplish this analysis, use thiscommand:combo +t*MOT +t%spa sample.cha +s'want^to' +d

The +t*MOT switch tells the program to select only the main lines associated with the
speaker *MOT. The +t%spa tells the program to add the %spa tier to the *MOT main
speaker tiers. By default, the dependent tiers are excluded from the analysis. After this,comes the file name, which can appear anywhere after the program name. The +s"want^to"then tells the program to select only the *MOT clusters that contain the phrase want to.
You can then run programs like FREQ or MLU on the output.
Sometimes researchers want to maintain a copy of their data that is stripped of thevarious coding tiers. This can be done by this command:
combo +s* +o@ -t% +f *.cha

The +o switch controls the addition of the header material that would otherwise be excluded from the output and the -t switch controls the deletion of the dependent tiers. It isalso possible to include or exclude individual speakers or dependent tiers by providing
additional +t or -t switches. The best way to understand the use of limiting for controlling
data display is to try the various options on a small sample file.

7.6.9 Adding Codes with COMBO
Often researchers leave a mark in a transcript indicating that a certain sentence has
matched some search pattern. For example, imagine that you want to locate all sentenceswith a preposition followed immediately by the word “the” and then tag these sentences insome way. You can use the COMBO +d4 switch to do this. First, you would create a filePart 2: CLAN

67

with all the prepositions (one on each line) and call it something like prep.cut. Then you
would create a second support file called something like combo.cut with this line:"@prep.cut^the" "$Pthe" "%cod:"

The first string in this line gives the term used by the standard +s search switch. The second
string says that the code produced will bye $Pthe. The third string says that this codeshould be placed on a %cod line under the utterance that is matched. If there is no %codline there yet, one will be created. The COMBO command that uses this information would
then be:combo +s"@combo.cut" +d4 filename.cha

The resulting file will have this line added:
%cod:

$Pthe

You can include as many lines as you wish in the combo.cut file to control the addition of
additional codes and additional coding lines. Once you are done with this, you can use
these new codes to control better inclusion and exclusion of utterances and other types ofsearches.

7.6.10 Unique Options
COMBO has the following unique options:+bN COMBO usually works on only one cluster at a time. However, when you want tolook at several clusters in a row, you can use this switch. For example, +b4 would
allow COMBO to search across a sequence of four clusters.
+dNormally, COMBO outputs the location of the tier where the match occurs. Whenthe +d switch is turned on you can output only each matched sentence in a simple
legal CHAT format.
+d1 This switch outputs legal CHAT format along with line numbers and file names.
+d2 This switch outputs files names once per file only.
+d3 This switch outputs legal CHAT format, but with only the actual words matched by
the search string, along with @Comment headers that are ignored by other programs.
+d4 Use of the +d4 switch was described in the previous section.
+d7 Search for words linked between two tiers.
+g1 COMBO can operate in either string-oriented or word-oriented mode. The default
mode is word-oriented. The +g1 switch changes the mode to string-oriented. Wordoriented search assumes that the string of characters requested in the search string issurrounded by spaces or other word delimiting characters. The string-oriented searchdoes not make this assumption. It sees a string of characters simply as a string ofcharacters. In most cases, there is no need to use this switch, because the default
word-oriented mode is usually more useful.
The interpretation of metacharacters varies depending on the search mode. In wordoriented search mode, an expression with the asterisk metacharacter, such as
air*^plane, will match air plane as well as airpline plane or airy plane. It will notmatch airplane because, in word-oriented mode, the program expects to find twowords. It will not match air in the plane because the text is broken into words byassuming that all adjacent nonspace characters are part of the same word, and a spacemarks the end of that word. You can think of the search string air as a signal for thePart 2: CLAN

+g2:
+g3:
+g4:
+g5:

+o

+s

-s
+t

68

computer to search for the expressions: _air_, _air., air?, air!, and so forth, where theunderline indicates a space.
The same expression air*^plane in string-oriented search mode will match airline
plane, airy plane, air in the plane or airplane. They will all be found because thesearch string, in this case, specifies the string consisting of the letters “a,” “i,” and“r”, followed by any number of characters, followed by the string “p,” “l,” “a,” “n,”and “e.” In string-oriented search, the expression (air^plane) will match airplane but
not air plane because no space character was specified in the search string. In
general, the string-oriented mode is not as useful as the word-oriented mode. One of
the few cases when this mode is useful is when you want to find all but some given
forms. For example, if you are looking for all the forms of the verb kick except theing form, you can use the expression “kick*^! ^!ing” and the +g switch.
Do a string-oriented search on just one word. This option is for searching for strings
within each word.
Do not continue searching on a tier after first failure. This option is in cases users do
not want to look for word patterns further down the tier, if the first match fails. This
option is used for searches with the "not", "!", operator.
Exclude utterance delimiters from search. This will remove all utterance delimitersfrom the search string. It is useful, if you want to find the last word on the tier.
Make search <s1>^<s2> identical to search <s2>^<s1>. This option is used as ashort cut. Normally words specified this way "word1^word2" are searched for in a
specific order. This option will match for word1 and word2 regardless whether
word1 precedes word2 or follows it on the tier. Otherwise user will have to specify
this: (word1^word2)+(word2^word1). By default, the ^ operator means followedby, but the +g6 options turns ^ into a true AND operator. So COMBO search will
succeed only if all words separated by "^" are found anywhere on the cluster tier.
This also takes care of the situation when dependent tiers are not always in the sameorder.
The +t switch is used to control the addition or deletion of particular tiers or lines
from the input and the output to COMBO. In some cases, you may want to include
a tier in the output that is not being included in the input. This typically happens
when you want to match a string in only one dependent tier, such as the %mor tier,but you want all tiers to be included in the output. To do this you would use acommand of the following shape:
combo +t%mor +s"*ALL" +o% sample2.chaThis option is obligatory for COMBO. It is used to specify a regular expression to
search for in a data line(s). This option should be immediately followed by theregular expression itself. The rules for forming a regular expression are discussed indetail earlier in this section.
This switch allows you to exclude certain line from a COMBO search. It can beused in combination with the +s switch.
Dependent tiers can be included or excluded by using the +t option immediately
followed by the tier code. By default, COMBO excludes the header and dependentcode tiers from the search and output. However, when the dependent code tiers areincluded by using the +t option, they are combined with their speaker tiers into
clusters. For example, if the search expression is the^*^kitten, the match would bePart 2: CLAN

69

found even if the is on the speaker tier and kitten is on one of the speaker’s associateddependent tiers. This feature is useful if one wants to select for analyses only speakertiers that contain specific word(s) on the main tier and some specific codes on thedependent code tier. For example, if one wants to produce a frequency count of thewords want and to when either one of them is coded as an imitation on the %spa
line, or neat when it is a continuation on the %spa line, the following two commands
could be used:
combo +s(want^to^*^%spa:^*^$INI*)+(neat^*^%spa:^*^$CON*)+t%spa +f +d sample.cha
freq +swant +sto +sneat sample.cmb

In this example, the +s option specifies that the words want, to, and $INI may occur
in any order on the selected tiers. The +t%spa option must be added to allow the
program to look at the %spa tier when searching for a match. The +d option is used
to specify that the information produced by the program, such as file name, line
number and exact position of words on the tier, should be excluded from the output.
This way the output is in a legal CHAT format and can be used as an input to another
CLAN program, FREQ in this case. The same effect could also be obtained by using
the piping feature.
COMBO also uses several options that are shared with other commands. For a complete
list of options for a command, type the name of the command followed by a carriage returnin the Commands window. Information regarding the additional options shared acrosscommands can be found in the chapter on Options.

7.7 COOCUR
The COOCCUR program tabulates co-occurences of words. This is helpful for analyzing syntactic clusters. By default, the cluster length is two words, but you can reset thisvalue just by inserting any integer up to 20 immediately after the +n option. The second
word of the initial cluster will become the first word of the following cluster, and so on.
cooccur +t*MOT +n3 sample.cha +f

The +t*MOT switch tells the program to select only the *MOT main speaker tiers. The
header and dependent code tiers are excluded by default. The +n3 option tells the program
to combine three words into a word cluster. The program will then go through all of *MOT
main speaker tiers in the sample.cha file, three words at a time. When COOCCUR reaches
the end of an utterance, it marks the end of a cluster, so that no clusters are broken acrossspeakers or across utterances. Co-ocurrences of codes on the %mor line can be searchedusing commands such as this example:
cooccur +t%mor -t* +s*def sample2.cha

7.7.1 Unique Options
COOCCUR has the following unique options:+dStrip the numbers from the output data that indicate how often a certain cluster
occurred.
+n
Set cluster length to a certain number. For example, +n3 will set cluster length to 3.

Part 2: CLAN
+s

70

Select either a word or a file of words with @filename to search for.

7.8 DISTThis program produces a listing of the average distances between words or codes in a
file. DIST computes how many utterances exist between occurrences of a specified key
word or code. The following example demonstrates a use of the DIST program.
dist +t%spa -t* +b: sample.cha

This command line tells the program to look at the %spa tiers in the file sample.cha forcodes containing the : symbol. It then does a frequency count of each of these codes, as agroup, and counts the number of turns between occurrences. The -t* option causes the program to ignore data from the main speaker tiers.
You can also use DIST to search for distances between words in the main line. For
example, when looking for the word frog in CHILDES/Frogs/WolfHemp/08/01w8.cha,
you would use this command:
dist +sfrog +g 01w8.cha

and you would get this result:From file <01w8.cha>
There were 59 turns.
Occurrence
First
Last
AverageWord
Count
OccursOccursDistance----------------------------------------------------------frog
830
51
2.6250Note that the information about where the word first occurs, last occurs, and the distance
between occurrences is given in terms of turns, not line numbers.

7.8.1 Unique Options
DIST has the following unique options:+bThis option allows you to specify a special character after the +b. This character issomething like the colon that you have chosen to use to divide some complex code
into its component parts. For example, you might designate a word as a noun on thedependent tier then further designate that word as a pronoun by placing a code on
the dependent tier such as $NOU:pro. The program would analyze each element of
the complex code individually and as a class. For the example cited earlier, theprogram would show the distance between those items marked with a $NOU (alarger class of words) and show the distance between those items marked with
$NOU:pro as a subset of the larger set. The +b option for the example would look
like this with a colon following the +b:
dist +b: sample.cha+dOutput data in a form suitable for statistical analysis.
+gIncluding this switch in the command line causes the program to count only one
occurrence of each word for each utterance. So multiple occurrences of a word orcode will count as one occurrence.
+oThis option allows you to consider only words that contain the character specified

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71

by the b option, rather than all codes in addition to those containing your special
character.
DIST also uses several options that are shared with other commands. For a complete
list of options for a command, type the name of the command followed by a carriage returnin the Commands window. Information regarding the additional options shared acrosscommands can be found in the chapter on Options.

7.9 FREQ
One of the most powerful programs in CLAN is the FREQ program for frequency analysis. In its basic form, without special switches, it is also one of the easiest programs to use
and a good program to start with when learning to use CLAN. FREQ constructs a frequency
word count for user-specified files. A frequency word count is the calculation of thenumber of times a word occurs in a file or set of files. FREQ produces a list of all the words
used in the file, along with their frequency counts, and calculates a type–token ratio. The
type–token ratio is found by calculating the total number of unique words used by aselected speaker (or speakers) and dividing that number by the total number of words usedby the same speaker(s). It is generally used as a rough measure of lexical diversity. Of
course, the type–token ratio can only be used to compare samples of equivalent size,
because the ratio of types to tokens tends to vary with sample size.

7.9.1 What FREQ ignores
The CHAT manual specifies two special symbols that are used when transcribing difficult material. The xxx symbol is used to indicate unintelligible speech and the www symbol is used to indicate speech that is untranscribable for technical reasons. FREQ ignores
these symbols by default. Also excluded are all the words beginning with one of thefollowing characters: 0, &, +, -, #. If you wish to include them in your analyses, list them,along with other words you are searching for, using the +s/-s option. The FREQ program
also ignores header and code tiers by default. Use the +t option if you want to include
headers or coding tiers.

7.9.2 Studying Lexical Groups using the +s@file switch
The simplest use of FREQ is to ask it to give a complete frequency count of all thewords in a transcript. However, FREQ can also be used to study the development and useof lexical groups. For example, if you want to study how children use personal pronouns
between the ages of 2 and 3 years, a frequency count of these forms would be helpful.
Other lexical groups that might be interesting to track could be the set of all conjunctions,
all prepositions, all morality words, names of foods, and so on. To get a listing of thefrequencies of such words, you need to put all the words you want to track into a text file,
one word on each line by itself, and then use the +s switch with the name of the file
preceded by the @ sign, as in this example:
freq +s@articles.cut +f sample.cha

This command would conduct a frequency analysis on all the articles that you have put inthe file called articles.cut. You can create the articles.cut file using either the CLAN editor
in Text Mode or some other editor saving in “text only.” The file looks like this:Part 2: CLAN

72

a
the
an

If you wish to use this feature to search for items on the %mor tier, then use this format:
m;runm;playm;jump7.9.3 FREQ for %mor and %gra combinations
Because the %mor and %gra lines are in one-to-one alignment, you can use the +d7
switch with FREQ to search for items that have a particular characterization on both lines.
For example, these commands will find words that are marked as relativizers on the %morline and which are tagged as having the OBJ grammatical relation role on the %gra tier:freq sample.cha +d7 +sm|rel +sg|OBJ

This feature also works when used with KWAL and COMBO.

7.9.4 Searches in Multilingual Corpora
FREQ can be used with the –l switch to track a variety of multilingual patterns. Hereis a FREQ count of Spanish nouns (n|*) on the %mor tier with their corresponding words
on speaker tier:freq +l +s*@s:spa +sm|n +d7 *.cha

Here is a FREQ count of all Spanish words on %mor tier:freq +l +s*@s:spa +d7 *.cha

Here is a FREQ count of all Spanish words on speaker tier:freq +l +s*@s:spa *.cha

To count the frequencies of words on tiers marked with the [- spa] pre-code:freq +s"[- spa]" *.cha

To count the number of tiers with [- spa] pre-code only:
freq +s"<- spa>" *.cha

Counting only the English tiers:
freq +s"<- eng>"*.cha

The language pre-code has a space in it, so it is important that when you specify thislanguage pre-code that you include the space and use quotes:+s"[- spa]" +s"[- eng]" +s"<- spa>" +s"<- eng>"

7.9.5 Building Concordances with FREQ
CLAN is not designed to build final, publishable concordances. However, you can
produce simple concordance-like output using the +d0 switch with FREQ. Here is a
fragment of the output from the use of this command. This fragment shows 8 matches forthe word “those” and 3 for the word “throw.”
8 thosePart 2: CLAN

73

File "0012.cha": line 655.
*MOT:
look at those [= toys] .
File "0012.cha": line 931.
*MOT:
don’t you touch those wires .
File "0012.cha": line 1005.
*MOT:
you can’t play in those drawers .
File "0012.cha": line 1115.
*MOT:
those [= crayons] are (y)icky .
File "0012.cha": line 1118.
*MOT:
those [= crayons] are (y)icky .
File "0012.cha": line 1233.
*MOT:
you can’t eat those [= crayons] .
File "0012.cha": line 1240.
*MOT:
no (.) you can’t eat those [= crayons] .
File "0012.cha": line 1271.
*MOT:(be)cause you're gonna [: going to] put those [= crayons] inyour mouth .
3 throw
File "0012.cha": line 397.
*MOT:
can you <throw that [= football] ?> [>]File "0012.cha": line 702.
*MOT:
yeah (.) can we throw it [= ball] ?
File "0012.cha": line 711.
*MOT:
can you throw that [= ball] to Mommy ?

7.9.6 Using Wildcards with FREQ
Some of the most powerful uses of FREQ involve the use of metacharacters including
wildcards. CLAN's wildcards include*
matches any string and the results are not merged% matches any string and the results are merged_
matches a single word
Wildcards are particularly useful when you want to analyze the frequencies for various
codes that you have entered in coding lines. Here is an example of the use of wildcards
with codes. One line of Hungarian data in sample2.cha has been coded on the %mor line
for syntactic role and part of speech, as described in the CHAT manual. It includes thesecodes: N:A|duck-ACC, N:I|plane-ACC, N:I|grape-ALL, and N:A|baby-ALL, where thesuffixes mark accusative and illative cases and N:A and N:I indicate animate and inanimate
nouns. If you want to obtain a frequency count of all the animate nouns (N:A) that occur
in this file, use this command line:
freq +t%mor +s"N:A|*" sample2.cha

The output of this command will be:
1 n:a|baby-all
1 n:a|ball-acc
1 n:a|duck-acc

Note that material after the +s switch is enclosed in quotation marks to guarantee thatwildcards will be correctly interpreted. For Macintosh and Windows, the quotes are thebest way of guaranteeing that a string is correctly interpreted. On Unix, only single quotes
can be used. In Unix, single quotes are necessary when the search string contains a $, |, orPart 2: CLAN

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> sign.
The next examples give additional search strings with asterisks and the output they will
yield when run on the sample file. Note that what may appear to be a single underline in
the second example is actually two underline characters.
String*-acc*-a__

N:*|*-all

Output
1 n:a|ball-acc
1 n:a|duck-acc
1 n:i|plane-acc
1 n:a|baby-all
1 n:a|ball-acc
1 n:a|duck-acc
1 n:i|grape-all
1 n:i|plane-acc
1 N:A|baby-all
1 N:I|grape-all

These examples show the use of the asterisk as a wildcard. When the asterisk is used,
FREQ gives a full output of each of the specific code types that match. If you do not want
to see the specific instances of the matches, you can use the percentage wildcard, as in thefollowing examples:
String
N:A|%
%-ACC
%-A__
N:%|%-ACC
N:%|%

Output3 N:A|
3 -ACC
3 -ACC
2 -ALL
3 N:|-ACC
5 N:|

It is also possible to combine the use of the two types of wildcards, as in these examples:
StringN:%|*-ACC
N:*|%

Output1 N:|ball-acc
1 N:|duck-acc
1 N:|plane-acc
3 N:A|
2 N:I|

Researchers have also made extensive use of FREQ to tabulate speech act and interactional codes. Often such codes are constructed using a taxonomic hierarchy. For example,
a code like $NIA:RP:NV has a three-level hierarchy. In the INCA-A system discussed in
the chapter on speech act coding in the CHAT manual, the first level codes the interchange
type; the second level codes the speech act or illocutionary force type; and the third level
codes the nature of the communicative channel. As in the case of the morphological example cited earlier, one could use wildcards in the +s string to analyze at different levels. The
following examples show what the different wildcards will produce when analyzing the%spa tier. The basic command here is:freq +s"$*" +t%spa sample.cha
String$*$*:%$%:*:%

Outputfrequencies of all the three-level
codes in the %spa tierfrequencies of the interchange types
frequencies of the speech act codes

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$RES:*: %$*:sel:%75
frequencies of speech acts within the
RES category
frequencies of the interchange types that have SEL
speech acts

If some of the codes have only two levels rather than the complete set of three levels,
you need to use an additional % sign in the +s switch. Thus, the switch+s"$%:*:%%"

will find all speech act codes, including both those with the third level coded and thosewith only two levels coded.
As another example of how to use wild cards in FREQ, consider the task of counting
all the utterances from the different speakers in a file. In this case, you count the threeletter header codes at the beginnings of utterances. To do this, you need the +y switch to
make sure FREQ sees these headers. The command is:freq +y +s”\**:” *.cha

7.9.7 FREQ for the %mor line
Searches for material on the %mor line can be difficult to compose. To help in thisregard, we have composed a variant of FREQ searches that takes advantage of the rigid
syntax of the %mor tier. To see the available options here, you can type “freq +sm” on thecommand line and you will see the following information:
Morphosyntactic markers specify the nature of the following string# prefix marker| part-of-speech marker; stem of the word marker- suffix marker& nonconcatenated morpheme marker= English translation for the stem marker@ error word preceding [: ...] code marker on the main line
* error code inside [* ...] code marker on the main line
and then optionally - or + and followed by one of these:*find any match%
erase any match
stringfind "string"o erase all other elements not specified by user
o% erase all other elementso~ erase postclitic element, if present
o$ erase preclitic element, if present,separates alternative elements
Postclitic AND Preclitic exception:
Find postclitics with specific Morphosyntactic marker example:|*,~|*
OR;*,~;*
Find preclitic with specific Morphosyntactic marker example:|*,$|*
OR;*,$;**
find any match
stringfind "string"
For example:
find all stems and erase all other markers:+sm;*,o%find all stems and erase all other markers including all postclitics,
if present:Part 2: CLAN

76+sm;*,o%,o~find all stems of all "adv" and erase all other markers:+sm;*,|adv,o%find all forms of "be" verb:+sm;befind all stems and parts-of-speech and erase other markers:+sm;*,|*,o%find only stems and parts-of-speech that have suffixes and eras othermarkers:+sm;*,|*,-*,o%find all stems, parts-of-speech and distinguish those with suffix
and erase other markers:+sm;*,|-*,-+*,o-%find only stems and parts-of-speech that do not have suffixes anderase other markers:-sm-* +sm;*,|*,o%find only noun words with "poss" parts-of-speech postclitic:+sm|n,|n:*,~|possfind all noun words and show postclitics, if they have any:+sm|n,|n:* +r4
find all noun words and erase postclitics, if they have any:+sm|n,|n:*,o~If you are using an include file with the +s@filename option, the format of this file is:m;firstwordm;anotherwordetc.

In other words, the format of the include file is the same as the format of the +sm
options, minus the +s. When using the +sm switch with FREQ, you must also include the+t%mor switch to instruct FREQ to include searches of the %mor tier.

7.9.8 Errors for morphological codes
FREQ allows you to locate all the errors that correspond to specified morphologicalcodes. You can do this by using the +sm switch described in the previous section. For
example, to find all PASTs with all errors, you can use this command:freq+u +t*CHI +sm-PAST,** +sm&PAST,** *cha

The output from this will have this form:
From file <adler01a.cha>
Speaker: *PAR:
1 v|deliver-PAST@dəlivd@u*n:kThis output shows first the frequency, then the code from the %mor line and then the error
as coded on the main line. To find all PASTs with only "neg" errors, as in "word [* neg]",
you can use this command:freq+u +t*CHI +sm-PAST,*neg +sm&PAST,*neg *cha

7.9.9 Directing the Output of FREQ
When FREQ is run on a single file, output can be directed to an output file by using the+f option:

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freq +f sample.cha

This results in the output being sent to sample.frq.cex. If you wish, you may specify a fileextension other than .frq.cex for the output file. For example, to have the output sent to a
file with the extension .mot.cex, you would specify:
freq +fmot sample.cha

Suppose, however, that you are using FREQ to produce output on a group of files rather
than on a single file. The following command will produce a separate output file for each.cha file in the current directory:
freq +f *.cha

To specify that the frequency analysis for each of these files be computed separately but
stored in a single file, you must use the redirect symbol (>) and specify the name of theoutput file. For example:
freq *.cha > freq.all

This command will maintain the separate frequency analyses for each file separately andstore them all in a single file called freq.all. If there is already material in the freq.all file,
you may want to append the new material to the end of the old material. In this case, you
should use the form:
freq *.cha >> freq.all

Sometimes, however, researchers want to treat a whole group of files as a single database.
To derive a single frequency count for all the .cha files, you need to use the +u option:
freq +u *.cha

Again, you may use the redirect feature to specify the name of the output file, as in thefollowing:
freq +u *.cha > freq.all

7.9.10 Limiting in FREQ
An important analytic technique available in clan is the process of “limiting” whichallows you to focus your analysis on the part of your data files that is relevant by excluding
all other sections. Limiting is based on use of the +s, +t, and +z switches. Limiting is available in most of the clan string search programs but cannot be done within special purpose
programs such as CHSTRING or CHECK.
1. Limiting by including or excluding dependent tiers. Limiting can be used to selectout dependent tiers. By using the +t and -t options, you can choose to include certain
dependent tiers and ignore others. For example, if you select a main speaker tier, youwill be able to choose the dependent tiers of only that speaker. Each type of tier mustbe specifically selected by the user, otherwise the programs follow their default
conditions for selecting tiers.
2. Limiting by including or excluding main tiers. When the -t* option is combined witha switch like +t*MOT, limiting first narrows the search to the utterances by MOT andthen further excludes the main lines spoken by MOT. This switch functions in adifferent way from -t*CHI, which will simply exclude all of the utterances of CHI andthe associated dependent tiers.

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3. Limiting by including or excluding sequential regions of lines or words. The nextlevel of limiting is performed when the +z option is used. At this level only the specified
data region is chosen out of all the selected tiers.
4. Limiting by string inclusion and exclusion. The +s/-s options limit the data that ispassed on to subsequent programs.
Here is an example of the combined use of the first four limiting techniques. There are
two speakers, *CHI and *MOT, in sample.cha. Suppose you want to create a frequency
count of all variations of the $ini codes found on the %spa dependent tiers of *CHI only inthe first 20 utterances. This analysis is accomplished by using this command:
freq +t*CHI +t%spa +s"$INI*" -t* +z20u sample.cha

The +t*CHI switch tells the program to select the main and dependent tiers associatedonly with the speaker *CHI. The +t%spa tells the program to further narrow the selection.
It limits the analysis to the %spa dependent tiers and the *CHI main speaker tiers. The -t*
option signals the program to eliminate data found on the main speaker tier for NIC fromthe analysis. The +s option tells the program to eliminate all the words that do not match
the $INI* string from the analysis. Quotes are needed for this +s switch to guarantee correctinterpretation of the asterisk. In general, it is safest to always use pairs of double quotes
with the +s switch. The +z20u option tells the program to look at only the first 20
utterances. Now the FREQ program can perform the desired analysis. This command line
will send the output to the screen only. You must use the +f option if you want it sent to a
file. By default, the header tiers are excluded from the analysis.
The +/-s switch can also be used in combination with special codes to pick out sectionsof material in code-switching. For example, stretches of German language can be marked
inside a transcript of mostly English productions with this form:*CHI:<ich meine> [@g] cow drinking.

Then the command to ignore German material would be:freq –s”<@g>” *.cha

7.9.11 Creating Crosstabulations in FREQ
You are use FREQ to create Excel output with crosstabulation between variables on
dependent tiers. For example, if you have two coding tiers called %xarg and %xsem, you
can crosstabulate using these commands:
freq +d8 +t%xarg +t%xsem +d2 sample.cha
freq +d8 +t%xarg +t%xsem +c5 +d2 sample.cha
freq +d8 +t%xarg +t%xsem sample.cha
freq +d8 +t%xarg +t%xsem +c5 sample.cha

If you want to get results across all input files, then:freq +d8 +t%xarg +t%xsem +d2 +u sample.cha
freq +d8 +t%xarg +t%xsem +c5 +d2 +u sample.cha

7.9.12 TTR for Lemmas
If you run FREQ on the data on the main speaker tier, you will get a type-token ratio

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that is grounded on whole word forms, rather than lemmas. For example, “run,” “runs,”and “running” will all be treated as separate types. If you want to treat all forms of thelemma “run” as a single type, you should run the file through MOR and POST to get adisambiguated %mor line. Then you can run FREQ in a form such as this to get a lemmabased TTR.
freq +sm;*,o% sample.mor.pst

Depending on the shape of your morphological forms, you may need to add some additional +s switches to this sample command.

7.9.13 Studying Unique Words and Shared Words
With a few simple manipulations, FREQ can be used to study the extent to which words
are shared between the parents and the child. For example, we may be interested in understanding the nature of words that are used by the child and not used by the mother as a wayof understanding the ways in which the child’s social and conceptual world is structured
by forces outside of the immediate family. To isolate shared and unique words, you can go
through three steps. To illustrate these steps, we will use the sample.cha file.
Run FREQ on the child’s and the mother’s utterances using these two commands:
freq +d1 +t*MOT +f sample.cha
freq +d1 +t*CHI +f sample.cha

The first command will produce a sample.frq.cex file with the mother’s words and thesecond will produce a sample.fr0.cex file with the child’s words. Next you should run
FREQ on the output files:
freq +y +o +u sample.f*

The output of these commands is a list of words with frequencies that are either 1 or 2. All
words with frequencies of 2 are shared between the two files and all words with frequenciesof 1 are unique to either the mother or the child.

7.9.14 Grammatical Complexity Analysis through FREQ
Systems such as LARSP work to compute grammatical complexity from embeddings
in a parse tree structure. However, this type of analysis must be done almost completely
by hand and eye. One can compute many of these same relations automatically by counting
the occurrences of particular grammatical relations (GRs) in transcripts that have a %graline.
If the %gra line was produced by one of the Universal Dependencies (UD) taggers, therelevant GRs, as defined at https://universaldependencies.org/u/dep are:csubj: the finite clausal subject of another clause
ccomp: the clausal complement of a verb
xcomp: a clausal complement without its own subject
acl: clausal modifier of a noun
advcl: adverbial clausal modifier
expl: reflexive markers

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The top of that page has the core GRs, but just below are these further subtypes which areimportant for most languages. Although the web documentation uses lowercase for thesetags, the actual %gra output is in uppercase.
•
•
•
•
•
•
•
•
•
•
•

acl:relcl for relative adnominal clauses
advcl:relcl for relative clauses whose antecedent is a clause
aux:pass for the passive auxiliary
csubj:outer for outer clausal subjects of predicates that are clauses
csubj:pass for clausal subjects of passive clauses
expl:impers for reflexive markers of impersonal clauses
expl:pass for reflexive markers of middle or passive clauses
expl:pv for reflexive clitics with inherently reflexive verbs
nsubj:outer for outer nominal subjects of predicates that are clauses
nsubj:pass for nominal subjects of passive clauses
obl:agent for demoted agents in passive clauses

If the %gra was produced by the earlier MOR system, the relevant GRs are:
CSUBJ: the finite clausal subject of another clause
COMP: the clausal complement of a verb
CPRED: a full clause that serves as the predicate nominal of verbs
CPOBJ: a full clause that serves as the object of a preposition
COBJ: a full clause that serves as the direct object
CJCT: a finite clause that attaches to a verb, adjective, or adverb
XJCT: a non-finite clause that attaches to a verb, adjective, or adverb
NJCT: the head of a complex NP with a PP attached as an adjunct of a noun. The inclusionof this GR is optional.
CMOD: a finite clause that is a nominal modifier or complement
XMOD: a non-finite clause that is a nominal modifier or complement.
To locate and sum all of the complex constructions, one can use this command for UD:
freq +sg|CSUBJ* +sg|CCOMP +sg|XCOMP +sg|ACL* +sg|ADVCL*+d2 +t*PAR *.cha+sg|NSUBJ*

or this command for MOR:
freq +sg|CSUBJ +sg|COMP +sg|CPRED +sg|CPOBJ +sg|COBJ+sg|XJCT +sg|NJCT +sg|CMOD +sg|XMOD +d2 +t*PAR *.cha+sg|CJCTThe result is a stat.frq.xls file that you can open in Excel. It has everything you need tocompute the index for each of the input files, except for the number of tokens in the files.
To get that, you can run this command:
freq +t%gra +t*PAR +s% *.cha

You can then cut and paste those numbers into the first spreadsheet into a column called
TokenAllGR. Then you create another Excel column, which we can call
TokenComplexGRs, that sums all the frequencies of the various complexity GRs. Finally,
you create a third column that divides TokenComplexGR by TokenAllGR, and that is yourcomplexity index. Thanks to Kimberly Mueller for formulating this procedure. Using her

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test files, this procedure spotted 74 embeddings. Of these two were false alarms and there
was one miss. So, overall accuracy of this procedure is at about 95% which compares
favorably with results from human coders.

