ENGLISH LANGUAGE EDUCATION PROGRAM FACULTY OF …
Transcript of ENGLISH LANGUAGE EDUCATION PROGRAM FACULTY OF …
VOCABULARY PROFILE IN THE 8 GRADE ENGLISH
TEXTBOOK USED IN ANAK TERANG JUNIOR HIGH
SCHOOL
THESIS
Submitted in Partial Fulfillment of
the Requirements for the Degree of Sarjana Pendidikan
Annisa Putri
112012079
ENGLISH LANGUAGE EDUCATION PROGRAM
FACULTY OF LANGUAGE AND LITERATURE
SATYA WACANA CHRISTIAN UNIVERSITY
2016
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VOCABULARY PROFILE IN THE 8 GRADE ENGLISH
TEXTBOOK USED IN ANAK TERANG JUNIOR HIGH
SCHOOL
THESIS
Submitted in Partial Fulfillment of
the Requirements for the Degree of Sarjana Pendidikan
Annisa Putri
112012079
Approved by
Thesis Supervisor Thesis Examiner
Anne Indrayanti Timotius, M.Ed Prof. Dr. Gusti Astika, M.A.
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COPYRIGHT STATEMENT
This thesis contains no such material as has been submitted for examination in
any course or accepted for the fulfillment of any degree or diploma in any
university. To the best of my knowledge and my belief, this contains no material
previously published or written by any other person except where due the
reference is made in the text.
Copyright@ 2016. Annisa Putri and Anne Indrayanti Timotius, M.Ed.
All rights reserved. No part of this thesis may be reproduced by any means
without the permission of at least one of the copyright owners or the English
Language Education Program, Faculty of Language and Literature, Satya Wacana
Christian University, Salatiga.
Annisa Putri
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PUBLICATION AGREEMENT DECLARATION
As a member of (SWCU) Satya Wacana Christian University academic
community, I verify that:
Name: Annisa Putri
Student ID Number: 112012079
Study Program: PBI (Pendidikan Bahasa Inggris)
Faculty: Language and Literature
Kind of Work: Undergraduate Thesis
In develop in my knowledge, I agree to provide SWCU with a non-exclusive
royalty free right for my intellectual property and the content therein entitled:
VOCABULARY PROFILE IN THE 8 GRADE ENGLISH TEXTBOOK USED
IN ANAK TERANG JUNIOR HIGH SCHOOL
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With the non-exclusive royalty free right, SWCU maintains the right to copy,
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name is still included as the writer.
Made in :
Date :
Verified by signee,
Annisa Putri
Approved by
Thesis Supervisor Thesis Examiner
Anne Indrayanti Timotius, M.Ed Prof. Dr. Gusti Astika, M
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TABLE OF CONTENT
Cover Page .............................................................................................................. i
Approval Page ........................................................................................................ ii
Copyright Statement ............................................................................................. iii
Publication Agreement Declaration ...................................................................... iv
Table of Content ...................................................................................................... v
Abstract ....................................................................................................... 1
Introduction ................................................................................................. 1
Literature Review ........................................................................................ 4
Definition of Vocabulary .................................................................. 4
The Importance of Vocabulary ....................................................... 4
Vocabulary Profile .......................................................................... 4
Words Frequency ............................................................................ 5
Relevant Previous Studies ............................................................... 8
The Study ..................................................................................................... 9
Context of the Study ....................................................................... 9
Material ......................................................................................... 10
Data Collection Instrument ........................................................... 10
Data Collection Procedures ........................................................... 10
Data Analysis Procedure ............................................................... 11
Findings and Discussions .......................................................................... 11
Overall Result ................................................................................ 11
Negative Vocabulary Profiles of The Textbook . .......................... 13
Block Frequency Output of Off-List Words ................................. 17
Comparison of Vocabulary Frequency Across Units .................... 19
Conclusion ................................................................................................ 24
References ................................................................................................. 27
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Acknowledgements ................................................................................... 29
Appendixes ................................................................................................ 30
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VOCABULARY PROFILE IN THE 8 GRADE ENGLISH TEXTBOOK
USED IN ANAK TERANG JUNIOR HIGH SCHOOL
Annisa Putri
ABSTRACT
In the English language teaching and learning, vocabulary is important as the
basis of the language. Many students especially in Indonesia still have problems
in vocabulary, therefore they need to know about the vocabulary of a foreign
language. By knowing the vocabulary, the learners are able to understand the
message of the language. There are three objectives of this study, analyzing the
vocabulary profile of the textbook for grade 8 used in Anak Terang Junior High
School, producing list of vocabulary that are not covered in the textbook, and
producing the token (word) recycling index of the textbook. The title of the
textbook was ‘English in Mind’ for grade 8 that was published in 2010 by
Cambridge University Press, and the material for this study was taken from all the
units from the textbook. To find out the vocabulary profile, this study used an
online tool named The Compleat Lexical Tutor, v.4. There are 85.30% of K-1
words in the textbook that students can comprehend, but the rest is 14.7% that
students need more effort to study. There are 7.19% of K-2 words from the
textbook, AWL words was 1.74%, and the percentage of Off-list words was
5.77%. There are 24.69% of K-1, 58.72% of K-2, and 79.26% of academic words
that were not found in the textbook. The result of comparison analysis of Unit 3
and Unit 13 showed that there were 77.83% similar words, and the result of
comparison analysis of Unit 12 and Unit 14 showed that there were 82.75%
similar (shared) words, and the total words of the similar words from these units
were 2850 words.
Keywords: Vocabulary, Vocabulary profile.
INTRODUCTION
Vocabulary is an important basic element of a language because without
knowing the vocabulary, the learner will not be able to understand the message of
the language. According to Chapelle and Jamieson (2008), vocabulary words and
phrases are the building blocks, and grammar is the glue which makes them as
one. If the learners do not know the vocabulary, they will get many difficulties in
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learning that language. According to Cahyono and Widiati (cited in Priyono,
2004: 26) students’ limited vocabulary is the main problem of Indonesian EFL
student. Therefore, the learners need to improve their vocabulary knowledge
because it is important to help the learners to be easier to understand the message
and also the meaning of the language.
As a basic element of language learning, learning vocabulary as the foreign
language is very important because the learners have to use the vocabulary
whether inside or outside the classroom. According to Nation (1990) knowing a
word also defined as knowing its spelling, pronunciation, collocations (i.e. words
it co-occurs with), and appropriateness. The learners have to master vocabulary as
the basic element in learning a foreign language.
Farjami (2014) said that vocabulary profile gives information about the
frequency of function and content words. Vocabulary profile is effective to help
learners choose the appropriate vocabulary in the learning process. Morris and
Cobb (2004) stated, “Vocabulary profiles proved to be useful in carrying out a
finer assessment of the language skills of high proficiency of non-native speakers
than an oral review can offer.” By knowing the vocabulary profile, the teacher can
choose the appropriate vocabulary to assess the learners’ foreign language skills.
Vocabulary profile is needed for teachers and learners because both of
teachers and learners have mutual support in the interaction and communication in
the teaching learning process. The researcher believes that investigating the
vocabulary profile is important especially in teaching and learning vocabulary
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because teachers and students should understand the vocabulary level which
suitable for them. According to Capel (2012), vocabulary profile is needed
because it is a reference source for teachers that provide a fully searchable listing
of words and phrases in English at each level of words, phrases, phrases verbs,
and idioms.
Considering to the effectiveness and the need of vocabulary profile, the
investigation of this study was completed to assess the result of the vocabulary
profile in the 8 grade English textbook materials used in Anak Terang Junior High
School.
There were three research questions of the study:
1. What is the vocabulary profile in the 8 grade English textbook used in
Anak Terang Junior High School?
2. What is the proportion of vocabulary that is not covered in the textbook?
3. What is the token (word) recycling index of the textbook?
The objectives of the study were:
a. to investigate the vocabulary profile of 8 grade English textbook used in
Anak Terang Junior High School,
b. to produce list of vocabulary that are not covered in the textbook,
c. to produce the token (word) recycling index of the textbook.
The significance of this study is to provide teachers with important
information about the proportion of vocabulary in each frequency group, to gives
information for teachers about words that necessary to teach to Junior High
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School students, and also to help students to choose the vocabulary that they have
to understand.
At the end, this study tries to reveal the vocabulary profile of 8 grade
English textbook used in Anak Terang Junior High School. This study will apply
descriptive research by analyzing the vocabulary profile based on the category
above. By doing this research, it is expected that this study could be beneficial for
the teacher because by knowing the vocabulary profile of the textbook, the teacher
can pay attention to the words contained in the textbook and decide the suitable
words for the learners, hopefully the learners will be able understand the meaning
of the language easily.
LITERATURE REVIEW
Definition of Vocabulary
In the process of teaching and learning English as both second and foreign
language, vocabulary becomes basic elements which is very important. According
to Olmos (2009), “Vocabulary is the basic tool for shaping and transmitting
meaning”. By knowing the vocabulary as the basic element, the learners know
something about the message and the sense of those words as their achievement in
learning language. Therefore, the first step for the learners to do is to understand
the vocabulary. White, Graves, and Slated (1990, as cited in Wessels, 2011) said
that vocabulary knowledge is the best tool to determine the learners’ achievement
in learning language.
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The Importance of Vocabulary
As a basic element of English language teaching and learning process,
learning vocabulary is an important thing because the learners will use that
vocabulary to speak and write in the foreign language. Nation (1990) stated that
knowing a word was also defined as knowing the spelling, pronunciation,
collocations, and the appropriateness. Therefore, it was important to the learners
to master vocabulary as the basic element in learning foreign language since it
will help them to be easier to understand the meaning of the language.
Vocabulary Profile
Vocabulary profile is an aggregation of word frequencies (Graves, 2005). It
describes the frequency of word usage and the word groups where it belongs. It is
necessary to identify the vocabulary profile of the words contained in a textbook.
By knowing the vocabulary profile in that book, students in could learn the
vocabulary step by step depend on their level, from low to high-frequency words.
This vocabulary profile is very important in teaching and learning vocabulary
because students and teachers should understand which parts of vocabulary have
to be taught based on the learners’ level, it can be for beginners, intermediate, or
advanced learner.
Word Frequency
Word frequency, as Yoon and Zechner (2001) said, is a class of words that
is grouped based on their frequencies of actual usage in corpora. According to
Nation (1990 p. 19), there are four types of word frequency: high-frequency
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words or K-1 words (1-1000) and K2 words (1001-2000), academic vocabulary or
AWL words (academic), technical vocabulary, and low-frequency words. The
explanation of the types of word frequency will be explained in the next
paragraph.
First of all, according to Coper (2002, p. 5), high-frequency words are the
words most commonly appeared in the texts. According to Schmitt (2012), “High-
frequency word consists of 1000 and 1001 – 2000 family words”. As written in
the previous paragraph, according to Nation (1990 p. 19), “1 - 1000 family words
were the K-1 words and 1001 – 2000 family words was the K2 words ....” Then,
Schmitt (2012) argue that high-frequency vocabulary should be explicitly
addressed because it is very useful for the learners and around 2000 words are
considered as sufficient use and engage in daily conversation. Because of that, the
learners have to know and be familiar with the words. Farjami (2014) said that
vocabulary profile gives information about the frequency of function and content
words. Cook (2004) stated, “Function words are prepositions, articles,
conjunctions and so on, such as ‘of’, ‘the’, or ‘but’.” Content words are nouns,
verbs, adjectives and so on, like ‘glass’, ‘pay’ or ‘red’ (Cook, 2004). Both of
function words and content words are appeared in the result of the K-1 words.
Vadasy (2012) explained that academic words (AWL) making up 10% of
academic text. It is highly possible that if the result of the vocabulary profile
covers 10%, it means that the text is indicated as academic text. Academic word is
different from the word that usually used in daily conversation. It depends on the
area and the context. Usually, academic words are used in the university with
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different level. The examples of academic words such as ‘analyze’, ‘assess’,
‘concept’, ‘approach’.
Technical words occur in the small area. Technical words cannot be avoided
because it should be depend on the topic given form the texts, and it can make
some students face difficulties to understand the meaning of that vocabulary. For
example, a Chinese student will get difficulties in reading a literary text on
religious poetry and found the words liturgy and Eucharist because of his/her lack
knowledge in Christian worship (Nation, 1990). Nation (1990, p. 19) state that the
number of technical words is about 1000 to 2000 for each subject and it occur,
sometimes frequently, in specialized texts and the coverage of text is about 2% of
the running words in a specialized text. The examples of technical vocabulary for
electrical engineering field are ‘ampacity’, ‘ampere’, ‘computer’, ‘battery’,
‘bluetooth’, ‘controller’, ‘boost’, ‘brightness’, ‘floating’, ‘centimeter’, ‘chip’,
‘framer’, ‘circuit’, ‘chrome’, ‘frequency’.
According to Yoon and Zechner (2001), low-frequency word (Off-List) is
considered as difficult and sophisticated word. Generally, low-frequency words
consist of 2000 words. Examples of low-frequency vocabulary from Nation
(1990, p. 18) such as: abduct, aberration, abrasion, adventurous, afflict, coffle,
bane, bankrupt, barter, bespeak, caste, categorical, chameleon, caprice, carnage,
and cannibal.
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Relevant Previous Studies
There are several relevant previous studies related to this topic. The first
one, Graves (2005) use vocabulary profile was aimed to know the vocabulary
profile of letters and novels of Jane Austen and her contemporaries. About the
method, Graves (2005) state that his methods compare the word frequencies of
two or more texts from the same author, to evolve a profile word set for each
author that can be used to differentiate works by that author from those of other
authors. Then, the result of this analysis of the vocabulary profile of Jane Austen
shows a close relation to the frequency of words and three-word combination in
Austen’s novels and letters, and shows the sense of familiarity felt by readers of
both texts. This study also shows that Austen’s and Burney’s have a stronger
relation between the use of words in novels and letters than Edgeworth’s.
The second relevant previous study from McNeill (N.D) use vocabulary
profile was aimed to know about the use of vocabulary profiler of Lexical
Frequency Profile (LFP) as the learning tools to promote second language writing.
About the method, McNeill stated that “In order to promote the higher English
vocabulary targets for Hong Kong school leavers, the Education Bureau
commissioned a study of the vocabulary needs of Hong Kong primary and
secondary students, with a view to developing an English vocabulary curriculum
for primary and secondary education”. Then, the result is that LFP has always
been an assessment tool which can help language learners to improve the quality
of their writing product. Thereby, students will benefit more from using LFP as a
learning tool.
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Both of the relevant studies have the similarity in the general aim. Which
aims know the vocabulary profile of each sample. Then, compared to both of the
relevant studies, this study is different. The difference is about the sample. In this
study, the sample is the 8 grade English textbook used in Anak Terang Junior
High School. That is known as the school that adopts Singaporean curriculum into
the curriculum system of the school. Therefore, this kind of sample has never
been analyzed by other researchers.
THE STUDY
This study used the descriptive method to analyze the data. According to
Rivera (2007), descriptive method is used to find facts on which professional
judgment or theories could be based. In this study, the vocabulary in the textbook
were analyzed according to the category of high frequency words known as K-1
words (1-1000) and K2 words (1001-2000), academic words known as AWL
words (academic), and low-frequency words known as Off-List words.
Context of the Study
In this study, the object is Anak Terang Junior High School. The school is
located at Jl. Jendral Sudirman No. 105b Salatiga. This Junior High School has a
different kind of curriculum compared to other schools. It adopted Singaporean
curriculum, so it has different kind of system including the textbooks which the
students used to study. Therefore, the reasons why the researcher chose this
context was that: this school was adopting Singaporean curriculum, and use
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different English textbooks compared to other schools. This school is a bilingual
school that uses English as the medium of instruction in most classes.
