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

ii

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.

iii

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

iv

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

Along with any pertinent equipment.

With the non-exclusive royalty free right, SWCU maintains the right to copy,

reproduce, print, publish, post, display, incorporate, store in or scan into a

retrieval system or database, transmit, broadcast, barter or sell my intellectual

property, in whole or in part without my express written permission, as long as my

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

v

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

vi

Acknowledgements ................................................................................... 29

Appendixes ................................................................................................ 30

1

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

2

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

3

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

4

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.

5

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

6

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

7

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.

8

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.

9

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

10

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

11

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.

12

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.

13

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

14

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

15

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

16

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

17

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

18

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

19

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’

1

102. favour 2

103. few 3

104. field 1

105. fill 1

106. film 5

107. finger 2

108. fish 1

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