7.9.15 Unique Options
FREQ has the following unique options:+bN This option calculates the lexical diversity of a sample using the Moving Average
Type-Token Ratio (MATTR). This index is based on a moving window thatcomputes TTRs for each successive window of fixed length (N). Initially, a window
length is selected (e.g., 10 words) and the TTR for words 1-10 is estimated. Then,the TTR is estimated for words 2-11, then 3-12, and so on to the end of the text. For
the final score, the estimated TTRs are averaged.
+cfind capitalized words only
+c1 find words with upper case letters in the middle only
+c2 find matches for every string specified by +s option (default: only first match iscounted)+c3 find multi-word groups anywhere and in any order on a tier+c4 find match only if tier consists solely of all words in multi-word group, even a single
word+c5 when combined with +d7 option it will reverse tier's priority (default: first tier is on
top)+c6 count only repeat segments when searching for words with ↫+c7 for multi-word groups, this searches without expanding wildcards. For example, in
combination with +s"set *" it will give the count of words occuring combination
with "set".
+dCN output only words used by <, <=, =, => or > than N percent of speakers+dPerform a particular level of data analysis. By default, the output consists of all selected words found in the input data file(s) and their corresponding frequencies. The+d option can be used to change the output format. Try these commands:
freq sample.cha +d0
freq sample.cha +d1
freq sample.cha +d2 +tCHI
Each of these three commands produces a different output.
+d0 When the +d0 option is used, the output provides a concordance with the frequencies
of each word, the files and line numbers where each word, and the text in the linethat matches.
+d1 This option outputs each of the words found in the input data file(s) one word perline with no further information about frequency. Later this output could be used asa word list file for KWAL or COMBO programs to locate the context in which thosewords or codes are used.
+d2 With this option, the output is sent to a file in a form that can be opened directly in
Excel. To do this, you must include information about the speaker roles you wish
to include in the output spreadsheet.
freq +d2 +t@ID=”*|Target_Child|*” *.cha+d3 This output is essentially the same as that for +d2, but with only the statistics on

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+d4
+d5

+d6

82

types, tokens, and the type–token ratio. Word frequencies are not placed into the
output. You do not need to use the +f option with +d2 or +d3, since this is assumed.
This switch allows you to output just the type–token information.
This switch will output all words you are searching for, including those that occur
with zero frequency. This could happen, for example, if you use the +s switch tosearch for a specific word and that word does not occur in the transcript. This switch
can be combined with other +d switches.
When used for searches on the main line, this switch will output matched forms witha separate tabulation of replaced forms, errors, partial omissions, and full forms, asin this example for “going” as the target word:
17 going
3 going
11 gonna [: going to]
2 goin(g)1 go [: going] [* 0ing]

This switch can also be used on the %mor line in a form such as this:freq +d6 adler01a.cha +sm|n*,o%will produce separate counts for all instantiations of a given part of speech, organized bypart of speech. For example, the output for n:gerund would be:
2 n:gerund
1 n:gerund|go-PRESP
1 n:gerund|look-PRESP+d7

This command links forms on a “source” tier with their corresponding words on a“target” tier, yielding output such as this:12 pro|you
8 you
4 you'reIn this example, and by default, the first line gives the form on the %mor line as thesource tier, and the following lines give the corresponding main line or “target”
words. If you add the name of a tier, such as %gra, then that becomes the source.
This switch expects that the items on the two tiers be in one-to-one correspondence.
If you want to switch the display so that the target becomes the source, you can add
the +c5 switch. You can also specify a match between two dependent tiers, as inthis example:
freq +d7 +sm|cop +sg|ROOT +t%gra +t%mor t.cha+d8+ooutputs words and frequencies of cross tabulation of one dependent tier with another
Normally, the output from FREQ is sorted alphabetically. This option can be used
to sort the output in descending frequency. The +o1 level will sort to create a reverse
concordance.
+o1 sort output by reverse concordance+o2 sort by reverse concordance of first word; non-CHAT, preserve the whole line+o3 By default, FREQ tabulates separate frequencies for each speaker. To see the
combined results across all speakers, use this switch.
+pS add S to word delimiters. (+p_ will break New_York into two words)
FREQ also uses several options that are shared with other commands, such as +f, +k,

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+l, +y, +r, +s, +u, +x, +z, and others. For a complete list of options for a command, typethe name of the command followed by a carriage return in the Commands window.
Information regarding the additional options shared across commands can be found in thechapter on Options.

7.9.16 Further Illustrations
Davida Fromm has created this list of further examples of the use of FREQ:
1.
If you want the frequency of all words used by Participants on the %mor line indescending order of frequency, file by file, with their part of speech label, use:
freq +t%mor +t*PAR -t* +o *.cha

If you want to merge results across files, add +u to the above command. If you want to
exclude unintelligible words and neologisms, add -sm|neo,|unk" to the above command.
2.
If you want to do this on the speaker line and exclude unintelligible words andneologisms, use:
freq +t*PAR +o -s"xx" -s"<*\* n:uk*>" *.cha

If you want to get information about error productions, add +d6 to the above command.
If you want to send the results to a file instead of having them appear on the computer
screen, add +f to the above command.
3.
If you want the frequency of all words from the %mor line in descending order,
by stems, with information on parts of speech, bound morphemes, and error codes, use:
freq +t*PAR +d6 +o +s"@r-*,o-%" *.cha

4.
If you want a list and frequency count of all prefixes used by Participants indescending order of frequency, merged across files in the folder, use:
freq +t*PAR +o +s"@r-*,#-*,o-%" +u *.cha

If you want the %mor line printed out with that information, use +d.
5.
To get a frequency count of Participant word-level errors (see the Error Coding
sheet at the website for a description of these error codes), file by file, a basic command is:freq +s"\[\* *\]"

+t*PAR*.cha

If you want to include errors that were repeated and revised, add +r6 to the above command.
6.
If you want to specify which errors you want listed and counted, you can list themas in the following command. Remember to add +r6 to the command if you want wordlevel errors within repetitions and retracings (e.g., *s:r, *s:r-rep, *s:r-ret).
freq +s"[\* s*]" +s"[\* p*]" +s"[\* n*]" +s"[\* d*]" +s"[\* m*]"+s"[\* f*]" +t*PAR *.cha7.
Alternatively, you can create a CLAN cut file of all error types and use thatinstead. Put the cut file in the same folder as the files you are analyzing and use:
freq +s@error.cut +t*PAR *.cha

Remember to add +r6 to the command if you want to include errors within repetitions andretracings.
If you want to create a list with frequencies of each error and the CHAT transcript line that

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includes that error, add +d to the above command line.
If you want a tally and list of the actual errors with the intended target word, add +d6 tothe above command line.
If you want the context (CHAT transcript line) as well, add +d to the above command line
instead of +d6.
8.
If you want the data to be in a file that can be opened in Excel, use:
freq +s"[\* s:r]" +s"[\* s:ur]" +s"[\* s:uk]" +s"[\* s:per]" +s"[\*p:w]" +s"[\* p:n]" +s"[\* p:m]" +t*PAR +d2 +fS +2 *.cha

Triple click on the line with the filename at the end of the CLAN output to open the Excel
file. The Excel file itself will be in the folder you put as your working directory.
9.
To get a frequency count of Participant errors at the sentence level (see the Error
Coding sheet at the website for a description of these error codes), a basic command is:freq +s"<+ >" +t*PAR *.cha

If you want a certain type of sentence error, for example jargon, use <+ jar> inside thequotation marks.
10.
If you want to see all error productions associated with a target word, for example,
Cinderella, use:
freq +s"<: Cinderella>" *.cha

11.
To list all parts of speech that occur in the files, merged across all the files, with
their corresponding frequencies, in descending order of frequency, use:
freq +t*PAR +d5 +o +sm|*,o% +u *.cha

12.
If you want to list and count the frequency of all verb forms, stems ony, merged
across files in a folder, use:
freq +t*PAR +sm;*,|v*,|aux*,|part*,|mod*,|cop*,o% +u *.cha

13.
If you want the total number of nouns (of all types), stems only, merged acrossfiles in a folder, use:
freq +t*PAR +d5 +o +sm|n,|n:*,o-% +u *.cha

14.

If you want to count and list the nouns, stems only, merged across files, use:
freq +t*PAR +d5 +o +sm;*,|n,|n:*,o% +u *.cha

15.
If you want word-level errors and other part of speech and bound morpheme info
about the noun, merged across files, use:
freq +t*PAR +d6 +o +sm;*,|n,|n:*,o% +u *.cha

16.
If you want to list and count the frequency (in descending order) of all
prepositions used by the Participant merged across files in a folder, use:
freq +t*PAR +d5 +o +sm;*,|prep*,o% +u *.cha

If you want to see the lines in which they're used, use +d instead of +d5.
17.
If you want to list and count the frequency (in descending order) of all adverbs
used by the Participant, by stems, merged across files in a folder, use:
freq +t*PAR +d5 +o +sm;*,|adv*,o% +u *gem.cex

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18.
If you want to list and count the frequency (in descending order) of all adjectives
used by the Participant, by stems, merged across files in a folder, use:
freq +t*PAR +d5 +o +sm;*,|adj*,o% +u *gem.cex

7.10 FREQMERG
If you have collected many FREQ output files and you want to merge these counts
together, you can use freqmerg to combine the outputs of several runs of the FREQ
program. For example, you could run this command:
freq sample*.cha +f

This would create sample.frq.cex and sample2.frq.cex. Then you could merge these twocounts using this command:
freqmerg *.frq.cex

The only option that is unique to freqmerg is +o, which allows you to search for a specificword on the main speaker tier. To search for a file that contains a set of words use the form
+o@filename.

7.11 FREQPOSThe FREQPOS program is a minor variant of freq. What is different about FREQPOS
is the fact that it allows the user to track the frequencies of words in initial, final, and second
position in the utterance. This can be useful in studies of early child syntax. For example,
using FREQPOS on the main line, one can track the use of initial pronouns or auxiliaries.
For open class items like verbs, one can use FREQPOS to analyze codes on the %mor line.
This would allow one to study, for example, the appearance of verbs in second position,
initial position, final position, and other positions.
To illustrate the running of freqpos, let us look at the results of this simple command:
freqpos sample.cha

Here are the first six lines of the output from this command:
1
1
1
3
1
1

a
initial =anyinitial =areinitial =chalkinitial =chalk+chalkinitial =delicious initial =0, final =0, final =0, final =0, final =0, final =0, final =0, other =0, other =1, other =3, other =1, other =0, other =1, one word =1, one word =0, one word =0, one word =0, one word =1, one word =

0
0
0
0
0
0

We see here that the word “chalk” appears three times in final position, whereas the word“delicious” appears only once and that is not in either initial or final position. To study
occurrences in second position, we must use the +d switch as in:
freqpos +d sample.cha

7.11.1 Unique Options
FREQPOS has the following unique options:+dCount words in either first, second, or other positions. The default is to count by
first, last, and other positions.

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+g
-s

86

Display only selected words in the output. The string following the +g can be either
a word or a file name in the @filename notation.
The effect of this option for FREQPOS is different from its effects in the other
CLAN commands. Only the negative -s value of this switch applies. The effect ofusing -s is to exclude certain words as a part of the syntactic context. If you want tomatch a word with FREQPOS, you should use the +g switch rather than the +s
switch.

7.12 GEM
Researchers use gem markers for different purposes. In some CHILDES corpora, they
are used to mark the dates or numbers of diary entries. In studies of narratives and bookreading, they are used to mark page numbers. In tasks with object and picture description,
they may indicate the number or name of the picture. In some corpora, they are used justto enter descriptive remarks.
One important and interesting use of gems is to facilitate later retrieval and analysis.
For example, some studies with children make use of a fixed sets of activities such as
MotherPlay, book reading, and story telling. For these gems, it can be useful to compare
similar activities across transcripts. To support this, we have entered the possible gems ina corpus that uses gems in this way into the TalkBankDB facility in a pulldown menu.
Descriptions of the gems used in a corpus can be found in the homepage for that corpus.
The GEM program is designed to allow you to pull out parts of a transcript for further
analysis. Separate header lines are used to mark the beginning and end of each interesting
passage you want included in your gem output. These header tiers may contain “tags” thatwill affect whether a given section is selected or excluded in the output. If no tag
information is being coded, you should use the header form @Bg with no colon. If you areusing tags, you must use the colon, followed by a tab. If you do not follow these rules,
check will complain.

7.12.1 Sample Runs
By default, GEM looks for the beginning marker @Bg without tags and the ending
marker @Eg, as in this example command:
gem sample.cha

If you want to be more selective in your retrieval of gems, you need to add code words ortags to both the @Bg: and @Eg: lines. For example, you might wish to mark all cases ofverbal interchange during the activity of reading. To do this, you must place the word
“reading” on the @Bg: line just before each reading episode, as well as on the @Eg: line
just after each reading episode. Then you can use the +sreading switch to retrieve only thistype of gem, as in this example:
gem +sreading sample2.cha

Ambiguities can arise when one gem without a tag is nested within another or when
two gems without tags overlap. In these cases, the program assumes that the gem being
terminated by the @Eg line is the one started at the last @Bg line. If you have any sort ofoverlap or embedding of gems, make sure that you use unique tags.

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GEM can also be used to retrieve responses to specific questions or particular stimuli
used in an elicited production task. The @Bg entry for this header can show the number
and description of the stimulus. Here is an example of a completed header line:@Bg:

Picture 53, truck

One can then search for all the responses to picture 53 by using the +s"53" switch in GEM.
The / symbol can be used on the @Bg line to indicate that a stimulus was described out
of its order in a test composed of ordered stimuli. Also, the & symbol can be used toindicate a second attempt to describe a stimulus, as in 1a& for the second description of
stimulus 1a, as in this example:
@Bg:
*CHI:
@Bg:
*CHI:
@Bg:
*CHI:

1b /
a &b ball.
1a /a dog.
1a &and a big ball.

Similar codes can be constructed as needed to describe the construction and ordering ofstimuli for specific research projects.
When the user is sure that there is no overlapping or nesting of gems and that the endof one gem is marked by the beginning of the next, there is a simpler way of using GEM,
which we call lazy GEM. In this form of GEM, the beginning of each gem is marked by@G: with one or more tags and the +n switch is used. Here is an example:
@G:
*CHI:
@G:
*CHI:
@G:
*CHI:
@G:

reading
nice kitty.
offstage
who that?
readinga big ball.
dinnerIn this case, one can retrieve all the episodes of “reading” with this command:
gem +n +sreading

7.12.2 Limiting with GEM
GEM also serves as a tool for limiting analyses. The type of limiting that is done by
GEM is very different from that done by KWAL or COMBO. In a sense, GEM works likethe +t switches in these other programs to select segments of the file for analysis. Whenyou do this, you will want to use the +d and +f switches, so that the output is in CHAT
format foir analysis by further commands.
gem +sreading +d +f sample2.cha

Note also that you can use any type of code on the @Bg line. For example, you mightwish to mark well-formed multi-utterance turns, teaching episodes, failures in communications, or contingent query sequences.

7.12.3 Unique Options
GEM has the following unique options:Part 2: CLAN
+d
+g

+n

+s

88

The +d0 level of this switch produces simple output that is in legal CHAT format.
The +d1 level of this switch adds information to the legal CHAT output regarding
file names, line numbers, and @ID codes.
If this switch is used, all the tag words specified with +s switches must appear on
the @Bg: header line to make a match. Without the +g switch, having just one of
the +s words present is enough for a match.
gem +sreading +sbook +g sample2.chaThis will retrieve all the activities involving reading of books.
Use @G: lines as the basis for the search. If these are used, no overlapping or nestingof @G: gems is possible and each @G: must have tags. In this case, no @Eg isneeded, but CHECK and GEM will simply assume that the gem starts at the @G:
and ends with the next @G:.
This option is used to select file segments identified by words found on the @Bg:
tier. Do not use the -s switch. See the example given above for +g. To search for agroup of words found in a file, use the form +s@filename.

7.13 GEMFREQ
This program combines the basic features of FREQ and GEM. Like GEM, it analyzes
portions of the transcript that are marked off with @Bg: and @Eg: markers. For example,
gems can mark off a section of bookreading activity with @Bg: bookreading and @Eg:
bookreading. Once these markers are entered, you can then run GEMFREQ to retrieve abasic FREQ-type output for each of the various gem types you have marked. For example,
you can run this command:
gemfreq +sarriving sample2.cha

and you would get the following output:
GEMFREQ +sarriving sample2.cha
Wed May 12 15:54:35 1999
GEMFREQ (04-May-99) is conducting analyses on:
ALL speaker tiersand ONLY header tiers matching: @Bg:; @Eg:;****************************************
From file <sample2.cha>
2 tiers in gem " arriving":
1 are
1 fine1 how
1 you

7.13.1 Unique Options+d+g

The d0 level of this switch produces simple output that is in legal CHAT format.
The d1 level of this switch adds information to the legal CHAT output regarding filenames, line numbers, and @ID codes.
If this switch is used, all the tag words specified with +s switches must appear on
the @Bg: header line to make a match. Without the +g switch, having just one of
the +s words present is enough for a match.
gem +sreading +sbook +g sample2.cha

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+n

+o

89

This will retrieve all the activities involving reading of books.
Use @G: lines as the basis for the search. If these are used, no overlapping or nestingof gems is possible and each @G must have tags. In this case, no @Eg is needed,and both CHECK and GEMFREQ will simply assume that the gem starts at the @G
and ends with the next @g.
Search for a specific word on the main speaker tier. To search for a file of words use
the form +o@filename.

7.14 GEMLISTThe GEMLIST program provides a convenient way of viewing the distribution of gems
across a collection of files. For example, if you run GEMLIST on both sample.cha andsample2.cha, you will get this output:
From file <sample.cha>
12 @Bg3 main speaker tiers.
21 @Eg1 main speaker tiers.
24 @Bg3 main speaker tiers.
32 @EgFrom file <sample2.cha>
18 @Bg: just arriving
2 main speaker tiers.
21 @Eg: just arriving
22 @Bg: reading magazines
2 main speaker tiers.
25 @Eg: reading magazines
26 @Bg: reading a comic book2 main speaker tiers.
29 @Eg: reading a comic book

GEMLIST can also be used with files that use only the @G lazy gem markers. In thatcase, the file should use nothing by @G markers and GEMLIST will treat each @G as
implicitly providing an @Eg for the previous @g. Otherwise, the output is the same as with
@Bg and @Eg markers.
The only option unique to GEMLIST is +d which tells the program to display only thedata in the gems. GEMLIST also uses several options that are shared with other commands.
For a complete list of options for a command, type the name of the command followed bya carriage return in the Commands window. Information regarding the additional options
shared across commands can be found in the chapter on Options.

7.15 KEYMAPThe KEYMAP program is useful for performing simple types of interactional andcontingency analyses. KEYMAP requires users to pick specific initiating or beginning
codes or “keys” to be tracked on a specific coding tier. If a match of the beginning code or
key is found, KEYMAP looks at all the codes on the specified coding tier in the next utterance. This is the “map.” The output reports the numbers of times a given code maps onto
a given key for different speakers.

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7.15.1 7 Sample Runs
Here is a file fragment with a set of codes that will be tracked by KEYMAP:*MOT: here you go.
%spa: $INI
*MOT: what do you say?
%spa: $INI
*CHI: thanks.
%spa: $RES
*MOT: you are very welcome.
%spa: $CON

If you run the KEYMAP program on this data with the $INI as the +b key symbol, theprogram will report that $INI is followed once by $INI and once by $RES. The key ($INI
in the previous example) and the dependent tier code must be defined for the program. On
the coding tier, KEYMAP will look only for symbols beginning with the $ sign. All otherstrings will be ignored. Keys are defined by using the +b option immediately followed by
the symbol you wish to search for. To see how KEYMAP works, try this example:
keymap +b$INI* +t%spa sample.cha

For Unix, this command would have to be changed to treat the metacharacters as literal, as
follows:
keymap +b\$INI\* +t%spa sample.cha

KEYMAP produces a table of all the speakers who used one or more of the key symbols, and how many times each symbol was used by each speaker. Each of those speakers
is followed by the list of all the speakers who responded to the given initiating speaker,
including continuations by the initial speaker, and the list of all the response codes andtheir frequency count.

7.15.2 Unique Options
KEYMAP has the following unique options:+bThis is the beginning specification symbol.
+oIn addition to tracking following codes, this option also tracks preceding codes.
+sThis option is used to specify the code or codes beginning with the $ sign to treat as
possible continuations. For example, in the sample.cha file, you might only want totrack $CON:* codes as continuations. In this case, the command would be as
follows.
keymap +b$* +s"$CON:*" +t%spa sample.cha

7.16 KWAL
The KWAL program outputs utterances that match certain user-specified search words.
The program also allows the user to view the context in which any given keyword is used.
To specify the search words, use the +s option, which allows you to search for either asingle word or a whole group of words stored in a file. It is possible to specify as many +s
options on the command line as you like.

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Like COMBO, the KWAL program works not on lines, but on “clusters.” A cluster is
a combination of the main tier and the selected dependent tiers relating to that line. Eachcluster is searched independently for the given keyword. The program lists all keywords
that are found in a cluster tier. A simple example of the use of KWAL is:kwal +schalk sample.cha

The output of this command tells you the file name and the absolute line number of thecluster containing the key word. It then prints out the matching cluster.

7.16.1 Tier Selection in KWAL
Sometimes you may want to create new files in which some of the tiers in your original
files are systematically deleted. For example, you may wish to drop out certain coding tiers
that interfere with the readability of your transcript, or you may wish to drop out a tier thatwill be later recomputed by a program. For example, to drop out the %mor tier for all
speakers, except CHI, you can use this command:
kwal +t*chi +t%mor +o@ +o* -o%mor +d +f t.cha

The two +t switches work as a matched pair to preserve the %mor tier for CHI. The first+o@ switch will preserve the header tiers. The second and third +o switches work as a pair
to exclude the %mor lines in the other speakers. However, the -o%mor switch keeps all of
the dependent tiers except for %mor. The +t switch is used for selecting parts of the transcript that may also be searched using the +s option. The +o switch, on the other hand,
only has an impact on the shape of the output. The +d switch specifies that the output
should be in CHAT format and the +f switch sends the output to a file. In this case, there
is no need to use the +s switch. Try out variations on this command with the sample files
to make sure you understand how it works.
Main lines can be excluded from the analysis using the -t* switch. However, this exclusion affects only the search process, not the form of the output. It will guarantee that nomatches are found on the main line, but the main line will be included in the output. If you
want to exclude certain main lines from your output, you can use the -o switch, as in:
kwal +t*CHI +t%spa -o* sample.cha

You can also do limiting and selection by combining FLO and KWAL:
kwal +t*CHI +t%spa +s"$*SEL*" -t* sample.cha +d +f
flo *.kwal.cex

To search for a keyword on the *MOT main speaker tiers and the %spa dependent tiers ofthat speaker only, include +t*MOT +t%spa on the command line, as in this command.
kwal +s"$INI:*" +t%spa +t*MOT sample.cha

If you wish to study only material in repetitions, you can use KWAL in this form:
kwal +s”+[//]” *.cha +d3 +d7.16.2 KWAL for breaking up files by code typeIn a variation of the procedures in the last section, you may want to break out a certain
type of utterance from a file for further analysis or even break up a file into several
segments. For example, if you have used a code such as $A on the %cod tier and want toPart 2: CLAN

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contrast sentence with and without that code, you can pull out all the utterances with $A
using this command:
kwal +d +o@ +t% +s"$A" +f$A filenames

This will produce the $A.cex output file. Next, you can créate a file with the utterances that
do not have $A using this command:
kwal +d +o@ +t% -s"$A" +fno$A filenames

This will produce the no$A.cex output file. Then you can run commands on the output files
separately.

7.16.3 KWAL for %mor and %gra combinations
You can use the +d7 switch in COMBO, FREQ, and KWAL to search for matches
between the %gra and %mor lines, because these two lines are in one-to-one
correspondence. Here is an example of a command to search for cases in which the SUBJ
on the %gra line is pro:per on the %mor line. In this command, the letter “g” after +s refers
to the %gra tier and the letter “m” after +s refers to the %mor tier.
kwal +d7 +sg|SUBJ +sm|pro:per filename.cha

You can also search for cases in which the SUBJ on the %gra line is pro:per|you on the%mor line. In this command, the letter “g” after the +s refers to the %gra tier and the letter“m” refers to the %mor tier.
kwal +d7 +sg|SUBJ +sm|pro:per,;you filename.cha

7.16.4 KWAL with signs and speech
When participants are using mostly nonspeech items coded on the %sin dependent tiers
but a few speech items on the main tier (coded as 0 items), then it is sometimes interesting
to track utterances in which speech is either combined or not combined with gesture. The+c switch can help in this analysis by selecting out utterances that have a small number ofwords on the main line, but extensive coding on the %sin line. Here are some sample
command sequences for this type of work:
1. Gesture only (at least one g: on the %sin tier, no words on the main tier and no s: on
the %sin tier).
kwal *.cha +t%sin +c0 -ss:* +d +fno-s
kwal *.no-s.cex +t%sin +sg:* +d +fgesture_only

2. Speech only (included at least one word on the main tier, but no g: or s: on the %sintier):
kwal *.cha +t%sin +c1 -ss:* -sg:* +d +fspeech_only

3. Sign only (included at least one s: on the %sin tier, but no words on the main line andno g: on the %sin tier):
kwal *.cha +t%sin +c0 -sg:* +d +fno-g
kwal *.no-g.cex +t%sin +ss:* +d +fsign_only

4. Gesture + speech only (included at least one g: on the %sin tier and at least one word

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on the main tier, but no s: on the %sin tier):
kwal *.cha +t%sin +c1 -ss:* +d +fno-s
kwal *.no-s.cex +t%sin +sg:* +d +fgesture+speech

5. Gesture + sign only (included at least one g: and one s: on the %sin tier but no words
on the main tier):
kwal *.cha +t%sin +c0 +ss:* +d +fsign_only
kwal *.sign_only.cex +t%sin +sg:* +d +fgesture+sign_only

6. Gesture + speech + sign (included at least one g: and one s: on the %sin tier and at least
one word on the main tier):
kwal *.cha +t%sin +c1 +ss:* +d +fsign_only
kwal *.sign_only.cex +t%sin +sg:* +d +fgesture+speech+sign

7.16.5 Unique Options
KWAL has the following unique options:+aSort the output alphabetically. Choosing this option can slow down processing
significantly.
+dNormally, KWAL outputs the location of the tier where the match occurs. When the+d switch is turned on you can in these formats:+d : outputs legal CHAT format+d1: outputs legal CHAT format plus file names and line numbers+d2: outputs file names once per file only
+d3: outputs ONLY matched items+d30: outputs ONLY matched items without any defaults removed.
The +d30 and the +d3 switches can be combined.
+d99: convert "word [x 2]" to "word [/] word" and so on+d4: outputs for Excel+d40: outputs for Excel, repeating the same tier for every keyword match+d7: compares items across dependent tiers, as in this example:
kwal +d7 +s@|-cop +sROOT +t%gra +t%mor t.cha+/-nS Include or exclude all utterances from speaker S when they occur immediately after
a match of a specified +s search string. For example, if you want to exclude all childutterances that follow questions, you can use this commandkwal +t*CHI +s"?" -nCHI *.cha+oThe +t switch is used to control the addition or deletion of particular tiers or lines
from the input and the output to KWAL. In some cases, you may want to include atier in the output that is not being included in the input. This typically happens when
you want to match a string in only one dependent tier, such as the %mor tier, but
you want all tiers to be included in the output. To do this you would use a commandof the following shape:
kwal +t%mor +s"*ACC" +o% sample2.cha
In yet another type of situation, you may want to include tiers in the KWAL output
that are not normally included. For example, if you want to see output with the agesof the children in a group of files you can use this command:
kwal +o@ID -t* *.cha+w It is possible to instruct the program to enlarge the context in which the keyword

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was found. The +w and -w options let you specify how many clusters after andbefore the target cluster are to be included in the output. These options must be
immediately followed by a number. Consider this example:
kwal +schalk +w3 -w3 sample.cha
When the keyword chalk is found, the cluster containing the keyword and the threeclusters above (-w3) and below (+w3) will be shown in the output.
+xCNinclude only utterances which are C (>, <, =) than N items (w, c, m), "+x=0w" forzero words+xS specify items to include in above count (Example: +xxxx +xyyy)-xS specify items to exclude from above count

7.17 MAXWD
This program locates, measures, and prints either the longest word or the longest utterance in a file. It can also be used to locate all the utterances that have a certain number ofwords or greater.
When searching for the longest word, the MAXWD output consists of: the word, its
length in characters, the line number on which it was found, and the name of the file where
it was found. When searching for the longest utterance with the +g option, the output consists of: the utterance itself, the total length of the utterance, the line number on which theutterance begins, and the file name where it was found. By default, MAXWD only analyzes
data found on the main speaker tiers. The +t option allows for the data found on the header
and dependent tiers to be analyzed as well. The following command will locate the longest
word in sample.cha.
maxwd sample.cha

You can also use MAXWD to track all the words or utterances of a certain length. For
example, the following command will locate all the utterances with only one word in them:maxwd -x1 +g2 sample.cha

Alternatively, you may want to use MAXWD to filter out all utterances below or above acertain length. For example, you can use this command to output only sentences with fouror more words in them:maxwd +x4 +g2 +d1 +o%

7.17.1 Unique Options+a

If you have elected to use the +c switch, you can use the +a switch to further finetune the output so that only one instance of each length type is included. Here is asample command:
maxwd +c8 +a +xw sample.cha+b+c

You can use this switch to either include or exclude specified morpheme delimiters.
By default, the morpheme delimiters #, ~, and - are understood to delimit separate
morphemes. You can force MAXWD to ignore all three of these by using the -b#-~form of this switch. You can use the +b switch to add additional delimiters to thelist.
This option is used to produce a given number of longest items. The following

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command will print the seven longest words in sample.cha.
maxwd +c7 sample.cha

If you want to print out all the utterances above a certain length, you can use this KWAL
commandkwal +x4w sample.cha+d+g

The +d level of this switch produces output with one line for the length level and thenext line for the word. The +d1 level produces output with only the longest words,
one per line, in order, and in legal CHAT format.
This switch forces MAXWD to compute not word lengths but utterance lengths. It
singles out the sentence that has the largest number of words or morphemes andprints that in the output. The way of computing the length of the utterance is determined by the number following the +g option. If the number is 1 then the length
is in number of morphemes per utterance. If the number is 2 then the length is in
number of words per utterance. And if the number is 3 then the length is in thenumber of characters per utterance. For example, if you want to compute the MLU
and MLT of five longest utterances in words of the *MOT, you can use the following
command:maxwd +g2 +c5 +d1 +t*MOT +o%mor sample.cha

Then you would run the output through MLU. The +g2 option specifies that theutterance length will be counted in terms of numbers of words. The +c5 option
specifies that only the five longest utterances should be sent to the output. The +d1
option specifies that individual words, one per line, should be sent to the output. The+o%mor includes data from the %mor line in the output sent to MLU.
+o

The +o switch is used to force the inclusion of a tier in the output. To do this you
would use a command of the following shape:
maxwd +c2 +j +o%mor sample2.cha

7.17.2 Unique Options
MAXWD has the following unique options:+a : consider ONLY unique length utterances/words+cN: display N longest utterances/words+gN: look for longest utterance instead of longest word
1 - number of morph; 2 - number of word; 3 - number of chars

7.18 MLTThe MLT program computes the mean number of utterances in a turn, the mean numberof words per utterance, and the mean number of words per turn. A turn is defined as asequence of utterances spoken by a single speaker. Overlaps are ignored in thiscomputation. Instead, the program simply looks for sequences of repeated speaker ID codesat the beginning of the main line. While the same speaker is talking, then each utterance isPart 2: CLAN

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a part of the current turn. These computations are provided for each speaker separately.
Note that none of these ratios involve morphemes on the %mor line. If you want to analyze
morphemes per utterances, you should use the MLU program.