Material
The material was selected from English textbook used in Anak Terang
Junior High School. The title of the textbook was ‘English in Mind’ for grade 8
that was published in 2010 by Cambridge University Press, and the material was
taken from the entire units from the textbook. The reason why the researcher
chose this material was because 8th grade is the middle level of Junior High
School, so the difficulty of the material would be in the middle of the easiest and
the hardest level.
Data Collection Instrument
The analysis used an electronic tool named Lextutor as the vocabulary
profiler. This tool is an online vocabulary profiler created by Tom Cobb in 1999
that can be accessed at http://www.lextutor.ca/. This tool can calculate the type
ratio (TTR) and the percentage of words of the text from high-frequency words or
K-1 words (1-1000) and K2 words (1001-2000), academic words known as AWL
words (academic), and low-frequency words known as Off-List words.
Data Collection Procedures
The first procedure was to type all the words of the words in each unit in
Microsoft Office Word. One file is for one unit, and the total of the file would be
14 files. All lexical items had to be selected except name of persons, name of
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towns, phonetic transcript, number, and to be-verbs. Second, the step of the
analyzing was to open the vocabulary profiler website on http://www.lextutor.ca/
to go to vocabulary profiler. Then, copy and paste the text from the unit that
would be analyzed and paste into the box provided. Finally, click submit button
under the box, and the result would appear.
Data Analysis Procedure
The data automatically appeared from the vocabulary profiler. The data
contain several kinds of words level: K-1 words (1-1000) and K2 words (1001-
2000), academic words known as AWL words (academic), and low-frequency
words known as Off-List words. In this study also analyzed list of vocabulary that
are not included in textbook and the token (word) recycling index of the textbook.
FINDINGS AND DISCUSSIONS
In this section presents the result of the analysis from the textbook entitled
‘English in Mind’. There were 36,154 words from the textbook analyzed using
The Compleat Lexical Tutor, V.4. The finding is divided into three parts. The first
part of this section shows the overall result from all units of the textbook
presenting the data about the vocabulary frequency classification. The second part
presents the negative vocabulary profile of K-1, K-2, and K-3 (AWL) with lists of
the vocabulary items that were not found in all units of the textbook.
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Table 1. The overall of vocabulary profile
FAMILIES
%
TYPES
%
TOKENS
%
CUMULATIVE
%
K-1 WORDS 733
58.08%
1426
45.90%
30840
85.30%
85.30%
K-2 WORDS 409
32.41%
603
19.41%
2601
7.19%
92.49%
AWL
(570 fams
TOT 2,570)
120
9.51%
169
5.43%
629
1.74%
94.23%
OFF LIST
? 909
29.26%
2084
5.77%
100%
TOTAL
1262+? 3107
100%
36154
100%
In the first row in Table 1 shows three terms; family, type and token. Words
family is a head word, for example, the family or head word of organization and
organizing is organize. While organization, organizing, and organized are
considered as the same type. Type is different words, for example, interest and
express are different words. Token is words in text; the total number of words in a
textbook. For example, if in a textbook there are give [4], run [4], small [2], and
vacation [5], the number of token is 15.
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Table 1 shows that the vocabulary used in the textbook was in most
frequently used 1000 words group (K-1). 7.19% of vocabulary fell under the
second 1000 words group (K-2) with the cumulative percentage of the word
coverage from the result was 92.49%, which is relatively close estimate for good
comprehension of the text in the textbook. According to Hirsch (2003), order to
comprehend a text, students need to understand about 90-95% of words in that
text. Based on the data in Table 1, this word knowledge band should include
knowledge as much as 1.74% of academic words. Academic words are those
words which are commonly used in academic texts. Selection is needed whether
the academic words that amounted 629 used in the textbook need to be introduced
to the students in Junior High Schools. Beside, another important point is the
number of off-list words that reached 2084 words. Off-list words are those words
that did not include into the frequency list and may occur infrequently (low
frequency words), it may have words that students at this level need to know. This
low frequency list should not be ignored in teaching since students at this level
need to know about this vocabulary, and teachers have to make a selection for
useful words in this category.
Negative Vocabulary Profiles of The Textbook
In this section presents the description of negative vocabulary profiles result
of the textbook. Negative vocabulary is vocabulary items that are not found in the
textbook. These missing words are derived from differences of words in the New
General Word List (NGWL) and words in the textbook. The list of negative K-1
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words below is useful for teachers in selecting vocabulary items that students may
find useful to develop their vocabulary knowledge.
1. Negative Vocabulary Profile of K-1
The analysis below shows all the word families (=head words) from the K-
1 level that were not found in the textbook. The summary of negative vocabulary
profile for K-1 level is presented below.
K-1 total words families : 964
K-1 families in input : 727 (75.41%)
K-1 families not in input : 238 (24.69%)
These percentages do not refer to tokens in text but rather number of families.
Based on the summary above, there was 75.41% of word families that were found
in the textbook, which means that there was 24.69% of word families were
‘missing’ or not found based on the words listed in New General Service List
(NGSL).
Below are some of the word families that are not found in the input
textbook. The complete word list has been put in Appendix A.
ACCOUNTABLE ACTRESS ADMIT ADOPT
AFFAIR AGAINST AGENT ALTHOUGH
ARMY
ASSOCIATE ATTEMPT BAR
BATTLE
BELONG BENEATH
BESIDE
BEYOND BILL BLUE BOARD
CASTLE CHIEF
COAL
COIN
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COLLEGE
COLONY
COMMITTEE CONCERN
DECLARE DEEP DEGREE
DEMAND
EAST
ELECT ELEVEN
ENEMY
ESCAPE
EXCEPT
EXCHANGE
FAMILIAR
2. Negative Vocabulary Profile of K-2
This following analysis shows all the word families (=head words) from K-2
that were not found in the textbook. The summary of negative vocabulary profile
for K-2 level is presented below.
K-2 Total word families : 986
K-2 families in input : 408 (41.38%)
K-2 families not in input : 579 (58.72%)
From the data above, the percentages do not refer to tokens or number of words in
the textbooks but rather number of word families. Depend on the summary above,
there was 41.38% of word families that were found in the textbooks, and there
was 58.72% of word families were not found based on the words listed in New
General Service List (NGSL).
The followings are some of the word families K-2 that were not found in the
input of the textbook. The complete word list has been put in Appendix B.
ABSENCE ABSENT ABSOLUTE
ABSOLUTELY
ARROW
ARTIFICIAL ASH ASHAMED
ASIDE
ASTONISH
ATTEND AVENUE
AVOID AWKWARD AXE BAKE
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BALANCE BARBER BARE BATH
BAY BEAK CAPE
CARRIAGE
CART CATTLE CAUTION CENTIMETRE
CHAIN
CHALK
DARE
DEAF
DEBT DECAY DECEIVE DECREASE
DEED DEER
DEFEND DELICATE
3. Negative Vocabulary Profile of K3 or AWL
This analysis below presents all the word families (=head words) from the
K-3 or AWL level that were not found in the textbook. The summary of negative
vocabulary profile for K-3 or AWL level is presented below.
K-3 Total word families : 569
K-3 families in input : 119 (20.91%)
K-3 families not in input: 451 (79.26%)
The percentages above do not refer to tokens in text but rather number of families.
Based on the summary above, there was 20.91% of word families were found in
the textbook, which means, there was 79.26% of word families were not found
based on the words listed in New General Service List (NGSL).
Below are some of the word families K-3 that were not found in the input
textbook. The complete word list has been put in Appendix C.
ABANDON
ABSTRACT ACADEMY ACCESS
ADAPT
ADEQUATE ADJACENT ADJUST
BEHALF BENEFIT BIAS BOND
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BULK
CAPABLE CAPACITY CATEGORY
CHANNEL
UNIT COMMISSION COMMIT
COMMODITY COMPILE
COMPLEMENT COMPONENT
COMPOUND COMPREHENSIVE
DATA DEBATE
DECADE DECLINE
DENOTE DENY
DEPRESS DERIVE ETHNIC
EVALUATE
EVIDENT EVOLVE
FINANCE
FINITE
Block Frequency Output of Off-List Words.
In this section presents the description of block frequency output of ‘Off-
list’ words of the textbook. Off-list words are words that are not listed in K-1, K-
2, or AWL. By using the tool in the Vocabulary Profiler, those words have been
frequency-block per ten words and have been arranged from high to low
frequency. However, depend on the result below, all of Off-list words are used
once, it means that all of words in Off-list words are different at all.
The list of words below may be useful for teachers to use some words that
are necessary for teaching. Then, it also helpful for the teachers to help them to
choose words from the list that are relevant to teach in Junior High School. Below
is the list of ‘Off-list’ words with 845 tokens and 845 types. Besides, RANK is
ranking of words, FREQ is words occurancce frequency, COVERAGE is the
percentage of word occurance, individual or cumulative, and the last column is the
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vocabulary item. For the information, the complete list of blocked frequency has
been put in Appendix D.
RANK FREQ COVERAGE
ind. cumulative WORD
1. 1 0.12% 0.12% ACCOMODATION
2. 1 0.12% 0.24% ACRES
3. 1 0.12% 0.36% AC
4. 1 0.12% 0.48% ADAPTOR
5. 1 0.12% 0.60% ADDICTED
6. 1 0.12% 0.72% ADJECTIVES
7. 1 0.12% 0.84% ADOLESCENCE
8. 1 0.12% 0.96% ADVERBS
9. 1 0.12% 1.08% ADVERB
10. 1 0.12% 1.20% AGIN
11. 1 0.12% 1.32% AIRES
12. 1 0.12% 1.44% AIRPORT
13. 1 0.12% 1.56% AI
14. 1 0.12% 1.68% ALBUMS
15. 1 0.12% 1.80% ALBUM
16. 1 0.12% 1.92% ALLERGIC
17. 1 0.12% 2.04% ALUMINIUM
18. 1 0.12% 2.16% AMAZING
19. 1 0.12% 2.28% AMBULANCE
20. 1 0.12% 2.40% ANKLES
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Comparison of Vocabulary Frequency Across Units of The Textbook
In this section of this part presents the comparison of vocabulary profile
accross units of the textbook. Table 2 below shows the comparison of vocabulary
across units of the textbook used in this study.
Table 2. Comparison of word frequency level
K-1
WORDS
(%)
K-2
WORDS
(%)
AWL (%) Off-list (%)
UNIT 1
CUMULATIVE %
86.09
86.09
7.10
93.19
1.16
94.35
5.65
100
UNIT 2
CUMULATIVE %
82.36
82.36
7.44
89.80
1.58
91.38
8.62
100
UNIT 3
CUMULATIVE %
82.02
82.02
7.49
89.51
2.24
91.75
8.25
100
UNIT 4
CUMULATIVE %
84.98
84.98
6.86
91.84
1.38
93.22
6.78
100
UNIT 5
CUMULATIVE %
86.31
86.31
6.23
92.54
1.99
94.53
5.47
100
UNIT 6
CUMULATIVE %
86.79
86.79
7.89
95.68
0.99
95.67
4.33
100
UNIT 7
CUMULATIVE %
84.15
84.15
6.91
91.06
2.64
93.70
6.30
100
UNIT 8 85.89 8.92 1.16 4.03
20
CUMULATIVE % 85.89 94.81 95.97 100
UNIT 9
CUMULATIVE %
86.71
86.71
6.28
92.99
2.43
95.42
4.58
100
UNIT 10
CUMULATIVE %
86.06
86.06
6.20
92.26
1.38
93.64
6.36
100
UNIT 11
CUMULATIVE %
85.21
85.21
7.42
92.63
1.91
94.54
5.46
100
UNIT 12
CUMULATIVE %
83.43
83.43
5.81
89.24
3.84
93.08
6.92
100
UNIT 13
CUMULATIVE %
87.61
87.61
7.25
94.86
0.99
95.85
4.15
100
UNIT 14
CUMULATIVE %
85.93
85.93
8.52
94.45
0.79
95.24
4.76
100
According to Table 2, the differences in percentages of K-1, K-2, AWL, and
Off-list words accross units in the textbook were not really significant because the
differences were very small. Depend on the result in Table 2, I can say that the
percentage of vocabulary in each frequency level in all units of the textbook are
relatively similar. However, in this case, I may expect that students may face
difficulty to learn the textbook since AWL words are usually used in academic
texts. In addition, the cumulative percentages of K-1 and K-2 words also provide
information about difficulty of understanding the textbook as the percentage of K-
21
1 and K-2 words are around 95% as the requirement for good understanding of a
text.
Depend on the result from Table 2, the percentage of K-1 in Unit 3 became
the lowest percentage compared to the other units, the percentage of K-1 was only
82.02%. In contrast, the highest percentage of K-1 from this textbook was in Unit
13, which was 87.61%. Besides, the AWL words from the entire units are became
the lowest percentage compared to others words frequency. Here, the percentage
of AWL words of Unit 14 became the lowest percentage, which only reach
0.79%. In contrast, the highest percentage of AWL words was reach 3.84% for
Unit 12.
Text Comparison Across Units of The Textbook
1. Comparison of Unit 3 vs. Unit 13
This section presents the comparison across unit of the textbook. The
comparison presents the token recycling index of the unit that already compared.
Recycling index is the ratio between words that are shared by two units and the
total number of words in the second unit that already compared. The index
provides important information about similar words in both unit and unique word
in the second unit. The first comparison was comparison between Unit 3 and Unit
13. Those units were compared because Unit 3 became the lowest K-1 compared
to all units of the textbook. In contrast, Unit 13 had the highest percentage of K-1.
The comparison result of Unit 3 and Unit 13 shows that the token recycling index
22
was 77.83%. The result indicated that there were 77.83% similar (shared) words
in Unit 3 and Unit 13, the amount of the similar words from these units were 1234
words. Then, there were 22.17% (100%-77.83%) unique words in Unit 13, the
amount of those unique words were as much as 1579 words. Table 3 shows the
similar (shared) and unique words in Unit 3 and Unit 13. The complete table has
been put in Appendix E.
Table 3. Shared and unique words in unit 3 and unit 13
TOKEN Recycling Index: (1243 repeated tokens : 1597 tokens in new text) = 77.83%
FAMILIES Recycling Index: (205 repeated families : 396 families in new text) = 51.77%
Table 3. Shared and unique words in unit 3 and unit 13
Table 3. Shared and unique words in unit 3 and unit 13
23
2. Comparison of Unit 12 vs. Unit 14
This part shows the comparison across unit of the textbook. The comparison
presents the token recycling index of two units that already compared. Recycling
index is the ratio between words that are shared by two units and the total number
of words in the second unit that already compared. The index shows important
information about similar words in both unit and unique word in the second unit.
The second comparison was comparison between Unit 12 and Unit 14. Those
units being compared since Unit 12 had the highest percentage of AWL words,
which was 3.84%. In contrast, Unit 14 became the lowest percentage of AWL
words which was only 0.79%.
The result of the comparison from Unit 12 and Unit 14 shows that the token
recycling index was 82.75%. The result indicated that there were 82.75% similar
(shared) words in Unit 12 and Unit 14, the total words of the similar words from
these units were 2850 words. Thus, there were 17.25% (100%-82.75%) unique
words in Unit 14, the total words of those unique words were as much as 3444
words. Table 4 presents the data of similar (shared) and unique words from Unit
12 and Unit 14 from the textbook that were used in this study. For the
information, the complete table has been put in Appendix F.
Table 4. Shared and unique words in unit 12 and unit 14
TOKEN Recycling Index: (2850 repeated tokens : 3444 tokens in new text) = 82.75%
FAMILIES Recycling Index: (331 repeated families : 654 families in new text) = 50.61%
24
CONCLUSION
The purpose of this study was to show the result of vocabulary profile of
Junior High School English textbook; the title is English in Mind for grade 8 that
was published in 2010 by Cambridge University Press. The second purpose of the
study is to find the proportion of vocabulary that are not appeared in the textbook,
and find the words recycling index of the textbook. From the overall result, it
showed the proportions of the vocabulary frequency classifications. Depend on
the overall result from all units of the textbook shows the percentage of K-1 words
25
(1-1000) was 85.30%, K-2 words (1000-2000) was 7.19%, AWL words was
1.74%, and the percentage of Off-list words was 5.77%.
The second result of this study was about the negative vocabulary profiles
of K-1, K-2 and AWL with lists of words that were not found in the textbook, and
also block frequency output of the Off-list words from all units of the textbook.
For the result of this part, as much as 964 words are the total families of K-1
words with the total of K-1 families in input was 727 words, and K-1 families that
were not in input was 238 words.
Second, the total families of K-2 words were 986 words with the amount of
K-2 families in input were 408 words, and the amount of K-2 families that not in
input was 579 words. Third, the total of AWL (K-3) word families was 569
words, the families in input for AWL words was only 199, and K-3 families that
not in input was 451 words. The last, the total of Off-list words was 845 as same
as the types of Off-list words also 845 types.
The last result showed the frequency levels from all units of the textbook
and two comparisons of units which selected depend on the significant differences
in form of percentage. For the first comparison, it was comparing the K-1 words
form Unit 3 and Unit 13. The result of this comparison shows the percentage of
the token recycling index was 77.83%. It means that there were 77.83% similar
words in Unit 3 and Unit 13, the total of words of the similar words from these
units was 1234 words. Besides, there were 22.17% unique words in Unit 13, with
1579 words as the total tokens. The comparison result of Unit 12 and Unit 14
26
showed that the token recycling index was 82.75%, it indicated that from these
units there were 82.75% similar (shared) words, and the total words of the similar
words from these units were 2850 words. Moreover, there were 17.25% (100%-
82.75%) unique words in Unit 14, the total words of those unique words were as
much as 3444 words.
For the further research, may the other researcher able to choose the same
title of the book, English in Mind that was published in 2010 by Cambridge
University Press. However, the researcher may choose the other grade, such as for
grade 7 and 9, to comparing the level of difficulty since Anak Terang Junior High
School using those books in their curriculum.
For the suggestion, I suggest that the teachers need to pay attention to the
most often most often occurring words that were used in the students’ textbook.
May the teachers have kind of words lists taken from the textbook that contains
words that indicated as difficult words, so the teachers are able to provide the
alternative words or the synonyms of that difficult words. By having that kind of
thing, it was good to help the teachers in giving a suitable explanation in the
classroom that may help the students easy to understand the main point of the
materials. In addition, knowing the information about vocabulary profile of the
textbook is important and it is helpful for the teachers to check the vocabulary in
the material that would be given to the students. Furthermore, this is good to help
the teacher in choosing suitable vocabulary assignments, exercises, home works,
and many other activities by knowing the difficulties of the vocabulary in each
unit.
27
REFERENCES
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Context: The State of The Art. UniversitasNegeriMalang: TEFLIN
JOURNAL.
Capel, A. (2012). Completing the English Vocabulary Profile: C1 and C2
Vocabulary English Profile Journal, 3(1), 1-14.
Chapelle, C. A., & Jamieson, J. (2008). Tips for Teaching with CALL: Practical
approaches to computer-assisted language learning. New York: Pearson
Education.
Cook, Vivian. (2004). The English Writing System. London: Hodder Arnold.
Coper, T. (2002). 100 Write-and-learn sign word practice pages: Engaging
reproducible activity pages that help kids recognize, write, and really
LEARN the top 100 high-frequency words that are key to reading
success. New York: Scholastic Inc.
Farjami, H. (2014). The vocabulary profile of Iranian English teaching school
books. Iranian Journal of Applied Linguistics, 17(2), 1-26.
Graves, D. (2005). A Publication of the Jane Austen Society of North America.
Vocabulary Profiles of Letters and Novels of Jane Austen and her
Contemporaries, 26 (1). Retrieved from http://www.jasna.org./index.html
Hirsch, E.D. (2003). Reading comprehension requires knowledge of words and
the world. American Educator, Spring. American Federation of Teachers.
Olmos, C. (2009). An Assesment of the Vocabulary Knowledge of Students in the
Final Year of Secondary Education is Their Vocabulary Extensive
Enough?. International Journal of English Studies, Special Issue, 73-90.
McNeill, A. (N.D). The Hong Kong University of Science and Technology. Using
Vocabulary Profiling Assessment Software to Promote Independent
Process Writing, Issue 2, 139-146. Retreived from
http://wlts.edb.hkedcity.net/filemanager/file/AandL2uNIT/A&L2_(11)%
20%20Artur%20Mcneill.pdf
Morris, L., & Cobb, T. (2004). Vocabulary Profile as Predictors of the Academic
Performance of Teaching English as a Second Language Trainees.
System 32, 75-87.
Nation, I. S. P. (1990). Teaching and Learning Vocabulary. Boston: Heinle & Heinle
Publisher.
28
Rivera, M., & Rivera, R. (2007). Practical guide to thesis and dissertation
writing. Quezon City: KHATA publishing, Inc.
Schmitt, N., & Schmitt, D. (2012). Frequency and vocabulary size. A
reassessment of frequency and vocabulary size in L2 vocabulary
teaching, 1 – 20. Retreived from http://journals.cambridge.org
Vadasy, P. F. (2012). Vocabulary Instruction for Struggling Students. New York:
A Division of Guilford Publications, Inc.
Wessels, S. (2011). Promoting vocabulary learning for English Learner. Teaching
Tip.
Yoon, S. Y., & Zechner, K. (2001). Vocabulary Profile as a Measure of
Vocabulary Sophistication. The 7th Workshop on the Innovative Use of
NLP for Building Education Applications, Special Issue, 180-189.
29
ACKNOWLEDGEMENTS
This thesis would not have been completed without any support and help
from many people around me. First of all, I would like to thank you for Allah
SWT who always blesses me, so I can complete this thesis and graduate in July
2016. Second, I would like to express my sincere appreciation to my thesis
supervisor, Anne Indrayanti Timotius, M.Ed, and the examiner, Prof. Dr. Gusti
Astika, M.A. By their guidance and knowledge, I could finish my thesis. Third, I
also would like to say thank you to Anak Terang Junior High School for the
opportunity to take some data to complete my thesis. Special thanks are also
dedicated to my beloved parents (Raharja and Siti Khoni’ah) and also my
brothers, Gilang Agung Saputra, and Geoprama Saputra. Many thanks to Agung
Basofi Rafsanjany for his support and prayer in finishing this study. Then, a
bunch of thanks is also addressed for Ulfa Awisti Wijayanti, Yayas Khomala
Sanjivani, Novi Puspitasari, Fajarini Prihastuti, Retrianti Prasetya, Yiskaningtyas
Nugraheni, Ika Runtiana, Luky Rias who always support me to finish this thesis
patiently. Last but not least, I want to say thanks to Twelvers for the great
friendship and the unforgettable memories.
30
Appendixes
Appendix A
Negative vocabulary profile of K1
ACCOUNTABLE
ACTRESS
ADMIT
ADOPT
AFFAIR
AGAINST
AGENT
ALTHOUGH
APPLY
APPOINT
ARISE
ARMY
ASSOCIATE
ATTEMPT
BAR
BATTLE
BELONG
BENEATH
BESIDE
FORMER
FORTH
FORTUNE
FURNISH
GAIN
GATHER
GENTLE
GOD
HARDLY
HEAVEN
HONOUR
HOWEVER
INCH
INDEED
INDEPENDENT
INDUSTRY
INFLUENCE
INSTEAD
IRON
REGARD
REMAIN
REMARK
REPRESENT
REPUBLIC
RESPECT
ROYAL
SALE
SCARCE
SEASON
SEAT
SENSITIVE
SEPARATE
SETTLE
SHADOW
SHAKE
SHALL
SHAPE
SHOULDER
31
BEYOND
BILL
BLUE
BOARD
BOAT
BRANCH
BREAD
BRIDGE
BRIGHT
BROAD
CASTLE
CHIEF
CLAIM
COAL
COAST
COIN
COLLEGE
COLONY
COMMAND
COMMITTEE
CONCERN
CONSIDER
CORN
JOINT
JOINTED
JOY
JUSTICE
LACK
LADY
LATTER
LENGTH
LIP
LORD
LOSS
MANNER
MANUFACTURE
MASS
MASTER
MEASURE
METAL
MINER
MINISTER
MISTER
MONDAY
MOON
MORAL
SIDE
SIGHT
SILVER
SIR
SOCIAL
SOCIETY
SOFT
SOLDIER
SON
SOUL
SPEED
SPITE
SPREAD
STAGE
STANDARD
STOCK
STORE
STREAM
STRENGTH
STRUGGLE
SUBSTANCE
SUPPORT
SURFACE
32
COUNCIL
COURT
CROWD
CROWN
CURRENT
DEAL
DECLARE
DEEP
DEGREE
DEMAND
DEPARTMENT
DESIRE
DETERMINE
DISTINGUISH
DISTRICT
DIVIDE
DOUBT
DUE
DUTY
EAST
EFFICIENT
EFFORT
ELECT
MOREOVER
MOTOR
MRS
NATION
NATIVE
NECESSITY
NEITHER
NOBLE
NOR
NUMERICAL
NUMEROUS
OBSERVE
OCCASION
OFFICIAL
OTHERWISE
OUGHT
OWE
PLAIN
POVERTY
PRESS
PRIVATE
PRODUCT
PROFIT
SURROUND
SWORD
TAX
TEAR
TERM
THEREFORE
THIRTEEN
THIRTY
THUS
TON
TRADE
TRUST
TUESDAY
UNION
VALLEY
VALUE
VARIETY
VARIOUS
VESSEL
VICTORY
VIEW
VIRTUE
WAGE