7.18.1 MLT defaults
The exact nature of the MLT calculation depends both on what the program includes
and what it excludes. By default, all utterances and words are included with these default
exceptions:
1. MLT excludes material followed by [/], [//], [///], [/?], and [/-]. This can be changed byusing the +r6 switch or by adding any of these switches: +s+"</>" +s+"<//>"
2. The following strings are also excluded as words: 0* &* +* -* $*. Here the asterisk
indicates any material following the first symbol until a delimiter.
3. The symbols xxx, yyy, and www are also excluded from the word count by default, but
the utterances in which they appear are not. If you wish to also exclude theseutterances, use the switches –sxxx, -syyy, and –swww.
4. Utterances with no words are included, although they can be excluded using the +aswitch.
To exclude utterances with a specific postcode, such as [+ bch], use -s"[+ bch]".
Similarly, you can use +s to include lines that would otherwise be excluded. Pairs ofutterances that use the +, and +. continuation codes are counted as single utterances. If apotential turn is composed only of utterances that are being excluded with the –s option,then the whole turn also disappears and the surrounding two turns become a single turn. In
addition, you can use the +g option to exclude any utterance composed exclusively of acertain set of words. Because MLT’s exclusions are not as extreme as those for MLU,
there are often more utterances included in MLT than in MLU.

7.18.2 Unique Options
MLT has the following unique options:+aThis switch causes all utterances to be counted, even if they have no words. If you
add “t” to make +at, you will only count empty utterances if they have the [+ trn]
postcode.
+cS Look for unit marker S. If you want to count phrases or narrative units instead ofsentences, you can add markers such as [^c] to make this segmentation of yourtranscript into additional units. Compare these two commands:
mlt sample.cha
mlt +c[^c] sample.cha+d

You can use this switch, together with the @ID specification to output data in aformat that can be opened in Excel, as in this command:
mlt +d +t@ID=”*|Target_Child*” sample.cha

The output of this command would be something like this:eng samp sample 0110 CHI

6

6

8 1.333 1.000 1.333

This output gives 11 fields in this order: language, corpus, file, age, participant id, number

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+d1

of utterances, number of turns, number of words, words/turn, utterances/ turn, andwords/utterance. The first five of these fields come from the @ID field. The next six
are computed for a given participant for a particular file. To run this type of analysis
you must have an @ID header for each participant you wish to track. Alternatively,
you can use the +t switch in the form +t*CHI. In this case, all the *CHI lines will be
examined in the corpus. However, if you have different names for children acrossdifferent files, you need to use the @ID fields.
This level of the +d switch outputs data in another systematic format, with data foreach speaker on a single line. However, this form is less adapted to input to astatistical program than the output for the basic +d switch. Also this switch works
with the +u switch, whereas the basic +d switch does not. Here is an example of this
output:
*CHI:
*MOT:

+g

+s

97

6
8

6 8 1.333 1.000 1.333
7 43 6.143 1.143 5.375

You can use the +g option to exclude utterances composed entirely of certain words.
For example, you might wish to exclude utterances composed only of hi, bye, orboth these words together. To do this, you should place the words to be excluded ina file, each word on a separate line. The option should be immediately followed by
the file name, i.e. there should not be a space between the +g option and the nameof this file. If the file name is omitted, the program displays an error message: “Nofile name for the +g option specified!”
This option is used to specify a word string that specifies which utterances should
be included. This switch selects whole utterances for inclusion, not individual
words, because MLT is an utterance-oriented program.

7.19 MLU
The MLU program computes the mean length of utterance, which is the ratio of
morphemes to utterances. By default, this program runs from a %mor line and uses thatline to compute the mean length of utterance (MLU) in morphemes. However, if you do
not have a %mor line in your transcript, you need to add the –t%mor switch to use it fromthe main line. In that case, you will be computing MLU in words, not morphemes.
The predecessor of the current MLU measure was the “mean length of response” or
MLR devised by Nice (1925). The MLR corresponds to what we now call MLUw or mean
length of utterance in Words. Brown (1973) emphasized the value of thinking of MLU in
terms of morphemes, rather than words. Brown was particularly interested in the ways inwhich the acquisition of grammatical morphemes reflected syntactic growth and hebelieved that MLUm or mean length of utterance in morphemes would reflect this growthmore accurately than MLUw. Brown described language growth through six stages ofdevelopment for which MLU values ranged from 1.75 to 4.5. Subsequent research (Klee,
Schaffer, May, Membrino, & Mougey, 1989) showed that MLU is correlated with age until
about 48 months. Rondal, Ghiotto, Bredart, and Bachelet (1987) found that MLU is highly
correlated with increases in grammatical complexity between MLU of 1 and 3. However,after MLU of 3.0, the measure was not well correlated with syntactic growth, as measured
by LARSP. A parallel study by Blake, Quartaro, and Onorati (1970)with a larger subject
group found that MLU was correlated with LARSP until MLU 4.5. Even better correlations

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between MLU and grammatical complexity have been reported when the IPSyn is used tomeasure grammatical complexity (Scarborough, Rescorla, Tager-Flusberg, Fowler, &Sudhalter, 1991)
Brown (1973, p. 54) presented the following set of rules for the computation (by hand)of MLU:
1. Start with the second page of the transcription unless that page involves a recitation ofsome kind. In this latter case, start with the first recitation free stretch. Count the first
100 utterances satisfying the following rules.
2. Only fully transcribed utterances are used; none with blanks. Portions of utterances,entered in parentheses to indicate doubtful transcription, are used.
3. Include all exact utterance repetitions (marked with a plus sign in records). Stuttering
is marked as repeated efforts at a single word; count the word once in the most complete
form produced. In the few cases where a word is produced for emphasis or the like(no, no, no) count each occurrence.
4. Do not count such fullers as mm or oh, but do count no, yeah, and hi.
5. All compound words (two or more free morphemes), propernames, and riualized
reduplications count as single words. Examples: birthday, rakety-booom, choo-choo,quack-quack, night-night, pocketbook, seesaw. Justification is that there is no evidence
that the constitutent morphemes functions as such for these children.
6. Count as one morpheme all irregular pasts of the verb (got, did, went, saw). Justificationis that there is no evidence that the child relates these to present forms.
7. Count as one morpheme all diminutives (doggie, mommie) because these children atleast to do not seem to use the suffix productively. Diminutives are the standard forms
used by the child.
8. Count as separate morphemes all auxiliaries (is, have, will, can, must, would). Also, allcatenatives: gonna, wanna, hafta. These latter counted as single morphemes rather
than as gong to or want to because evidence is that they function so for the children.
Count as separate morphemes all inflections, for example, possessive [s], plural [s],third person singular [s], regular past [d], and progressive [ing].
9. The range count follows the above rules but is always calculated for the totaltranscription rather than for 100 utterances.
Because researchers often want to continue to follow these rules, it is important tounderstand how to implement this system in CLAN. Here is a detailed description,corresponding to Brown’s nine points.
1. Brown recommended using 100 utterances. He also suggested that these should betaken from the second page of the transcript. In effect, this means that roughly the first
25 utterances should be skipped. The switch that would achieve this effect in the MLU
program is: +z25u-125u. This is the form of the command used for MLU-100 in the
KIDEVAL program.
2. The symbols xxx, yyy, and www are also excluded by default, as are the utterances inwhich they appear. If you wish to include the xxx forms and the utterances that containthem, then use the +sxxx option. The forms yyy and www are always excluded andcannot be included. Utterances with no words are excluded from the utterance count.

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3. If you mark repetitions and retraces using the CHAT codes of [/], [//], [///], [/?], and [/], the repeated material will be excluded from the computation automatically. This canbe changed by using the +r6 switch or by adding any of these switches: +s+"</>"+s+"<//>".
4. If you want forms to be treated as nonwords, you can precede them with the marker &,as in &mm. Alternatively, you can add the switch –smm to exclude this form or you
can have a list of forms to exclude. The following strings are also excluded by default:
uh um 0* &* +* -* $* where the asterisk indicates any material following the exclusion
symbol. If the utterance consists of only excludable material, the whole utterance willbe ignored. In addition, suffixes, prefixes, or parts of compounds beginning with a zeroare automatically excluded and there is no way to modify this exclusion. Brown
recommends excluding mm and uh by default. This is done by marking them as &-mmand &-uh.
5. You can use +s to include lines that would otherwise be excluded. For example, you
may want to use +s”[+ trn]” to force inclusion of lines marked with [+ trn]. You canalso use the +sxxx switch to change the exclusionary behavior of MLU. In this case,the program stops excluding sentences that have xxx from the count, but still excludes
the specific string “xxx”. You can also use the special form marker @a to force
treatment of an incomprehensible string as a word. This can happen when the sentential
and situational context is so clear that you know that the form was a word. For example,
the form xxx@a will appear on the %mor line as w|xxx and the form xxx@a$n willappear on the %mor line as n|xxx. In the place of the “n” you could place any part ofspeech code such as “v” or “adj”. This is because the @a is translated through the code“w” as a generic “word” and the part of speech code after the $ sign is translated as thepart of the speech of the incomprehensible word. These codes also apply to yyy,
yyy@a, and yyy@a$n.
6. When MLU is computed from the %mor line, the compound marker is excluded as amorpheme delimiter, so this restriction is automatic. If you compute MLU from themain line, then you need to add –b+ to your command to exclude the plus as amorpheme delimiter.
7. The ampersand (&) marker for irregular morphology is not treated as a morpheme
delimiter, so this restriction is automatic.
8. By default, diminutives are treated as real morphemes. In view of the evidence for the
productivity of the diminutive, it is difficult to understand why Brown thought they
were not productive.
9. The treatment of hafta as one morpheme is automatic unless the form is replaced by [:have to]. The choice between these codes is left to the transcriber.
It is also possible to exclude utterances by using postcodes. By default, MLU excludes
utterances marked with the specific postcode [+ mlue]. This works both for MLU as aseparate program and for MLU as a part of KIDEVAL. It is also possible to make further
exclusions for MLU as a separate program by using some other postcode such as [+ exc]
in the form of -s"[+ exc]". However, this non-default marking will not get picked up by
KIDEVAL.
The use of postcodes for exclusion needs to be considered carefully. Brown suggested

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that all sentences with unclear material be excluded. Brown wants exact repetitions to be
included and does not exclude imitations. However, other researchers recommend also
excluding imitation, self-repetitions, and single-word answers to questions.
The program considers the following three symbols to be morpheme delimiters: - # ~MOR analyses distinguish between these delimiters and the ampersand (&) symbol thatindicates fusion. As a result, morphemes that are fused with the stem will not be includedin the MLU count. If you want to change this list, you should use the +b option described
below. For Brown, compounds and irregular forms were monomorphemic. This means that+ and & should not be treated as morpheme delimiters for an analysis that follows his
guidelines. The program considers the following three symbols to be utterance delimiters:. ! ? as well as the various complex symbols such as +... which end with one of these threemarks.
The computation of MLU depends on the correct morphemicization of words. The best
way to do this is to use the MOR and POST programs to construct a morphemic analysis
on the %mor line. This is relatively easy to do for English and other languages for whichgood MOR grammars and POST disambiguation databases exist. However, if you areworking in a language that does not yet have a good MOR grammar, this process would
take more time. Even in English, to save time, you may wish to consider using MLU tocompute MLUw (mean length of utterance in words), rather than MLU. Malakoff, Mayes,
Schottenfeld, and Howell (1999) found that MLU correlates with MLUw at .97 for English.
Aguado (1988) found a correlation of .99 for Spanish, and Hickey (1991) found acorrelation of .99 for Irish. If you wish to compute MLUw instead of MLU, you can simply
refrain from dividing words into morphemes on the main line. If you wish to divide them,
you can use the +b switch to tell MLU to ignore your separators.

7.19.1 Exclude files for MLU and MLT
Researchers often wish to conduct MLU analyses on subsets of their data. This can bedone using commands such as:
kwal +t*CHI +t%add +s"mot" sample.cha +d +fThis command looks at only those utterances spoken by the child to the mother as addressee. You can then run MLU on the output of the KWAL command.
The inclusion of certain utterance types leads to an underestimate of MLU. However,there is no clear consensus concerning which sentence forms should be included or excluded in an MLU calculation. The MLU program uses postcodes to accommodate differing
approaches to MLU calculations. To exclude sentences with postcodes, the -s exclude
switch can be used in conjunction with a file of postcodes to be excluded. The exclude fileshould be a list of the postcodes that you are interested in excluding from the analysis. For
example, the sample.cha file is postcoded for the presence of responses to imitations [+ I],
yes/ no questions [+ Q], and vocatives [+ V].
For the first MLU pass through the transcript, you can calculate the child’s MLU on
the entire transcript by typing:mlu +t*CHI +t%mor sample.cha

For the second pass through the transcript you can calculate the child’s MLU following

Part 2: CLAN101

the criteria of Scarborough (1990). These criteria require excluding the following: routines[+ R], book reading [+ "], fillers [+ F], imitations [+ I], self-repetitions [+ SR], isolated
onomatopoeic sounds [+ O], vocalizations [+ V], and partially unintelligible utterances [+PI]. To accomplish this, an exclude file must be made which contains all these postcodes.
Of course, for the little sample file, there are only a few examples of these coding types.
Nonetheless, you can test this analysis using the Scarborough criteria by creating a filecalled “scmlu” with the relevant codes in angle brackets. Although postcodes are containedin square brackets in CHAT files, they are contained in angle brackets in files used by
CLAN. The scmlu file would look something like this:
[+ R]
[+ "]
[+ V]
[+ I]

Once you have created this file, you then use the following command:
mlu +t*CHI -s@scmlu sample.cha

For the third pass through the transcript, you can calculate the child’s MLU using a still
more restrictive set of criteria, also specified in angle brackets in postcodes and in a separate file. This set also excludes one-word answers to yes/no questions [$Q] in the file ofwords to be excluded. You can calculate the child’s MLU using these criteria by typing:mlu +t*CHI -s@resmlu sample.cha

In general, exclusion of these various limited types of utterances tends to increase thechild’s MLU.

7.19.2 Unique Options
MLU has the following unique options:+b
You can use this switch to either include or exclude certain morpheme delimiters.
By default, the morpheme delimiters ~, #, and - are understood to delimit separate
morphemes. You can force MLU to ignore all three of these by using the -b#-~switch. You can use the +b switch to add additional delimiters to the list.
+cS Look for unit marker S. If you want to count phrases or narrative units instead ofsentences, you can add markers such as [^c] to make this segmentation of yourtranscript into additional units. Compare these two commands:
mlu sample.cha
mlu +c[^c] sample.cha+d

You can use this switch, together with the ID specification to output data for Excel,
as in this command:
mlu +d +tCHI sample.cha

The output of this command should be:en|sample|CHI|1;10.4|female|||Target_Child|| 5

7 1.400 0.490

This output gives the @ID field, the number of utterances, number of morphemes,morphemes/utterances, and the standard deviation of the MLU. To run this type ofanalysis, you must have an @ID header for each participant you wish to track. You
can use the +t switch in the form +tCHI to examine a whole collection of files. In

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+d1

this case, all the *CHI lines will be examined in the corpus.
This level of the +d switch outputs data in another systematic format, with data foreach speaker on a single line. However, this form is less adapted to input to astatistical program than the output for the basic +d switch. Also, this switch works
with the +u switch, whereas the basic +d switch do es not. Here is an example ofthis output:
*CHI:
*MOT:

+g

+s

102

5 7 1.400 0.490
8 47 5.875 2.891

You can use the +g option to exclude utterances composed entirely of certain words
from the MLT analysis. For example, you might wish to exclude utterances
composed only of hi or bye. To do this, you should place the words to be excluded
in a file, each word on a separate line. The option should be immediately followedby the file name. There should not be a space between the +g option and the nameof this file. If the file name is omitted, the program displays an error message: “Nofile name for the +g option specified!”
This option is used to specify a word to be used from an input file. This option shouldbe immediately followed by the word itself. To search for a group of words stored
in a file, use the form +s@filename. The -s switch excludes certain words from theanalysis. This is a reasonable thing to do. The +s switch bases the analysis only on
certain words. It is more difficult to see why anyone would want to conduct such ananalysis. However, the +s switch also has another use. One can use the +s switch to
remove certain strings from automatic exclusion by MLU. The programautomatically excludes xxx, 0, uh, and words beginning with & from the MLU
count. This can be changed by using this command:
mlu +s+uh +s+xxx +s0* +s&* file.cha

MLU also uses several options that are shared with other commands. For a complete
list of options for a command, type the name of the command followed by a carriage returnin the Commands window. Information regarding the additional options shared acrosscommands can be found in the chapter on Options.

7.20 MODREPThe MODREP program matches words on one tier with corresponding words on another tier. It works only on tiers where every word on tier A matches one word on tier B. Whensuch a one-to-one correspondence exists, MODREP will output the frequency of all matches. Consider the following sample file distributed with CLAN as modrep.cha:@Begin@Participants:
CHI Child*CHI:
I want more.
%pho:aI wan mo%mod:aI want mor*CHI:
want more bananas.
%pho:
wa mo nAnA
%mod:want mor bAn&nAz*CHI:
want more bananas.
%pho:
wa mo nAnA
%mod:want mor bAn&nAz*MOT:
you excluded [//] excluded [/] xxx yyy www

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%pho:
%mod:
@End

103

&d do?
yu du
yu du

You can run the following command on this file to create a model-and-replica analysis forthe child’s speech:
modrep +b*chi +c%pho +k modrep.cha

The output of MODREP in this case should be as follows:
From file <modrep.cha>
1 I
1 aI
2 bananas
2 nAnA
3 more3 mo
3 want
1 wan
2 wa

This output tells us that want was replicated in two different ways, and that more was replicated in only one way twice. Only the child’s speech is included in this analysis and the%mod line is ignored. Note that you must include the +k switch in this command toguarantee that the analysis of the %pho line is case-sensitive. By default, all CLAN
commands except for FREQ, FREQMERGE, MORTABLE, PHONFREQ, RELY,TIMEDUR, and VOCD are case-insensitive.

7.20.1 Exclusions and Inclusions
By default, MODREP ignores certain strings on the model tier and the main tier. These
include xxx, yyy, www, material preceded by an ampersand, and material preceding theretracing markers [/] and [//]. To illustrate these exclusions, try this command:modrep +b* +c%pho +k modrep.cha

The output of this command will look like this:
MODREP +b* +c%PHO +k modrep.cha
Thu May 13 13:03:26 1999
MODREP (04-May-99) is conducting analyses on:
ALL speaker main tiersand those speakers' ONLY dependent tiers matching: %PHO;****************************************
From file <modrep.cha>
Model line:
you zzz do ?
is longer than Rep line:
yu du
In File "modrep.cha" in tier cluster around line 13.

If you want to include some of the excluded strings, you can add the +q option. For example, you could type:modrep +b* +c%pho +k modrep.cha +qwww

However, adding the www would destroy the one-to-one match between the model line andPart 2: CLAN104

the replica line. When this happens, CLAN will complain and then die. Give this a try to
see how it works. It is also possible to exclude additional strings using the +q switch. For
example, you could exclude all words beginning with “z” using this command:modrep +b* +c%pho +k modrep.cha -qz*

Because there are no words beginning with “z” in the file, this will not change the match
between the model and the replica.
If the main line has no speech and only a 0, MODREP will effectively copy this zeroas many times as in needed to match up with the number of units on the %mod tier that isbeing used to match up with the main line.

7.20.2 Using a %mod LineA more precise way of using MODREP is to construct a %mod line to match the %pho
line. In modrep.cha, a %mod line has been included. When this is done the following typeof command can be used:modrep +b%mod +c%pho +k modrep.cha

This command will compare the %mod and %pho lines for both the mother and the childin the sample file. Note that it is also possible to trace pronunciations of individual targetwords by using the +o switch as in this command for tracing words beginning with /m/:modrep +b%mod +c%pho +k +om* modrep.cha

7.20.3 MODREP for the %mor line
Because words on the main line stand in a one-to-one relation with words on the %mor
line, MODREP can also be used to match codes on the %mor tier to words on the mainline. For example, if you want to find all the words on the main line that match words on
the %mor line with an accusative suffix in the mother’s speech in sample2.cha, you can
use this command:modrep +b%mor +c*MOT +o"*ACC" sample2.cha

The output of this command is:
From file <sample2.cha>
1 n:a|ball-acc
1 labda't
1 n:a|duck-acc
1 kacsa't
1 n:i|plane-acc
1 repu"lo"ge'pet

If you want to conduct an even more careful selection of codes on the %mor line, you
can make combined use of MODREP and COMBO. For example, if you want to find all
the words matching accusatives that follow verbs, you first select these utterances by running COMBO with the +d switch and the correct +s switch and then analyze the output
using the MODREP command we used earlier.
combo +s"v:*^*^n:*-acc" +t%mor sample2.cha +d +f
modrep +b%mor +c*MOT +o"*acc"sample2.cmb.cex

The output of this program is the same as in the previous example. However, in a large

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input file, the addition of the COMBO filter can make the search much more restrictive
and powerful.

7.20.4 Unique Options
MODREP has the following unique options:+asort output by descending frequency+bThis switch is used to set the model tier name. There is no default setting. The modeltier can also be set to the main line, using +b* or +b*chi.
+c
You can use this switch to change the name of the replica tier. There is no default
setting.
+nThis switch limits the shape of the output from the replica tier in MODREP to somestring or file of strings. For example, you can cut down the replica tier output to only
those strings ending in “-ing.” If you want to track a series of strings or words, you
can put them in a file and use the @filename form for the switch.
+oThis switch limits the shape of the output for the model tier in MODREP to somestring or file of strings. For example, you can cut down the model tier output to only
those strings ending in “-ing” or with accusative suffixes, and so forth. If you want
to track a series of strings or words, you can put them in a file and use the @filename
form for the switch.
+s
The +s switch allows you to include particular symbols such as xxx or &* that are
excluded by default. The -s switch allows you to make further exclusions of particular strings. If you want to include or exclude a series of strings or words, you can
put them in a file and use the @filename form for the switch.
MODREP also uses several options that are shared with other commands. For a complete list of options for a command, type the name of the command followed by a carriage
return in the Commands window. Information regarding the additional options sharedacross commands can be found in the chapter on Options.

7.21 Phon and PhonTalk
PHON is Java program that performs extensive detailed analyses of phonologicalpatterns in transcripts linked to media. PHON was created by Greg Hedlund and Yvan
Rose at Memorial University of Newfoundland. Information on how to download, install,and learn PHON is available at https://www.phon.ca/phon-manual/misc/Welcome.html.
Although PHON is not a part of CLAN, the two systems use a common form of XML thatmakes it possible to convert CHAT files to PHON projects and also to convert PHON
projects to a series of CHAT files.
To do this, download Chatter fromhttps://talkbank.org/software/chatter.html and PhonTalk from https://www.phon.ca/phonmanual/chat/phontalk.html.
When converting from CHAT to Phon, first run Chatter to make sure that your files arein good CHAT format. To do this, you make sure that the buttons the select "from CHAT
to XML" are turned on. Then, you select the folder you want to convert using the Open in
the File pulldown menu. Then Chatter will run on that folder and create a new folder with
the name of the original plus -XML. For example, if you run on the Bliss folder, it will
create Bliss-xml.

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Next, you should open PhonTalk, and select the Open option in the File pulldown
menu. The program will run on each file and will list each as "Finished" once it isprocessed. By default, the output in the case of the Bliss corpus would be Bliss-xml-phon.
This is then a Phon project which you can analyze using Phon. You may want to rename
this output to remove the "xml" in the folder name, as in Bliss-phon.
To convert back to CHAT, you start PhonTalk and select Bliss-phon by choosing Openfrom the File menu. This will create a CHAT XML version of the Phon files. Then you
run Chatter to convert from CHAT XML to standard CHAT, this time selecting the radio
buttons for "XML to CHAT".

7.22 RELY
This program has five functions: (1) to examine agreement between two coders, (2) tocompute Cohen's kappa, (3) to evaluate correctness of a student coder against a masterdocument, (4) to check overall match between two transcripts, and (5) to combine codes
into a single file.
First, we will consider the function of checking reliability between two coders. When
you are entering a series of codes into files using the Coder Mode, you will often want tocompute the reliability of your coding system by having two or more people code a single
file or group of files. To do this, you can give each coder the original file, get them to enter
a %cod or %spa line and then use the RELY program to spot matches and mismatches. To
create an example, you could copy the sample.cha file in CLAN’s /examples folder to a
file called samplea.cha file and change one code in the samplea.cha file. In samplea.cha,change the code for the first utterance from “$INI:sel:in” to “$INI:sel:gone”. Then enter
the command:
rely sample.cha samplea.cha

The output in sample.rely.cex file will report the coding disagreements and you cantriple-click on the lines that give the line numbers, and they will open to the point of themismatch in each file. If you add +t%spa to this command, you will get a fuller report.
This will work in the same way, if your coding tier name is, for example, %cod orsomething else.
By default, RELY examines all main and dependent tiers. In the example discussed sofar, the two transcripts are identical except for any differences that would appears on thecoding tier. However, in some other cases, there could also be differences on the main line
or elsewhere. If you want the program to ignore any differences in the main line, headerline, or other dependent tiers that may have been introduced by the second coder, you can
add the +c switch. Then, you will need to use the +t switch to pick out a line or speaker toinclude, while ignoring all the others. If the command is:rely +c sample.cha samplea.cha +t%spa -t*

then the program will only report mismatches on the %spa tier. Note that, in addition toadding the +c switch, you must also add the -t* switch to make sure the RELY also ignoresthe main line in addition to ignoring other depending tiers. If you further add a +t*CHI
switch, it will only report mismatches for selected tier for the Target_Child. However, if
you use the +c switch with no additional +t inclusions, RELY will report unmatched tiers,
but not the actual mismatches. In the sample.rely.cex output, you can triple-click on lines

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with the line numbers given and CLAN will open to that place in the original file.
A second function of RELY is to compute Cohen's kappa by adding the +dN switch.
For this analysis, you need to specify the number of possible categories being checked.
Here is an example of computing kappa for %spa lines with three possible categories:
rely +c +d3 sample.cha samplea.cha +t%spa -t*

The third function of the RELY command is to evaluate the correctness of a student
transcript against a master transcript. The student is given a version of the complete mastertranscript without the coding tier and the goal is to add codes. In this case, it is assumed
that the coding in the master transcript is fully correct. The question is whether the student's
codes are correct. To specify that the first file given is the master, you use the +dm1 switch.
rely +dm1 sample.cha samplea.cha +t%spa

The output from this command provides two counts: precision and accuracy. These are
the two dimensions used in fields such as computational linguistics to evaluate correctness.
Precision is the percentage of fields in the master file that are matched by the student.
Accuracy is the percentage of fields in the student file that are matched to the master. A
failure in precision occurs when the student misses a code in the master file. A failure in
accuracy occurs when the student inserts a code that is not found in the master. If a
researcher is interested in going beyond these two scores, they can also compute the F score
which is the harmonic mean of precision and accuracy.
The fourth function of the RELY command is to estimate the overall match between
two transcripts on the main line. It is very difficult to define this type of comparison
precisely. Instead, RELY uses a rough-and-ready "bag of words" comparison method that
simply looks at the overall match of the main line items in the two versions. The commandfor this type of analysis adds the +d switch, and the output is the percentage of overall
overlap.
rely +d sample.cha samplea.cha

The fifth function of the RELY command is to allow multiple coders to add a series ofdependent tiers to a master file. The master file is the first one given in the command line.
The lines of the master file should remain untouched and the coder of the second file should
only be adding information on a single additional dependent tier. This function is accessed
through the +a switch, which tells the program the name of the coding line (given by the+t switch) from the secondary file that is to be added to the master file, as inrely +a +t%spa +t@ sample.cha samplea.cha

If, by mistake, some changes were made to the other coding lines, the output will ignore
the mismatches, keeping what is in the master file only. To get a full file merger, you needto add the +t@ switch to include the header tiers.
It is important to understand the detailed workings of comparison with the +a switch.
When used with +a and +t%cod, RELY looks at the first speaker tier in master and coder
files and, if they match, then it looks for the dependent tier in coder file that was specified
with +t%cod option. If it finds a %cod tier in coder file, then it looks in the master fileunder the corresponding speaker tier to see if the master file already has a %cod tier. If it
does (and it really shouldn’t), then the error message "** Duplicate tier found aroundlines:" is given and user must choose whether the master file tier or the coder file tier should

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be added to master file. If master file does not already have %cod tier, then the %cod tierfrom coder file is add to all the other dependent tiers in the master file under thecorresponding speaker tier.
RELY +a will also report an error, if it finds some other dependent tier, such as %com
in the coder file that is not in the master. In that case, it will report a message "**Unmatched tiers found around lines:" to inform user that there are tiers in the coder filethat are not in the master file and which have not been specified by the user to be addedwith +t%com option. This message is just an FYI.
RELY +a will only add tiers from coder file that are missing from master file and are
specified with +t option. At the same time, it will report if there are some other tiers incoder file that were not specified with +t option that are also missing from master file. In
other words, the coder file must be a subset of master file with only extra tiers that users
would want to add to master file. However, possibly not all those tiers are supposed to be
added and that is what the +t option is for.
If you want to conduct multiple runs with RELY, looking at different speakers anddifferent coding lines using the +c and +t switches, then you may also want to use the +2
switch to create differently named files from each run of RELY.