33
ELEVEN
EMPLOY
ENEMY
ESCAPE
EXCEPT
EXCHANGE
FAMILIAR
FELLOW
FIGHT
FIGURE
FIT
FIX
FLOW
FLOWER
FORCE
PROOF
PROPER
PROPERTY
PROPOSE
PROTECT
PROVE
PROVISION
PURPOSE
QUALITY
QUANTITY
QUARTER
RANK
RATE
RATHER
RECEIPT
WAVE
WEALTH
WEST
WESTERN
WHETHER
WHITE
WHOLE
WILD
WISE
WITHIN
WOUND
YIELD
34
Appendix B
Negative vocabulary profile of K2
ABSENCE
ABSENT
ABSOLUTE
ABSOLUTELY
ACCUSE
ACCUSTOM
AFFORD
AGRICULTURE
AIRPLANE
ALIKE
ALOUD
AMBITION
AMUSE
ANGLE
ANXIETY
APART
APOLOGY
APPLAUD
APPLAUSE
APPROVE
FRAME
FRY
FUNERAL
FUR
GALLON
GAY
GENEROUS
GLORY
GOAT
GRACE
GRADUAL
GRAIN
GRAM
GRASS
GRAVE
GREASE
GREED
GREET
GRIND
GUARD
RESIGN
RESIST
RETIRE
REVENGE
REVIEW
REWARD
RIBBON
RICE
RIPE
RISK
RIVAL
ROAR
ROAST
ROD
ROOT
ROPE
ROT
ROW
RUG
RUIN
35
ARCH
ARREST
ARROW
ARTIFICIAL
ASH
ASHAMED
ASIDE
ASTONISH
ATTEND
ATTENTION
ATTRACT
AUDIENCE
AVENUE
AVOID
AWAKE
AWKWARD
AXE
BAKE
BALANCE
BARBER
BARE
BARELY
BARGAIN
GUIDE
GUILTY
GUN
HABIT
HAMMER
HANDKERCHIEF
HARBOR
HASTE
HAT
HAY
HEAL
HEAP
HESITATE
HINDER
HOLLOW
HOLY
HOOK
HORIZON
HOST
HUMBLE
HUNT
HURRAH
IDEAL
RUSH
RUST
SACRED
SACRIFICE
SADDLE
SAKE
SAMPLE
SATISFY
SAUCE
SAUCER
SAWS
SCALE
SCATTER
SCENT
SCISSORS
SCOLD
SCORN
SCRAPE
SCRATCH
SCREW
SEED
SEIZE
SELDOM
36
BARREL
BASIN
BATH
BATHE
BAY
BEAK
BEAM
BEARD
BEAST
BEHAVIOUR
BELL
BELT
BEND
BERRY
BIND
BITTER
BLADE
BLAME
BOAST
BOIL
BOLD
BOTTOM
BOUND
IDLE
IMMENSE
INFORMAL
INFORMALLY
INN
INQUIRE
INSIDE
INSTANT
INSULT
INSURE
INTEND
INTERFERE
INTERRUPT
INWARD
JAW
JEWEL
KICK
KILOGRAM
KISS
KNEE
KNEEL
KNIFE
KNOT
SELF
SEVERE
SEW
SHADE
SHALLOW
SHARP
SHAVE
SHEEP
SHEET
SHELF
SHELL
SHELTER
SHIELD
SHILLING
SHOWER
SIGNAL
SILK
SINCERE
SINK
SKILL
SKIRT
SLAVE
SLIDE
37
BOUNDARY
BOW
BOWL
BRASS
BREATH
BRIBE
BRICK
BROADCAST
BRUSH
BUCKET
BUNCH
BUNDLE
BURIAL
BURY
BUSH
BUTTER
CAGE
CAPE
CARRIAGE
CART
CATTLE
CAUTION
CENTIMETRE
LAMP
LEAN
LEATHER
LID
LIMB
LOAF
LOAN
LODGING
LONE
LOOSE
LOYAL
LUMP
LUNG
MALE
MAP
MAT
MEANTIME
MEANWHILE
MEAT
MECHANIC
MELT
MEND
MERCHANT
SLIGHT
SLIP
SLOPE
SOAP
SOCK
SOIL
SOLEMN
SOLID
SOUP
SOUR
SOW
SPADE
SPARE
SPILL
SPIN
SPIT
SPLENDID
SPLIT
SPOIL
SPOON
STAFF
STAIN
STEADY
38
CHAIN
CHALK
CHEAT
CHEER
CHEQUE
CHICKEN
CHIMNEY
CLAY
CLERK
COARSE
COLLAR
COMB
COMBINE
COMPANION
COMPETE
CONFESS
CONFIDENCE
CONQUER
CONSCIENCE
CONSCIOUS
COPPER
COPY
CORK
MERCY
MERRY
MESSENGER
MILD
MILL
MILLIGRAM
MILLILITRE
MILLIMETRE
MINERAL
MODERATE
MODEST
MONKEY
MOTION
MUD
MULTIPLY
MURDER
NARROW
NEAT
NECK
NEGLECT
NEPHEW
NIECE
NOON
STEAM
STEEP
STEER
STEM
STIFF
STING
STIR
STOCKING
STOVE
STRAP
STRAW
STRETCH
STRICT
STRIP
STRIPE
SUCK
SUGAR
SUPPER
SUSPECT
SUSPICION
SWALLOW
SWEAR
SWEAT
39
COUGH
COWARD
CREATURE
CREEP
CRIME
CRIMINAL
CRITIC
CROP
CRUEL
CRUSH
CULTIVATE
CUPBOARDS
CURIOUS
CURL
CURSE
CURVE
CUSHION
CUSTOM
CUSTOMER
DAMP
DARE
DEAF
DEBT
NUISANCE
NUT
OAR
OBEY
OFFEND
OMIT
OPPOSE
ORGAN
ORIGIN
ORNAMENT
OUTLINE
PACK
PALE
PAN
PARCEL
PASSAGE
PASTE
PATH
PATRIOTIC
PATTERN
PAUSE
PAW
PEARL
SWEEP
SWELL
SYMPATHY
TAIL
TAILOR
TAME
TASTE
TEA
TELEGRAPH
TEMPER
TEMPT
TEND
TENDER
THICK
THIEF
THIN
THIRST
THORN
THOROUGH
THREAD
THUMB
THUNDER
TIDE
40
DECAY
DECEIVE
DECREASE
DEED
DEER
DEFEND
DELICATE
DELIVER
DESCEND
DESERVE
DESPAIR
DEVIL
DIG
DIP
DISAPPOINT
DISCIPLINE
DISGUST
DISMIS
DISTURB
DITCH
DIVE
DONKEY
DOT
PECULIAR
PEN
PENCIL
PENNY
PERFORM
PERMANENT
PERSUADE
PIG
PIGEON
PILE
PIN
PINCH
PINT
PITY
PLATE
PLENTY
PLOUGH
PLURAL
POISON
POLISH
POSTPONE
POUR
POWDER
TIGHT
TOBACCO
TOE
TONGUE
TOOL
TOWEL
TOWER
TRANSLATE
TRAY
TREMBLE
TRICK
TUBE
TUNE
UNIVERSE
UPPER
UPRIGHT
UPWARDS
URGE
VAIN
VEIL
VERSE
VOWEL
VOYAGE
41
DOZEN
DRAG
DRAWER
DROWN
DUCK
DULL
DUST
EAGER
EARNEST
EASE
EDGE
EDUCATE
ELASTIC
ELEPHANT
ENCLOSE
ENCOURAGE
ENVELOPE
ESSENCE
EVIL
EXAMINING
EXCESS
EXTRAORDINARY
FADE
PRACTICAL
PRAISE
PRAY
PREACH
PRECIOUS
PREJUDICE
PRESERVE
PRETEND
PRIDE
PRIEST
PRISON
PROCESSION
PROFESSION
PROGRAMME
PROMPT
PUMP
PUNCTUAL
PUNISH
PUPIL
PURE
PURPLE
QUALIFY
QUARREL
WAIST
WANDER
WARN
WEAPON
WEAVE
WEED
WHEAT
WHEEL
WHIP
WHISPER
WIDOW
WINE
WING
WITNESS
WOOL
WORM
WORSHIP
WRAP
WRECK
WRIST
42
FAINT
FANCY
FASTEN
FATE
FAULT
FEAST
FEATHER
FEVER
FIERCE
FIRM
FLAVOUR
FLESH
FLOAT
FLOUR
FOND
FORBID
FORGIVE
FORK
FORMAL
QUART
RAIL
RAKE
RAPID
RARE
RAT
RAW
RAY
RAZOR
REFER
REFLECT
REFRESH
REJOICE
REMEDY
REPAIR
REPLACE
REPRODUCE
REPUTATION
REQUEST
43
Appendix C
Negative Vocabulary Profile of K3
ABANDON
ABSTRACT
ACADEMY
ACCESS
ACCOMMODATE
ACCOMPANY
ACCUMULATE
ACCURATE
ACHIEVE
ACKNOWLEDGE
ACQUIRE
ADAPT
ADEQUATE
ADJACENT
ADJUST
ADMINISTRATE
DOCUMENT
DOMAIN
DOMESTIC
DOMINATE
DRAFT
DURATION
DYNAMIC
ECONOMY
EDIT
ELIMINATE
EMERGE
EMPIRICAL
ENABLE
ENCOUNTER
ENERGY
ENFORCE
OCCUR
OFFSET
ONGOING
ORIENT
OUTCOME
OUTPUT
OVERALL
OVERLAP
OVERSEAS
PANEL
PARADIGM
PARALLEL
PARAMETER
PARTICIPATE
PERCEIVE
PERSIST
44
ADVOCATE
AGGREGATE
AID
ALBEIT
ALLOCATE
ALTER
ALTERNATIVE
AMBIGUOUS
AMEND
ANALOGY
ANALYSE
ANNUAL
ANTICIPATE
APPARENT
APPEND
APPRECIATE
APPROACH
APPROPRIATE
ENHANCE
ENSURE
ENTITY
EQUATE
EQUIVALENT
ERODE
ESTABLISH
ETHIC
ETHNIC
EVALUATE
EVIDENT
EVOLVE
EXCEED
EXCLUDE
EXPAND
EXPLICIT
EXPLOIT
EXPORT
PERSPECTIVE
PHASE
PHENOMENON
PHILOSOPHY
PLUS
POLICY
PORTION
POSE
POTENTIAL
PRACTITIONER
PRECEDE
PRECISE
PREDOMINANT
PRELIMINARY
PRESUME
PREVIOUS
PRIMARY
PRIME
45
APPROXIMATE
ARBITRARY
ASPECT
ASSEMBLE
ASSESS
ASSIGN
ASSIST
ASSUME
ASSURE
ATTACH
ATTAIN
ATTITUDE
ATTRIBUTE
AUTHOR
AUTHORITY
AUTOMATE
AVAILABLE
AWARE
EXPOSE
EXTERNAL
FEATURE
FEDERAL
FEE
FINANCE
FINITE
FLEXIBLE
FLUCTUATE
FORMAT
FORMULA
FORTHCOMING
FOUNDATION
FOUNDED
FRAMEWORK
FUNCTION
FUND
FUNDAMENTAL
PRINCIPAL
PRINCIPLE
PRIOR
PRIORITY
PROCEED
PROCESS
PROFESSIONAL
PROHIBIT
PROMOTE
PROSPECT
PROTOCOL
PUBLICATION
PURCHASE
PURSUE
QUALITATIVE
QUOTE
RADICAL
RANDOM
46
BEHALF
BENEFIT
BIAS
BOND
BRIEF
BULK
CAPABLE
CAPACITY
CATEGORY
CEASE
CHALLENGE
CHANNEL
CHAPTER
CIRCUMSTANCE
CITE
CIVIL
CLARIFY
CODE
FURTHERMORE
GENDER
GENERATE
GRANT
GUARANTEE
GUIDELINE
HENCE
HIERARCHY
HIGHLIGHT
HYPOTHESIS
IDENTICAL
IDENTIFY
IDEOLOGY
IGNORANT
ILLUSTRATE
IMAGE
IMPACT
IMPLEMENT
RANGE
RATIO
RATIONAL
REFINE
REGIME
REGISTER
REGULATE
REINFORCE
REJECT
RELEASE
RELEVANT
RELUCTANCE
REMOVE
REQUIRE
RESEARCH
RESIDE
RESOLVE
RESOURCE
47
COHERENT
COINCIDE
COLLEAGUE
COMMENCE
COMMISSION
COMMIT
COMMODITY
COMMUNICATE
COMMUNITY
COMPATIBLE
COMPENSATE
COMPILE
COMPLEMENT
COMPONENT
COMPOUND
COMPREHENSIVE
COMPRISE
CONCEIVE
IMPLICATE
IMPLICIT
IMPLY
IMPOSE
INCENTIVE
INCIDENCE
INCLINE
INCOME
INCORPORATE
INDEX
INDUCE
INEVITABLE
INFER
INFRASTRUCTURE
INHERENT
INITIATE
INPUT
INSERT
RESPOND
RESTORE
RESTRAIN
RESTRICT
RETAIN
REVEAL
REVENUE
REVERSE
RIGID
ROUTE
SCENARIO
SCOPE
SECTOR
SECURE
SEEK
SELECT
SEQUENCE
SERIES
48
CONCEPT
CONCLUDE
CONCURRENT
CONDUCT
CONFER
CONFINE
CONFLICT
CONFORM
CONSENT
CONSIDERABLE
CONSTANT
CONSTITUTE
CONSTRAIN
CONSTRUCT
CONSULT
CONSUME
CONTACT
CONTEMPORARY
INSIGHT
INSPECT
INSTANCE
INSTITUTE
INTEGRAL
INTEGRATE
INTEGRITY
INTENSE
INTERMEDIATE
INTERNAL
INTERPRET
INTERVAL
INTERVENE
INTRINSIC
INVEST
INVESTIGATE
INVOKE
INVOLVE
SEX
SHIFT
SIGNIFICANT
SIMULATE
SO-CALLED
SOLE
SOMEWHAT
SPECIFIC
SPECIFY
SPHERE
STABLE
STATISTIC
STATUS
STRAIGHTFORWARD
STRATEGY
SUBMIT
SUBORDINATE
SUBSEQUENT
49
CONTEXT
CONTRACT
CONTRADICT
CONTRARY
CONTRAST
CONTRIBUTE
CONTROVERSY
CONVENE
CONVERSE
CONVINCE
COOPERATE
COORDINATE
CORE
CORPORATE
CORRESPOND
CRITERIA
CRUCIAL
CURRENCY
ISOLATE
ISSUE
JUSTIFY
LABEL
LAYER
LECTURE
LEGISLATE
LEVY
LIBERAL
LICENCE
LIKEWISE
LINK
LOCATE
MAINTAIN
MANIPULATE
MARGIN
MATURE
MAXIMISE
SUBSIDY
SUCCESSOR
SUFFICIENT
SUM
SUPPLEMENT
SURVEY
SUSPEND
SUSTAIN
SYMBOL
TARGET
TASK
TECHNICAL
TECHNIQUE
TEMPORARY
TERMINATE
THEME
THEORY
THEREBY
50
DATA
DEBATE
DECADE
DECLINE
DEDUCE
DENOTE
DENY
DEPRESS
DERIVE
DESPITE
DETECT
DEVIATE
DEVICE
DEVOTE
DIFFERENTIATE
DIMENSION
DIMINISH
DISCRETE
MECHANISM
MEDIA
MEDIATE
MEDIUM
MENTAL
METHOD
MIGRATE
MILITARY
MINIMAL
MINIMISE
MINISTRY
MODE
MODIFY
MONITOR
MOTIVE
MUTUAL
NEUTRAL
NEVERTHELESS
THESIS
TRACE
TRANSFER
TRANSFORM
TRANSIT
TRANSMIT
TRANSPORT
TREND
TRIGGER
ULTIMATE
UNDERGO
UNDERLIE
UNDERTAKE
UNIFY
UTILISE
VALID
VARY
VERSION
51
DISCRIMINATE
DISPLACE
DISPLAY
DISPOSE
DISTINCT
DISTORT
DISTRIBUTE
DIVERSE
NONETHELESS
NORM
NOTION
NOTWITHSTANDIN
G
OBJECTIVE
OBTAIN
OBVIOUS
OCCUPY
VIA
VIOLATE
VIRTUAL
VISION
VOLUME
WELFARE
WHEREAS
WHEREBY
WIDESPREAD
52
Appendix D
Block Frequency Output of Off-list Words
RANK FREQ COVERAGE
ind. Cumulative WORD
1. 1 0.12% 0.12% ACCOMODATION
2. 1 0.12% 0.24% ACRES
3. 1 0.12% 0.36% AC
4. 1 0.12% 0.48% ADAPTOR
5. 1 0.12% 0.60% ADDICTED
6. 1 0.12% 0.72% ADJECTIVES
7. 1 0.12% 0.84% ADOLESCENCE
8. 1 0.12% 0.96% ADVERBS
9. 1 0.12% 1.08% ADVERB
10. 1 0.12% 1.20% AGIN
11. 1 0.12% 1.32% AIRES
12. 1 0.12% 1.44% AIRPORT
13. 1 0.12% 1.56% AI
14. 1 0.12% 1.68% ALBUMS
15. 1 0.12% 1.80% ALBUM
16. 1 0.12% 1.92% ALLERGIC
17. 1 0.12% 2.04% ALUMINIUM
18. 1 0.12% 2.16% AMAZING
19. 1 0.12% 2.28% AMBULANCE
20. 1 0.12% 2.40% ANKLES
21. 1 0.12% 2.52% ANKLE
22. 1 0.12% 2.64% ANTARTICA
23. 1 0.12% 2.76% ANTISEPTICS
24. 1 0.12% 2.88% ANTISEPTIC
25. 1 0.12% 3.00% ANTONYMS
26. 1 0.12% 3.12% APARTMENT
53
27. 1 0.12% 3.24% ARCHAEOLOGISTS
28. 1 0.12% 3.36% ARCHAEOLOGY
29. 1 0.12% 3.48% ARCHERY
30. 1 0.12% 3.60% ARCHITECTS
31. 1 0.12% 3.72% ARCHITECT
32. 1 0.12% 3.84% ASNWER
33. 1 0.12% 3.96% ASTRONOMERS
34. 1 0.12% 4.08% ATHLETE
35. 1 0.12% 4.20% ATH
36. 1 0.12% 4.32% ATMOSPHERE
37. 1 0.12% 4.44% ATTIC
38. 1 0.12% 4.56% AUDIO
39. 1 0.12% 4.68% AUSTRALIA
40. 1 0.12% 4.80% AUXILIARY
41. 1 0.12% 4.92% AU
42. 1 0.12% 5.04% AWAT
43. 1 0.12% 5.16% AWFUL
44. 1 0.12% 5.28% BACKACHE
45. 1 0.12% 5.40% BALCONY
46. 1 0.12% 5.52% BALDNESS
47. 1 0.12% 5.64% BAMBOO
48. 1 0.12% 5.76% BANANAS
49. 1 0.12% 5.88% BANANA