7.22.1 Unique Options
RELY has the following unique options:+a
Add tiers from second file to the master file.
+b
Include media bullets in string comparison between first and master files and show
differences if there are any.
+c
Only check data on tiers selected with +t.
+c1 Only compare main part of code ($COD:EX: compare $COD, ignore :EX)+dCompute percentage agreement. By default, this is based only on the main line. To
compute percentage agreement on a dependent tier, such as %cod, you should add
the –t* switch to exclude the main line and then use +t%cod to include just thisdependent tier.
+dmN: Compute student correctness. (+dm1 - first file is control, +dm2 second file iscontrol)+dN Compute Cohen’s kappa coefficient, where N is the number of categories.
+m Merge files and place error flags inside output file.

7.23 SCRIPTThe SCRIPT command is useful if fixed scripts are used in clinical research. It willcompare a participant’s performance to that of the model. To run SCRIPT, you must firstprepare a Model Script and a Participant’s Script.

7.23.1 The Model Script
1. Transcribe the model script in CHAT format. It is not necessary to have a media fileor do any linking on this model script. If sample duration is important, however,include @ Time Duration as a header.

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2. Run CHECK (using esc-L) to verify that CHAT format is accurate and run mor +xl*.cha to make sure the words in the file are all recognizable. Run MOR, POST, and
CHECK. Put this file in your CLAN lib folder in the folder with the Participant’s files
you will be comparing to it.

7.23.2 The Participant’s Script
Transcribe the Participant’s script production in CHAT format and link it to the media.
The analysis compares the model and Participant transcript on a line-by-line basis, so it isnecessary for the Participant transcript lines to match those of the model.
1. If the Participant skips a line of script, enter 0 for that line (*PAR: 0.).
2. If the Participant makes comments (e.g., “I don’t know”, “oh no”) in the middle of anutterance, put those words inside angle brackets with [//] afterward so they will not becounted as extra words in the analysis.
3. Error productions that are followed by target replacements must be judged to be either:
1) close approximations of the target; or 2) not close approximations. CLAN will
include close approximation errors as correct matches to the target word when the
SCRIPT command is run in its default mode. This can be easily modified, if such ananalysis is not desired (see Variations below).
4. Utterances with the [+ exc] code are ignored.
5. If most listeners would be able to figure out what the person meant, the error should beconsidered a close approximation and should be followed by the target replacement
word like this: error [: target]. Usually this means the error production is a closesemantic synonym (gifts for giftware), a lexical error (e.g., have for had), or a
production with 1 or 2 phonemic errors (e.g., /əʃeʒə/ for aphasia. If in doubt, assume
it is not a close approximation.
6. If an error is judged not to be a close approximation and most listeners would be unable
to know what the speaker meant (e.g., May for March, say for attend, reading for write),transcribe the error word with the target replacement like this: error [:: target].
Once the transcription is complete, run CHECK (using esc-L) to verify that CHAT format
is accurate and run mor +xl *.cha to make sure the words in the file are all recognizable.
Run MOR, POST, and CHECK.

7.23.3 Running SCRIPT
Once you have prepared the two scripts, this command will compare a model script file(which we will call model.cha) to a participant’s script file (which we will callparticipant.cha):
script +t*PAR +smodel.cha participant.cha

The output will include an .xls file and a .cex file. The .xls file provides the following
information for both the model script and the Participant’s script production: TIMDUR
and # words produced. It provides the following information on the Participant’s script
only: # words correct, % words correct, # words omitted, % words omitted, # words added,# recognizable errors, # unrecognizable errors, # utterances with xxx, # utterances with 0.
Unrecognizable errors are those transcribed as xxx or coded as unknown neologistic orPart 2: CLAN

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semantic errors ([* n:uk] and [* s:uk]. (See the chapter on Error Coding in the CHAT
manual.) All other errors include target replacements and are considered recognizable.
The .cex file provides the following information for the Participant’s script production:
list of omitted words (with part of speech and bound morpheme), list of added words, andlist of errors (error production, intended word if known, error code and frequency info).

7.23.4 Variations
If you want to produce output for all the CHAT files in a folder you would use thiscommand:script +t*PAR +smodel.cha *.cha +u

The +u switch will list the results for each CHAT file instead of individual .cex and .xls
files.
The default mode for this command is to INCLUDE target replacements for errors
judged to be close approximations (lake [: like] [* p:w]) and EXCLUDE revisions andretracings (anything coded with [/] or [//]). Both of those defaults can be changed by adding
switches to the command line:+r5 excludes target replacements+r6 includes repetitions and revisions

7.23.5 Unique Options
MAXWD has the following unique options:+e : count error codes in retraces or repeats. (default: don't count)+sF: specify template script file F

7.24 TIMEDURThe TIMEDUR program computes the duration of the pauses between speakers andthe duration of overlaps. This program requires sound bullets at the ends of utterances orlines created through sonic CHAT. The data is output in a form that is intended for export
to a spreadsheet program. Columns labeled with the speaker’s ID indicate the length of theutterance. Columns labeled with two speaker ID’s, such as FAT-ROS, indicate the lengthof the pause between the end of the utterance of the first speaker and the beginning of theutterance of the next speaker. Negative values in these columns indicate overlaps.
The basic output format of TIMEDUR gives a profile of durations for all speakers
through the whole file. For a more succinct summary of durations for a given speaker, use
a command with the +t switch, such as:
timedur +d1 +d +t*PAR *.cha

This command creates a summary of time durations across files for just PAR. In effect,
it treats the +u switch as the default.
+doutputs default results in SPREADSHEET format+d1 outputs ratio of words and utterances over time duration+d10 outputs above, +d1, results in SPREADSHEET format

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7.25 VOCD
The VOCD command was written by Gerard McKee of the Department of Computer
Science, The University of Reading. The research project supporting this work was fundedby grants from the Research Endowment Trust Fund of The University of Reading and theEconomic and Social Research Council (Grant no R000221995) to D. D. Malvern and B.
J. Richards, School of Education, The University of Reading, Bulmershe Court, Reading,England RG6 1HY. The complete description of VOCD can be found in: Malvern, D.,
Richards, B., Chipere, N., & Purán, P. (2004). Lexical diversity and language development.
New York: Palgrave Macmillan.
Measurements of vocabulary diversity are frequently needed in child language researchand other clinical and linguistic fields. In the past, measures were based on the ratio ofdifferent words (Types) to the total number of words (Tokens), known as the type–token
Ratio (TTR). Unfortunately, such measures, including mathematical transformations of the
TTR such as Root TTR, are functions of the number of tokens in the transcript or language
sample — samples containing larger numbers of tokens give lower values for TTR andvice versa. This problem has distorted research findings. Previous attempts to overcome
the problem, for example by standardizing the number of tokens to be analyzed from eachchild, have failed to ensure that measures are comparable across researchers who use
different baselines of tokens, and inevitably waste data in reducing analyses to the size ofthe smallest sample.
The approach taken in the VOCD program is based on an analysis of the probability of
new vocabulary being introduced into longer and longer samples of speech or writing. This
probability yields a mathematical model of how TTR varies with token size. By comparing
the mathematical model with empirical data in a transcript, VOCD provides a new measureof vocabulary diversity called D. The measure has three advantages: it is not a function of
the number of words in the sample; it uses all the data available; and it is more informative,
because it represents how the TTR varies over a range of token size. The measure is basedon the TTR versus token curve calculated from data for the transcript as a whole, rather
than a particular TTR value on it.
D has been shown to be superior to previous measures in both avoiding the inherent
flaw in raw TTR with varying sample sizes and in discriminating across a wide range oflanguage learners and users (Malvern, Richards, Chipere, & Purán, 2004).

7.25.1 Origin of the Measure
TTRs inevitably decline with increasing sample size. Consequently, any single valueof TTR lacks reliability as it will depend on the length in words of the language sample
used. A graph of TTR against tokens (N) for a transcript will lie in a curve beginning at the
point (1,1) and falling with a negative gradient that becomes progressively less steep (seeMalvern & Richards, 1997a). All language samples will follow this trend, but transcripts
from speakers or writers with high vocabulary diversity will produce curves that lie above
those with low diversity. The fact that TTR falls in a predictable way as the token size
increases provides the basis for our approach to finding a valid and reliable measure. The
method builds on previous theoretical analyses, notably by Brainerd (Brainerd & Pressley,1982) and in particular Sichel (1986), which model the TTR versus token curvePart 2: CLAN

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mathematically so that the characteristics of the curve for a transcript yields a valid measureof vocabulary diversity.
Various probabilistic models were developed and investigated to arrive at a model
containing only one parameter which increases with increasing diversity and falls into a
range suitable for discriminating among the range of transcripts found in various language
studies. The model chosen is derived from a simplification of Sichel’s (1986) type– token
characteristic curve and is in the form an equation containing the parameter D. Thisequation yields a family of curves with the same general and appropriate shape, with different values for the parameter D distinguishing different members of this family (Malvern& Richards, 1997a). In the model, D itself is used directly as an index of lexical diversity.
To calculate D from a transcript, the VOCD program first plots the empirical TTR
versus tokens curve for the speaker. It derives each point on the curve from an average of
100 trials on subsamples of words of the token size for that point. The subsamples are madeup of words randomly chosen (without replacement) from throughout the transcript. The
program then finds the best fit between the theoretical model and the empirical data by acurve-fitting procedure which adjusts the value of the parameter (D) in the equation until
a match is obtained between the actual curve for the transcript and the closest member ofthe family of curves represented by the mathematical model. This value of the parameterfor best fit is the index of lexical diversity. High values of D reflect a high level of lexical
diversity and lower diversity produces lower values of D.
The validity of D has been the subject of extensive investigation (Malvern & Richards,
1997a, 1997b; Malvern et al., 2004; Richards & Malvern, 1996) on samples of childlanguage, children with SLI, children learning French as a foreign language, adult learnersof English as a second language, and academic writing. In these validation trials, theempirical TTR versus token curves for a total of 162 transcripts from five corpora covering
ages from 24 months to adult, two languages and a variety of settings, all fitted the model.
The model produced consistent values for D which, unlike TTR and even Mean Segmental
TTR (MSTTR) (Richards & Malvern, 1996, pp. 35-38), correlated well with other wellvalidated measures of language. These five corpora also provide useful indications of thescale for D.

7.25.2 Calculation of D
In calculating D, VOCD uses random sampling of tokens in plotting the curve of TTR
against increasing token size for the transcript under investigation. Random sampling has
two advantages over sequential sampling. Firstly, it matches the assumptions underlying
the probabilistic model. Secondly, it avoids the problem of the curve being distorted by theclustering of the same vocabulary items at points in the transcript.
In practice, each empirical point on the curve is calculated from averaging the TTRs of
100 trials on subsamples consisting of the number of tokens for that point, drawn at random
from throughout the transcripts. This default number was found by experimentation andbalanced the wish to have as many trials as possible with the desire for the program to run
reasonably quickly. The run time has not been reduced at the expense of reliability, however, as it was found that taking 100 trials for each point on the curve produced consistency
in the values output for D without unacceptable delays.

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Which part of the curve is used to calculate D is crucial. First, to have subsamples toaverage for the final point on the curve, the final value of N (the number of tokens in a
subsample) cannot be as large as the transcript itself. Moreover, transcripts vary hugely intotal token count. Second, the equation approximates Sichel’s (1986) model and applies
with greater accuracy at lower numbers of tokens. In an extensive set of trials, D has been
calculated over different parts of the curve to find a portion for which the approximation
held good and averaging worked well. Based on these trials, the default is for the curve tobe drawn and fitted for N=35 to N=50 tokens in steps of 1 token. Each of these points is
calculated from averaging 100 subsamples, each drawn from the whole of the transcript.
Although only a relatively small part of the curve is fitted, it uses all the information
available in the transcript. This also has the advantage of calculating D from a standard
part of the curve for all transcripts regardless of their total size, further providing for
reliable comparisons between subjects and between the work of different researchers.
The procedure depends on finding the best fit between the empirical and theoreticallyderived curves by the least square difference method. Extensive testing confirmed that thebest fit procedure was valid and was reliably finding a unique minimum at the least squaredifference.
As the points on the curve are averages of random samples, a slightly different valueof D is to be expected each time the program is run. Tests showed that with the defaults
chosen these differences are relatively small, but consistency was improved by VOCD
calculating D three times by default and giving the average value as output.

7.25.3 Sample Size
By default, the software plots the TTR versus token curve from 35 tokens to 50 tokens.
Each point on the curve is produced by random sampling without replacement. VOCD
therefore requires a minimum of 50 tokens to operate. However, the fact that the software
will satisfactorily output a value of D from a sample as small as 50 tokens does not guarantee that values obtained from such small samples will be reliable. It should also be notedthat random sampling without replacement causes the software to run noticeably more
slowly when samples approach this minimum level.

7.25.4 VOCD Running and Output
By default, VOCD runs from the %mor line. This is also true for the form of VOCD
that runs in KIDEVAL. This is necessary, because the %mor line provides the base formof each word or lemma, which is the best way to compute lexical diversity. Also, the %morline excludes repetitions.
If you do not have a %mor line in your transcript(s), then you should use the +o switch
to run VOCD from the main line instead. Of course, running in this mode will lead toresults based on surface forms, rather than lemmas.
Because VOCD runs by default off the %mor line, you should use the form of the +/-s
switch that conforms with the syntax of the %mor line. This form uses +sm, rather thansomething like +s"n|*". You can best understand the syntax of +sm by typing:
vocd +sm

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In the CLAN output window, you will then see a description of the various components of
the +sm switch, along with example usages. When you use the +sm version of the searchstring, you then make sure that VOCD is running from the %mor line.
To illustrate the functioning of VOCD, we can use a command that examines the child’s
output in the file 68.cha in /examples/transcripts/ne32. The command for doing this is:
vocd +t*CHI +sm;*,o% 68.cha

To also exclude affixes and neologisms (unintelligible words are already excluded fromthis analysis), use:
vocd +t*CHI +sm;*,o% -sm|neo +f *.cha

The output of this analysis has four parts:
1. A sequential list of utterances by the speaker selected shows the tokens that will be
retained for analysis.
2. Three tables that each show the number of tokens for each point on the curve, average
TTR and the standard deviation for each point, and the value of D obtained from theequation for each point. Three such tables appear, one for each time the program takes
random samples and carries out the curve-fitting.
3. At the foot of each of the three tables is the average of the Ds obtained from the equationand their standard deviation, the value for D that provided the best fit, and the residuals.
4. Finally, a results summary repeats the command line and file name and the type andtoken information for the lexical items retained for analysis, as well as giving the three
optimum values of D and their average.
For the command given above, the last of the three tables and the results summary are:tokens
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

samples
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100

ttr
0.7846
0.77390.7786
0.76260.77000.75500.75900.76260.75210.74230.74420.73910.73340.73040.72240.7268st.dev
0.059
0.055
0.057
0.063
0.057
0.056
0.064
0.055
0.057
0.052
0.053
0.059
0.052
0.053
0.060
0.058

D
50.00347.67750.67346.55450.26846.53349.01151.45049.05647.03248.72248.16747.41347.49646.07248.338

D: average = 48.404; std dev. = 1.541
D_optimum<48.34; min least sq val = 0.000>
VOCD RESULTS SUMMARY====================
Types,Tokens,TTR:
D_optimum values:
D_optimum average:<121,388,0.311856><49.61, 47.22, 48.34>
48.39

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7.25.5 Unique Options
VOCD has the following unique options:+doutputs a list of utterances processed and number of types, tokens andTTR,
but does not calculate D.
+d1: outputs only VOCD results summary on one line+d2: outputs only type/token information+d3: outputs only type/token information in Excel format+bvarious methods for controlling sampling+b0: D_optimum - use split half; even.
+b1: D_optimum - use split half; odd.
+bsN: D_optimum - size N of starting sample (default 35)+blN: D_optimum - size N of largest sample (default 50)+biN: D_optimum - size N of increments(default 1)+bnN: D_optimum - the N number of samples (default 100)
+br: D_optimum - random sampling with replacement (default: noreplacement)+be: D_optimum - use sequential sampling+g: Calls up the limiting relative diversity (LRD) sub-routine to compare the relative
diversity of two different word classes, the numerator and the denominator, coded on the%mor tier. This procedure extracts the words to be included from the %mor tier where theword classes are coded. The speaker, the %mor tier and the file name are specified in theusual way, plus the +g switch to invoke the subroutine. The following would compare verb
and noun diversity and limit the analysis to word stems:
vocd +t*CHI +t%mor +gn"m|v,;*,o%" +gd"m|n,;*,o%" filename

The first word class entered will be the numerator and the second willbe the denominator.
+gnS:
compute "limiting type-type ratio" S=NUMERATOR-gnS: compute "limiting type-type ratio" S=NUMERATOR+gdS:
compute "limiting type-type ratio" S=DENOMINATOR-gdS: compute "limiting type-type ratio" S=DENOMINATOR

7.26 WDLEN
The WDLEN program tabulates the lengths of words, utterances, and turns. The basic
command is:
wdlen sample.cha

The output from running this on the sample.cha file will be as displayed here:
Number of words of each length in characters
lengths:
1
2
3
4
5
6
7
8
9
*CHI:
0
0
0
4
4
0
0
0
0
*MOT:
2
4
11
11
11
0
0
0
2
------Number of utterances of each of these lengths in words
lengths:
1
2
3
4
5
6
7
8
9

Mean
4.50
4.00
10

Mean

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*CHI:
3
1
1
0
0
0
0
0
0
0
1.60
*MOT:
0
1
1
1
2
2
0
0
0
1
3.77
------Number of single turns of each of these lengths in utterances
lengths:
1
2
Mean
*CHI:
0
0
0.00
*MOT:
0
1
2.00
------Number of single turns of each of these lengths in words
lengths:
1
2
3
4
5
6
7
8
9
10
11
Mean
*CHI:
0
0
0
0
0
0
0
0
0
0
0
0.00
*MOT:
0
0
0
0
0
0
0
0
0
0
1
11.00
------Number of words of each of these morpheme lengths
lengths:
1
2
Mean
*CHI:
7
1
1.12
*MOT:
35
7
1.16
------Number of utterances of each in morpheme length:
1
2
3
4
5
6
7
8
9
10
11
12
Mean
*CHI:
3
0
2
0
0
0
0
0
0
0
0
0
1.80
*MOT:
0
0
2
0
1
2
2
0
0
0
0
1
4.46

The first four analyses are computed from the main line. For these, the default valueof the +r5 switch is shifted to “no replacement” so that word length is judged from theactual surface word produced. Also, the default treatment of forms with omitted material
uses +r3, rather than the usual +r1. Only alphanumeric characters are counted and theforms xxx, yyy, and www are excluded, as are forms beginning with & or 0 and any
material in comments. The last two analyses are computed from the %mor line. There,
the forms with xxx, yyy, or www are also excluded. For the segments of WDLEN that run
off the %mor line, you should use the form of the +/-s switch that conforms with the syntaxof the %mor line. This form uses +sm, rather than something like +s"n|*". You can best
understand the syntax of +sm by typing:
vocd +sm

The WDLEN command allows for a maximum of 100 letters per word and 100 words
or morphemes per utterance. If you input exceeds these limits, you will receive an error
message. The only option unique to WDLEN is +d that allows you to output the results in
a format that can be opened directly from Excel. Information regarding the additional
options shared across commands can be found in the chapter on Options.

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Profiling Commands
This section describes CLAN's profiling commands. They include:
1. C-NNLA: Northwestern Narrative Language Analysis
2. C-QPA: Quantitative Production Analysis
3. CORELEX: Core lexicon analysis for 5 AphasiaBank tasks
4. DSS: Developmental Sentence Score
5. EVAL: computation of a wide range of indices for aphasia
6. FluCalc: computation of a wide range of indices for stuttering
7. IPSyn: Index of Productive Syntax
8. KIDEVAL: computation of a wide range of indices for child language
9. MORTABLE: computation of occurrences of grammatical morphemes
10. SUGAR: Sampling Utterances and Grammatical Analysis Revised

8.1 C-NNLA
This command provides an automatic computation of the Northwestern Narrative
Language Analysis profile (Thompson et al., 1995). C-NNLA has been designed tocompute measures in accordance with the rules provided in the NNLA manual. Althougha few individual lexical codes are different from those given in the NNLA manual, allautomatically computed measures have been shown to have an acccuracy comparable tothat achieved manually by highly trained and experienced NNLA coders. Currently the
program is implemented for English only. The command depends on the presence andaccuracy of %mor and %gra lines, as described in the MOR manual.

8.1.1 CHAT File Format Requirements
For accurate computation of outcome measures according to NNLA rules, thefollowing CHAT conventions must be followed.
1. Exclusions. NNLA rules call for several exclusions in computing outcome
measures. Many of these exclusions are automatic. CLAN already excludes repetitions
marked with [/], revisions marked with [//], fillers transcribed with &-, and fragments
transcribed with &+. The C-NNLA command also automatically excludes the following
conjunctions when they are used the beginning of an utterance: and, but, or, then, so, well,and then, but then, and so. To exclude other words from C-NNLA analysis, as per the
NNLA manual (e.g. interjections, comments), transcribers must manually insert the [e]code after the word(s) to be excluded. For entire utterances to be excluded, use the [+ exc]
code after the final punctuation. Here are three examples:
*PAR:
*PAR:
*PAR:

the prince says oh [e] it is you.
<I think> [e] her name was Cinderella.
I can't do this. [+ exc]

2. Utterance level coding. Two codes are needed to mark grammatically flawed [+gram] and semantically flawed [+ sem] utterances, as per the NNLA manual.
*PAR: she was a really nice dress. [+ sem]
*PAR: he has a wonderful time. [+ sem](talking about Cinderella)*PAR: looking at the clock. [+ gram]*PAR: it is just stepmother and three stepsisters. [+ gram]

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3. Morphological error coding. The error-coding chapter in the CHAT manual
provides word-level error codes that can be used in CHAT files for phonological, semantic,
neologistic, and morphological errors. For accurate computation of several morphologicaloutcome measures in C-NNLA, it is important to mark the morphological errors, as in thefollowing examples. Note, that the target word is entered in square brackets with a single
colon, followed by the word-level error code.
*PAR: both was [: were] [* m:vsg:a] very mean. [+ gram]*PAR: it felled [: fell] [* m:+ed] out. [+ gram]*PAR: she was push [: pushing][* m:0ing] pins and needles. [+ gram]

8.1.2 C-NNLA Output
All C-NNLA measures are calculated from the %mor and %gra lines. All measures
ignore unintelligible utterances and utterances with the [+ exc] postcode. The basic formatof the command for analysis of speaker PAR is:
c-nnla +t*PAR filename.cha

This command will generate one Excel spreadsheet with all outcome measures. (Note: the.xls file is actually a CSV text file, so it is advisable to save these as .xlsx files in Excel.).
Following is a list of the values computed in C-NNLA. The first 12 fields come fromthe CHAT @ID header for the selected speaker. To match the Excel to the numbers in thefollowing fields you can select R1C1 column labeling in Excel. For Windows, this in in
Options/Calculation. For Mac, it is in Preferences/Calculation and it says “Use R1C1
reference style”.
1. filename
2. language
3. corpus name
4. code for the speaker
5. age6. sex7. group
8. race
9. SES
10. role of the speaker
11. education
12. custom_field: score on the Western Aphasia Battery (WAB).
13. duration: total time of the sample in seconds. Note: If the transcript is not linked,
this will not be calculated. An alternative is to add a TIME DURATION line to the
ID lines in the transcript (see the TIMEDUR section in this manual).
14. words per minute: total words divided by total time for speaker
15. total utterances: # of utterances
16. total words: # of words (tokens)
17. MLU words: MLU in words (excludes utterances with any unintelligible content
transcribed as xxx)
18. open-class: # of open-class words -- all nouns, all verbs excluding auxiliaries andmodals, all adjectives, all adverbs
19. % open-class/all words

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20. closed-class words: # of closed-class words -- all other words besides open-classwords, with the exception of words coded as communicators (e.g., “yeah”) andonomatopoeia (e.g., “woofwoof”)
21. % closed-class words/all words
22. open/closed ratio
23. nouns: # of nouns
24. % nouns/all words
25. verbs: # of verbs, including copulas and participles
26. % verbs/all words
27. noun/verb: ratio of nouns to verbs
28. adj: # of adjectives
29. adv: # of adverbs
30. det: # of determiners (det:art and det:dem)
31. pro: # of pronouns (excluding "wh" interrogative pronouns and "wh" relative
pronouns) and possessive determiners
32. aux: # of auxiliaries
33. conj: # of conjunctions (excluding "wh" conjunctions) and coordinators
34. complementizers: # of complementizers
35. modals: # of modals and modal auxiliaries
36. prep: # of prepositions
37. negation markers: # of negatives and "no" communicators
38. infinitival markers: # of infinitives
39. quantifiers: # of quantifiers, numbers (det:num), and post (e.g., "all", "both")
40. wh-words: # of "wh" relative pronouns, "wh" interrogative pronouns, "wh"conjunctions, and interrogative determiners
41. comparative suffixes: # of words with -CP
42. superlative suffixes: # of words with -SP
43. possessive markers: # of words with -POSS
44. regular plural markers: # of words with -PL
45. irregular plural forms: # of words with &PL
46. 3rd person present tense markers: # of words with -3S
47. regular past tense markers: # of words with -PAST
48. irregular past tense markers: # of words with &PAST
49. regular perfect aspect markers: # of words with -PASTP
50. irregular perfect participles: # of words with &PASTP
51. progressive aspect markers: # of words with -PRESP excluding gerunds (nouns)
52. % correct regular inflection: numerator = of all regular inflected verbs, copulas,participles, auxiliaries, and "does" modal that do not have any morphological error
codes next to them; denominator = all regular inflected verbs, copulas, participles,
auxiliaries, and "does" modal
53. % correct irregular inflection: numerator = numerator = of all irregular inflected verbs,
copulas (except for "is"), participles, auxiliaries (except for "is" and "are"), and "did"modal that do not have any morphological error codes next to them; denominator = allirregular inflected verbs, copulas (except for "is"), participles, auxiliaries (except for"is" and "are"), and "did" modal
54. % sentences produced: numerator = utterances that have at least one verb, copula,Part 2: CLAN

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modal, or participle; denominator = all utterances counted on speaker tier except nonword utterances
55. % sentences with correct syntax, semantics: numerator = utterances that have at least
one verb, copula, modal, or participle and do not have a [+ gram] or [+ sem] postcode; denominator = all sentences (numerator from % sentences produced)
56. % sentences with flawed syntax: numerator = utterances that have a [+ gram] postcode and have at least one verb, copula, modal, or participle; denominator = allsentences (numerator from % sentences produced)
57. % sentences with flawed semantics: numerator = utterances that have a [+ sem] postcode and have at least one verb, copula, modal, or participle; denominator = allsentences (numerator from % sentences produced)
58. sentence complexity ratio: numerator = # of sentences that have at least 1 verb, copula,modal, or participle and have at least one CSUBJ, COMP, CPRED, CPOBJ, COBJ,
XJCT, CJCT, CMOD, XMOD on the %gra tier; denominator = sentences that have atleast one verb, copula, modal, or participle and do not have any of those codes on the%gra tier (Note: there are some additional rules about this computation that can befound at the C-NNLA link at the AphasiaBank website in the Discourse Analysis
section)59. # embedded clauses/sentence: numerator = # sentences with one embedded clause +# of sentences with two embedded clauses x 2 + # of sentences with three embedded
clauses x 3, etc.; denominator = # of sentences with zero embeded clauses + # ofsentences with one embedded clause, two embedded clauses + # of sentences withthree embedded clauses, etc. For details on how embedded clauses are counted on the%gra tier, see the C-NNLA link at the AphasiaBank website in the Discourse Analysis
section.

8.2 C-QPA
This command provides an automatic computation of the Quantitative Production
Analysis profile (Berndt, Wayland, Rochon, Saffran, & Schwartz, 2000). C-QPA has been
designed to compute measures in accordance with the rules set out by the QPA authors.
Currently the program is implemented for English only. Use of the command requires thepresence of accurate %mor and %gra lines.

8.2.1 CHAT File Format Requirements
For accurate computation of outcome measures according to QPA rules, the following
CHAT conventions must be followed.
1. Sentence exclusions. Use the [+ exc] post-code after final punctuation to exclude:a. sentences that respond to directive questioning from the Examinerb. comments made by PAR (e.g., " I can't remember what happens next.")c. sentences that consist only of direct discourse markers (e.g., "he said", "thefairy godmother told her")
2. Required determiners. Mark any missing required determiners for nouns with
0det, for example:
*PAR:
*PAR:

she tried to fit 0det shoe on her foot.
0det sisters were mean to Cinderella.

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121

mother is washing dishes at 0det sink.