50. 1 0.12% 6.00% BANDAGE
51. 1 0.12% 6.12% BARN
52. 1 0.12% 6.24% BASEBALL
53. 1 0.12% 6.36% BASEMENT
54. 1 0.12% 6.48% BASKETBALL
55. 1 0.12% 6.60% BATHROOM
56. 1 0.12% 6.72% BATTERIES
54
57. 1 0.12% 6.84% BATTERY
58. 1 0.12% 6.96% BEACHES
59. 1 0.12% 7.08% BEACH
60. 1 0.12% 7.20% BECAUS
61. 1 0.12% 7.32% BEDTIME
62. 1 0.12% 7.44% BEES
63. 1 0.12% 7.56% BELIEFE
64. 1 0.12% 7.68% BERIMBAU
65. 1 0.12% 7.80% BERLIN
66. 1 0.12% 7.92% BICYCE
67. 1 0.12% 8.04% BIGGST
68. 1 0.12% 8.16% BIKES
69. 1 0.12% 8.28% BIKE
70. 1 0.12% 8.40% BISCUITS
71. 1 0.12% 8.52% BISTROS
72. 1 0.12% 8.64% BLANK
73. 1 0.12% 8.76% BLOGGING
74. 1 0.12% 8.88% BLOG
75. 1 0.12% 9.00% BOLT
76. 1 0.12% 9.12% BOMB
77. 1 0.12% 9.24% BONNET
78. 1 0.12% 9.36% BOOKED
79. 1 0.12% 9.48% BOOTH
80. 1 0.12% 9.60% BOOTS
81. 1 0.12% 9.72% BOOT
82. 1 0.12% 9.84% BORED
83. 1 0.12% 9.96% BORING
84. 1 0.12% 10.08% BOTHER
85. 1 0.12% 10.20% BOUNCED
86. 1 0.12% 10.32% BOUTIQUE
55
87. 1 0.12% 10.44% BOWIE
88. 1 0.12% 10.56% BOYFRIEND
89. 1 0.12% 10.68% BRACKETS
90. 1 0.12% 10.80% BRETT
91. 1 0.12% 10.92% BRILLIANTLY
92. 1 0.12% 11.04% BRILLIANT
93. 1 0.12% 11.16% BRITAIN
94. 1 0.12% 11.28% BROADBAND
95. 1 0.12% 11.40% BROCHURE
96. 1 0.12% 11.52% BROWSER
97. 1 0.12% 11.64% BROWSES
98. 1 0.12% 11.76% BUENOS
99. 1 0.12% 11.88% BULB
100. 1 0.12% 12.00% BUNGALOW
101. 1 0.12% 12.12% BURRS
102. 1 0.12% 12.24% BUR
103. 1 0.12% 12.36% BYE
104. 1 0.12% 12.48% CABLE
105. 1 0.12% 12.60% CAB
106. 1 0.12% 12.72% CAFE
107. 1 0.12% 12.84% CANADA
108. 1 0.12% 12.96% CANDEAL
109. 1 0.12% 13.08% CARAVAN
110. 1 0.12% 13.20% CARLINHOS
111. 1 0.12% 13.32% CARPET
112. 1 0.12% 13.44% CARTOONS
113. 1 0.12% 13.56% CASH
114. 1 0.12% 13.68% CASSETTES
115. 1 0.12% 13.80% CASSETTE
116. 1 0.12% 13.92% CDS
56
117. 1 0.12% 14.04% CELEBRATED
118. 1 0.12% 14.16% CELEBRATE
119. 1 0.12% 14.28% CELL
120. 1 0.12% 14.40% CERTIFICATE
121. 1 0.12% 14.52% CHAMPIONSHIP
122. 1 0.12% 14.64% CHAMPIONS
123. 1 0.12% 14.76% CHAMPION
124. 1 0.12% 14.88% CHARGER
125. 1 0.12% 15.00% CHARITY
126. 1 0.12% 15.12% CHAT
127. 1 0.12% 15.24% CHEMIST
128. 1 0.12% 15.36% CHEVALIER
129. 1 0.12% 15.48% CHEWING
130. 1 0.12% 15.60% CHIPS
131. 1 0.12% 15.72% CHOCOLATE
132. 1 0.12% 15.84% CHOIR
133. 1 0.12% 15.96% CHOOS
134. 1 0.12% 16.08% CHORUS
135. 1 0.12% 16.20% CHRASED
136. 1 0.12% 16.32% CHRISMAST
137. 1 0.12% 16.44% CIGARETTES
138. 1 0.12% 16.56% CIGARETTE
139. 1 0.12% 16.68% CINEMA
140. 1 0.12% 16.80% CIRCULATION
141. 1 0.12% 16.92% CIRCUS
142. 1 0.12% 17.04% CLARINET
143. 1 0.12% 17.16% CLASSROOM
144. 1 0.12% 17.28% CLIMATE
145. 1 0.12% 17.40% CLOVERS
146. 1 0.12% 17.52% CLUES
57
147. 1 0.12% 17.64% COACH
148. 1 0.12% 17.76% COCOA
149. 1 0.12% 17.88% COKE
150. 1 0.12% 18.00% COLDPLAY
151. 1 0.12% 18.12% COLUMNS
152. 1 0.12% 18.24% COLUMN
153. 1 0.12% 18.36% COMEDY
154. 1 0.12% 18.48% COMICS
155. 1 0.12% 18.60% COMIC
156. 1 0.12% 18.72% COMPARATIVES
157. 1 0.12% 18.84% COMPOSTION
158. 1 0.12% 18.96% COMTINUE
159. 1 0.12% 19.08% CONCERT
160. 1 0.12% 19.20% CONCETRATE
161. 1 0.12% 19.32% CONDITIONING
162. 1 0.12% 19.44% CONDUCTOR
163. 1 0.12% 19.56% COOKIES
164. 1 0.12% 19.68% CORRIDORS
165. 1 0.12% 19.80% COUNTRYSIDE
166. 1 0.12% 19.92% CRAZY
167. 1 0.12% 20.04% CROCODILES
168. 1 0.12% 20.16% CROCODILE
169. 1 0.12% 20.28% CRUTCHES
170. 1 0.12% 20.40% CRYSTALS
171. 1 0.12% 20.52% CUTURE
172. 1 0.12% 20.64% CYLINDERS
173. 1 0.12% 20.76% DAVID
174. 1 0.12% 20.88% DAWN
175. 1 0.12% 21.00% DAYDREAMS
176. 1 0.12% 21.12% DDT
58
177. 1 0.12% 21.24% DEADLINE
178. 1 0.12% 21.36% DECORATE
179. 1 0.12% 21.48% DEFEAR
180. 1 0.12% 21.60% DEFINISIONS
181. 1 0.12% 21.72% DENTISTS
182. 1 0.12% 21.84% DENTIST
183. 1 0.12% 21.96% DESEASE
184. 1 0.12% 22.08% DETACHED
185. 1 0.12% 22.20% DETERMINERS
186. 1 0.12% 22.32% DIAGRAM
187. 1 0.12% 22.44% DIALOGUES
188. 1 0.12% 22.56% DIALOGUE
189. 1 0.12% 22.68% DIARY
190. 1 0.12% 22.80% DIESEL
191. 1 0.12% 22.92% DISABILITY
192. 1 0.12% 23.04% DISABLED
193. 1 0.12% 23.16% DISASTERS
194. 1 0.12% 23.28% DISASTER
195. 1 0.12% 23.40% DISCO
196. 1 0.12% 23.52% DISCUS
197. 1 0.12% 23.64% DISC
198. 1 0.12% 23.76% DISHWASHER
199. 1 0.12% 23.88% DISKS
200. 1 0.12% 24.00% DI
201. 1 0.12% 24.12% DOLPHINS
202. 1 0.12% 24.24% DONATIONS
203. 1 0.12% 24.36% DONT
204. 1 0.12% 24.48% DOWNLOADED
205. 1 0.12% 24.60% DOWNLOAD
206. 1 0.12% 24.72% DOWNTOWN
59
207. 1 0.12% 24.84% DRAPES
208. 1 0.12% 24.96% DRUGS
209. 1 0.12% 25.08% DUSTBIN
210. 1 0.12% 25.20% DU
211. 1 0.12% 25.32% DWELLINGS
212. 1 0.12% 25.44% EARTHQUAKERS
213. 1 0.12% 25.56% EASTER
214. 1 0.12% 25.68% EATER
215. 1 0.12% 25.80% ED
216. 1 0.12% 25.92% EG
217. 1 0.12% 26.04% ELEVATORS
218. 1 0.12% 26.16% ELEVATOR
219. 1 0.12% 26.28% EMAILS
220. 1 0.12% 26.40% EMAIL
221. 1 0.12% 26.52% EMAI
222. 1 0.12% 26.64% EMBARRASING
223. 1 0.12% 26.76% EMBARRASISING
224. 1 0.12% 26.88% EMERGENCY
225. 1 0.12% 27.00% EMILY
226. 1 0.12% 27.12% EMISSION
227. 1 0.12% 27.24% ENDINGS
228. 1 0.12% 27.36% ENGLAND
229. 1 0.12% 27.48% ENTHUSIASTIC
230. 1 0.12% 27.60% EPIDEMIC
231. 1 0.12% 27.72% EPISODE
232. 1 0.12% 27.84% ERASER
233. 1 0.12% 27.96% ERUPTED
234. 1 0.12% 28.08% ERUPTION
235. 1 0.12% 28.20% ER
236. 1 0.12% 28.32% ESE
60
237. 1 0.12% 28.44% ETC
238. 1 0.12% 28.56% ETERNAL
239. 1 0.12% 28.68% EUROPEAN
240. 1 0.12% 28.80% EUROPE
241. 1 0.12% 28.92% EXAMPES
242. 1 0.12% 29.04% EXAMS
243. 1 0.12% 29.16% EXAM
244. 1 0.12% 29.28% EXHAUST
245. 1 0.12% 29.40% EXPEDITION
246. 1 0.12% 29.52% EYESIGHT
247. 1 0.12% 29.64% FAMINE
248. 1 0.12% 29.76% FANTASTIC
249. 1 0.12% 29.88% FAUCET
250. 1 0.12% 30.00% FESTIVAL
251. 1 0.12% 30.12% FICTION
252. 1 0.12% 30.24% FIJI
253. 1 0.12% 30.36% FINF
254. 1 0.12% 30.48% FIREFIGHTERS
255. 1 0.12% 30.60% FIREMEN
256. 1 0.12% 30.72% FLUENTLY
257. 1 0.12% 30.84% FLUENT
258. 1 0.12% 30.96% FLUTE
259. 1 0.12% 31.08% FLU
260. 1 0.12% 31.20% FOIL
261. 1 0.12% 31.32% FOM
262. 1 0.12% 31.44% FOOTBALLER
263. 1 0.12% 31.56% FOREVER
264. 1 0.12% 31.68% FRANCES
265. 1 0.12% 31.80% FRANCE
266. 1 0.12% 31.92% FRENCH
61
267. 1 0.12% 32.04% FRIDGE
268. 1 0.12% 32.16% FRIEDS
269. 1 0.12% 32.28% FRIENS
270. 1 0.12% 32.40% FRONTS
271. 1 0.12% 32.52% FROWN
272. 1 0.12% 32.64% FUMES
273. 1 0.12% 32.76% FURUTE
274. 1 0.12% 32.88% FUTHER
275. 1 0.12% 33.00% GARBAGE
276. 1 0.12% 33.12% GENIUS
277. 1 0.12% 33.24% GEOGRAPHY
278. 1 0.12% 33.36% GETGROW
279. 1 0.12% 33.48% GE
280. 1 0.12% 33.60% GHOSTS
281. 1 0.12% 33.72% GHOST
282. 1 0.12% 33.84% GINE
283. 1 0.12% 33.96% GIRLFRIEND
284. 1 0.12% 34.08% GIT
285. 1 0.12% 34.20% GIVED
286. 1 0.12% 34.32% GIVER
287. 1 0.12% 34.44% GLASESS
288. 1 0.12% 34.56% GLIDER
289. 1 0.12% 34.68% GOLDFISH
290. 1 0.12% 34.80% GOLDIE
291. 1 0.12% 34.92% GOTHS
292. 1 0.12% 35.04% GOTH
293. 1 0.12% 35.16% GOVERMENT
294. 1 0.12% 35.28% GRAMOPHONES
295. 1 0.12% 35.40% GRAMOPHONE
296. 1 0.12% 35.52% GRAPHICS
62
297. 1 0.12% 35.64% GREECE
298. 1 0.12% 35.76% GREENHOUSE
299. 1 0.12% 35.88% GROWNUP
300. 1 0.12% 36.00% GRUMPY
301. 1 0.12% 36.12% GUIDEBOOK
302. 1 0.12% 36.24% GUINEA
303. 1 0.12% 36.36% GUITARIST
304. 1 0.12% 36.48% GUITAR
305. 1 0.12% 36.60% GULY
306. 1 0.12% 36.72% GUM
307. 1 0.12% 36.84% GUSESSING
308. 1 0.12% 36.96% GYMNASTICS
309. 1 0.12% 37.08% GYM
310. 1 0.12% 37.20% HABE
311. 1 0.12% 37.32% HAIRDRESSER
312. 1 0.12% 37.44% HALFHOUR
313. 1 0.12% 37.56% HAMBURGERS
314. 1 0.12% 37.68% HAMBURGER
315. 1 0.12% 37.80% HANDSTAND
316. 1 0.12% 37.92% HARRY
317. 1 0.12% 38.04% HAVA
318. 1 0.12% 38.16% HAYSTACK
319. 1 0.12% 38.28% HEADACHES
320. 1 0.12% 38.40% HEADACHE
321. 1 0.12% 38.52% HEADPHONES
322. 1 0.12% 38.64% HEADQUARTERS
323. 1 0.12% 38.76% HELICOPTER
324. 1 0.12% 38.88% HIPPOPOTAMUS
325. 1 0.12% 39.00% HIP
326. 1 0.12% 39.12% HISHER
63
327. 1 0.12% 39.24% HMM
328. 1 0.12% 39.36% HOBBIES
329. 1 0.12% 39.48% HOCKEY
330. 1 0.12% 39.60% HOLLYWOOD
331. 1 0.12% 39.72% HOMESTAY
332. 1 0.12% 39.84% HOMEWORK
333. 1 0.12% 39.96% HOOD
334. 1 0.12% 40.08% HOP
335. 1 0.12% 40.20% HORRIBLE
336. 1 0.12% 40.32% HUGE
337. 1 0.12% 40.44% HUMOUR
338. 1 0.12% 40.56% HUN
339. 1 0.12% 40.68% HURRICANES
340. 1 0.12% 40.80% HURRICANE
341. 1 0.12% 40.92% HURRICANS
342. 1 0.12% 41.04% ICONS
343. 1 0.12% 41.16% IDOL
344. 1 0.12% 41.28% IER
345. 1 0.12% 41.40% IMAN
346. 1 0.12% 41.52% IMMUNE
347. 1 0.12% 41.64% IMPATIENT
348. 1 0.12% 41.76% IMPRESSED
349. 1 0.12% 41.88% IMPROTANT
350. 1 0.12% 42.00% IMPROVISATION
351. 1 0.12% 42.12% IMPROVISE
352. 1 0.12% 42.24% INCREDIBLE
353. 1 0.12% 42.36% INDIANS
354. 1 0.12% 42.48% INFINITIVE
355. 1 0.12% 42.60% INFROMATION
356. 1 0.12% 42.72% INJECTION
64
357. 1 0.12% 42.84% INSPIRATION
358. 1 0.12% 42.96% INSPIRE
359. 1 0.12% 43.08% INTELLIGENCES
360. 1 0.12% 43.20% INTENSIFIERS
361. 1 0.12% 43.32% INTERNET
362. 1 0.12% 43.44% INTERPERSONAL
363. 1 0.12% 43.56% INTERTAINMENT
364. 1 0.12% 43.68% INTERVIEWER
365. 1 0.12% 43.80% INTERVIEW
366. 1 0.12% 43.92% INTONATION
367. 1 0.12% 44.04% INTRAPERSONAL
368. 1 0.12% 44.16% INVENTATIONS
369. 1 0.12% 44.28% INVENTATION
370. 1 0.12% 44.40% IPOD
371. 1 0.12% 44.52% ISTHEY
372. 1 0.12% 44.64% ITALIAN
373. 1 0.12% 44.76% ITALICS
374. 1 0.12% 44.88% ITALY
375. 1 0.12% 45.00% JACK
376. 1 0.12% 45.12% JAKE
377. 1 0.12% 45.24% JAMS
378. 1 0.12% 45.36% JANE
379. 1 0.12% 45.48% JANIE
380. 1 0.12% 45.60% JAPANESE
381. 1 0.12% 45.72% JAVELIN
382. 1 0.12% 45.84% JEAND
383. 1 0.12% 45.96% JEANS
384. 1 0.12% 46.08% JENNY
385. 1 0.12% 46.20% JESS
386. 1 0.12% 46.32% JIGSAW
65
387. 1 0.12% 46.44% JIMMY
388. 1 0.12% 46.56% JOCKEYS
389. 1 0.12% 46.68% JOEL
390. 1 0.12% 46.80% JOGGING
391. 1 0.12% 46.92% JOHN
392. 1 0.12% 47.04% JONES
393. 1 0.12% 47.16% JONI
394. 1 0.12% 47.28% JOOKING
395. 1 0.12% 47.40% JUGGLE
396. 1 0.12% 47.52% JUGJE
397. 1 0.12% 47.64% JUNGLE
398. 1 0.12% 47.76% KEYBOARD
399. 1 0.12% 47.88% KIDDING
400. 1 0.12% 48.00% KIDNAPPED
401. 1 0.12% 48.12% KIDS
402. 1 0.12% 48.24% KILOS
403. 1 0.12% 48.36% KIMONO
404. 1 0.12% 48.48% KIM
405. 1 0.12% 48.60% KINDERGARTEN
406. 1 0.12% 48.72% KM
407. 1 0.12% 48.84% KNOWNING
408. 1 0.12% 48.96% LANDLORD
409. 1 0.12% 49.08% LANES
410. 1 0.12% 49.20% LAPTOP
411. 1 0.12% 49.32% LASCAUX
412. 1 0.12% 49.44% LATENIGHT
413. 1 0.12% 49.56% LAUNCHED
414. 1 0.12% 49.68% LAURE
415. 1 0.12% 49.80% LAYOUTS
416. 1 0.12% 49.92% LEASDALE
66
417. 1 0.12% 50.04% LETBE
418. 1 0.12% 50.16% LIBERTE
419. 1 0.12% 50.28% LIFESTYLE
420. 1 0.12% 50.40% LIF
421. 1 0.12% 50.52% LIKA
422. 1 0.12% 50.64% LINGUITIC
423. 1 0.12% 50.76% LION
424. 1 0.12% 50.88% LISTER
425. 1 0.12% 51.00% LITIGATE
426. 1 0.12% 51.12% LITTER
427. 1 0.12% 51.24% LOCUSTS
428. 1 0.12% 51.36% LOCUST
429. 1 0.12% 51.48% LOGGED
430. 1 0.12% 51.60% LONGUE
431. 1 0.12% 51.72% LORRY
432. 1 0.12% 51.84% LOTTERY
433. 1 0.12% 51.96% LOUISA
434. 1 0.12% 52.08% LOWS
435. 1 0.12% 52.20% LSITEN
436. 1 0.12% 52.32% LUGGAGE
437. 1 0.12% 52.44% LUIS
438. 1 0.12% 52.56% LUTHER
439. 1 0.12% 52.68% LUXURIOUS
440. 1 0.12% 52.80% LYON
441. 1 0.12% 52.92% LYRICS
442. 1 0.12% 53.04% MAGAZINES
443. 1 0.12% 53.16% MAGAZINE
444. 1 0.12% 53.28% MAGICAL
445. 1 0.12% 53.40% MAGIC
446. 1 0.12% 53.52% MAKEUP
67
447. 1 0.12% 53.64% MARATHON
448. 1 0.12% 53.76% MARCO
449. 1 0.12% 53.88% MARIA
450. 1 0.12% 54.00% MARIE
451. 1 0.12% 54.12% MARS
452. 1 0.12% 54.24% MARTIN
453. 1 0.12% 54.36% MATHEMATICAL
454. 1 0.12% 54.48% MATHEMATICS
455. 1 0.12% 54.60% MATHS
456. 1 0.12% 54.72% MEANINGFULLY
457. 1 0.12% 54.84% MEDALLISTS
458. 1 0.12% 54.96% MEDAL
459. 1 0.12% 55.08% MEMORABLE
460. 1 0.12% 55.20% MENS
461. 1 0.12% 55.32% MESSY
462. 1 0.12% 55.44% MESS
463. 1 0.12% 55.56% METRO
464. 1 0.12% 55.68% MEXICAN
465. 1 0.12% 55.80% MIDDLEAGED
466. 1 0.12% 55.92% MIKE
467. 1 0.12% 56.04% MIRACLE
468. 1 0.12% 56.16% MIRROR
469. 1 0.12% 56.28% MISUNDERSTANDING
470. 1 0.12% 56.40% MITCHELL
471. 1 0.12% 56.52% MOBILE
472. 1 0.12% 56.64% MODAL
473. 1 0.12% 56.76% MOGHT
474. 1 0.12% 56.88% MONSTERS
475. 1 0.12% 57.00% MOOD
476. 1 0.12% 57.12% MOVIE
68
477. 1 0.12% 57.24% MOZART
478. 1 0.12% 57.36% MUSCLE
479. 1 0.12% 57.48% MUSEUMS
480. 1 0.12% 57.60% MUSEUM
481. 1 0.12% 57.72% MUSTN
482. 1 0.12% 57.84% MUS
483. 1 0.12% 57.96% NADIA
484. 1 0.12% 58.08% NAIVE
485. 1 0.12% 58.20% NANCY
486. 1 0.12% 58.32% NATALIE
487. 1 0.12% 58.44% NATSUMI
488. 1 0.12% 58.56% NATURALISTIC
489. 1 0.12% 58.68% NEGARIVE
490. 1 0.12% 58.80% NERVOUS
491. 1 0.12% 58.92% NILL
492. 1 0.12% 59.04% NIOWRA
493. 1 0.12% 59.16% NOISER
494. 1 0.12% 59.28% NON
495. 1 0.12% 59.40% NOTEBOOK
496. 1 0.12% 59.52% NUMBERA
497. 1 0.12% 59.64% NUMBERB
498. 1 0.12% 59.76% OBLIGATION
499. 1 0.12% 59.88% OFFLINE
500. 1 0.12% 60.00% OFTHE
501. 1 0.12% 60.12% OGOD
502. 1 0.12% 60.24% OK
503. 1 0.12% 60.36% OLDMAN
504. 1 0.12% 60.48% OLIVIA
505. 1 0.12% 60.60% OLYMPICS
506. 1 0.12% 60.72% OLYMPIC
69
507. 1 0.12% 60.84% ONLINE
508. 1 0.12% 60.96% ORCHESTRA
509. 1 0.12% 61.08% ORROW
510. 1 0.12% 61.20% OT
511. 1 0.12% 61.32% OUTFITS
512. 1 0.12% 61.44% OUTFIT
513. 1 0.12% 61.56% OXYGEN
514. 1 0.12% 61.68% PACIFIC
515. 1 0.12% 61.80% PALACES
516. 1 0.12% 61.92% PANE
517. 1 0.12% 62.04% PANTS
518. 1 0.12% 62.16% PAPUA
519. 1 0.12% 62.28% PARADISE
520. 1 0.12% 62.40% PARALYMPICS
521. 1 0.12% 62.52% PARETS
522. 1 0.12% 62.64% PARISIAN
523. 1 0.12% 62.76% PARIS
524. 1 0.12% 62.88% PARTICIPLE
525. 1 0.12% 63.00% PASSWORD
526. 1 0.12% 63.12% PASTA
527. 1 0.12% 63.24% PAS
528. 1 0.12% 63.36% PATIENTS
529. 1 0.12% 63.48% PATRICK
530. 1 0.12% 63.60% PAVED
531. 1 0.12% 63.72% PAVEMENT
532. 1 0.12% 63.84% PEDESTRIANS
533. 1 0.12% 63.96% PENICILLIN
534. 1 0.12% 64.08% PENSIONER
535. 1 0.12% 64.20% PEOPE
536. 1 0.12% 64.32% PERCUSSIONISTS
70
537. 1 0.12% 64.44% PERCUSSION
538. 1 0.12% 64.56% PERION
539. 1 0.12% 64.68% PETER
540. 1 0.12% 64.80% PETE
541. 1 0.12% 64.92% PETROL
542. 1 0.12% 65.04% PHONOGRAPHS
543. 1 0.12% 65.16% PHOTOS
544. 1 0.12% 65.28% PHOTO
545. 1 0.12% 65.40% PHRASES
546. 1 0.12% 65.52% PHYSICS
547. 1 0.12% 65.64% PIANOS
548. 1 0.12% 65.76% PIANO
549. 1 0.12% 65.88% PICASSO
550. 1 0.12% 66.00% PICNIC
551. 1 0.12% 66.12% PIERRE
552. 1 0.12% 66.24% PILLOW
553. 1 0.12% 66.36% PILLS
554. 1 0.12% 66.48% PIONEERS
555. 1 0.12% 66.60% PIZZAS
556. 1 0.12% 66.72% PIZZA
557. 1 0.12% 66.84% PLANETS
558. 1 0.12% 66.96% PLANET
559. 1 0.12% 67.08% PLASTIC
560. 1 0.12% 67.20% PLUG
561. 1 0.12% 67.32% PLUMBER
562. 1 0.12% 67.44% PLUMB
563. 1 0.12% 67.56% PODCASTS
564. 1 0.12% 67.68% POEPLE
565. 1 0.12% 67.80% POLAND
566. 1 0.12% 67.92% POLAR
71
567. 1 0.12% 68.04% POLLUTE
568. 1 0.12% 68.16% POLLUTION
569. 1 0.12% 68.28% POP
570. 1 0.12% 68.40% PORTABLE
571. 1 0.12% 68.52% PORTUGAL
572. 1 0.12% 68.64% POSSITIVE
573. 1 0.12% 68.76% POSTCARD
574. 1 0.12% 68.88% POTATOES
575. 1 0.12% 69.00% PREPOSITIONS
576. 1 0.12% 69.12% PREPOSITION
577. 1 0.12% 69.24% PRESCRIPTION
578. 1 0.12% 69.36% PRESENTATIONS
579. 1 0.12% 69.48% PRIMITIVE
580. 1 0.12% 69.60% PRINTER
581. 1 0.12% 69.72% PROFESSOR
582. 1 0.12% 69.84% PROFILES
583. 1 0.12% 69.96% PROGRAMMES
584. 1 0.12% 70.08% PRONOUNCIATION
585. 1 0.12% 70.20% PRONOUNS
586. 1 0.12% 70.32% PRONOUN
587. 1 0.12% 70.44% PRONUNCIATION
588. 1 0.12% 70.56% PS
589. 1 0.12% 70.68% PXSIVENEE
590. 1 0.12% 70.80% PYRAMIDS
591. 1 0.12% 70.92% PYRAMID
592. 1 0.12% 71.04% QUESTIONNAIRE
593. 1 0.12% 71.16% QUET
594. 1 0.12% 71.28% QUEUE
595. 1 0.12% 71.40% QUIZ
596. 1 0.12% 71.52% RADIUM
72
597. 1 0.12% 71.64% RAINBOWS
598. 1 0.12% 71.76% RAINBOW
599. 1 0.12% 71.88% RAINFALL
600. 1 0.12% 72.00% RAINFORESTS
601. 1 0.12% 72.12% RAINFOREST
602. 1 0.12% 72.24% REAS
603. 1 0.12% 72.36% RECEPTIONIST
604. 1 0.12% 72.48% RECEPTION
605. 1 0.12% 72.60% RECKON
606. 1 0.12% 72.72% RECORDERS
607. 1 0.12% 72.84% RECORDER
608. 1 0.12% 72.96% RECORDINGS
609. 1 0.12% 73.08% RECYCLE
610. 1 0.12% 73.20% RECYCLING
611. 1 0.12% 73.32% REFEREE
612. 1 0.12% 73.44% REMOTE
613. 1 0.12% 73.56% REWRITE
614. 1 0.12% 73.68% RHYMING
615. 1 0.12% 73.80% RHYTHMS
616. 1 0.12% 73.92% RHYTHM
617. 1 0.12% 74.04% RIDICULOUS
618. 1 0.12% 74.16% ROLLERBLADING
619. 1 0.12% 74.28% ROMANCE
620. 1 0.12% 74.40% ROMANTIC
621. 1 0.12% 74.52% ROME
622. 1 0.12% 74.64% SALLY
623. 1 0.12% 74.76% SAMA
624. 1 0.12% 74.88% SANDRA
625. 1 0.12% 75.00% SANDWICHES
626. 1 0.12% 75.12% SAN
73
627. 1 0.12% 75.24% SARAH
628. 1 0.12% 75.36% SAXOPHONE
629. 1 0.12% 75.48% SAYED
630. 1 0.12% 75.60% SAYINGS
631. 1 0.12% 75.72% SCARED
632. 1 0.12% 75.84% SCARY
633. 1 0.12% 75.96% SCATTERBRAINED
634. 1 0.12% 76.08% SCORED
635. 1 0.12% 76.20% SCORE
636. 1 0.12% 76.32% SCRIPT
637. 1 0.12% 76.44% SCROFULA
638. 1 0.12% 76.56% SCULPUTURES
639. 1 0.12% 76.68% SEMI
640. 1 0.12% 76.80% SERIAL
641. 1 0.12% 76.92% SERIUOS
642. 1 0.12% 77.04% SERIUS
643. 1 0.12% 77.16% SGE
644. 1 0.12% 77.28% SHAMPOO
645. 1 0.12% 77.40% SHOOTONG
646. 1 0.12% 77.52% SHY
647. 1 0.12% 77.64% SIDEWALKS
648. 1 0.12% 77.76% SIDEWALK
649. 1 0.12% 77.88% SILLY
650. 1 0.12% 78.00% SINGIN
651. 1 0.12% 78.12% SKATEBOARDING
652. 1 0.12% 78.24% SKATES
653. 1 0.12% 78.36% SKIING
654. 1 0.12% 78.48% SKULLS
655. 1 0.12% 78.60% SLAM
656. 1 0.12% 78.72% SLOT
74
657. 1 0.12% 78.84% SMEYS
658. 1 0.12% 78.96% SOCCER
659. 1 0.12% 79.08% SOCKET
660. 1 0.12% 79.20% SOEAK
661. 1 0.12% 79.32% SOFTWARE
662. 1 0.12% 79.44% SOMEON
663. 1 0.12% 79.56% SONY
664. 1 0.12% 79.68% SOPHIE
665. 1 0.12% 79.80% SPACESHIPS
666. 1 0.12% 79.92% SPACESHIP
667. 1 0.12% 80.04% SPANISH
668. 1 0.12% 80.16% SPECIALISED
669. 1 0.12% 80.28% SPEEDBOAT
670. 1 0.12% 80.40% SPELLINGS
671. 1 0.12% 80.52% SPIDERS
672. 1 0.12% 80.64% SPOKESMAN
673. 1 0.12% 80.76% SPORTSPEOPLE
674. 1 0.12% 80.88% SPORTSPERSON
675. 1 0.12% 81.00% SPRINTING
676. 1 0.12% 81.12% SPRINT
677. 1 0.12% 81.24% STADIUM
678. 1 0.12% 81.36% STARED
679. 1 0.12% 81.48% STARVING
680. 1 0.12% 81.60% STATUE
681. 1 0.12% 81.72% STEREO
682. 1 0.12% 81.84% STEVE
683. 1 0.12% 81.96% STORTY
684. 1 0.12% 82.08% STREETCARS
685. 1 0.12% 82.20% STUDIO
686. 1 0.12% 82.32% SUBWAY
75
687. 1 0.12% 82.44% SUCCESSES
688. 1 0.12% 82.56% SUE
689. 1 0.12% 82.68% SUFFIXES
690. 1 0.12% 82.80% SUNGLASSES
691. 1 0.12% 82.92% SUNSET
692. 1 0.12% 83.04% SUPERLATIVE
693. 1 0.12% 83.16% SUPERMARKETS
694. 1 0.12% 83.28% SUPERMARKET
695. 1 0.12% 83.40% SUPERSTAR
696. 1 0.12% 83.52% SUPERSTITIOUS
697. 1 0.12% 83.64% SUPRISED
698. 1 0.12% 83.76% SUPRISE
699. 1 0.12% 83.88% SUPRISING
700. 1 0.12% 84.00% SURFED
701. 1 0.12% 84.12% SURFING
702. 1 0.12% 84.24% SURF
703. 1 0.12% 84.36% SURGEON
704. 1 0.12% 84.48% SWARMS
705. 1 0.12% 84.60% SWARM
706. 1 0.12% 84.72% SWEATY
707. 1 0.12% 84.84% SWITCHED
708. 1 0.12% 84.96% SWITCH
709. 1 0.12% 85.08% SYLLABLES
710. 1 0.12% 85.20% SYNTHESISER
711. 1 0.12% 85.32% TABLETS
712. 1 0.12% 85.44% TAGS
713. 1 0.12% 85.56% TAG
714. 1 0.12% 85.68% TAKED
715. 1 0.12% 85.80% TALENTED
716. 1 0.12% 85.92% TALENTS
76
717. 1 0.12% 86.04% TALENT
718. 1 0.12% 86.16% TANGO
719. 1 0.12% 86.28% TATTOOS
720. 1 0.12% 86.40% TATTOO
721. 1 0.12% 86.52% TEENAGERADOLESCENT
722. 1 0.12% 86.64% TEENAGERS
723. 1 0.12% 86.76% TEENAGER
724. 1 0.12% 86.88% TEENAGE
725. 1 0.12% 87.00% TEEN