3. Required subjects. Mark any missing subjects in sentences with a [+ 0subj] postcode. Do not use this code for imperatives like "clean the floor". Example:*PAR:make her his wife. [+ 0subj]

4. Imperatives. Mark any imperatives preceded by proper nouns or pronouns withvocative or summons marker ‡ (F2-v), for example:*PAR: Cinderella ‡ clean the floor.
*PAR: hey ‡ watch out!
5. Syntax errors. Mark any sentences NOT syntactically well-formed with [+ gram]post-code. Semantically anomolous sentences are okay. Examples:a. missing obligatory argumentsb. agreement errors
c. deleted elements
6. Lexical exclusions. Use [e] next to an individual word or next to a phrase in angle
brackets, for direct discourse markers, habitually used starters, and anything else
not automatically excluded by CLAN. Examples:
*PAR:
*PAR:

her fairy godmother appeared <and said> [e].
okay [e] his stool is tipping over.

By default, CLAN automatically excludes repetitions, revisions, fillers, andsound fragments from analysis. In adherence to the QPA rules, C-QPA will also
automatically exclude the following items:
1. frozen words elements -- once upon a time, happily ever after
2. words associated with these error codes -- [* n:uk], [* s:uk], [* n:uk:s]
3. habitually used starters -- and, but, so, then, and then, so, well
4. sentences with with the [+ exc] post-code, words followed by the [e] code

8.2.2 C-QPA Output
All measures are calculated from the %mor and %gra lines. All measures ignore
unintelligible utterances and utterances with [+ exc] postcode. The basic format of thecommand for analysis of speaker PAR is:
c-qpa +t*PAR filename.cha

The command will generate two .xls spreadsheets: the sentence-by-sentence analysis
spreadsheet, and the summary spreadsheet. (Note: the .xls files are text files, so it isadvisable to save them as .xlsx files in Excel.) To match the Excel to the numbers in thefollowing fields you can select R1C1 column labeling in Excel. For Windows, this in in
Options/Calculation. For Mac, it is in Preferences/Calculation and it says “Use R1C1
reference style”.
Analysis Spreadsheet
1. utterance – actual speaker utterance
2. sentence utterance (1,0) – 1 for sentences that have a noun (SUBJ) and main verb
(ROOT); 1 for imperative sentences (ROOT without [+0 subj] post-code); 0 forPart 2: CLAN

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sentences that do not have a noun and main verb
3. other utterance (1,0) – 1 for sentences that do not have a noun (SUBJ) and main verb
(ROOT); 0 for sentences that do have a noun and main verb
4. # narrative wds – total words, tokens
5. # open class wds -- all nouns, verbs (excluding auxiliaries and modals), participles,
adjectives, and adverbs with -ly endings
6. # nouns – all nouns
7. # Ns req det (NRDs) -- # of nouns, excluding proper nouns, nouns with numbers (e.g.,"three girls"), 2nd noun in compound noun phrase ("queen" in "the king and queen")
8. # NRDs w/dets -- # of nouns with determiners (articles, demonstratives, possessives)
minus nouns preceded by 0det
9. # pronouns -- # of pronouns (excluding relative pronouns, interrogative pronouns,
demonstrative pronouns, existential pronouns) and possessive determiners
10. # verbs -- # of verbs, copulas, and participles
NOTE: All measures below (11-19) are not computed if Sentence Utterance = 0
11. # matrix verbs -- # of main verbs (verb, copula, or participle) in the sentence, which
will only be more than 1 for conjoined verb phrases
12. AUX score -- # of copulas plus # of verbs (except if it immediately follows infinitive"to") plus # of auxiliaries plus # of modals plus # of participles plus # of inflections(except for copula and auxiliary "is") plus # of "not" immediately following copula,modal, or auxiliary
13. # embeddings -- # of these codes on %gra tier: CSUBJ; CJCT; COBJ; CMOD; COMP
if SUBJ directly ties to it; CPRED if SUBJ directly ties to it; XJCT if SUBJ directly
ties to it; XMOD if SUBJ directly ties to it (%gra codes are explained in the MOR
manual, section 10.6 MEGRASP)
14. S well-formed -- # of utterances that do not have [+ gram] post-code
15. # phrases SNP -- # of nouns and pronouns on the %mor tier that correspond to thesegrammatical relations on %gra tier: SUBJ directly linked to ROOT; PRED directly
linked to ROOT; COORD directly linked to CONJ linked to SUBJ linked to ROOT;POBJ directly linked to NJCT linked to SUBJ
16. # phrases VP -- # of the following: ROOT; ROOT with CONJ followed by COORD
where CONJ links to ROOT and COORD links to CONJ; ENUM linked to ROOT;LINK that corresponds to coordinator on the %mor tier and links to CJCT which links
to ROOT on the %gra tier
17. # open class words + pron SNP -- # of nouns, copulas, adjectives, pronouns (indefinite,personal, subject, demonstrative), numbers as determiners, adverbs with -ly endings in
the SNPs; count 1 for imperatives (where ROOT is the first or only code on the %gratier or follows a BEGP, with no 0subj post-code)
18. # open class words + pron VP -- # of the following parts of speech that correspond to
ROOT or are linked to ROOT on the %gra tier: nouns, verbs, participles, copulas,adjectives, pronouns (indefinite, subject, object, demonstrative), and adverbs with -ly
endings
19. # wds in Ss -- # of words in each utterance (where Sentence Utterance = 1)
Summary Spreadsheet

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Following is a list of the values provided in the summary spreadsheet for C-QPA. To
match the Excel to the numbers in the following fields you can select R1C1 column
labeling in Excel. For Windows, this in in Options/Calculation. For Mac, it is in
Preferences/Calculation and it says “Use R1C1 reference style”. The first 12 fields in this
output come from the CHAT @ID header for the selected speaker. Several of thesemeasures are the same as the ones in the Analysis Spreadsheet above and will not be redefined here.
1. filename
2. language
3. corpus name
4. code for the speaker
5. age6. sex7. group
8. race
9. SES
10. role of the speaker
11. education
12. custom_field: score on the Western Aphasia Battery (WAB).
13. duration -- total time of the sample in seconds. Note: If the transcript is not linked,
this will not be calculated. An alternative is to add a TIME DURATION line to the
ID lines in the transcript (see the TIMEDUR section in the CHAT manual).
14. # narrative words
15. # words per minute -- total narrative words divided by total time for speaker
16. # open class words
17. # closed class words -- all other parts-of-speech that are not open class, excluding
onomatopoeia and communicators
18. proportion closed class words -- # closed class words divided by # narrative words
19. nouns
20. # NRDs
21. # NRDs w/determiners
22. DET index -- # NRDs with determiners divided by # NRDs
23. # pronouns
24. proportion pronouns -- # pronouns divided by # pronouns plus # nouns
25. # verbs
26. proportion verbs -- # of verbs divided by # of pronouns plus # of nouns
27. # matrix verbs
28. total aux score29. aux complexity -- [total aux score divided by # of matrix verbs] minus 1
30. # Ss -- # of the following: utterances that include a SUBJ linked to ROOT on the%gra tier and is not an incomplete utterance (trailed off, interrupted); utteranceswith ROOT as the first or only code on the %gra tier or the first code following
BEGP (with no 0subj post-code)
31. # words in Ss
32. proportion words in Ss -- # of words in sentences divided by # of narrative words
33. # well-formed Ss

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34. proportion well-formed Ss -- # of well-formed sentences divided by # of sentences
35. # SNPs
36. # words in SNPs
37. mean SNP length -- # of words in SNPs divided by # of SNPs
38. SNP elaboration -- [# of words in SNPs divided by # of SNPs] minus 1
39. # VPs
40. # words in VPs
41. mean VP length -- # of words in VPs divided by # of VPs
42. VP elaboration -- [# of words in VPs divided by # of VPs] minus 1
43. S elaboration -- SNP elaboration plus VP elaboration
44. # embeddings
45. embedding index -- # of embeddings divided by # of sentences
46. # utterances
47. mean utterance length -- # of narrative words divided by # of utterances

8.3 CORELEXThis command provides automatic computation of core lexicon lists for the five
AphasiaBank Discourse Protocol tasks, as published in Dalton et al. (2020). The
command will automatically extract the appropriate gem (task) and create a spreadsheet
showing which words from the core lexicon were used. The "Types" column in the
spreadsheet will show how many words from the list were used. The other columns willshow which specific words from the list were used and how frequently. Be sure to savethe spreadsheet as an .xlsx Workbook in Excel.
8.3.1

CHAT File Format Requirements

For accurate computation, the following CHAT conventions must be followed:
1. The transcript must have a gem heading (e.g., @G:<tab>Window) at the beginning of
the task. The current list of gem headings for the CORELEX command is: Window,
Umbrella, Cat, Cinderella, Sandwich.
2. Enter the command — corelex +lcat +t*par filename.cha . Substitute the appropriate
task name (e.g., sandwich, window) for "cat" in the above example. Use *.cha toanalyze all files in a folder.
3. The command counts words in utterances marked with [+ exc] codes (which is how
the norms for Corelex were computed), but if you used that code in your transcripts
and want those utterances excluded, use this command -- corelex +lcat +t*par -s"<+exc>" filename.cha .
If you want to compare your results to the norms reported in Dalton et al. (2020), youneed to do two steps before running the CORELEX command because the norms included
revised words and excluded target replacements for semantic paraphasias. The CORELEX
program counts lemmas on the %mor tier (to capture different forms of a word — e.g.,"was" for "be"), and the %mor tier excludes revisions and includes target replacements. To
fix that (include revised words, exclude target replacements):
1. Run this command on your CHAT file(s) -- chstring +q1 filename.cha -- to remove
revision codes in the transcript ([//]) and replace target replacements for semantic

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paraphasias (e.g., grandmother [: godmother]) with double colons instead of single
colons. That will create a new file with a .chstr.cex extension.
2. Re-run the MOR command -- mor filename.chstr.cex-- on the new file(s).
3. Run the CORELEX command -- corelex +lcat +t*par filename.chstr.cex -- on the
new file(s). Now, CORELEX will count every word the participant said and will notcount any target replacements that may be in the transcripts for semantic paraphasias.
Remember to substitute the appropriate task name (e.g., sandwich, window) for "cat"in the above example and use *.cex (or *.chstr.cex) instead of filename.chstr.cex for
multiple files in a folder.
Note: If you are transcribing from scratch and your transcript does not have revision
markings or target replacements, you would not need to do this to be able to compare
your results with the norms.

8.3.2 Unique Options+lF: specify words group with the name F. If the name has a dash (e.g., cookieshort.cut), then the time values from the %wor line are computed.
+w
finds the first time values for words from a *.cut file with a dash in the file name,as specified in the -l switch
Note: Use of the dash in +l and use of +w require a %wor tier in the transcript, and thecut file needs to have all possible versions of the core lexicon words (e.g., boy, boys,
boy’s) because the search is done on the %wor tier, not the %mor tier.

8.4 DSS
This program provides an automatic computation of the Developmental Sentence Score(DSS) (Lee, 1974). This score is based on the assignment of scores for a variety ofsyntactic, morphological, and lexical structures across eight grammatical domains. The
computation of DSS relies on the part of speech (POS) analysis of the %mor tier.

8.4.1 CHAT File Format Requirements
1.
2.
3.
4.
5.

For DSS to run correctly on a file, the following CHAT conventions must be followed:
All utterances must have delimiters, and imperatives must end with an exclamation
mark.
Incomplete or interrupted utterances must end either with the +... or the +/. codes.
Only the pronoun “I” and the first letter of proper nouns should be in uppercase.
Utterances that contain a noun and a verb in a subject-predicate relation in an unusual
word order must contain a [+ dss] postcode after the utterance delimiter to be included.
DSS automatically excludes any child utterances that are imitations of the immediately
preceding adult utterance. If, however, the analyst feels that there are additional childutterances that are imitations and should be excluded from the analysis, the [+ dsse]
postcode must be included for these utterances. This exclusion will also apply to
KIDEVAL.

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8.4.2 Selection of a 50-sentence Corpus
DSS scores are based on analysis of a corpus of 50 sentences. The dss program is designed to extract a set of 50 sentences from a language sample using Lee’s six inclusion
criteria.
1. The corpus should contain 50 complete sentences. A sentence is considered
complete if it has a noun and a verb in the subject-predicate relationship. To check for
this, the program looks for a nominal phrase followed by a verb. Imperatives such as“Look!” also are included. Imperative sentences must have end with an exclamation
mark. Immature sentences containing word order reversals such as “car a garage comeout” or “hit a finger hammer Daddy” also should be included. However, these sentencesmust contain the [+ dss] code after the utterance delimiter on the main tier to be
included in the analysis.
2. The speech sample must be a block of consecutive sentences. To be representative,
the sentences constituting the corpora must occur consecutively in a block, ignoring
incomplete utterances. The analyst may use his or her discretion as to which block ofsentences are the most representative. The DSS program automatically includes thefirst 50 consecutive sentences in the transcript. If you wish to start the analysis at someother point, you can use the +z switch in combination with KWAL and piping to DSS.
3. All sentences in the language sample must be different. Only unique child sentences
will be included in the corpora. DSS automatically analyzes each sentence and excludes
any repeated sentences.
4. Unintelligible sentences should be excluded from the corpus. The DSS programautomatically excludes any sentences containing unintelligible segments. Thus, any
sentence containing the xxx, yyy and www codes on the main tier will be excluded
from the analysis.
5. Echoed sentences should be excluded from the corpus. Any sentence that is arepetition of the adult’s preceding sentence is automatically excluded. Additionally,
any sentences containing a [+ dsse] post-code are excluded.
6. Incomplete sentences should be excluded. Any sentence that has the +... or the +/.
sentence delimiters, indicating that they were either incomplete or interrupted, will not
be included in the analysis.
7. DSS analysis can only be used if at least 50% of the utterances are complete
sentences as defined by Lee. If fewer than 50% of the sentences are complete
sentences, then the Developmental Sentence Type analysis (DST) is appropriate
instead.

8.4.3 Automatic Calculation of DSS
To compute DSS, the user must first complete a morphological analysis of the file using
the MOR program. The command mor *.cha runs the MOR, PREPOST, POST,
POSTMORTEM, and MEGRASP commands one after another to create a %mor line forpart-of-speech analysis and a %gra line for grammatical relation analysis. To make surethat MOR recognizes each word properly, it is best to first run the mor +xb *.cha
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on morphosyntactic analysis. Once the disambiguated %mor is created, the user can run
DSS to compute the Developmental Sentence Analysis. A basic DSS command has thisshape:
dss +b*CHI +le *.cha

8.4.4 Sentence Points
DSS assigns a sentence point to each sentence if it “meets all the adult standard rules”(Lee, p. 137). Sentence points are withheld for all errors and omissions. Errors, including
neologisms, should be marked with the [*] codes used in the CHAT error coding system.
Omissions should be marked with a code such as 0det or 0aux, for example, to mark a
missing determiner or a missing auxiliary.
Warning: MOR cannot distinguish between non-complementing infinitive structures,such as I stopped to play, which receives 3 points as secondary verb and infinitival complement structures, such as I had to go, which receive 5 points as secondary verbs. When thelatter occurs, you must edit the output table to assign two more points.

8.4.5 DSS Output
Once all 50 sentences have been assigned sentence points, the DSS program automatically generates a table. Each sentence is displayed on the leftmost column of the table withthe corresponding point values, sentence point, and the DSS score. The Developmental
Sentence Score is calculated by dividing the sum of the total values for each sentence by
the number of sentences in the analysis.
The output of the table has specifically been designed for users to determine “at aglance” areas of strength and weakness for the individual child for these eight grammaticalcategories. The low points values for both the indefinite and personal pronoun (IP, PP)
categories in the table below indicate that this child used earlier developing forms
exclusively. In addition, the attempt mark for the main verb (MV) and interrogative
reversal (IR) categories suggest possible difficulties in question formulation.
Sentence|IP |PP |MV |SV |NG |CNJ|IR |WHQ|S|TOT|
I like this.
| 1 | 1 | 1 ||||||1| 4|
I like that.
| 1 | 1 | 1 ||||||1| 4|
I want hot dog. || 1 | 1 ||||||0| 2|
I like it .
| 1 | 1 | 1 ||||||1| 4|
what this say.
| 1 |
| - |
|
|
| - | 2 |0| 3|
Developmental Sentence Score: 3.4(i.e. 17/5)

8.4.6 DSS Summary
DSS has been designed to adhere as strictly as possible to the criteria for both sentence
selection and scoring outlined by Lee. The goal is the calculation of DSS scores based upon
Lee’s (1974) criteria, as outlined below. The numbers indicate the scores assigned for eachtype of usage.
Indefinite Pronouns (IP) (A)1
it, this, that
3ano, some, more, all, lot(s), one(s), two (etc.), other(s), anotherPart 2: CLAN
3b
4
7a
7b
7c

something, somebody, someone
nothing, nobody, none, no one
any, anything, anybody, anyone
every, everyone, everything, everybody
both, few, many, each, several, most, least, last, second, third (etc.)
Personal Pronouns (PP) (B)1
1st and 2nd person: I, me, my, mine, your(s)2
3rd person: he, him, his, she, her(s)3aplurals: we, us, our(s) they, them, their
3b
these, those5reflexives: myself, yourself, himself, herself, itself, themselves, ourselves
6Wh-pronouns: who, which, whose, whom, what, how much
Wh-word + infinitive: I know what to do, I know who(m) to take.
7(his) own, one, oneself, whichever, whoever, whatever
Each has his own. Take whatever you like.
Main Verb (MV) (C)1auninflected verb
1b
copula, is or ’s. It’s red.
1c
is + verb + ing
2a-s and -ed
2birregular past, ate, saw
2c
copula am, are, was, were\2d
auxiliary am, are, was, were4acan, will may + verb
4bobligatory do + verb
4cemphatic do + verb
6a
could, would, should, might + verb
6bobligatory does, did + verb
6cemphatic does, did +verb
7apassive including with get and be
7bmust, shall + verb
7c
have + verb + en
7dhave got
8ahave been + verb + ing, had been + verb + ing
8bmodal + have + verb + en
8cmodal + be + verb + ing
8dother auxiliary combinations (e.g., should have been sleeping)
Secondary Verbs (SV) (D)2afive early developing infinitives
2b
I wanna see, I’m gonna see, I gotta see, Lemme see, Let’s play3
noncomplementing infinitives: I stopped to play4
participle, present or past: I see a boy running. I found the vase broken.
5aearly infinitives with differing subjects in basic sentences:
I want you to come5blater infinitival complements: I had to go

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5c
5d
7

obligatory deletions: Make it [to] go
infinitive with wh-word: I know what to getpassive infinitive with get: I have to get dressedwith be: I want to be pulled.
8
gerund: Swinging is fun.
Negative (NG) (E)1
it, this, that + copula or auxiliary is, ’s + not: It’s not mine.
This is not a dog.
4
can’t don’t
5
isn’t won’t
7auncontracted negatives with have: I have not eaten it.
7b
any other pro-aux + neg forms: you’re not, he’s not
7c
any other aux-negative contractions: aren’t, couldn’t
Conjunction (CNJ) (F)
3
and
5a
but
5b
so, and so, so that
5cor, if8awhere, when, how, while, whether, (or not), till, until, unless, since,
before, after, for, as, as + adjective + as, as if, like, that, than
8d
wh-words + infinitive: I know how to do it.
Interrogative Reversal (IR) (G)1reversal of copula: isn’t it red?
4
reversal of auxiliary be: Is he coming?
6aobligatory -do, -does, -did Do they run?
6b
reversal of modal: Can you play?
6c
tag questions: It’s fun isn’t it?
8areversal of auxiliary have: Has he seen you?
8b
reversal with two auxiliaries: Has he been eating?
8creversal with three auxiliaries: Could he have been going?
Wh-question (WHQ) (H)2awho, what, what + noun
2bwhere, how many, how much, what....do, what....for4
when, how, how + adjective
7
why, what it, how come, how about + gerund8
whose, which, which + noun

8.4.7 DSS for Japanese
DSS can be used for Japanese data to provide an automatic computation of theDevelopmental Sentence Score for Japanese (DSSJ; Miyata, & al., 2013) based on theDevelopmental Sentence Score of Lee (1974). The DSSJ scores are based on a corpus of
100 utterances with disambiguated %mor tiers. The basic command has this shape:
dss +lj +djpn.cut +b*CHI +c100 +e *.cha

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The items scored by DSSJ are listed below. The numbers indicate the scores assigned
for each type of usage. The morpological codes refer to the codes used in JMOR06 and
WAKACHI2002 v.5.
Verb Final Inflection (Vlast)
1 PAST (tabeta), PRES (taberu), IMP:te (tabete!)
2 HORT (tabeyoo), CONN (tabete…)
3 COND:tara (tabetara)
4 CONN&wa (tabecha), GER (tabe), NEG&IMP:de (tabenaide!)
5 IMP (tabero), NEG&OBL (tabenakucha)
Verb Middle Inflection (Vmid)
1 COMPL (tabechau), NEG (tabenai), ASP/sub|i (tabeteru/tabete iru)
2 DESID (tabetai), POT (taberareru/tabereru), POL (tabemasu),
sub|ku (tabete kuru), sub|ik (tabete iku)
3 sub|mi (tabete miru), sub|ar (tabete aru), sub|ok (tabete oku),
sub|age (tabete ageru)
4 PASS (taberareru)
5 sub|moraw (tabete morau), sub|kure (tabete kureru)
Adjective Inflection (ADJ)
1 A-PRES (oishii)
3 A-NEG- (oishikunai), A-ADV (oishiku)
4 A-PAST (oishikatta)
Copula (COP)
1 da&PRES (da)3 de&wa-NEG-PRES (janai), de&CONN (gakusee de)
4 da-PAST (datta), da&PRES:na (gakusee na no), ni&ADV (gakusee ni naru)
5 de&CONN&wa (kami ja dame)
Adjectival Nouns + Copula (AN+COP)
4 AN+da&PRES (kiree da), AN+ni&ADV (kiree ni), AN+da&PRES:na (kiree na)
Conjunctive particles (CONJ ptl)
2 kara=causal (kiree da kara)
3 to (taberu to ii), kara=temporal (kaette kara taberu), kedo (tabeta kedo)
4 shi (taberu shi), noni (tabeta noni)
Conjunctions (CONJ)
4 datte (datte tabeta mon), ja (ja taberu), de/sorede (de tabeta), dakara (dakara
tabeta)5 demo (demo tabechatta)
Elaborated Noun Phrases (NP)2 N+no+(N) (ringo no e), A+N (oishii ringo)
3 N+to+N (ringo to nashi), Adn+N (ironna ringo), V+N (tabeta ringo)
5 AN+na+N (kiree na ringo), V+SNR (tabeta no ga chigau)
Formal Nouns (FML)Part 2: CLAN

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4 koto (tabeta koto aru)
5 hoo (tabeta hoo ga ii)
Compounds (COMP)
1 N+N (geta+bako)
5 PROP+N (Nagoya+eki), V+V (tabe+owaru), N+V (jagaimo+hori)
Case and Post Particles (CASE)
1 ga (ringo ga oishii), ni (ringo ni tsukeru)
2 to (Papa to asonda), de (ie de taberu)
3 o (ringo o taberu), kara (ringo kara deta)
5 made (ie made hashiru)
Topic, Focus, Quotation Particles (TOP, FOC, QUOT)
1 wa (ringo wa ii), mo (ringo mo ii)
2 tte (ringo tte nani?)
3 dake (ringo dake), to (quot) (ringo to iu to…)
5 kurai (sannin kurai ita), shika (sannin shika inai)
Adverbs (ADV)
2 motto (motto taberu), moo (moo tabenai), mata (mata taberu)
3 mada (mada tabetenai), chotto (chotto taberu), ippai (ippai taberu)
4 ichiban (ichiban oishii), nanka (nanka oishii), sugu (sugu taberu)
5 yappari (yappari taberu), sakki (sakki taberu)
Sentence Final Particles (SFP)
1 yo (taberu yo), no (taberu no), ne (taberu ne)
2 kanaa (taberu kanaa), mon (taberu mon), ka (taberu ka), naa (taberu naa)
3 no+yo (taberu no yo)
4 yo+ne (taberu yo ne), kke (tabeta kke)
Sentence Modalizers (SMOD)
3 desu (oishii desu)
4 mitai (ringo mitai ), deshoo (tabeta deshoo)
5 yoo (ikeru yoo ni suru), jan (tabeta jan)
Miyata, S., MacWhinney, B. Otomo, K. Sirai, H., Oshima-Takane, Y., Hirakawa, M.,
Shirai, Y., Sugiura, M. and Itoh, K. (2013). Developmental Sentence Scoring for Japanese.
First
Language
33,
2,
x-x.
https://fla.sagepub.com/content/early/recent
www2.aasa.ac.jp/people/smiyata/papers/Miyata&al_DSSJ_FirstLanguage2013.pdf

8.4.8 How DSS works
DSS relies on a series of rules stated in the rules file in CLAN/lib/dss, such as eng.cut.
These rules are listed in a “STARTRULES” line at the beginning of the file. They are firedin order from left to right. Each rule matches one of the top-level categories in the DSS.
For example, the D rules (fourth letter of the alphabet) match the SV category that is the
fourth category in the DSS. Within each rule there is a series of conditions. These
conditions each have a focus and points. Here is an example:
FOCUS: pro|it+pro:dem|that+pro:dem|thisPart 2: CLAN

132

POINTS: A1

This condition checks for the presence of the pronouns it, that, or this and assigns one
A1 points if they are located. The pattern matching for the Focus uses the syntax of a
COMBO search pattern. This means that the asterisk is a wild card for “anything”; the plus
means “or”, and the up arrow means “followed by”. DSS goes through the sentence one
word at a time. For each word, it checks for a match across all the rules. Within a rule,
DSS checks across conditions in order from top to bottom. Once a match is found, it adds
the points for that match and then moves on to the next word. This means that, if acondition assigning fewer points could block the application of a condition assigning morepoints, you need to order the condition assigning more points before the condition
assigning fewer points. Specifically, the C1 condition for main verbs is ordered after C2and C7 for this reason. If there is no blocking relation between conditions, then you do nothave to worry about condition ordering.
The Japanese implementation of DSS differs from the English implementation in one
important way. In Japanese, after a match occurs, no more rules are searched and theprocessor moves directly on to the next word. In English, on the other hand, after a matchoccurs, the processor moves on to the next rules before moving on to the next word.
Miyata, S., Hirakawa, M., Ito, K., MacWhinney, B., Oshima-Takane, Y., Otomo, K.
Shirai, Y., Sirai, H., & Sugiura, M. (2009). Constructing a New Language Measure forJapanese: Developmental Sentence Scoring for Japanese. In: Miyata, S. (Ed.) Developmentof a Developmental Index of Japanese and its application to Speech Developmental
Disorders. Report of the Grant-in Aid for Scientific Research (B)(2006-2008) No.
18330141, Head Investigator: Susanne Miyata, Aichi Shukutoku University. 15-66.

8.4.9 Unique Options
DSS has the following unique options:+c Determine the number of sentences to be included in analysis. The default for thisoption is 50 sentences. These sentences must contain both a subject and a verb, be
intelligible, and be unique and non-imitative. A strict criterion is used in the developmentof the corpora. Any sentences containing xxx yyy and www codes will be excluded fromthe corpora.
+l This option allows you to select a specific version of DSS. Currently, thepossibilitities are +leng for standard American English, +lbss for African-American
English, and +ljpn for Japanese.
+s This switch has specific usage with DSS. To include sentences marked with the[+ dss] code, the following option should be included on the command line: +s"[+ dss]".
To exclude sentences with the [+ imit] postcode, the user should include the following
option on the command line: -s"[+ imit]". These are the only two uses for the +s/-s option.
Additional options shared across commands can be found in the chapter on Options.