726. 1 0.12% 87.12% TELEVISION
727. 1 0.12% 87.24% TENNIS
728. 1 0.12% 87.36% TENSES
729. 1 0.12% 87.48% TERRACED
730. 1 0.12% 87.60% TE
731. 1 0.12% 87.72% THATTHE
732. 1 0.12% 87.84% THEATER
733. 1 0.12% 87.96% THEA
734. 1 0.12% 88.08% THETREES
735. 1 0.12% 88.20% THET
736. 1 0.12% 88.32% THIGS
737. 1 0.12% 88.44% THIRSTY
738. 1 0.12% 88.56% THOMAS
739. 1 0.12% 88.68% THONDS
740. 1 0.12% 88.80% THOND
741. 1 0.12% 88.92% THOOSE
742. 1 0.12% 89.04% TICK
743. 1 0.12% 89.16% TIDIER
744. 1 0.12% 89.28% TINY
745. 1 0.12% 89.40% TITTLE
746. 1 0.12% 89.52% TOBE
77
747. 1 0.12% 89.64% TODDLER
748. 1 0.12% 89.76% TOIT
749. 1 0.12% 89.88% TOM
750. 1 0.12% 90.00% TONNES
751. 1 0.12% 90.12% TONY
752. 1 0.12% 90.24% TOOTACHE
753. 1 0.12% 90.36% TOOTHACHE
754. 1 0.12% 90.48% TORCH
755. 1 0.12% 90.60% TOR
756. 1 0.12% 90.72% TRAFFIC
757. 1 0.12% 90.84% TRAMPOLINE
758. 1 0.12% 90.96% TRAMS
759. 1 0.12% 91.08% TRAM
760. 1 0.12% 91.20% TRAPEZE
761. 1 0.12% 91.32% TREATHEN
762. 1 0.12% 91.44% TREINERS
763. 1 0.12% 91.56% TRESS
764. 1 0.12% 91.68% TRICKY
765. 1 0.12% 91.80% TROPICAL
766. 1 0.12% 91.92% TROUSERS
767. 1 0.12% 92.04% TRUCK
768. 1 0.12% 92.16% TRUMPET
769. 1 0.12% 92.28% TSUNAMIS
770. 1 0.12% 92.40% TSUNAMI
771. 1 0.12% 92.52% TUVALU
772. 1 0.12% 92.64% TV
773. 1 0.12% 92.76% TWEENTIETH
774. 1 0.12% 92.88% TYRES
775. 1 0.12% 93.00% UDIFTICFL
776. 1 0.12% 93.12% UK
78
777. 1 0.12% 93.24% UNDERGROUND
778. 1 0.12% 93.36% UNDERLINED
779. 1 0.12% 93.48% UNDERLINE
780. 1 0.12% 93.60% UNINHABITABLE
781. 1 0.12% 93.72% UNLUCKIEST
782. 1 0.12% 93.84% UPLOADING
783. 1 0.12% 93.96% USA
784. 1 0.12% 94.08% VACATION
785. 1 0.12% 94.20% VACCINATION
786. 1 0.12% 94.32% VANCOUVER
787. 1 0.12% 94.44% VARIUOS
788. 1 0.12% 94.56% VEGETABLES
789. 1 0.12% 94.68% VELIB
790. 1 0.12% 94.80% VELO
791. 1 0.12% 94.92% VERBAL
792. 1 0.12% 95.04% VERTEBRA
793. 1 0.12% 95.16% VICTIMS
794. 1 0.12% 95.28% VIDEOS
795. 1 0.12% 95.40% VIDEO
796. 1 0.12% 95.52% VINYL
797. 1 0.12% 95.64% VIOLIN
798. 1 0.12% 95.76% VIP
799. 1 0.12% 95.88% VOCABLARY
800. 1 0.12% 96.00% VOCABULARYMEDICINE
801. 1 0.12% 96.12% VOCABULARY
802. 1 0.12% 96.24% VODCASTS
803. 1 0.12% 96.36% VOLCANIC
804. 1 0.12% 96.48% VOLCANO
805. 1 0.12% 96.60% VOLLEYBALL
806. 1 0.12% 96.72% VS
79
807. 1 0.12% 96.84% WASHINGUP
808. 1 0.12% 96.96% WATHCING
809. 1 0.12% 97.08% WEARED
810. 1 0.12% 97.20% WEBISTE
811. 1 0.12% 97.32% WEBSITES
812. 1 0.12% 97.44% WEBSITE
813. 1 0.12% 97.56% WEB
814. 1 0.12% 97.68% WERY
815. 1 0.12% 97.80% WES
816. 1 0.12% 97.92% WHEB
817. 1 0.12% 98.04% WHEELCHAIR
818. 1 0.12% 98.16% WHETEHER
819. 1 0.12% 98.28% WHE
820. 1 0.12% 98.40% WHI
821. 1 0.12% 98.52% WHOOPS
822. 1 0.12% 98.64% WIFI
823. 1 0.12% 98.76% WILLIAMS
824. 1 0.12% 98.88% WINDSCREEN
825. 1 0.12% 99.00% WIRELESS
826. 1 0.12% 99.12% WOLL
827. 1 0.12% 99.24% WONDERB
828. 1 0.12% 99.36% WORKSHOP
829. 1 0.12% 99.48% WORLDWIDE
830. 1 0.12% 99.60% WOTH
831. 1 0.12% 99.72% WOW
832. 1 0.12% 99.84% WRIGHT
833. 1 0.12% 99.96% WRITINF
834. 1 0.12% 100.00% WTH
835. 1 0.12% 100.00% WUTH
836. 1 0.12% 100.00% YEAHHER
80
837. 1 0.12% 100.00% YEAH
838. 1 0.12% 100.00% YESTEDAY
839. 1 0.12% 100.00% YHE
840. 1 0.12% 100.00% YONSI
841. 1 0.12% 100.00% YORK
842. 1 0.12% 100.00% YO
843. 1 0.12% 100.00% YSE
844. 1 0.12% 100.00% ZINES
845. 1 0.12% 100.00% ZINE
81
Appendix E
Shared and Unique Words in Unit 3 and Unit 13
Unique to
first
580 tokens
272 families
001. if 29
002. will 24
003. won’ 18
004. pollute
15
005. might 13
006. don’ 12
007. problem
12
008. traffic
11
009. unless
10
010. scheme 9
011.
environment 8
012. town 8
013. big 6
014. idea 6
015. paris 6
016. best 5
017. fume 5
018. jam 5
019. list 5
020. may 5
021. much 5
022. reduce 5
023. bike 4
024. chorus 4
025.
condition 4
026. factory
4
027. future 4
028. litter 4
029. match 4
030.
rainforest 4
031. rise 4
032.
temperature 4
033. air 3
034. amy’ 3
Shared
1243 tokens
205 families
001. the
136
002. a 59
003. have
51
004. in 47
005. be 45
006. to 43
007. i 35
008. and 32
009. he 26
010. it 26
011. of 26
012. we 26
013. they
24
014. go 18
015. this
16
016. for 14
017. see 14
018. when
14
019. you 14
020. do 12
021. city
11
022. find
11
023. she 11
024. tell
11
025. there
11
026.
because 10
027. but 9
028. get 9
029. some 9
030. listen
8
031.
sentence 8
032. time 8
Unique to
second
354 tokens
191 families
Freq first
(then alpha)
001. past 13
002. before
10
003. perfect
9
004. who 9
005. jungle 8
006. build 7
007. lose 6
008. story 6
009. ’ 6
010. explore
5
011. noun 5
012. paint 5
013. act 4
014. cave 4
015. century
4
016. climb 4
017. didn’ 4
018. into 4
019. local 4
020. old 4
021. already
3
022. civilise
3
023. couldn’
3
024. famous 3
025. happen 3
026. home 3
027. house 3
028. hundred
3
029. key 3
030. sail 3
031. show 3
032. snow 3
033. top 3
VP novel items
Same list
Alpha first
001. act 4
002. advance
1
003.
adventure 1
004. age 1
005. almost 1
006. already
3
007.
ambulance 1
008.
archaeology 2
009. are: 1
010. art 1
011.
astronomy 1
012. bad 2
013. bag 1
014. bandage
1
015. before
10
016. between
2
017.
breakfast 1
018. build 7
019. call 2
020. can’ 1
021. cave 4
022. cell 1
023. centre 2
024. century
4
025. choose 1
026. circus 1
027. civilise
3
028. class 2
029. clear 1
030. climb 4
82
035.
atmosphere 3
036. by 3
037. fail 3
038. fresh 3
039. level 3
040. number 3
041. recycle
3
042. sure 3
043. tomorrow
3
044. velib 3
045. want 3
046. world’ 3
047. + 2
048. also 2
049. amy 2
050. away 2
051. believe
2
052. bottle 2
053. bus 2
054. buy 2
055. cause 2
056. clause 2
057. clean 2
058. climate
2
059.
disappear 2
060. doesn’ 2
061. drama 2
062. end 2
063. exam 2
064. exhaust
2
065. express
2
066. global 2
067. half 2
068. ice 2
069. journey
2
070. lane 2
071. life 2
072. litre 2
073. message
2
074. nice 2
075. opinion
2
076. paradise
2
033. what 8
034. from 7
035. know 7
036. not 7
037. on 7
038. people
7
039. tree 7
040. year 7
041. about
6
042. day 6
043. live 6
044. look 6
045. no 6
046. one 6
047. person
6
048.
picture 6
049. two 6
050. write
6
051. again
5
052. answer
5
053. at 5
054. last 5
055. or 5
056. other
5
057. put 5
058. use 5
059. with 5
060. – 5
061. after
4
062. as 4
063. box 4
064. change
4
065. circle
4
066.
complete 4
067.
correct 4
068. how 4
069. i’ 4
070. leave
4
071. make 4
072. many 4
034.
archaeology 2
035. bad 2
036. between
2
037. call 2
038. centre 2
039. class 2
040. collect
2
041. dream 2
042. early 2
043. eat 2
044. empire 2
045. excite 2
046. govern 2
047. gum 2
048. hide 2
049.
immediate 2
050. india 2
051. island 2
052. left 2
053. little 2
054. mention
2
055. mountain
2
056. plumb 2
057.
programme 2
058. pyramid
2
059.
reception 2
060. report 2
061. send 2
062. ski 2
063. stress 2
064. suffix 2
065. summer 2
066. system 2
067. table 2
068. temple 2
069. watch 2
070. advance
1
071.
adventure 1
072. age 1
073. almost 1
074.
ambulance 1
075. are: 1
031. closes 1
032. collect
2
033.
competition 1
034. couldn’
3
035. country
1
036. cover 1
037. cow 1
038. cry 1
039. decide 1
040. decorate
1
041. diary 1
042. didn’ 4
043.
difficult 1
044. disaster
1
045. dream 2
046. early 2
047. easter 1
048. eat 2
049.
emergency 1
050. empire 2
051. excite 2
052.
expedition 1
053.
experience 1
054. explore
5
055. eye 1
056. fall 1
057. famous 3
058.
fantastic 1
059. floor 1
060. follow 1
061. forget 1
062. friday 1
063. fright 1
064. gold 1
065. govern 2
066. gum 2
067.
hamburger 1
068. happen 3
069. happy 1
070.
helicopter 1
83
077.
paragraph 2
078. pave 2
079. phrase 2
080. play 2
081. possible
2
082. predict
2
083. rest 2
084. rubbish
2
085. secret 2
086. solve 2
087. song 2
088. sport 2
089. spot 2
090. street 2
091. taxi 2
092. warm 2
093. we’ll 2
094. yellow 2
095. ‘em 2
096. ‘ll 2
097. above 1
098. acre 1
099. add 1
100. aim 1
101. alicia 1
102. always 1
103. animal 1
104.
antartica 1
105. apple 1
106. aren’ 1
107. around 1
108. awful 1
109. basket 1
110. beach 1
111. bed 1
112. bee 1
113. bird 1
114. book 1
115. boutique
1
116. break 1
117. breathe
1
118. cap 1
119. cars’ 1
120. cat 1
121. certain
1
122. club 1
073. now 4
074. out 4
075. place
4
076.
question 4
077. read 4
078. start
4
079. text 4
080. then 4
081. thing
4
082. verb 4
083. where
4
084. why 4
085. word 4
086. work 4
087.
another 3
088. every
3
089.
example 3
090. girl 3
091. give 3
092. good 3
093. hotel
3
094. man 3
095. page 3
096. really
3
097. think
3
098. up 3
099. = 2
100. ago 2
101. angry
2
102. any 2
103. arrive
2
104. check
2
105. come 2
106.
exercise 2
107. first
2
108. form 2
109. great
2
076. art 1
077.
astronomy 1
078. bag 1
079. bandage
1
080.
breakfast 1
081. can’ 1
082. cell 1
083. choose 1
084. circus 1
085. clear 1
086. closes 1
087.
competition 1
088. country
1
089. cover 1
090. cow 1
091. cry 1
092. decide 1
093. decorate
1
094. diary 1
095.
difficult 1
096. disaster
1
097. easter 1
098.
emergency 1
099.
expedition 1
100.
experience 1
101. eye 1
102. fall 1
103.
fantastic 1
104. floor 1
105. follow 1
106. forget 1
107. friday 1
108. fright 1
109. gold 1
110.
hamburger 1
111. happy 1
112.
helicopter 1
113. holiday
1
114. huge 1
071. hide 2
072. holiday
1
073. home 3
074. house 3
075. huge 1
076. hundred
3
077.
immediate 2
078. include
1
079. india 2
080. inform 1
081. into 4
082. invent 1
083. invite 1
084. island 2
085. journal
1
086. juggle 1
087. jungle 8
088. key 3
089.
kilometre 1
090. king 1
091. kitchen
1
092. language
1
093. lascaux
1
094. left 2
095. little 2
096. local 4
097. lose 6
098. louisa’
1
099. luck 1
100.
magazine’ 1
101. mark 1
102. mean: 1
103. mention
2
104. milk 1
105. miss 1
106. month 1
107. mother 1
108. mountain
2
109. negative
1
110. nervous
84
123. company
1
124. compare
1
125. comtinue
1
126. contain
1
127. continue
1
128. course 1
129. cream 1
130. cut 1
131. cuture 1
132. dad 1
133. danger 1
134. ddt 1
135. die 1
136. dirty 1
137. discuss
1
138. disease
1
139. dollar 1
140. door 1
141. drink 1
142. each 1
143. empty 1
144. evening
1
145. expert 1
146. explain
1
147. facility
1
148. fact 1
149. family 1
150. feel 1
151. final 1
152. flu 1
153. france 1
154. free 1
155. freedom’
1
156. freeze 1
157. further
1
158. furute 1
159. game 1
160. gine 1
161. glass 1
162. grey 1
163. group 1
164. guess 1
110. help 2
111. late 2
112. long 2
113. next 2
114. only 2
115. order
2
116. parent
2
117. part 2
118.
partner 2
119. rule 2
120. run 2
121. same 2
122. speak
2
123. still
2
124. study
2
125. test 2
126. than 2
127. travel
2
128.
underline 2
129. very 2
130.
vocabulary 2
131. walk 2
132.
weather 2
133. which
2
134. : 1
135. a: 1
136. across
1
137. all 1
138. area 1
139.
article 1
140. ask 1
141. ball 1
142. begin
1
143.
bicycle 1
144.
brother 1
145. can 1
146. car 1
147. catch
115. include
1
116. inform 1
117. invent 1
118. invite 1
119. journal
1
120. juggle 1
121.
kilometre 1
122. king 1
123. kitchen
1
124. language
1
125. lascaux
1
126. louisa’
1
127. luck 1
128.
magazine’ 1
129. mark 1
130. mean: 1
131. milk 1
132. miss 1
133. month 1
134. mother 1
135. negative
1
136. nervous
1
137. off 1
138. online 1
139. over 1
140. palace 1
141.
particular 1
142.
pedestrian 1
143. prize 1
144.
pronunciation
1
145. quick 1
146. radio 1
147. reach 1
148. reas 1
149.
recognize 1
150. region 1
151. religion
1
152. sandwich
1
111. noun 5
112. off 1
113. old 4
114. online 1
115. over 1
116. paint 5
117. palace 1
118.
particular 1
119. past 13
120.
pedestrian 1
121. perfect
9
122. plumb 2
123. prize 1
124.
programme 2
125.
pronunciation
1
126. pyramid
2
127. quick 1
128. radio 1
129. reach 1
130. reas 1
131.
reception 2
132.
recognize 1
133. region 1
134. religion
1
135. report 2
136. sail 3
137. sandwich
1
138. save 1
139. school’
1
140. second 1
141. section
1
142. send 2
143. service
1
144. ship 1
145. show 3
146. since 1
147. ski 2
148. sleep 1
149. small 1
85
165. halfhour
1
166. health 1
167. hear 1
168. he’ll 1
169. hot 1
170. hour 1
171. hullo 1
172. increase
1
173.
interview 1
174.
introduce 1
175. isn’ 1
176. it’sthey
1
177. jane 1
178. joni 1
179. just 1
180. large 1
181. lead 1
182. lesson 1
183. liberte
1
184. love 1
185. lyon 1
186. mar 1
187. marco 1
188. mark’ 1
189. mean 1
190. means: 1
191. meet 1
192. metro 1
193. million
1
194. mind 1
195. minute 1
196. mitchell
1
197. moght 1
198. museum 1
199. new 1
200. oil 1
201. oldman 1
202. open 1
203. pair 1
204. pass 1
205. peope 1
206. per 1
207.
photograph 1
208. piano 1
209. pick 1
1
148. charge
1
149. cycle
1
150. down 1
151. drop 1
152. europe
1
153. even 1
154. farm 1
155. foot 1
156. friend
1
157.
grammar 1
158. hard 1
159. heavy
1
160. here 1
161.
homework 1
162.
important 1
163.
improve 1
164. it’ 1
165. like 1
166. lot 1
167.
mathematics
1
168. money
1
169. more 1
170. most 1
171. need 1
172. night
1
173. park 1
174. party
1
175. pay 1
176. planet
1
177. please
1
178.
politic 1
179.
positive 1
180. power
1
181.
1
153. save 1
154. school’
1
155. second 1
156. section
1
157. service
1
158. ship 1
159. since 1
160. sleep 1
161. small 1
162. soon 1
163. special
1
164. square 1
165. star 1
166. storm 1
167. strange
1
168. strong 1
169. sunday 1
170. terrible
1
171. thetrees
1
172. three 1
173. through
1
174. tire 1
175. tour 1
176. treasure
1
177. trip 1
178. tropics
1
179. true 1
180. turn 1
181.
tweentieth 1
182. uncle 1
183. upset 1
184. visit 1
185. wait 1
186. wasn’ 1
187. weak 1
188. while 1
189. win 1
190. yes 1
191. ‘ 1
150. snow 3
151. soon 1
152. special
1
153. square 1
154. star 1
155. storm 1
156. story 6
157. strange
1
158. stress 2
159. strong 1
160. suffix 2
161. summer 2
162. sunday 1
163. system 2
164. table 2
165. temple 2
166. terrible
1
167. thetrees
1
168. three 1
169. through
1
170. tire 1
171. top 3
172. tour 1
173. treasure
1
174. trip 1
175. tropics
1
176. true 1
177. turn 1
178.
tweentieth 1
179. uncle 1
180. upset 1
181. visit 1
182. wait 1
183. wasn’ 1
184. watch 2
185. weak 1
186. while 1
187. who 9
188. win 1
189. yes 1
190. ‘ 1
191. ’ 6
86
210. pink 1
211. plan 1
212. plan: 1
213. plant 1
214. pocket 1
215. poland 1
216. polar 1
217. probable
1
218. problem:
1
219. promise
1
220. rainfall
1
221. result 1
222. return 1
223.
revolution 1
224. river 1
225. road 1
226.
rollerblading
1
227. safe 1
228. screen 1
229. seem 1
230. serious
1
231. seriuos
1
232. shop 1
233. sick 1
234.
skateboarding
1
235. slam 1
236. space 1
237.
spokesman 1
238. state 1
239. sun 1
240. swing 1
241.
television 1
242. tennis 1
243. they’ll
1
244. thousand
1
245. title 1
246. ton 1
247. tonight
1
present 1
182. repeat
1
183. right
1
184. rule:
1
185. say 1
186. school
1
187. seven
1
188. short
1
189. simple
1
190.
situate 1
191. so 1
192.
station 1
193. take 1
194. talk 1
195. teach
1
196.
teenage 1
197.
telephone 1
198. there’
1
199. today
1
200.
together 1
201. train
1
202. unit 1
203. water
1
204. week 1
205. world
1
87
248. too 1
249. total 1
250. try 1
251. under 1
252.
understand 1
253. until 1
254. usa 1
255. velo 1
256.
vocabulary: 1
257. waste 1
258. weather’
1
259. website
1
260. where’ 1
261. worry 1
262. worse 1
263. worse’ 1
264. york 1
265. you’ve 1
266. yse 1
267. ‘bicycle
1
268. ‘free’ 1
269.
‘safewater’ 1
270. ‘the 1
271. ‘these 1
272. ‘velib’
1
88
Appendix F
Shared and Unique Words in Unit 12 and Unit 14
Unique to
first
523 tokens
265 families
001. girl 21
002. internet
21
003. book 15
004. own 15
005. web 12
006. create 8
007. podcasts
7
008. show 7
009. advice 6
010. design 6
011. most 6
012. english
5
013. father 5
014. percent
5
015. surf 5
016. as 4
017. battery
4
018.
competition 4
019. connect
4
020. laptop 4
021. lead 4
022. let 4
023. library
4
024. often 4
025.
paragraph 4
026. post 4
027.
recommend 4
028. shame 4
029. stuff 4
030. teenage
4
031. websites
4
Shared
2850 tokens
331 families
001. the 162
002. i 126
003. a 124
004. to 101
005. you 92
006. be 86
007. he 84
008. have 69
009. say 63
010. and 60
011. this 57
012. in 43
013. do 39
014. she 38
015. go 35
016. if 34
017. of 34
018. we 32
019. with 30
020. it 27
021. on 26
022. tell 25
023. for 22
024. what 22
025. i’ 21
026. not 21
027. they 21
028. would 20
029. at 19
030. know 19
031. get 17
032. some 17
033. how 16
034. ’ 16
035. – 16
036. about 15
037. look 15
038. really
15
039. so 15
040. use 15
041. didn’ 14
042. why 14
043. but 13
Unique to
second
594 tokens
323 families
Freq first
(then alpha)
001. luck 41
002. report
13
003. speech 9
004. year 9
005. break 7
006. sun 7
007.
chevalier 6
008. past 6
009. accident
5
010. direct 5
011. down 5
012. explain
5
013. film 5
014. hope 5
015. noun 5
016. what’ 5
017. apology
4
018. blind 4
019. car 4
020. converse
4
021. end 4
022. into 4
023. nervous
4
024. orrow 4
025. tire 4
026. train 4
027. accept 3
028. ankle 3
029. begin 3
030. brother
3
031. evening
3
032. few 3
VP novel items
Same list
Alpha first
001. above 1
002. accept 3
003. accident
5
004.
accomodation 1
005. across 1
006. advance
1
007. alive 1
008. allow 1
009. almost 1
010. already
2
011. animal 2
012. ankle 3
013. apology
4
014.
archaeology 1
015. arrange
2
016. arrive 2
017. art 2
018. ath 1
019. aunt 1
020. band 2
021. beach 1
022. beat 2
023. bed 1
024. begin 3
025. birth 1
026. bless 1
027. blind 4
028. both 1
029. break 7
030.
breakfast 1
031. bring 2
032. brother
3
033. burst 1
89
032. world 4
033. + 3
034. active 3
035. blog 3
036. chat 3
037. clothe 3
038. definite
3
039. doesn’ 3
040. download
3
041. hour 3
042. keyboard
3
043. magazine
3
044. plug 3
045.
preposition 3
046. print 3
047. relation
3
048. shop 3
049. talent 3
050.
technology 3
051. topic 3
052. worth 3
053. young 3
054. adapt 2
055. ahead 2
056.
ambulance 2
057.
brilliant 2
058. class 2
059. content
2
060. cover 2
061.
dictionary 2
062. dog 2
063. enter 2
064. expert 2
065. file 2
066. girl’ 2
067. hide 2
068. hobby 2
069. interact
2
070. log 2
071. market 2
072.
mathematics 2
044. when 13
045. exam 12
046. good 12
047. late 12
048. listen
12
049. very 12
050. can 11
051. complete
11
052. from 11
053. out 11
054. see 11
055. think 11
056. want 11
057. well 11
058. wouldn’
11
059. come 10
060. could 10
061. day 10
062. oh 10
063. or 10
064. sentence
10
065. buy 9
066. feel 9
067. find 9
068. happen 9
069. it’ 9
070. just 9
071. like 9
072. read 9
073. talk 9
074. then 9
075. time 9
076. word 9
077. write 9
078. because
8
079. best 8
080. correct
8
081. put 8
082. again 7
083. answer 7
084. bad 7
085. fall 7
086. form 7
087. i’ve 7
088. lot 7
089. than 7
090. thing 7
091. another
033. fly 3
034. forget 3
035. hotel 3
036. i’ll 3
037. join 3
038. kill 3
039. little 3
040. marry 3
041. miss 3
042. must 3
043. please 3
044. rainbow
3
045. road 3
046. saturday
3
047. song 3
048. treat 3
049. wake 3
050. wood 3
051. would’ve
3
052. wrong 3
053. ‘i’ 3
054. already
2
055. animal 2
056. arrange
2
057. arrive 2
058. art 2
059. band 2
060. beat 2
061. bring 2
062. busy 2
063.
calculate 2
064.
children’ 2
065. cinema 2
066. cold 2
067.
complicate 2
068. cross 2
069. dear 2
070. desk 2
071. early 2
072. emily 2
073.
entertain 2
074. equip 2
075. eye 2
076. favour 2
077. finger 2
034. busy 2
035. cafe 1
036.
calculate 2
037. camp 1
038. car 4
039. card 1
040. care 1
041. carpet 1
042. case 1
043. cat 1
044. catch 1
045.
chevalier 6
046.
children’ 2
047. cinema 2
048. city 1
049. clever 1
050. clover 1
051. club 1
052. cold 2
053. collect
1
054. comedy 1
055. comfort
1
056.
commercial’ 1
057.
complicate 2
058. converse
4
059. crazy 1
060. credit 1
061. cross 2
062. crystal
1
063. dad’ 1
064. dance 1
065. dark 1
066. date 1
067. dawn 1
068. dear 2
069. death 1
070. defear 1
071. defeat 1
072. desk 2
073. di 1
074. die 1
075. direct 5
076. dirty 1
077. door 1
078. down 5
90
073. mouse 2
074. net 2
075. network
2
076. patient
2
077. pioneer
2
078. power 2
079.
programme 2
080. result 2
081. right 2
082. same 2
083. save 2
084. site 2
085. slot 2
086. switch 2
087. video 2
088. while 2
089. woman 2
090.
according 1
091. addict 1
092. address
1
093. airport
1
094. among 1
095. amoung 1
096. area 1
097. away 1
098. awful 1
099. base 1
100. become 1
101. black 1
102. blogging
1
103. bother 1
104. brett 1
105. browse 1
106. bulb 1
107. business
1
108. button 1
109. cable 1
110. cause 1
111. certain
1
112. chair 1
113. charge 1
114. cheap 1
115. chip 1
116. choice 1
6
092. company
6
093. don’ 6
094. first 6
095. friend 6
096. man 6
097. more 6
098. need 6
099. off 6
100. school 6
101. state 6
102.
telephone 6
103. three 6
104. watch 6
105. work 6
106.
yesterday 6
107. agree 5
108. bit 5
109. bus 5
110. by 5
111. enjoy 5
112. exercise
5
113. give 5
114. hard 5
115. invite 5
116. last 5
117. love 5
118. meet 5
119. never 5
120. no 5
121. only 5
122. page 5
123. people 5
124. play 5
125. sorry 5
126. student
5
127. sure 5
128. there 5
129. too 5
130. try 5
131. two 5
132. up 5
133. verb 5
134. way 5
135. all 4
136. always 4
137. before 4
138. can’ 4
139. chance 4
078. gift 2
079. glass 2
080. happy 2
081. hit 2
082. house 2
083. improve
2
084. injure 2
085. jack 2
086. journey
2
087. jump 2
088. king 2
089. land 2
090. left 2
091. luther 2
092. martin 2
093. million
2
094. mirror 2
095. mother 2
096. mouth 2
097.
opportunity 2
098. pot 2
099. rude 2
100. seven 2
101. shock 2
102. shouldn’
2
103. sleep 2
104. sport 2
105. suffix 2
106.
superstitious
2
107. test 2
108. they’ 2
109. tour 2
110. tree 2
111. trip 2
112. twist 2
113. window 2
114. won’ 2
115. world’ 2
116. worse 2
117. you’re 2
118. ’clock 2
119. above 1
120.
accomodation 1
121. across 1
122. advance
1
079. during 1
080. early 2
081. earn 1
082. either 1
083. else 1
084. emily 2
085. end 4
086.
entertain 2
087. equip 2
088. etc 1
089. eternal
1
090. evening
3
091. exact 1
092. excuse 1
093. expect 1
094. expense
1
095.
experience 1
096. explain
5
097. explode
1
098. eye 2
099. fact 1
100. famous 1
101. father’
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103. few 3
104. field 1
105. fill 1
106. film 5
107. finger 2
108. fish 1
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110. follow 1
111. fool 1
112. foot 1
113. foot’ 1
114. forever
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116. four 1
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118. ge 1
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120.
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155. grand 1
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158. reason 4
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