8.5 EVAL and EVAL-D
EVAL is a CLAN program that analyzes transcripts and yields a spreadsheet displaying
many language characteristics. EVAL is used with data collected with the AphasiaBan

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protocol and EVAL-D is a variant of EVAL used for data collected with the DementiaBank
protocol. The program can be used in three ways:
1. Both EVAL and EVAL-D can analyze a participant's performance on a discourse task.
2. EVAL can analyze a participant's performance of a discourse task from theAphasiaBank protocol and compare the results to those of a reference group from thatdatabase. EVAL-D can do the same for DementiaBank protocol data. The resulting
spreadsheet displays the participant's analysis side-by-side with the mean scores of thecomparison group and indicates where the participant and the comparison group differ
by one or more standard deviations.
3. The programs can analyze the baseline performance, then you can re-administer andanalyze the discourse task after a period of therapy. The spreadsheet displays the preand post- therapy results side-by-side, allowing a comparison of performance at
different time points.
The use of EVAL is described in tutorial screencasts
https://talkbank.org/screencasts . The basic EVAL commandavailable

from

8.5.1 Explanation of EVAL Measures
Following is a list of the measures computed for EVAL. To match the Excel to thenumbers in the following fields you can select R1C1 column labeling in Excel. For
Windows, this in in Options/Calculation. For Mac, it is in Preferences/Calculation and itsays “Use R1C1 reference style”. The first 12 fields in this output come from the CHAT
@ID header for the selected speaker. All measures are calculated from the %mor tierexcept for retracing, repetition, and duration which are calculated from the speaker tier.
FREQ numbers are calculated based on lemmas.
1. filename
2. language
3. corpus name
4. code for the speaker
5. age6. sex7. group
8. race
9. SES
10. role of the speaker
11. education
12. custom_field: score on the Western Aphasia Battery (WAB).
13. Duration: total time of the sample in hours:minutes:seconds. For simplified EVAL
transcripts, which are not linked with time bullets, users who want this informationneed to add a TIME DURATION line to the ID lines in the transcript (see EVAL
Manual, Section 4.c). For linked transcripts EVAL calculates this item automatically.
14. Total Utts: total utterances. Includes all utterances used in computing MLU, plus
utterances with xxx (unintelligible). Excludes non-word utterances, for example agesture only, coded as *PAR: &=head:yes.
15. MLU Utts: number of utterances used to compute MLU. Excludes utterances with xxx

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code and non-word utterances (*PAR: 0).
16. MLU Words: MLU in words. Excludes words in utterances with xxx, yyy or www
codes.
17. MLU Morphemes: MLU in morphemes. Excludes morphemes in utterances with xxx,
yyy or www codes.
18. FREQ types: total word types as counted by FREQ. The default does not include
repetitions and revisions.
19. FREQ tokens: total word tokens as counted by FREQ. The default does not include
repetitions and revisions.
20. FREQ TTR: type/token ratio
21. Words/min: words per minute (FREQ tokens/Duration converted to minutes)
22. Verbs/Utt: verbs per utterance: (roughly corresponds to clauses per utterance). Includes
verbs, copulas, and past or present participles (that appear with auxiliaries); does notinclude modals.
23. % Word Errors: percentage of words that are coded as errors [*]. These numbers are
based on the speaker tier and include revised and repeated material. This item can bedisplayed as a raw number rather than a percentage by adding +o4 to the EVAL
command.
24. Utt Errors: number of utterances coded as errors. For EVAL transcriptions the code is[+ *]. For transcripts with full error coding, the [+ *] code subsumes utterance-level
errors from the Error Coding section of the CHAT manual ([+ jar], [+ es], [+ cir], [+per], and [+ gram]). This code is intended to flag errors for closer examination. It does
not allow accurate comparison with the control database, since any utterance-level
errors of controls are not coded.
25. Density: measure of propositional idea density. This measure was adapted withpermission, from CPIDR3 (Computerized Propositional Idea Density Rater, third
major version), developed under the direction of Michael A. Covington, University ofGeorgia Artificial Intelligence Center, August 17, 2007. CPIDR replicates Turner andGreen’s rules (1977) for extracting propositions from text, based on the theory ofKintsch (1974) and the work of Snowdon et al. (1996) who who showed that
propositional idea density can be approximated by the number of verbs, adjectives,
adverbs, prepositions, and conjunctions divided by the total number of words. For a listof the words counted as propositions from each CHAT file, add +e2 to the EVAL
command.
The next 15 items (#14-28) are expressed as percentages by default. For parts of speech(e.g., nouns, verbs, auxiliaries, modals, adverbs, adjectives), the denominator is total
words; for % plurals, the denominator is total nouns; for items 19-23, the denominator is
total verbs. To display these results as raw numbers instead of percentages, add +o4 to the
EVAL command.
26. % Nouns
27. % Plurals
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29. % Aux: auxiliaries
30. % Mod: modals
31. % 3S: third person singular
32. % 1S/3S: identical forms for first and third person (e.g., I was, he was)
33. % Past
34. % PastP: past participle
35. % PresP: present participle
36. % prep: prepositions
37. %adv: adverbs
38. %adj: adjectives
39. % conj: conjunctions
40. % det: determiners (includes articles, demonstratives, interrogatives, numbers, andpossessives used as determiners)
41. % pro: pronouns
42. noun/verb ratio: total # of nouns ÷ total # of verbs (excluding auxiliaries and modals)
43. open/closed ratio: total # of open class words ÷ total # of closed class words; openclass includes all nouns, adverbs, adjectives, and all verbs including copulas andparticiples but excluding auxiliaries and modals; closed class includes all other parts ofspeech; co(municator) and on(omatopoeia) are excluded from both counts.
44. #open-class: total # of open class words
45. #closed-class: total # of closed class words
46. retracing [//]: number retracings (self-corrections or changes)
47. repetition [/]: number of repetitions
By default, sentences marked by [+ exc] are excluded from the analysis. For cases
where target replacements are in the transcript next to error productions with missing
morphemes (e.g., he is kick [: kicking] [* m:0ing] the ball), the EVAL and MORTABLE
programs will reflect the speaker's morphological production (e.g., v|kick) and not countanything that was not produced (e.g., part|kick-PRESP). For cases where target
replacements are in the transcript next to error productions for superfluous morphemes(e.g., there is one birds [: bird] [* m:+s] in the tree), the EVAL and MORTABLE programs
will not count the superfluous morphemes (e.g., n|bird-PL) because they were not usedcorrectly.

8.5.2 EVAL Demo
To run a demo of the EVAL program, follow these steps:
1. To start the comparison, push the Progs (for programs) button in the CLAN
commands window, and select Eval from the drop-down menu of CLAN commands.
Push the Option button, and you will see a list of the files in your working directory
folder. Navigate to the /examples/eval folder, and double click on eval_demo.cha andit will be displayed on the rightside of the box. When you have chosen the right filePart 2: CLAN

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for analysis, click on Done.
A new window, shown below, will appear for selecting EVAL options. For thisdemonstration, you want to compare eval_demo.cha to a sample from theAphasiaBank database. Since eval_demo.cha is a transcript of an anomic aphasic
person, we suggest choosing Anomic as the aphasia type for comparison. Under Age
Range, since the eval_demo participant is 55 years old, we suggest choosing 45-65 (inthis format). If you want only males or only females, choose the appropriate circle. If
you want both genders included, leave those circles blank, which is the choice wesuggest here. Next select the AphasiaBank discourse task you want to compare toeval_demo.cha. The eval_demo.cha file is a transcript of the Sandwich Gem, so selectSandwich under Gem choices. Your window will now look like this:3.

Press the OK button, and the Commands window will echo this full command you
have constructed:
eval @ +t*PAR: +d”Anomic45-65” +g”Sandwich” +u
4. Press Run and CLAN will produce an output file called eval_demo.xls, as listed in the
CLAN Output window. Click three times on that last line and the results will open in
Excel. If Excel warns you about opening the files, just say “yes”. The columns list
various outputs in terms of indices and part of speech frequencies.

8.5.3 EVAL Output
After triple-clicking on the output from this demo, you open up a spreadsheet. Belowis the first part of that spreadsheet. Data from the eval_demo transcript are displayed acrossthe first row, and the information for the comparison database is displayed in the rows
below. The top line of the first column identifies the name of the file being analyzed. Belowthat is a row specifying the percentage of a standard deviation that eval_demo differs fromthe database. When the difference is a standard deviation or more, the next row indicatesthis with one asterisk for one standard deviation or more, and two asterisks for two or more.
The next three rows provide information about the comparison database: the mean, theminimum, the maximum, and the standard deviation for each of the language measures
EVAL produces. Below this the characteristics of the comparison database are listed (inthis case the Sandwich Gem and Anomic participants from 45-65 years old). The next line

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specifies the number of files in the AphasiaBank database that met the criteria for thecomparison database; in this case it was 25. The final line is the CLAN command that wasused to generate this spreadsheet. The second column is the ID information for the person
whose language is in the transcript.

8.5.4 Comparing Multiple Transcripts
EVAL can also be used to analyze and compare files (e.g., measure change over timein therapy) without any reference to the AphasiaBank database or the AphasiaBankdiscourse tasks. As an example, you can use the sample files eval_demo02a.cha andeval_demo02b.cha that are in your examples/eval folder.
Next, push the PROGS button in the CLAN Commands window, and select eval fromthe dropdown menu of CLAN commands. Push the option button, and you will see a listof the files in your working directory folder. Double click on the files you want to analyze
and they will be displayed on the right side of the box. When you have finished yourselections, click the Done button.
A new window, shown below, will appear for selecting database options. Under theheading Database types, choose Deselect Database. If your transcriptions contain only one
gem or none of the AphasiaBank protocol gems, you can bypass the rest of the choices inthe box, and just press OK. If your transcriptions contain more than one gem from theAphasiaBank protocol, and you want to choose one for analysis, choose the circle next tothe one you want to analyze and then push OK. The command box will show theappropriate command for your selections. Press run, and the CLAN output will appear on
your desktop. Triple click on the last line of the CLAN output to open the Excel
spreadsheet displaying your data in one spreadsheet.

8.5.5 Unique Options
EVAL has the following unique options:+bS: add S characters to the morpheme delimiters list (default: -#~+)

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-bS: remove S characters from the morphemes list (-b: empty morphemes list)+cN:
create database file (N == 1: eval +c1 +t"*par")+dS:
specify database keyword(s) “S”. The choices are: Anomic, Global, Broca,
Wernicke, TransSensory, TransMotor, Conduction, NotAphasicByWAB, Control, Fluent,
Nonfluent, Fluent, AllAphasia+e1: create list of database files used for comparisons+e2: create proposition word list for each CHAT file+g: gem tier should contain all words specified by +gS-g: look for gems in database only
+gS: select gems which are labeled by label S+n: gem is terminated by the next @G (default: automatic detection)-n: gem is defined by @Bg and @Eg (default: automatic detection)+o4: output raw values instead of percentage values

8.6 FLUCALCThe FLUCALC program works very much like KIDEVAL. It tracks the frequencies ofthe various fluency indicators summarized in the section of the CHAT manual on“Disfluency Transcription” such as retraces, blockings, and initial segment repetitions.
Like KIDEVAL, it requires the presence of a %mor tier and the selection of a certain
speaker as the target. For example, the +t*PAR switch would select utterances from PAR
for analysis. Using the tom.cha file in CLAN's examples/fluency folder, the command fora basic analysis would be;
flucalc tom.cha +t*TOM
The output will go into a file called tom.flucalc.xls which you can open in Excel, after
accepting the warning message.
FLUCALC will perform a fluency analysis of a language sample, in both raw counts
and percentages of intended words. It will also provide a “beta” weighted disfluency value
over words, based on the formula proposed by the Illinois Stuttering Project (Yairi &Ambrose, 1999) for computations made on syllable counts.
You must use fluency codes specified in the CHAT manual and the Clinician’s Guide
to CLAN. You must run MOR for the appropriate language to then run FluCalc. The same
speech/language sample can be used for both language sample analysis (KidEval, EVAL)
and fluency appraisal via FluCalc. The output is in CSV (comma-separated variables)
spreadsheet format. Spreadsheets in this format can be imported directly to Excel. Whenopening them, Excel will warn you that the spreadsheet is not in Excel XLS format, but
you can just ignore that warning and proceed to open and analyze using Excel.
The user can select to have FLUCALC use either words or syllables as the denominator
for the computation of percentages (indicated below by %). For indices that look at
intended words, the forms on the %mor line are used, because that line excludes repetitions
and nonwords. When counting pauses, FLUCALC ignores pauses that are utterance
external. This means that pauses at the beginnings and ends of utterances are not counted,

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because they often represent features of conversational exchange, rather than purer
measures of disfluency.
By default, phonological fragments are considered typical disfluencies (TD). This isappropriate for children. However, for adults, it may be better to consider phonologicalfragments as stuttering like disfluencies (SLD). This is because fragments may represent
word avoidance behaviors in adults who stutter. To treat phonological fragments as SLD,
you can add the +c1 switch to the FLUCALC command. This option moves soundfragments from the TD computations to the SLD computations.
If the sample has been processed for ASR (automatic speech recognition) using thebatchalign system at https://github.com/talkbank there will be a new %wor line that can beused when the +a switch is selected to derive more accurate times for word and pause
duration. These additional times are then summarized in 9 columns are the end of theoutput.

8.6.1 FluCalc Measures
Following is a list of the measures computed for FLUCALC. To match the Excel tothe numbers in the following fields you can select R1C1 column labeling in Excel. For
Windows, this in in Options/Calculation. For Mac, it is in Preferences/Calculation and itsays “Use R1C1 reference style”. The first 12 fields in this output come from the CHAT
@ID header for the selected speaker. The final 9 columns (61-69) are only produced if the
input has a %wor line with time values.
1. Filename
2. Language
3. Corpus Name
4. Participant Code
5. Age in months
6. Sex7. Group
8. Race
9. SES
10. Participant Role
11. Education
12. Custom field
13. mor Utts: total utterances in the sample
14. mor Words: total intended words, as given on the %mor line
15. mor syllables: total syllables of the words on the %mor line
16. words/minute: words from the %mor line
17. syllables/minute: syllables in the words in the %mor line
18. #Prolongation: raw count of sound prolongations
19. %Prolongation
20. #Broken word
21. %Broken word
22. #Block
23. %Block
24. #PWR: part-word repetition

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25. %PWR
26. #PWR-RU: part-word repetition units, these are sometimes called iterations, or theactual number of repetitions in a part-word repetition unit. This column totals all RUs
seen in the sample, for use in the weighted disfluency score27. %PWR-RU
28. #WWR: whole word repetition
29. %WWR
30. #WWR-mono: monosyllabic repetition
31. %WWR-mono
32. #WWR-RU: repetition units; please see PWR above
33. %WWR-RU
34. #WWR-RU-mono: repetition units; please see PWR above
35. %WWR-RU-mono
36. mean RU = (PWR-RU + WWR-RU) / (PWR+WWR)
37. #Phonological fragment: these are best viewed as abandoned word attempts, e.g.
&+fr- tadpole, where the speaker appears to change word choices; this code wasoriginal to CLAN programs.
38. %Phonological fragment
39. #Phrase repetitions
40. %Phrase repetitions
41. #Word revisions
42. %Word revisions
43. #Phrase revisions
44. %Phrase revisions
45. #Pauses: only utterance-internal unfilled pauses are counted
46. %Pauses
47. #Filled pauses
48. %Filled pauses
49. #TD: typical disfluencies; this is the sum of phrase repetitions, word revisions, phrase
revisions, pause counts, phonological fragments, and filled pauses.
50. %TD: total typical disfluencies over total words or total syllables.
51. #SLD: stutter-like disfluencies; this the sum of prolongations, broken words, blocks,
PWRs, and monosyllabic WWRs.
52. %SLD: proportion of stutter-like disfluencies over total intended words
53. #Total (SLD+TD): this sums all forms of disfluency, both stutter-like and typical, seenin the sample
54. %Total (SLD+TD)
55. SLD Ratio: SLD/(SLD+TD)
56. Content_words_ratio: open_class_words with disfluency/total disfluencies
This includes n, v, part, cop adj, adv (except adv:int which is closed)
57. Function_words_ratio: closed_class_words with disfluency/total disfluencies
This includes all items not in the content/open class, except co and on
58. Content_allwords_ratio: all content words with and without disfluency
59. Function_allwords_ratio: all function words with and without disfluency
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between typical disfluency and stuttering profiles in young children. It was originatedby Yairi & Ambrose (1999) and referenced against a standard sample of 100 syllables.
This formula penalizes the severity of the segment repetition profile as well as thepresence of prolonged sounds and blocks, which are virtually absent in any sample oftypically fluent speech. The formula is:((PWR + mono-WWR) * ((PWR-RU + mono-WWR-RU)/(PWR + mono-WWR))) +(2 * (prologations + blocks))
61. IW_Dur: total inter-word pause duration for target speaker. FLUCALC requires
specifying just one target speaker
62. Utt_dur: total utterances duration for target speaker
63. IW_Dur / Utt_dur
64. Switch_Dur: total inter-utterance pause duration time when target utterances follow
different speakers
65. #_Switch: # of times consecutive utterances are from different speakers
66. Switch_Dur/#_Switch
67. No_Switch_dur: total inter-utterance pause when target utterances follow the sametarget speaker
68. #_No_Switch: # of times consecutive utterances are from the same speaker
69. No_Switch_Dur/#_No_Switch

8.6.2 Unique Options+a : get pauses time value from %wor tier (default: disregard %wor tier)+b : select word mode analyses (default: syllables mode)+b1: select word with repetition, retraces mode analyses+c : compute Pause_duration from pause notation like (2.4)+c1: add phonological fragment to SLD instead of TD (default: add it to TD instead)+c5: when combined with +p option it will reverse tier's priority+dN: specify sample size of N (s, w), +d100s means first 100 syllables+e1: create file with words and their syllable count+e2: create file with perfectly fluent utterances, both TD and SLD are zero
+e3: create file with perfectly fluent utterances, but with TD is non-zero+e4: create file with perfectly fluent utterances, but with SLD is non-zero+e5: create file with disfluencies, both TD and SLD are non-zero+pS: search for word S and match it to corresponding POS+p@F: search for words in file F and match it to corresponding POS

8.7 IPSYN
The IPSyn Command computes the Index of Productive Syntax (Altenberg, Roberts,& Scarborough, 2018; Scarborough, 1990). Computation of this index requires thepresence of an accurate %mor line. Currently the program is implemented only forPart 2: CLAN

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English. The full form of the IPSyn requires 100 acceptable utterances. However, Yanget al. (2021) have shown that IPSyn works equally well with samples of 50 utterances. To
allow for this, IPSyn now requires 50 utterances by default and uses the new rule set
recommend by Yang et al. The basic form of the command is:
ipsyn +t*CHI –leng filename.cha

If you wish to run the classic version of IPSyn, then you should use the +o switch whcih
will use the old rule set and 100 utterances by default. The classic rule set is designed toconform to the IPSyn-R revised version of the scale from 2018 (Altenberg et al., 2018).
Inaccuracies in CLAN's 2019 version that were noted by Roberts et al. (2020) are largely
corrected in the current version.
If you wish to change the treatment of a given utterance, you can use the [+ ipe]
postcode to exclude it or the [+ ip] to include it. This exclusion code will also apply to
IPSyn inside KIDEVAL. IPSyn excludes the whole utterance if it has an [+ ipe] postcode
or if the whole utterance is just xxx, yyy, or zzz. IPSYN also excludes repeated utterances.
If an utterance has been spoken verbatim by the speaker before, then it is excluded fromanalyses, unless the [+ ip] postcode is specified on that utterance. To better see what IPSYN
does, run this command on the 98.cha file in the /examples/transcripts/ne32 folder.
ipsyn +t*CHI +leng 98.cha

The output from this command will be 98.ipsyn.cex. Triple-click on that name in CLAN’s
output window and you will see how IPSYN assigned points for each relevant utterance inthat sample. This same run of the IPSYN command will also produce the file 98.ipcore.cex.
You can open that file to see which utterances were included in the IPSyn analysis.

8.7.1 Rule Syntax
The computation of IPSyn relies on a series of rules for point assignment that are given
in files in the /CLAN/lib/ipsyn folder such as eng.cut for English or zho.cut for Chinese.
Each rule searches through the 50 acceptable utterances for a match to the strings given in
the INCLUDE line. If there is a match, then one point is assigned for that rule. In order toassign a second point, the search must then use the information on the
DIFFERENTSTEMS line to decide how different a second match must be from the first to
be counted. Here is an example for the N6 point which searches for strings like this one
goes or my turn is next with a two-word NP before a verb or preposition:
RULENAME: N8
if
INCLUDE:$MOD ^ $N ^ $V
DIFFERENT_STEMS:>2

The notation of >2 should really be >=2, because the program interprets it as greater thanor equal to 2. Given an utterance such as my dog barks and my cat meows, this rule would
match my dog barks for the first point and my cat meows for the second point. If theutterance were my dog barks and my dog runs, then there would not be a second point,
because there would only be one new stem. The matching strings can be anywhere in the
100-utterance sample; it is not necessary that they be in the same utterance. In this case,the requirement that there be two or more different stems would be fulfilled, because twoof the stems are different, although one is the same. DIFFERENT_STEMS_POS sets

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further conditions on the different stems. The statement DIFFERENT_STEMS_POS: 1means that the first word in the sequence of stems must be different. This occurs in the V3
rule, as shown here:
RULENAME:
if
INCLUDE:|prep^
EXCLUDE:(lookit ^ this) + (in ^DIFFERENT_STEMS_POS:
if
POINT: 2
INCLUDE:(in ^ there) + (on ^ there)

V3
there)+(on^$NPthere)
1

This rule will assign a second point if the two strings are for the sheep and besides the
sheep, but not if the two strings are for the sheep and for a sheep.
On the INCLUDE line, the ^ or caret indicates that the element after the caret must
directly follow the one before. The categories here – $MOD for modifier, $N for noun,
and $V for verb – are defined in the eng.cut file.
It is also possible to have additional restrictions placed on the assignment of the points,
as in the above example from V3, where the EXCLUDE term disallows a first point forthree specific phrases: lookit this, in there, or on there. For the second point, the two
exclusions from the first point are now allowed, if neither word matches stems from thefirst point.
You can see how IPSyn is assigning points by looking at the results for individual
words in the *.ipsyn.cex output file. The *.ipcore.cex file shows you which utteranceswere selected for the analysis.

8.7.2 Cascading Credit
For the classic version of IPSyn, as described in the "credit" column of the Appendix
to the IPSyn-R paper (Altenberg et al., 2018), IPSyn-R specifies that a child's successful
use of certain higher-order patterns should lead in certain cases to also assigning credit forlower-order patterns. For example, credit on N8 should "cascade" to credit on N4. Thismeans that, if a child places a two-word NP before a verb (N8), then they should also getcredit for producing a two-word NP (N4). In addition to these various rules for primarycascading credit, it is also possible to have secondary cascading credit. For example, if achild negates an auxiliary with the form wasn't, credit is assigned for Q7. But then thisalso means that the child can produce a negative between the auxiliary and the verb which
requires credit for Q5. However, if Q5 receives credit, then one must also credit Q3, which
only requires that the child produce a negation of any type. So, there is a secondary cascadeof Q7 > Q5 > Q3. The 11 secondary cascades tracked by CLAN's IPSyn are these: V11 >V9 > V5; V13 > V6 > V5; V16 > V4 > V1; Q6 > Q4 > Q1; Q6 > Q4 > Q2; Q7 > Q5 > Q3;
Q11 > Q6 > Q4; Q11 > Q7 > Q5; Q11 > Q8 > Q1; Q11 > Q8 > Q2; S13 > S10 > S5. In
addition, there are 11 secondary cascades leading from S6 and S3 back to S1, which is theitem requiring a two-word utterance. However, S1 is already so likely to receive full credit,that we do not enforce these further 11 secondary cascades.

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8.7.3 IPSYN Rules
This section summarizes the IPSyn rules that govern CLAN’s computations of
IPSynprofiles. IPSyn includes scores for four dimensions: noun phrases, verb phrases,
questions and negation, and sentence structure. Below there are four tables - one for eachdimension. For each of these, there are IPSyn rules that make use of higher-level categories
that combine basic part-of-speech categories on the %mor line. These categories arearticulated in rules that are given in the beginning of the eng-cut file:$N = |n,|n:prop,|n:pt$V = |v,|cop,|aux$AMC = |aux,|mod,|cop$PRO = |pro:per,|pro:indef,|pro:refl,|pro:obj,|pro:sub$MOD += |adj,|det:art,|det:poss,|det:num,|det:dem,|qn$ART = |det:art$DET = |det:dem$ADV = |adv,|adv:loc,|adv:tem,|adv:int$NP = $N + $PRO + ($MOD ^ $N) + ($DET ^ $N) + ($ART ^ $N) +($ART ^ $MOD ^ $N) + ($DET ^ $MOD ^ $N)
The six columns in these four tables encode the features of each rule. The first column
gives the rule number. The second column gives the string match for the rule. Here the plus
is used for combination, although in the actual rules the up-arrow symbol is used for this.
The third column shows that, if you credit this rule, you should also credit some otherrule(s). This is called "cascading credit." The fourth column gives and example structurethat matches the rule. The fifth column lists structures to exclude, even if they match thestring in the second column. The sixth column gives the criteria that allow for theassignment of a second point for the rule. In this column, the word "stem" refers to the factthat the program uses the lemma or stem from the %mor line, rather than the surface formof the word.

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Noun Phrase Points
Level Include

Also

ExampleExclude2nd point (min)N1$Ndog, Bill

stem different

N2

$PRO

he, mine

stem different

N3

$MOD

red, thisstem different

N4$MOD + $N

my dog1 stem different

N5

det ^ $N

the dog, a saw,
my orange

1 stem different

N6$V + $MOD + $N
prep + $MOD + n

N4ride a car,on the road2 stems different

N7

$N-PL

N1

dog-PL

N8$MOD + $N + $V

N4

my dog runs

2 stems different

N9$MOD + $MOD + N4
$Nlittle red hen

2 stems different

N10$ADV + $MOD
qn +$N

very niceonly menot + X

1 stem different

N11n-X, adj-X

book-ish,happy-COMP-POSS, -PL

stem different

V8

n:pt

stem different

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Verb Phrase Points
Level

Include

V1

$V

V2

prep

V3

prep + $NP

V4

Also

ExampleExclude2nd point

look, stop

stem different
stem different

V2

into the truck, at lookit thisschool, of themdifferent part ofspeech$NP + v:cop + $NP V1$NP + v:cop +$MOD

we is monsters
he is okaydiff form of
copula, or diffstructure

V5$CAT +$V

hafta eatdiff aux or v

V6

v:aux + $V

V5

she is coming

diff aux or v

V7

-PROG

V1

runningstem different

V8

adv

V9

mod + vV5

can go, will go

V10

-3S

V1

runs, wants

V11

would,
could, V9should, might

V12-PAST, -PERF

V1

jumped, fixed

stem diffV13

v:aux|x-PASTV6

did say, had gotten

stem or head diff

V14

$ADV + $V$ADV + $MOD

V8

V151

$N + |cop

V4

I am.

stem different

V152$N + |aux

V6

he was.

stem different

V153$N + |mod

V9Bill could.

stem different

V16

v:cop|x&PAST

V4

were, was, havestem different

V17

x#v| or –LY

how are you?

now, so, hardly, I think so, stem different
yet, neverhere, there
stem or head diffdoes, says

stem different
stem or head diffstem different

undo, bravely, recut hardly, really, stem different or
repeatsuffix different

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Question and Negation Points
Also

IncludeQ1

any question

want some?mine?

stem different

Q2

pro:int

what that? why he..? why?

stem different

Q3

neg|notor~neg|not or qn|nono book, can’t

stem or head
diff

Q4

pro:int + v

Q1, Q2

who made it?

stem different

Q5$NP + neg + v

Q3

I not going.

different VP

Q6

pro:wh + aux

Q4

Where was that?

Q7

aux + neg
aux|x~neg|not

Q5

It doesn’t work.
He might not go.

different VP

Q8

aux + $NP

Q1, Q2Can I have it? Is
she tall?

different Q

Q9why, when, which, Q1
whose

Why? Which one
is taller?

stem different

Q10

Unicode 201E „I’ll be the mom„okay?

tag different

Q11

aux + neg + $NP
Q6, Q7, Isn’t he a neatdragon?
Why
aux|x~neg|not+ Q8
didn’t you say so?
$NPdifferent Q

Q1, Q2

ExampleExclude2nd point

Level

wh as SUBJ

Notes:
Q4: count wh + v:cop ONLY as second point, unless V4 has two points

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Sentence Structure Points
Level

IncludeS1

x + x (any 2 words)

S2$NP + v

S3

Also

ExampleExclude2nd point

here hammer

1 stem diff

S1

Mommy fell.

all different

v + $NPS1

help me,all different

S4$NP + v + $NP

S2, S3

I need thatall different

S5

conj|and conj|or

S6

v+v

S1

I like play. You wanna playsaw fall.

different VP

S7

x + and + x
x + or + x

S1, S5

red and blue, youor medifferentphrasesS8

inf + (X) + v

S6, V5 I like to swim. see below
Want to feed her?

S9

let/make/help/watch+v

S6

let’s play this. help make it pretty different conj
it stand. make her (nosecond
quiet
verb)

S10

conj

S5

but, because, after, and, or, then different conj
since(those are S5)

S11*

v + COMPS6

I know you brokeit.

different
clauseS12*

S + and + S

S5, S6

I start it and you
finish it.

different
clauses

S13*

v + wh clause

S6,
S10

Here is where it
hurts. Tell me if it
hurts.

different
clause

S14*

OBJ + IOBJ

S3Read me the book. Here’s one for different
Read the book you.
phrases
to/for me.
Gimme that.

S15

v + (X) + v + (X) + v S6

She told them tohurry and run.

different verbs

S16*

relative clauseS6

the one that fitsdiff clause

S17*

COMP new subject

S8

I need you to help

diff clause

S18

n:gerundThisishammeringfordiff clause

S19*

Subject Clause

S6

The one I like bestisdiff clause

S20*

passive, tags

S11This guy is killedby the dragon.

diff clausestem different

different verb

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Notes:
S8 excludes: wanna feed her (V5 only); I like swim (S6 only); to ride on (no main verb)
S11, 12, 13, 14, 16, 17, 19, 20 require use of the %gra line.
S20 is ill-definedCascading credits for S1 are not computed, because children always get full on S1.

8.7.4 Unique Options
IPSYN has the following unique options:+cN: analyse N complete unique utterances. (default: 50 utterances)+d : do not show file and line number where points are found.
+lF: specify ipsyn rules file name F

8.8 KIDEVALKIDEVAL is a program that provides automatic analysis of a language sample that has
been transcribed in the CHAT format. Using various components of the CLAN program,
KIDEVAL automatically computes these measures, which are entered into a spreadsheet.
The KIDEVAL spreadsheet includes columns for each measure. If the analysis operates
on multiple transcripts from the same or different children, each transcript will have values
in its own row.

8.8.1 KIDEVAL for summaries
KIDEVAL performs analyses like those of EVAL, but in forms that are better suited tothe analysis of child language data. It combines the various measures computed by DSS,
FREQ, MORTABLE, MLU, TIMEDUR, and VOCD into a single analytic report. Unlike
EVAL, KIDEVAL uses a configuration file that allows users to tailor analyses for theirproblems and interests.
Users may note that the numbers produced by KIDEVAL analyses often do not match
those produced by separate runs of FREQ, MLU, and VOCD. The reasons for this dependon properties of each program. In MLU "Number of Utts" refers to MLU utterances which
in KIDEVAL is called "MLU Utts". For FREQ, the default version of the program counts
surface word forms on the main line. This means that, without additional switches, FREQ
counts full words such as dog and dogs on the main line as two different word types.
However, KIDEVAL bases its count of word types on lemmas on the %mor tier. On that
line, n|dog-PL and n|dog both have the same lemma or word type. VOCD uses a random
number generator, so if you run the same VOCD command twice you will get two different
results. Every time you run VOCD on the same data you will get slightly different results.
To run KIDEVAL, you can make use of CLAN’s dialog facility. Use the Progs button
to select KIDEVAL and then click the Option button. You will be asked to choose thefiles you wish to analyze.
For example, you could navigate to the/examples/transcripts/ne32 folder and then choose to add all five files and click Done. You
will then see this dialog:

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If you select “do not compare to database” the dialog will change to this form:

If you select the options displayed above, CLAN will compile and use this command:
kideval @ +leng +t*CHI:

The result will be sent to an Excel file, as noted in CLAN’s output window with this line:
Output file </Applications/CLAN/examples/ne32/14.kideval.xls>

You can triple-click on that line, and it will fire up Microsoft Excel. Excel will ask you if
you really want to open the file and just say “yes”. The output (with most columns removed
to fit on this page) will look like this:Using a similar process for Mandarin Chinese, we can select all the files in the Tong corpus
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For various reasons, a file can be specifically excluded from KIDEVAL analysis byinclusion of this line in the transcript. Once a file has this line, KIDEVAL will ignore it.
@Comment: KIDEVAL DATABASE EXCLUDE

Following is a list of the measures computed by KIDEVAL. To match the Excel to thenumbers in the following fields you can select R1C1 column labeling in Excel. For
Windows, this in in Options/Calculation. For Mac, it is in Preferences/Calculation and itsays “Use R1C1 reference style”. The first 12 fields in this output come from the CHAT
@ID header for the selected speaker. Fields 13-15 come from the @Types header. Items
43-56 are the 14 English morphemes studied by Brown (1973).
1. Filename
2. Language
3. Corpus Name
4. Participant Code
5. Age in months
6. Sex7. Group
8. Race
9. SES
10. Participant Role
11. Education
12. Custom field
13. Design
14. Activity
15. Group
16. Total Utts: total utterances,
17. MLU Utts: number of utterances, as used for computing MLU,
18. MLU Words: MLU in words,
19. MLU Morphemes: MLU in morphemes,
20. MLU 100 Utts: MLU of the first 100 child utterances in morphemes,

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21. MLU 100 Words: MLU of the first 100 child utterances in words,
22. MLU 100 Morphemes: MLU of the first 100 child utterances in morphemes,
23. FREQ types: total word types, as used for computing FREQ
24. FREQ tokens: total word tokens,
25. FREQ TTR: type/token ratio,
26. NDW 100: number of different words in the first 100 words in the sample,
27. VOCD score: KIDEVAL will warn if the sample is too small to compute VOCD,
28. Verbs/Utt: verbs per utterance. This can be less than 1.0 for young children,
29. TD Words: total number of words for each speaker, as used for TIMEDUR
30. TD Utts: total number of utterances for each speaker (no exclusionary criteria),
31. TD Time: total duration in seconds of utterances for each speaker,
32. TD Words/Time: words per second,
33. TD Utts/Time: utterances per second,
34. Word Errors: number of words involved in errors,
35. Utt Errors: number of utterances involved in errors,
36. Retracing [//]: number of retracings,
37. Repetition [/]: number of repetitions,38. DSS Utterances: number of DSS-eligible utterances (default 50 required),
39. DSS: Developmental Sentence Score,
40. IPSyn Utterances: number of IPSyn-eligible utterances (default 50 required),
41. IPSyn Total
42. MOR words: the number of words according to the %mor tier
43. -PRESP the present participle -ing, as in swimming.
44. inthe preposition in, as in the cheese is in the bag.
45. onthe preposition on, as in put it on.
46. -PLthe regular plural, as in dogs.
47. &PAST the irregular past, as in fell.
48. ~possthe possessive clitic, as in John’s
49. copthe uncontractible copula as in Is Meg nice? Meg is.
50. det:art
the determiner, as in the ball.
51. -PAST
the regular past, as in jumped.
52. -3S
the regular third person singular present, as in runs.
53. &3S
the irregular third person singular present, as in does or has.
54. aux
the uncontractible auxiliary, as in Is John running? Yes, he is.
55. ~cop
the contracted copula, as in Meg’s tall.
56. ~aux
the contracted auxiliary, as in John’s going.
57. Total non-zero MOR: This is the count of the number of Brown's morphemes that had
at least one instance. For example, if 10 of the 14 morphemes appeared, this number
would be 10.
The first 22 of the measures in KIDEVAL are the same for all languages. Fields 23 and
24 are only computed for English and Japanese, because only those two languages have
DSS. Also, fields 25 and 26 are only meaningful for English, since IPSyn only exists so
far for English. For fields 43-56, the actual morphemic forms that are being tracked vary
totally from language to language. The best way to follow these is to run KIDEVAL on asingle file and then to look at the column headers for each field after mor_Words. We canmodify this set by adding or removing morphemes, based on user requests.

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To run KIDEVAL, a transcript must have at least 50 utterances. If the transcript does
not contain enough utterances for a given index, N/A (not applicable) is inserted.
KIDEVAL now uses the new smaller rule set recommended by Yang et al., as described in
the section on IPSyn. For the MLU computation, sentences marked by [+ mlue] areexcluded from the analysis. For the DSS computation, sentences marked by [+ dsse] areexcluded. For the IPSyn computation sentences marked by [+ ipe] are excluded. If atranscript does not have time values entered, then columns 15-18 will not be meaningful.

8.8.2 Creating a custom language script
You can either use built-in KIDEVAL indices or you can configure your own set ofindices. If you wish to use KIDEVAL to compare a file with the larger database, you must
use the built-in index files and the built-in commands.
However, if you need to track a different set of indices, you may want to develop yourown custom language file. If you do this, then the +l switch should use the name of thelanguage file you created which must be put into the CLAN/lib/kideval folder. For
example, for Dutch, it should have the name nld.cut. When you use an external language
file, then you can only use KIDEVAL to compute summaries and not to compare with a
database.
It is possible to control the operation of KIDEVAL by editing the script files forindividual languages that are stored in the /lib/kideval folder. To use one of these files,
just add its name to the +l switch, as in +lfranew.cut. If you use the switch +leng or +lfra,
then the program relies on a built-in script file, instead of your user-defined file.
Within the script file, each line defines a type of search string. For example, this line
searches for all instances of masculine singular marking in French:+&m,&sg +&m,-SG "m-sg"

Items separated by a comma are treated as AND; items separated by a space are treated as
OR. To include combinations of morphemes in a KIDEVAL spreadsheet, you must run aseparate FREQ program, such as this one that looks for adj+noun or noun+adj
combinations in French:
freq +sm"|adj |n" +sm"|n |adj" +d2 *.cha

This command will create an Excel output structured like that for KIDEVAL and you may
wish to cut and paste the relevant columns from that output into your overall KIDEVAL
spreadsheet.

8.8.3 KIDEVAL with a comparison database
In addition to providing summary profiles for a single file or a collection of files,
KIDEVAL can compare results for a single file with results from a large comparison
database of CHAT transcripts. This comparison then assigns a standard deviation scorewith significance levels to each score in the transcript being compared. By default, thecomparison is against samples of children in the same 6-month age range.
Currently, comparison databases are available for English (ENG), Japanese (JPN), andMandarin (ZHO). Before beginning a comparison database analysis, you should select “Get
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preference folder on your computer. If you use the KIDEVAL dialog to select acomparison database, the dialog will change to this form:

Now, the dialogs will be creating this CLAN command:
kideval @ +LinkAge +leng +t*CHI: +dfp~"2;6-2;11|female"Comparisons involve comparing a single transcript with the overall comparison database.
For example, if we compare the barry.cha file in the CLAN examples folder with theEnglish database, we will get this output (with many columns removed):

In this output, the asterisks in row 4 indicate that this sample differs significantly from thecomparison group on several measures. The corpora that are used for the English-NA
KIDEVAL comparison databases are
Bates/Free20

Bates/Free28

Bernstein/Children

Bliss

Bloom70

Bloom73

Braunwald

Brown

Clark

Demetras1

Demetras2

FeldmanGathercole

Gleason/Father

Gleason/MotherHall

Higginson

HSLLD/HV1/TP

HSLLD/HV2/TP HSLLD/HV3/TP MacWhinney
McCuneNewEngland

Post

Providence

Sachs

Snow

SuppesTardifValianVanHouten

VanKleeck

WarrenWeist
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French.

8.8.4 Unique Options
KIDEVAL has the following unique options:+bS: add S characters to the morpheme delimiters list (default: -#~+)-bS: remove S characters from the morphemes list (-b: empty morphemes list)+d : debug+g: gem tier should contain all words specified by +gS-g: look for gems in database only
+gS: select gems which are labeled by label S+lF: specify language script file F+n: gem is terminated by the next @G (default: automatic detection)-n: gem is defined by @Bg and @Eg (default: automatic detection)+o4: output raw values instead of percentage values

8.9 MORTABLE
MORTABLE uses the %mor line to create a frequency table of parts of speech andaffixes in a format that can be opened directly in Excel. The command line needs to include
the script file which is in the CLAN/lib/mortable folder, for example:
mortable +t*PAR +leng *.cha

Columns M-AF provide the percentage of each part-of-speech — e.g., adjectives, adverb,
auxiliaries, conjunctions. The script for these percentage calculations uses an “OR” format,
so that the data in each column is mutually exclusive. Columns AE-AS provide thepercentages of each affix. These are calculated in a non-exclusive fashion.
If you want the actual count of the items found instead of percentages, add +o4 to thecommand line. If you want MORTABLE to automatically calculate cumulative totals ofparts-of-speech, you can make modifications to the eng.cut file found in
CLAN/lib/mortable. Here is an example of how to do this.
AND# +|v,|v:*"% v,v:*"

If you wanted a cumulative total of all pronouns (including pro:indef, pro:per, pro:wh,
pro:refl, pro:poss, and pro), you could enter the following into the script file under the AND
section and you would see a column in your spreadsheet called “pro-total”:+|pro,|pro:*"pro-total"

For cases where target replacements are in the transcript next to error productions withmissing morphemes (e.g., he is kick [: kicking] [* m:0ing] the ball), the EVAL and
MORTABLE programs will reflect the speaker's morphological production (e.g., v|kick)and not count anything that was not produced (e.g., part|kick-PRESP). For cases wheretarget replacements are in the transcript next to error productions for superfluous
morphemes (e.g., there is one birds [: bird] [* m:+s] in the tree), the EVAL and
MORTABLE programs will not count the superfluous morphemes (e.g., n|bird-PL)
because they were not used correctly.

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8.10 SUGAR
This program computes the SUGAR (Sampling Utterances and Grammatical Analysis
Revised) profile (Pavelko & Owens, 2017). For a discussion of problems with SUGAR
see (Guo, Eisenberg, Bernstein Ratner, & MacWhinney, 2018). For SUGAR, the corpus
of utterances includes the first 50 utterances. The basic format of the command is:sugar +t*CHI filename.cha
There are just four metrics to be computed:
1. MLU-S: This measure is the same as the MLU currently in CLAN.
2. TNW: This counts the total number of words in the 50 utterances.
3. WPS: This computes (number of words) / (number of sentences). For this, eachutterance with a verb counts as a sentence.
4. CPS: This computes (number of clauses) / (number of sentences). For this, eachsentence counts as one clause and then each of the GRs marking embedded structuresin the %gra line (section 7.8.14 of the CLAN manual) counts as an additional clause.

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Morphological Analysis: UD and MOR

The Universal Dependencies (UD) framework at https://universaldependencies.org
provides a consistent framework for the analysis of the grammatical structures of over 100
languages. UD provides a common vocabulary or nomenclature for the parts of speech,
lexical features, and grammatical relations that occur across all these languages. The pageat https://universaldependencies.org/guidelines.html provides links to each of thesesystems, as well as guidelines for annotation.
Within TalkBank, the application of UD to corpora is handled through us of the
Batchalign program written by Houjun Liu. That program, which is downloadable fromhttps://github.com/talkbank, is designed not only to create morphosyntactic analysis for
CHAT transcripts, but also to perform automatic speech recognition (ASR) and forcedalignment (FA) from raw media to create CHAT transcripts. For this chapter, we only
consider the functions in Batchalign that create UD analysis. Batchalign is not a CLAN
command, and it must be run in a terminal window with a command prompt. Here is an
example of a Batchalign command for morphosyntactic analysis:batchalign morphosyntax --lang=fra ~/input ~/output

In this example, the first argument starts Batchalign (assuming it has been installed); thesecond argument starts morphosyntactic analysis; the third argument specifies the input as
French; the fourth argument locates the input folder; and the last argument locates theoutput folder. The input directory can contain any number of CHAT files or folders of
CHAT files and that same structure will be preserved when files appear recursively in the
output folder. As Batchalign completes UD tagging of a file, that file will appear in the
output folder. If processing fails on a file for some reason, such as incorrect CHAT format,
processing will move on to the next file.
Batchalign relies on the Stanza system (https://stanfordnlp.github.io/stanza/) to apply
the various UD taggers. Users of Stanza can cite this paper: Qi, P., Zhang, Y., Zhang, Y.,
Bolton, J., & Manning, C. D. (2020). Stanza: A Python natural language processing toolkit
for many human languages. arXiv preprint arXiv:2003.07082. Stanza is currently
maintained by John Bauer.
Although UD taggers are better than MOR in terms of constructing dependency
structures on the %gra line, they do not perform analysis on the %mor in as much detail as
the MOR taggers that work within the CLAN programs. Moreover, UD does none of thechecking for lexical accuracy and typos that is provide by MOR. However, UD makes up
for these deficiencies by providing wide language coverage and consistent nomenclature
for parts of speech, lexical features, and grammatical relations across languages. For thosereasons, we have shifted most languages in CHILDES to UD, while continuing to maintainthe well-developed MOR taggers for English, Spanish, and Chinese that are available athttps://talkbank.org/morgrams. That page also provides grammars for Hebrew andJapanese because we have not succeeded in applying UD to those languages. We will
eventually create both MOR and UD taggings for English, Spanish, and Chinese. For all
languages except English, CLAN programs such as KIDEVAL are now based on use of
the tags produced by UD.
Given the increasing availability of UD taggers and the possibility of creating web
services for running these analyses, we do not expect that users will be motivated to create

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new MOR grammars. However, the MOR manual is still available on the web for users of
MOR for English.

9.1 Alignment
The default file format for UD is the CONLL format. However, to maintaincompatibility with the CLAN programs and provide better readability, Batchalign converts
CONLL to CHAT. In this format, there must be a one-to-one correspondence between
words on the main line and words on the %mor tier. In order to achieve this one-to-one
correspondence, the following rules are observed:
1. Each word group (see below) on the %mor line is surrounded by spaces or an initial
tab to correspond to the corresponding space-delimited word group on the main line.
The correspondence matches each %mor word (morphological word) to a main line
word in a left-to-right linear order in the utterance.
2. Forms on the main line that begin with & are not considered to be words.
3. Utterance delimiters are preserved on the %mor line to facilitate readability andanalysis. These delimiters should be the same as the ones used on the main line.
4. Along with utterance delimiters, the satellite markers of ‡ for the vocative and „ for tag
questions or dislocations are also included on the %mor line in a one-to-one alignment
format.
5. Retracings and repetitions are excluded from this one-to-one mapping, as are nonwords
such as xxx or strings beginning with &.
6. When a replacing form is indicated on the main line with the form [: text], the materialon the %mor line corresponds to the replacing material in the square brackets, not thematerial that is being replaced. For example, if the main line has gonna [: going to], the%mor line will code going to.
7. The [*] symbol that is used on the main line to indicate errors is not duplicated on the%mor line.

9.2 %mor item syntax
Each item or word on the %mor line uses a special left-to-right format which consistsof: part of speech, pipe symbol, lemma, and tags, as in this example for the word dogs
noun|dog&Plur

Cliticized words with two or more parts have their parts joined by the tild ~ character.

9.3%gra syntax and graphs

For transcripts in which UD or MOR have created a %gra line, it is possible to view agraph of the dependency structure by triple-clicking on the %gra line. This calls up theGraphViz program which runs over the web as a web service. This means that it will only
work when connected to the web. Here is an illustration of how an utterance appears on
the %gra line as produced by English MOR and then as displayed throgh GraphViz:*CHI:
wanna have some coffee ?
%mor:
v|want~inf|to v|have qn|some n|coffee ?
%gra:
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In this example, the first number in the terms on the %gra line indicates the order of theword in the utterance. The second number indicates the item to which it is connected
through a grammatical relation (GR); and the last term labels the grammatical relation.
Running this same utterance through UD for English, we get this result and a slightly
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Format Conversion Commands

CLAN includes several commands that convert transcripts from other formats to CHAT
and from CHAT to these other formats. These commands convert to other formats:• CHAT2ANVIL• CHAT2CA• CHAT2CONLL• CHAT2CA• CHAT2ELAN• CHAT2PRAAT• CHAT2SRT• CHAT2TEXT• CHAT2XMAR
These commands convert from other formats to CHAT.
• ANVIL2CHAT• CONLL2CHAT• ELAN2CHAT• LAB2CHAT• LENA2CHAT• LIPP2CHAT• PRAAT2CHAT• RTF2CHAT• SALT2CHAT• SRT2CHAT• TEXT2CHAT
Descriptions of each of these commands follow here alphabetically in this section.

10.1 CHAT2ANVIL
This program converts a CHAT file to ANVIL format.

10.2 CHAT2CAThe CHAT2CA program will convert a CHAT file to a format that is closer to standard
CA (Conversation Analysis) format. This is a one-way conversion, since we cannot
convert back to CHAT from CA. Therefore, this conversion should only be done when you
have finished creating your file in CHAT or when you want to show you work in more
standard CA format. The conversion changes some of the non-standard symbols to theirstandard equivalent. For example, the speedup and slowdown are marked by inward andoutward pointing arrows.

10.3 CHAT2ELAN
This program converts a CHAT file to the ELAN format for gestural analysis. For
conversion in the opposite direction, use ELAN2CHAT. You can download the ELAN

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program from https://www.mpi.nl/tools/elan.html. The conversion of CHAT files to ELAN
is fairly straightforward. You just run this command in the CLAN Commands window:
chat2elan +emp4 *.cha

The +e switch is used to specify the media file type, which could be mp4, mov, wav, or
mp3. CHAT main tiers appear in ELAN with the speaker's name, such as *BET whichbecomes “BET”. Dependent tiers, which are called “child tiers” in ELAN, are then codedas owned by a speaker, as in gpx@BET for the %gpx tier linked to the BET speaker. These
screenshots show a simple CHAT file (which is included in the CLAN distribution inn the/examples/chat2elan folder) and then how it looks after conversion to ELAN format:

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10.4 CHAT2PRAATThis program converts a CHAT file to the Praat format. When running this, you needto add the file type of the audio using the +e switch as in +emp3.

10.5 CHAT2SRTThis program converts a CHAT file to SRT format for captioning video. The use ofthis program is described in a screencast available from https://talkbank.org/screencasts.
On the Mac, you will need to purchase Subtitle Writer for $4.99 from the App Store.
1.
Run this command: chat2srt shoes.cha to produce shoes.srt
2.
Open Subtitle Writer.
3.
Click Add and add shoes.srt
4.
Select the language as English.
5.
Click Import Movie and select shoes.mp4
6.
Click Save Subtitled Video
7.
Open and play the resultant captioned movie.
If you want to use the English gloss in the %glo line in a file instead of the main line forthe captions, then use this command: chat2srt +t%glo shoes.cha to produce the .srt file.

10.6 CHAT2TEXTThis program converts a CHAT file to a series of text lines for analysis by concordance
programs such as AntConc. This command is implemented as a simple alias based on the
FLO command: flo +cr +t* The FLO program with the +cr switch removes all the various
markup of CHAT.

10.7 CHAT2XMARThis program converts a CHAT file to the EXMARaLDA format for Partitur analysis.
For conversion in the opposite direction, use XMAR2CHAT. You can download the
EXMARaLDA program from https://www1.uni-hamburg.de/exmaralda/.

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10.8 ANVIL2CHATThis program converts an ANVIL file to a CHAT file. For conversion in the opposite
direction, you can use CHAT2ANVIL

10.9 ELAN2CHATThis program converts ELAN files to CHAT files. Use CHAT2ELAN for conversion
in the opposite direction. The command is just: ELAN2CHAT filename.cha. If your filebegan in CHAT, then the tiers probably already have names that will pass on without
problems during the conversion. You can see how this might look in the section above on
CHAT2ELAN. Looking at that section and running the example given there is the best way
to understand the format required in ELAN.
However, if your file was produced originally in ELAN, it will be necessary to rename
the tiers to CHAT format before running elan2chat. There are basically two different
pathways for conversion of ELAN to CHAT.
CHAT-compatible ELAN files: The first pathway is the simplest. In this method,
you create ELAN files from scratch that maximize CHAT compatibility. To do this, you
give the main tier a 3-letter name, such as BET in the screencast example. Then, the %gpx
dependent tier for that speaker is named gpx@BET and it should be a child under the BET
top level tier. You should avoid use of Time Subdivision and Symbolic Subdivision tiertype. Instead, please make use of the Included In tier type.
ELAN files that must be reorganized: In the second pathway, you would be working
with ELAN files that had not been structured from the beginning to maximize CHAT
compatibility. In that case, there are ways to reorganize the ELAN files to increase
compatibility. The steps are:

1. Renaming tiers. For this, you need to select one tier as the top-level tier. Usually
this would be the main speaker tier(s) with one such tier for each speaker.
2. Then you would align the child or dependent tiers with the top level or main tiers.
If you are coding three types of dependent information and you have three speakers,
you would then end up with 9 tiers.
3. It is also possible to have tiers that are children of child tiers, if they are fullyaligned. However, it is not possible to go further into embedding with a third levelof dependency.
4. If a corpus has a mix of aligned and unaligned annotations on a Symbolic
Subdivision tier, you should either convert the tier to Included In or make surethat all annotations are aligned.
5. Finally, you may wish to remove some tiers that don’t adapt well to the CHAT
structure or which are not important for your general analysis.
When reorganizing ELAN tiers, you need to consider these operations:

1. Changing the hierarchy of tiers in ELAN is only possible via a copy operation (there
is an option Reparent Tier but that also creates a copy of the tier). If the result is
acceptable, the original tier can be removed. It is implemented this way becausechanging the hierarchy can involve changing the type of the tier and, therefore, theconstraints applied to annotations (annotations can be removed or concatenated etc.

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by this operation).
2. Renaming of tiers can be applied to a set of files / a corpus (File->Multiple File
Processing->Edit Multiple Files...). This (mainly) makes sense if there is someconsistency of tier names in that corpus/set of files.

10.10 LAB2CHATThis program converts files in WaveSurfer format, such as the files from Stockholm
University to CHAT format. Each utterance is on a separate line and the line begins withthe begin and end times in seconds. The WaveSurfer or LABB format has separate files
for each speaker, so there must be an attribs.cut file, like the one in /lib/fixes/wavesurfattribs.cut to declare who is who, as in:;"Chat Speaker" "Original tag litman"*MOT
mammanMother*CHI
barnetTarget_Child

10.11 LENA2CHATThis program is designed to convert LENA *.its files into CHAT format. It assumes
that all the *.its files for a given child, along with the *.wav audio files, are collected intoa single folder that has a 3- or 4-character name, such as AB04 or 0134. The command of"lena2chat *.its" is issued from within the folder. After running the command, please create
a new folder inside this folder called 0its and put the *.its files into that folder. The *.cha
files created by the program stay at the top level and they will be given names based on thefolder name and the age of the child. The .wav files will also be renamed in this way. So,
if the child's age is 2;04.05 and the folder name is AB04, then the file will be named
AB04_020405.cha and the corresponding audio file will be named AB04_020505.wav.
The @Media line will use the new name of the media file, but the original name of themedia file will be preserved in a line such as:@Comment: old media file name is e20100728_143446_003489

The program is designed to convert whole folders at a time, rather than single files,
although a folder with only one file will also be converted. To convert a series of folders
at once, you can add the +re switch. So, conversion of a whole corpus would use this formof the command:lena2chat +re *.its

10.12 LIPP2CHAT
To convert LIPP files to CHAT, you need to go through two steps. The first step is to
run this command:
cp2utf –c6 *.lip

This will change the LIPP characters to CLAN’s UTF8 format. Next you run thiscommand:lipp2chat +leng *.cp2utf.cex

This will produce a set of *.utf.cha files which you can then rename to *.cha. The
obligatory +l switch requires you to specify the language of the transcripts. For example,

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“en” is for English and “fr” is for French.

10.13 PRAAT2CHATThis program converts files in the PRAAT format to files in CHAT format. The/examples/praat2chat folder contains a 0readme.txt file that explains how to use theprogram. The following material is the same as what is given in that file:
The files in this folder illustrate how to convert Praat files to CHAT and vice versa.
The simplest case arises when you first convert a CHAT file like clip.cha to Praat, you use
this command:
chat2praat clip.cha +emp3

The +e switch specifies the format of the audio as either mp3 or wav. The output of thiscommand is clip.c2praat.textGrid. You can convert this back to CHAT format using thiscommand:praat2chat clip.c2praat.textGrid

In this case, no attribs.cut file is needed, because the orginal file was already in CHAT
format. The output of this command is clip.xh2praat.praat.cha. Note that this file isidentical to clip.cha.
However, if you begin with a Praat file that was never converted to CHAT, you willneed to create an attribs.cut file and you use this command:
praat2chat -opcl +dattribs.cut praat.textGrid

This command uses the declarations in the attribs.cut file illustrated in this folder andproduces praat.praat.cha as output. The +d swtich with an attribs.cut file is only needed
when converting from a Praat file created originally inside Praat, not when the Praat file
was created from CHAT, and not when converting from CHAT to Praat. The attribs.cut
file tells the program which Praat tag gives the Speaker role and which ones give dependent
tiers. After running praat2chat, you will need to fix the headers, run DELIM to add periods
for utterances and perform various similar operations required by CHAT format.

10.14 RTF2CHATThis program is used to take data that was formatted in Word and convert it to CHAT.

10.15 SALT2CHATThis program takes SALT formatted files and converts them to the CHAT format.
SALT is a transcript format developed by Jon Miller and Robin Chapman at the Universityof Wisconsin. By default, SALT2CHAT sends its output to a file. Here is the most common
use of this program:salt2chat file.cut

It may be useful to note a few details of the ways in which SALT2CHAT operates on
SALT files:
1.
When SALT2CHAT encounters material in parentheses, it translates this material
as an unspecified retracing type, using the [/?] code.

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3.

4.
5.

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Multiple comments are placed on separate lines on a single comment tier.
SALT codes in square brackets are converted to CHAT comments in square brackets
and left in the original place they occurred, unless the + symbol is added to the
SALT code. If it is present, the code is treated as a postcode and moved to the endof the utterance when SALT2CHAT runs. The CHSTRING program can be used toinsert + in the desired codes or in all codes, if required.
Times in minutes and seconds are converted to times in hours:minutes:seconds.
A %def tier is created for coding definitions.

10.15.1 Unique Options+hSome researchers have used angle brackets in SALT to enter comments. When theoriginal contains text found between the < and the > characters this option instructs
SALT2CHAT to place it between [% and ]. Otherwise, material in angle brackets
would be coded as a text overlap.
+l
Put all codes on a separate %cod line. If you do not select this option, codes will beplaced on the main line in the [$text] format.
After running SALT2CHAT, you will probably want to use the LOWCASE program torecapitalize proper nouns.

10.16 SRT2CHATThis program converts files in SRT format to CHAT format. For the file headers, it
uses TXT as the speaker and English as the default language. Times are encoded in thebullets. After conversion, you may want to put the individual lines into full CHAT format.
Alternatively, you can insert the @Options: heritage line to preserve the initial transcripts
and timing.

10.17 TEXT2CHATThe TEXT2CHAT program is quite simple. It takes a set of sentences in paragraphform and converts them to a CHAT file. Blank lines are treated as possible paragraphbreaks and are noted with @Blank headers. To illustrate the operation of TEXT2CHAT,
here are the results of running TEXT2CHAT on the previous three sentences:@Begin@Languages:eng@Participants: TXT Text
@ID:
ent|text|TXT|||||Text|||*TXT:
the text2chat program is quite simple.
*TXT:
it takes a set of sentences in paragraph form andconverts them to a chat file.
*TXT:
blank lines are considered to be possible paragraphbreaks and are noted with @blank headers.
@End

Problems can arise when there is extraneous punctuation in the original, as in forms suchas St. Louis which would generate a new line at Louis. To avoid this, one can either remove
these problems beforehand or in the resultant CHAT file.

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168

Reformatting Commands

These commands are useful when a researcher wants to add features to files that havealready passed CHECK and are in good CHAT format. Some of these add additional
material to files; others move around information already in the files. Clicking in the page
number will take you to the relevant command.
CommandPage

Function

CHSTRING

168

Changes one string to another, often using a changes file.

DATES

170

Given a birthday and current data, computes the age.

FLO

170

Adds a simplified %flo line to each line in a transcript.

INDENT

171

Realigns overlaps in CA transcripts.

LONGTIER

171

Makes each utterance one line long.

REPEAT

171

Marks repeated sequences of words as repetitions.

RETRACE

171

Marks retraced segments.

TIERORDER

172

Places all dependent tiers into a user-specified order.

TRIM

172

Remove specified dependent tiers.

11.1 CHSTRING
This program changes one string to another string in an ASCII text file. CHSTRING isuseful when you want to correct spelling, change subjects’ names to preserve anonymity,
update codes, or make other uniform changes to a transcript. This changing of strings canalso be done on a single file using a text editor. However, CHSTRING is much faster andallows you to make a whole series of uniform changes in a single pass over many files.
By default, CHSTRING is word-oriented, as opposed to string-oriented. This meansthat the program treats the as the single unique word the, rather than as the string of theletters “t”, “h”, and “e”. If you want to search by strings, you need to add the +w option.
If you do, then searching for the with CHSTRING will result in retrieving words such asother, bathe, and there. In string-oriented mode, adding spaces can help you to limit yoursearch. Knowing this will help you to specify the changes that need to be made on words.
Also, by default, CHSTRING works only on the text in the main line and not on thedependent tiers or the headers.
When working with CHSTRING, it is useful to remember the functions of the various
metacharacters, as described in the metacharacters section. For example, the following
search string allows you to add a plus mark for compounding between “teddy” and “bear”
even when these are separated by a newline, since the underscore character matches any
one character including space and newline. You need two versions here, since the firstwith only one space character works within the line and the second works when “teddy” isat the end of the line followed by first a carriage return and then a tab:+s"teddy_bear" "teddy+bear” +s"teddy__bear" "teddy+bear"Part 2: CLAN

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11.1.1 Removing bullets
CHSTRING can also be used to remove the bullets in CHAT files that link to media,
using this command:
chstring +cbullets.cut *.cha

Unique Options+bWork only on material that is to the right of the colon which follows the tier ID.
+c
Often, many changes need to be made in data. You can do this by using a text editor
to create an ASCII text file containing a list of words to be changed and what theyshould be changed to. This file should conform to this format:"oldstring" "newstring"
You must use the quotation marks to surround the two strings. The default name forthe file listing the changes is changes.cut. If you don’t specify a file name at the +c
option, the program searches for changes.cut. If you want to another file, the nameof that file name should follow the +c. For example, if your file is called
mywords.cut, then the option takes the form +cmywords.cut.
To test out the operation of CHSTRING with +c, try creating the following filecalled changes.cut:"the" "wonderful""eat" "quark"
Then try running this file on the sample.cha file with the command:
chstring +c sample.cha
Check over the results to see if they are correct. If you need to include the double
quotation symbol in your search string, use a pair of single quote marks around thesearch and replacement strings in your include file. Also, note that you can include
Unicode symbols in your search string.
+dThis option turns off several CHSTRING clean-up actions. It turns off deletion ofblank lines, removal of blank spaces, removal of empty dependent tiers, replacementof spaces after headers with a tab, and wrapping of long lines. All it allows is thereplacement of individual strings.
+lWork only on material that is to the left of the colon which follows the tier ID. For
example, if you want to add an “x’ to the %syn to make it %xsyn, you would use
this command:
chstring +s"%mor:" "%xmor:" +t% +l *.cha+q
CHAT requires that a three letter speaker code, such as *MOT:, be followed by atab. Often, this space is filled by three spaces instead. Although this is undetectablevisually, the computer recognizes tabs and spaces as separate entities. The +q option
brings the file into conformance with CHAT by replacing the spaces with a tab. It
also reorganizes lines to wrap systematically at 80 characters.
+sSometimes you need to change just one word, or string, in a file(s). These strings
can be put directly on the command line following the +s option. For example, if
you wanted to mark all usages of the word gumma in a file as child-based forms, theoption would look like this:+s"gumma" "gumma@c"Part 2: CLAN
+w

+x

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Do string-oriented search and replacement, instead of word-oriented search andreplacement. CAUTION: Used incorrectly, the +w switch can lead to serious lossesof important data. Consider what happens when changing all occurrences of “yes”
to “yeah.” If you use this command with the +w switch included,
chstring +w +s"yes" "yeah" myfile.cha
every single occurrence of the sequence of letters y-e-s will be changed. This
includes words, such as “yesterday,” “eyes,” and “polyester,” which would become“yeahterday,” “eyeah,” and “polyeahter,” respectively. However, if you omit the +w
switch, you will not have these problems. Alternatively, you can surround thestrings with spaces, as in this example:
chstring +w +s" yes " " yeah " myfile.cha
If you want to treat the asterisk (*), the underline (_), and the backslash (\) as theliteral characters, instead of metacharacters, you must add this switch.

11.2 DATESThe DATES program takes two time values and computes the third. It can take thechild’s age and the current date and compute the child’s date of birth. It can take the dateof birth and the current date to compute the child’s age. Or it can take the child’s age andthe date of birth to compute the current date. For example, if you type:dates +a 2;03.01 +b 12-jan-1962

you should get the following output:@Age of Child: 2;03.01@Birth of Child: 12-JAN-1962@Date: 13-APR-1964

You can also use the date format of MM/DD/YY, as in this version of the command:
dates +b 08/31/63 +d 07/30/64

If your files have the child's age in the @ID header, and if you know the child's date ofbirth, but do not have the @Date field, you can create a set of new files with the @Date
information, using this version of the command:
dates +bCHI 08/31/63 *.cha

Unique Options+aFollowing this switch, after an intervening space, you can provide the child’s age in
CHAT format.
+b
Following this switch, after an intervening space, you can provide the child’s birthdate in day-month-year format.
+dFollowing this switch, after an intervening space, you can provide the current dateor the date of the file you are analyzing in day-month-year format.

11.3 FLOThe FLO program creates a simplified version of a main CHAT line. This simplified
version strips out markers of retracing, overlaps, errors, and all forms of main line coding.
The only unique option in FLO is +d, which replaces the main line, instead of just adding
a %flo tier.

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11.4 INDENTThis program is used to realign the overlap marks in CA files. The files must be in a
fixed width font such as CAFont.

11.5 LINESThis program inserts line numbers that can be saved when closing the file, based on thenumbering system used by the "Show Line Numbers" option in the Mode menu. Apartfrom the options available to other programs, LINES uses the +n option to remove all theline/tier numbers.

11.6 LONGTIERThis program removes line wraps on continuation lines so that each main tier and eachdependent tier is on one long line. It is useful for cleaning up files, because it eliminates
having to think about string replacements across line breaks.

11.7 MEDIALINEThis program inserts the @Media field, based on the name of the transcript, along with
options for declaring a=audio, v=video, m=missing, and u=unlinked. So, this command
medialine +a +u test.cha

will insert this line in the test.cha file:@Media:test, audio, unlinked

11.8 REPEATREPEAT if two consecutive main tiers are identical then the postcode [+ rep] is inserted
at the end of the second tier.

11.9 RETRACERETRACE inserts [/] after repeated words as in this example:
*FAT:
%ret:

+^ the was the was xxx ice+cream .
+^ <the was> [/] the was xxx ice+cream .

If +c is used, then the main tier is replaced with the reformatted material and no additional%ret tier is created.

11.10 ROLESThe ROLES program is used to correct the role assignments that are produced duringrunning of the Batchalign program for automatic speech recognition(https://github.com/talkbank/batchalign2). That program assigns roles such as PAR0,
PAR1, etc. To convert these to roles in the format described in the CHAT manual, youcreates a roles.cut file in this shape:

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PAR0
PAR1
PAR2
...

CHI
FAT
MOT

172
Target_Child
FatherMotherand then use this command:roles +croles.cut *.cha

This will redo the @Participants and @ID lines, as well as the *PAR codes throughout the
file. Depending on the shape of your corpus and the Batchalign output, you maywant tohave a different roles.cut file for each transcript.

11.11 SEGMENT
This command is used to modify the utterance segmentation produced in the Batchalignpipeline for automatic speech recognition (https://github.com/talkbank). Once Batchalignhas finished, you can use continuous playback (Esc-8) to play through the transcript tomake sure that words are correctly recognized and that utterances are properly segmented.
If one utterance should be joined to the following utterance, you can add &&& at the endof the line after the time bullet and remove the terminator on the first utterance. If anutterance should be broken up, then you can enter &&& at the break location preceded by
the terminator needed for the first utterance. When doing this, SEGMENT assumes that
there are no additional dependent tiers other than %wor. So, if there are %mor and %gratiers, you can use TRIM to remove them and then rerun MOR after completing SEGMENT.
Before running SEGMENT, make sure to save and close your file. Also, you may want toavoid use of the +1 switch for file replacement until you are comfortable with use of
SEGMENT.
An alternative to using SEGMENT is to use this command to remove the %wor lines
in the output from Batchalign (the +re is optional):
trim -t%wor +re *.cha +1

Then you can insert periods to mark sentence ends or join utterances as needed and thenuse FIXIT to separate out utterances at the period you have marked.

11.12 TIERORDER
TIERORDER puts the dependent tiers into a consistent alphabetical order. The/lib/fixes/tierorder.cut file can be used to control this order by rearranging the order of tiersin that file.

11.13 TRIM
This command is designed to allow you to remove coding tiers from CHAT files. For
example, to remove all the %mor lines from files without changing anything else, you can
use this command:
trim –t%mor *.cha +1

This command is equivalent to this more verbose KWAL command:
kwal –t%mor +t@ +t% +d +f *.cha

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To remove all dependent tiers, you can use this command:
trim -t%* *.cha +1

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174

Format Repair Commands

These commands are used to repair the format of files that are not yet in the correct
current CHAT format. These could either be files that were created by older versions of
CLAN or files that were not run through CHECK during their creation. Researchers who
are using current versions of CLAN and who have files that are passing CHECK do notneed to use any of these commands.

12.1 COMBTIERCOMBTIER corrects a problem that typically arises when transcribers create several%com lines. It combines two %com lines into one by removing the second header andmoving the material after it into the tier for the first %com.

12.2 CP2UTF
CP2UTF converts code page ASCII files and UTF-16 into UTF-8 Unicode files. If
there is an @Font tier in the file, the program uses this to guess the original encoding. If
not, it may be necessary to add the +o switch to specify the original language, as in +opcct
for Chinese traditional characters on the PC. If the file already has a @UTF8 header, theprogram will not run, unless you add the +d switch and you are sure that the line in question
is not already in UTF. The +c switch uses the unicode.cut file in CLAN/lib/fixes directory
to effect translation of ASCII to Unicode for IPA symbols, depending on the nature of the
ASCII IPA being used. For example, the +c3 switch produces a translation from IPAPhon.
The +t@u switch forces the IPA translation to affect main line forms in the text@u format.
+b : add BOM symbol to the output files+cN: specify column number (3-7) (default: 4, IPATimes)+d: convert ONLY tiers specified with +t option+d1: remove bullets from data file+d2: add BOM encoding information at the beginning of a CHAT file to help
applications, such as NVivo or MS-Word, to read it better+oS: specify code page. Please type "+o?" for full listing of codes
utf16 - Unicode UTF-16 data filemacl - Mac Latin (German, Spanish ...)
pcl - PC Latin (German, Spanish ...)

12.3 DELIM
DELIM inserts a period at the end of every main line if it does not currently have one.
To do this for all tiers use the +t* switch.

12.4 FIXBULLETS
This program is used to fix the format of the bullets that are used in CLAN to link a
CHAT file to audio or video. Without any additional switches, it fixes old format bullets
that contain the file name to new format bullets and inserts an @Media tier at the beginningof the file. The various switches can be used to fix other problems. The +l switch can beused to make the implicit declaration of second language source explicit. The +o switch

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can be used to change the overall start time for a file.
+bmerge multiple bullets per line into one bullet per tier+oN time offset value N (+o+800 means add 800, +o-800 means subtract)+fS send output to file (program will derive filename)-fsend output to the screen+l
add language tag to every word
+re run program recursively on all sub-directories.
+tS include tier code S-tS exclude tier code S+/-2 +2 do not create different versions of output file names / -2 create them
File names can be "*.cha" or a file of list of names "@:filename"12.5 FIXIT
FIXIT is used to break up tiers with multiple utterances into the standard format with
one utterance per main line.

12.6 LOWCASEThis program is used to fix files that were not transcribed using CHAT capitalization
conventions. Most commonly, it is used with the +c switch to only convert the initial wordin the sentence to lowercase. To protect certain proper nouns in first position from theconversion, you can create a file of proper noun exclusions with the name caps.cut and add
the +d switch to avoid lowercasing these. You may also want to use the /lib/fixes/caps.cut
file. It contains over 8000 proper nouns, many based on children's first names.

12.7 QUOTESThis program moves quoted material to its own separate tier.

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Supplementary Commands

13

CLAN also includes these basic operating system type commands which allow you tomanage file and folder structure.

13.1 batch
You can place a group of commands into a text file which you then execute as a batch.
The word batch should be followed by the name of a file in your working directory, suchas commands.bat. Each line of that file is then executed as a CLAN command.

13.2 cdThis command will change your working directory. For example, is you have asubfolder called ne20, then the command cd ne20 will set your current working directory
to ne20. To move up, you can type cd ..

13.3 dirThis command will provide a listing of the files in your current working directory.

13.4 info
Just typing "info" will list all the available CLAN commands.

13.5 ren(ame)This command allows you to change file names in a variety of ways. You can changecase by using -u for upper and -l for lower. The -c and -t switches allow you to change thecreator signature and file types recognized by Macintosh. The –f switch forces filereplacement. Here are some examples:
Rename a series of files with names like “child.CHA (Word 5)”:ren '*.CHA (Word 5)' *.cha

Rename the output from FREQ and replace the original files:
ren –f *.frq.cex *.cha

Taking out spaces (because CLAN has trouble with spaces in names):
ren'* -e.cha' '*-e.cha'

If you have two variables in your input, you can control which is replaced by adding
either \1 or \2 to the output. So, if you use \1, then the first variable is preserved, and if
you use \2, then the second variable is preserved. So, if the input is aki12_boofoo.cha, thenthis command creates 12.cha as output:
ren aki*_*.cha \1*.cha

and this command creates boofoo.cha as output:
ren aki*_*.cha \2*.cha

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13.6 rm
This command will delete files. It can be used with wild cards and recursively, as inrm -re *.mor.cex

It is important to be careful with the use of this command. A good practice is to make a
copy of the folder(s) to which it applies before running the command.

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178

Options

This chapter describes the various options or switches that are shared across the CLAN
commands. To see a list of options for a given program such as KWAL, type kwal followed
by a carriage return in the Commands window. You will see a list of available options in
the CLAN Output window.
Each option begins with a + or a -. There is always a space before the + or -. Multiple
options can be used and they can occur in any order. For example, the command:
kwal +f +t*MOT sample.cha

runs a KWAL analysis on sample.cha. The selection of the +f option sends the output fromthis analysis into a new file called sample.kwa.cex. The +t*MOT option confines the analysis to only the lines spoken by the mother. The +f and +t switches can be placed in either
order.

14.1 +F Option
This option allows you to send output to a file rather than to the screen. By default,
nearly all the programs send the results of the analyses directly to the screen. You can,however, request that your results be inserted into a file. This is accomplished by inserting
the +f option into the command line. The advantage of sending the program’s results to a
file is that you can go over the analysis more carefully, because you have a file to which
you can later refer.
The -f switch is used for sending output to the screen. For most programs, -f is thedefault and you do not need to enter it. You only need to use the -f switch when you want
the output to go to the screen for CHSTRING, FLO, and SALT2CHAT. The advantage ofsending the analysis to the screen (also called standard output) is that the results are
immediate and your directory is less cluttered with nonessential files. This is ideal for quick
temporary analysis.
The string specified with the +f option is used to replace the default file name extension
assigned to the output file name by each program. For example, the command
freq +f sample.cha

would create an output file sample.frq.cex. If you want to control the shape of the extension
name on the file, you can place up to three letters after the +f switch, as in the command
freq +fmot sample.cha

which would create an output file sample.mot.cex. If the string argument is longer than
three characters, it will be truncated. For example, the command
freq +fmother sample.cha

would also create an output file sample.mot.cex.
On the Macintosh, you can use the third option under the File menu to set the directory
for your output files. On Windows, you can achieve the same effect by using the +f switch
with an argument, as in:+fc: This will send the output files to your working directory on c:.
+f".res" This sets the extension for your output files.

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+f"c:.res" This sends the output files to c: and assigns the extension .res.
When you are running a command on several files and use the +f switch, the output
will go into several files – one for each of the input files. If what you want is a combined
analysis that treats all the input files as one large file, then you should use the +u switch. If
you want all the output to go into a single file for which you provide the name, then use
the > character at the end of the command along with an additional file name. The > option
can not be combined with +f.

14.2 +K Option
This option controls case-sensitivity. A case-sensitive program is one that makes a distinction between uppercase and lowercase letters. Many of the CLAN commands are casesensitive by default. If you type the name of each command, you will see a usage page
indicating the default setting for the +k switch. Use of the +k option overrides the default
state whatever that might be. For instance, suppose you are searching for the auxiliary verb
“may” in a text. If you searched for the word “may” in a case-sensitive program, you would
obtain all the occurrences of the word “may” in lower case only. You would not obtain any
occurrences of “MAY” or “May.” Searches performed for the word “may” using the +k
option produce the words “may,” “MAY,” and “May” as output.

14.3 +L Option
The +l option is used to provide language tags for every word in a bilingual corpus.
Use of this switch does not actually change the file; rather these tags are represented incomputer memory for the files and are used to provide full identification of the output of
programs such as FREQ or KWAL. For examples of the operation of the +l switch in thecontext of the FREQ program, see the section of the FREQ program description thatexamines searches in bilingual corpora.
An additional variation on the +l switch is +l1 which serves to insert a language precodesuch as [- spa] for every utterance, including those that are unmarked without the use ofthis switch. If this switch is used, then it is possible to trace between language codeswitching on the whole utterance level using commands such as the two following, where
the first one tracks changes from French to Spanish and the second tracks changes from
Spanish to French:
combo +b2 -l1 +s"\**:^[- fra]^*^\**:^[- spa]" *.cha
combo +b2 -l1 +s"\**:^[- spa]^*^\**:^[- fra]" *.cha

14.4 +P Option
This switch is used to change the way in which CLAN processes certain word-internal
symbols. Specifically, the programs typically consider compounds such as black+bird tobe single words. However, if you add the switch +p+, then the plus symbol will be treatedas a word delimiter. This means that a program like FREQ or MLU would treat black+bird
as two separate words. Another character that you may wish to treat as a word separator
is the underscore. If you use the switch +p_, then New_York would be treated as twowords.

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14.5 +R Option
This option deals with the treatment of material in parentheses.
+r1 Removing Parentheses. Omitted parts of words can be marked by parentheses,
as in “(be)cause” with the first syllable omitted. The +r1 option removes the parentheses
and leaves the rest of the word as is.
+r2 Leaving Parentheses. This option leaves the word with parentheses.
+r3 Removing Material in Parentheses. This option removes all the omitted part.
Here is an example of the use of the first three +r options and their resulting outputs, if
the input word is “get(s)”:OptionOutput"no option"
gets
"+r1"
gets
"+r2"
get(s)"+r3"get+r4 Removing Prosodic Symbols in Words. By default, symbols such as #, /, and :are ignored when they occur inside words. Use this switch if you want to include them inyour searches. If you do not use this switch, the strings cat and ca:t are regarded as thesame. If you use this switch, they are seen as different. The use of these prosodic marker
symbols is discussed in the CHAT manual.
+r5 Text Replacement. By default, material in the form [: text] replaces the material
preceding it in the string search programs. The exception to this rule is for the WDLEN
program. If you do not want this replacement, use this switch.
+r6 Retraced Material. By default, material in retracings is included in searches andcounts. The exceptions are the EVAL, FREQ, MLT, MLU, and MODREP programs, forwhich retracings are excluded by default. The +r6 switch is used to change these default
behaviors for those programs.
+r7 Do not remove prosodic symbols (/~^:) in words+r8: Combine %mor tier items with replacement word [: …] and error code [* …] if
any from speaker tier.

14.6 +S Option
This option allows you to search for a certain string. The +s option allows you to specify
the keyword you desire to find. You do this by putting the word in quotes directly after the+s switch, as in +s"dog" to search for the word “dog.” You can also use the +s switch to
specify a file containing words to be searched. You do this by putting the file name after+s@, as in +s@adverbs, which will search for the words in a file called adverbs.cut. If you
want to use +s to look for the literal character @, you need to precede it with a backslash
as in +s"\@".
By default, the programs will only search for matches to the +s string on the main line.
If you want to include a search on other tiers, you need to add them with the +t switch.
Also by default, unless you explicitly include the square brackets in your search string, thesearch will ignore any material that is enclosed in square brackets.

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It is possible to specify as many +s options on the command line as you like. If you
have several +s options specified, the longest ones will be applied first. Use of the +s option
will override the default list. For example, the command
freq +s"word" data.cut

will search through the file data.cut looking for “word.”
The +s/-s switch can be used with five types of material: (1) words, (2) codes or
postcodes in square brackets, (3) text in angle brackets associated with codes within square
brackets, (4) whole utterances associated with certain postcodes, and (5) particular
postcodes themselves. Moreover, the switch can be used to include, exclude, or add
information. The effect of the switch for the five different types across the three functions
is described in the following three tables:
Search Strings for Inclusion of Five Types of Material
Material

SwitchResults

word+s"dog"find only the word “dog”[code]+s"[//]"

find only this code itself<text>[code]+s"<//>"

find only text marked by this codeutterance+s"[+ imi]"+s"[- eng]"

find only text marked with this postcode orprecodepostcode+s"<+ imi>"+s”<- eng>”

find only this postcode or precode itself

Search Strings for Exclusion of Four Types of Material
Material

SwitchResults

word-s"dog"find all words except the word “dog”<text>[code]-s"<//>"

find all text except text marked by thiscodeutterance-s"[+ imi]"

find all utterances except utterancesmarked with this postcode

Search Strings for Addition of Material excluded by default
Material

SwitchResultsword+s+xxxadd “xxx”[code]+s+"[//]"

find all text, plus this codeutterance+s+"[+ bch]"find all utterances, including those marked
with the [+ bch] postcode

postcode+s+"<+ imi>"

find all text, including this postcode itself

You can use either single or double quotation marks. However, for Unix and CLAN

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commands on the web interface, you need to use single quotation marks. When your searchstring does not include any metacharacters or delimiters, you can omit the quotation marks
altogether.
Multiple +s strings are matched as exclusive or’s. If a string matches one +s string, itcannot match the other. The most specific matches are processed first. For example, if yourcommand isfreq +s$gf% +s$gf:a +t%cod

and your text has these codes$gf $gf:a $gf:b $gf:cyour output will be
$gf
$gf:a

3
1

Because $gf:a matches specifically to the +s$gf:a, it is excluded from matching +s$gf%.
One can also use the +s switch to remove certain strings from automatic exclusion. For
example, the MLU program automatically excludes xxx, 0, uh, and words beginning with& from the MLU count. This can be changed by using this command:
mlu +s+uh +s+xxx +s+0* +s+&* file.cha

14.7 +T Option
This option allows you to include or exclude tiers. In CHAT formatted files, there exist
three tier code types: main speaker tiers (denoted by *), speaker-dependent tiers (denotedby %), and header tiers (denoted by @). The speaker-dependent tiers are attached to
speaker tiers. If, for example, you request to analyze the speaker *MOT and all the %cod
dependent tiers, the programs will analyze all the *MOT main tiers and only the %cod
dependent tiers associated with that speaker.
The +t option allows you to specify which main speaker tiers, their dependent tiers, andheader tiers should be included in the analysis. All other tiers, found in the given file, willbe ignored by the program. For example, the command:
freq +t*CHI +t%spa +t%mor +t"@Group of Mot" sample.cha

tells FREQ to look at only the *CHI main speaker tiers, their %spa and %mor dependent
tiers, and @Situation header tiers. When tiers are included, the analysis will be done on
only those specified tiers.
The -t option allows you to specify which main speaker tiers, their dependent tiers, andheader tiers should be excluded from the analysis. All other tiers found in the given fileshould be included in the analysis, unless specified otherwise by default. The command:
freq -t*CHI -t%spa -t%mor -t@"Group of Mot" sample.cha

tells FREQ to exclude all the *CHI main speaker tiers together with all their dependent
tiers, the %spa and %mor dependent tiers on all other speakers, and all @Situation header
tiers from the analysis. All remaining tiers will be included in the analysis.
When the transcriber has decided to use complex combinations of codes for speakerIDs such as *CHI-MOT for “child addressing mother,” it is possible to use the +t switch
with the # symbol as a wildcard, as in these commands:

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freq +t*CHI-MOT sample.cha
freq +t*#-MOT sample.cha
freq +t*CHI-# sample.cha

When tiers are included, the analysis will be done on only those specified tiers. Whentiers are excluded, however, the analysis is done on tiers other than those specified. Failure
to exclude all unnecessary tiers will cause the programs to produce distorted results. Therefore, it is safer to include tiers in analyses than to exclude them, because it is often difficult
to be aware of all the tiers present in any given data file.
If only a tier-type symbol (*, %, @) is specified following the +t/-t options, the programs will include all tiers of that symbol type in the analysis. Using the option +t@ isimportant when using KWAL for limiting (see the description of the KWAL program),because it makes sure that the header information is not lost.
The programs search sequentially, starting from the left of the tier code descriptor, forexactly what the user has specified. This means that a match can occur wherever what has
been specified has been found. If you specify *M on the command line after the option, theprogram will successfully match all speaker tiers that start with *M, such as *MAR, *MIK,*MOT, and so forth. For full clarity, it is best to specify the full tier name after the +t/-t
options, including the : character. For example, to ensure that only the *MOT speaker tiers
are included in the analysis, use the +t*MOT: notation.
As an alternative to specifying speaker names through letter codes, you can use theform:+t@id=idcode

In this form, the “idcode” is any character string that matches the type of string that has
been declared at the top of each file using the @ID header tier.
All of the programs include the main speaker tiers by default and exclude all of thedependent tiers, unless a +t% switch is used.

14.8 +U Option
This option merges the output of searches on specified files together. By default, when
the user has specified a series of files on the command line, the analysis is performed oneach individual file. The program then provides separate output for each data file. If thecommand line uses the +u option, the program combines the data found in all the specified
files into one set and outputs that set as a whole. For most commands, the switch merges
all data for a given speaker across files. The commands that do this are: CHAINS, CHIP,
COOCCUR, DIST, DSS, FREQ, FREQPOS, GEM, GEMFREQ, IPSYN, KEYMAP,MAXWD, MLT, MLU, MODREP, PHONFREQ, and WDLEN. There are several othercommands for which there is a merged output, but that output separates data from different
input files. These commands are COMBO, EVAL, KIDEVAL, KWAL, MORTABLE,
TIMEDUR, and VOCD. If too many files are selected, CLAN may eventually be unable
to complete this merger.

14.9 +V Option
This switch gives you the date when the current version of CLAN was compiled.

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14.10 +W Option
This option controls the printing of additional sentences before and after a matchedsentence. This option can be used with either KWAL or COMBO. These programs are used
to display tiers that contain keywords or regular expressions as chosen by the user. By
default, KWAL and COMBO combine the user-chosen main and dependent tiers into“clusters.” Each cluster includes the main tier and its dependent tiers. (See the +u option
for further information on clusters.)
The -w option followed by a positive integer causes the program to display that numberof clusters before each cluster of interest. The +w option followed by a positive integer
causes the program to display that number of clusters after each cluster of interest. For
example, if you wanted the KWAL program to produce a context larger than a single
cluster, you could include the -w3 and +w2 options in the command line. The program
would then output three clusters above and two clusters below each cluster of interest.

14.11 +X Option
This option is available in most of the analysis programs. It allows you to control thetype and number of items in utterances being selected for analysis.
+xCNT: C (condition) can be greater than >, less than <, or equal = (>, <, =)N (number) is the number of items to be included
T (type) is the type of item which can be words (w), characters (c), or morphemes(m). If “m” is used, there must be a %mor line.
+x<10c means to include all utterances with less than 10 characters+x=0w means to include all utterances with zero words+xS:
include certain items in above count (Example: +xxxx +xyyy)-xS:
exclude certain items from above countIn the MOR and CHIP programs, +x has a different meaning.

14.12 +Y Option
This option allows you to work on non-CHAT files. Most of the programs are designed
to work best on CHAT formatted data files. However, the +y option allows the user to use
these programs on non-CHAT files. It also permits certain special operations on CHAT
files. The program considers each line of a non-CHAT file to be one tier. There are twovalues of the +y switch. The +y value works on lines and the +y1 value works on utterances
as delimited by periods, question marks, and exclamation marks. Some programs do notallow the use of the +y option at all. Workers interested in using CLAN with
nonconversational data may wish to first convert their files to CHAT format using theTEXTIN program to avoid having to avoid use of the +y option.
If you want to search for information in specific headers, you may need to use the +yoption. For example, if you want to count the number of utterances by CHI in a file, you
can use this command:
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14.13 +Z Option
This option allows the user to select any range of words, utterances, or speaker turns tobe analyzed. The range specifications should immediately follow the option. For example:+z10w
analyze the first ten words only.
+z10u
analyze the first ten utterances only.
+z10t
analyze the first ten speaker turns only.
+z10w-20w analyze 11 words starting with the 10th word.
+z10u-20u
analyze 11 utterances starting with the 10th utterance.
+z10t-20t
analyze 11 speaker turns starting with the 10th turn.
+z10wanalyze from the tenth word to the end of file.
+z10uanalyze from the tenth utterance to the end of file.
+z10tanalyze from the tenth speaker turn to the end of file.
If the +z option is used together with the +t option to select utterances from a certain
speaker, then the counting will be based only on the utterances of that speaker. For example, this command:
mlu +z50u +t*CHI 0611.cha

will compute the MLU for the first 50 utterances produced by the child. If the +z option
is used together with the +s option, the counting will be dependent on the working of the+s option and the results will seldom be as expected. To avoid this problem, you should
first use KWAL with +z to extract the utterances you want and then run MLU on that
output.
kwal +d +z50u +t*CHI sample.cha
kwal +sMommy sample.kwa.cex

If the +z switch specifies more items than exist in the file, the program will analyze
only the existing items. If the turn or utterance happens to be empty, because it consists ofspecial symbols or words that have been selected to be excluded, then this utterance or turnis not counted.
The usual reason for selecting a fixed number of utterances is to derive samples thatare comparable across sessions or across children. Often researchers have found thatsamples of 50 utterances provide almost as much information as samples of 100 utterances.
Reducing the number of utterances being transcribed is important for clinicians who havebeen assigned a heavy case load.
You can also use postcodes to further control the process of inclusion or exclusion.

14.14 Metacharacters for Searching
Metacharacters are special characters used to describe other characters or groups ofcharacters. Certain metacharacters may be used to modify search strings used by the +s/-s
switch. However, to use metacharacters in the CHSTRING program a special switch must
be set. The CLAN metacharacters are:*Any number of characters matched%

Any number of characters matched and removed%% As above plus remove previous character

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Any single character matched\

Quote character

Suppose you would like to be able to find all occurrences of the word “cat” in a file.
This includes the plural form “cats,” the possessives “cat's,” “cats’'” and the contraction“cat’s”. Using a metacharacter (in this case, the asterisk) would help you to find all of these
without having to go through and individually specify each one. By inserting the string
cat* into the include file or specifying it with +s option, all these forms would be found.
Metacharacters can be placed anywhere in the word.
The * character is a wildcard character; it will find any character or group of continuous
characters that correspond to its placement in the word. For example, if b*s were specified,
the program would match words like “beads,” “bats,” “bat's,” “balls,” “beds,” “breaks,”and so forth.
The % character allows the program to match characters in the same way as the *symbol. Unlike the * symbol, however, all the characters matched by the % will be ignoredin terms of the way of which the output is generated. In other words, the output will treat“beat” and “bat” as two occurrences of the same string, if the search string is b%t. Unlessthe % symbol is used with programs that produce a list of words matched by given keywords, the effect of the % symbol will be the same as the effect of the * symbol.
When the percentage symbol is immediately followed by a second percentage symbol,
the effect of the metacharacter changes slightly. The result of such a search would be thatthe % symbol will be removed along with any one character preceding the matched string.
Without adding the additional % character, a punctuation symbol preceding the wildcard
string will not be matched ane will be ignored.
The underline character _ is like the * character except that it is used to specify any
single character in a word. For example, the string b_d will match words like “bad,” “bed,”“bud,” “bid,” and so forth. For detailed examples of the use of the percentage, underline,
and asterisk symbols, see the section special characters.
The quote character (\) is used to indicate the quotation of one of the characters being
used as metacharacters. Suppose that you wanted to search for the actual symbol (*) in a
text. Because the (*) symbol is used to represent any character, it must be quoted by inserting the (\) symbol before the (*) symbol in the search string to represent the actual (*)
character, as in “string\*string.” To search for the actual character (\), it must be quoted
also. For example, “string\\string” will match “string” followed by “\” and then followed
by a second “string.”

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187

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