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VOCABULARY PROFILE OF THE NINTH GRADE ENGLISH

TEXTBOOK USED IN SMPN 7 SALATIGA

THESIS

Submitted in Partial Fulfilment

of the Requirements for the Degree of

Sarjana Pendidikan

Elis Herdiani Alfian

112012140

ENGLISH LANGUAGE EDUCATION PROGRAM

FACULTY OF LANGUAGE AND LITERATURE

SATYA WACANA CHRISTIAN UNIVERSITY

SALATIGA

2016

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TABLE OF CONTENT

COVER PAGE…………………………………………………………………………………… i

APPROVAL PAGE……………………………………………………………………………….ii

COPYRIGHT STATEMENT…………………………………………………………………….iii

PUBLICATION AGREEMENT DECLARATION...................................................................... iv

TABLE OF CONTENT…………………………………………………………………………..v

LIST OF TABLE………………………………………………………………………………..vii

ABSTRACT……………………………………………………………………………………… 1

INTRODUCTION……………………………………………………………………………….. 1

LITERATURE REVIEW…..……………………………………………………………………. 3

The Importance of Vocabulary…………………………………………………………... 3

How to Successfully Learn and Teach Vocabulary…………………………………….... 3

Problems in Learning Vocabulary………………………………………………………. 4

Vocabulary Profiler……………………………………………………………………… 4

Four Types of Word Frequency…………………………………………………………. 5

Relevant Studies Related to the Topic…………………………………………………... 7

THE STUDY…………………………………………………………………………………….. 8

Context of the Study……………………………………………………………………... 8

Material…………………………………………………………………………………... 8

Data Collection Instruments……………………………………………………………... 9

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Data Collection Procedure……………………………………………………………….. 9

Data Analysis Procedure……………………………………………………………….… 9

FINDINGS AND DISCUSSION................................................................................................. 10

A. Overall Vocabulary Profile of the Textbook……………………………………..… 10

B. Negative vocabulary profiles of the textbook…………………………………….….12

C. Comparison of Vocabulary frequency levels every chapter in the textbook……….. 16

CONCLUSION…………………………………………………..…………………………...… 21

REFERENCES..……………….....................…………………….……………………………. 23

ACKNOWLEDMENTS..........…………………………………….…………………………… 24

APPENDIX…………………………………………………………..…………………………. 25

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LIST OF TABLE

Table 1. K1/the most frequent words (1-1000)……………………………………………..……. 5

Table 2. K2/less frequent words (1001-2000)………………………………………………..….. 5

Table 3. Academic vocabulary (AWL words)………………………………………………..….. 6

Table 4. Low frequency (off list)……………………………………………………………….... 6

Table 5.Overall Vocabulary Profile…………………………………………………………….. 11

Table 6. Comparison of word frequency levels……………………………………………....… 17

Table 7.Shared and Unique words in Chapter 5 vs. Chapter 14……………………….…….…. 19

Table 8. Comparison of Chapter 4 vs. Chapter 14………………………………………...….… 20

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INTRODUCTION

Vocabulary is one of main elements to foreign language students’ understanding of what

they read in books, what they hear from English teachers in school, and to communicate

successfully with other people. In fact, it is important to build up a wide store of English words

in a students’ brain since English become a lingua franca in the world. Kweldju and Priyono

(2004, as cited in Widiati & Cahyono, 2008) stated that vocabulary is believed to be the most

crucial element in a learning process. They also recommend a solution can be found by good

handling and by choosing an appropriate vocabulary.

Sometimes English teacher may face difficulties in teaching vocabulary words because

they do not notice or find suitable words from textbook to teach students. Students’ lack of

vocabulary knowledge may prevent them for understanding textbook being used. With the

continuous progress of the technology, there is a tool called vocabulary profiler. Vocabulary

profiler is a tool to measure the vocabulary production from a text (Astika, 2014). Therefore, the

English teacher can use the tool to describe and calculate the frequency of words used in a

textbook and then decide in which groups each word belongs to.

The aim or objective of this research was to analyze vocabulary profile in English course

textbook used in SMP N 7 Salatiga entitled ‘‘Think Globally Act Locally’ for grade IX. The

book was published in 2014 by Kementrian Pendidikandan Kebudayaan Republik Indonesia.

This research has to be done because hopefully it will help teachers in choosing

appropriate vocabulary to teach to the students. Cobb (2010) stated that this tool has been used

all over the world to determine word frequency quickly. Furthermore, this tool is trusted,

effective, and efficient to use as a benchmark to check book’s qualities. Junior High School was

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chosen since as my experienced teaching PPL ninth grader students in SMPN 7 Salatiga, they

find difficulties in learning process used that book, so I wanted to investigate the words

frequency of the textbook.

It is important to find out because this research might be helpful for the teachers in Junior

High School to know which part of vocabulary is proper for the beginner, intermediate, or

advanced students. Also, teachers can teach vocabulary based on the arrangement of the word list

whether it is include as academic words, low frequency words, or high frequency words depend

on vocabulary profile’s result. However it also important to the writer’ publisher to improve their

arrangement of beginner to academic vocabulary in their book became better than before.

Furthermore, the research questions that will be used to address the aim of this study are:

1. What is Vocabulary Profile of The Ninth Grade English Textbook used in SMPN 7 Salatiga?

2. What is the proportion of vocabulary that is not covered in the textbook?

3. What is the token (words) recycling index off the textbook?

LITERATURE REVIEW

The Importance of Vocabulary

Vocabulary is one crucial part in learning processes in class. The statement is supported

by McKeown (2002). He stated that vocabulary is one of the important things in language

learning, because by the vocabulary knowledge, the learner can build up the language in any

language skill. Therefore, Cahyono and Widiati (2008) stated that in vocabulary learning the

learner also has to realize concepts of unfamiliar words, achieve as many as words, and use the

words successfully for communicate. As Nation (2002) stated, vocabulary development is such

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an important part of language acquisition that have to be monitored and controlled not only in

speaking skill and writing skill (productive) but also in listening skill and reading skill

(receptive).

How to Successfully Learn and Teach English Vocabulary

In order to make learners successfully learn or teach English vocabulary, teachers have to

use appropriate way to teach English in the class. Beck, McKeown, and Kucan (2002)

recommended that teachers should choose academic words that appear in a broad variety of

texts, provide student-friendly explanations for them, create instructional contexts that supply

useful information about new words, and engage students in actively dealing with word

meanings using academic words. However, the classification of four type word frequency

especially academic words may useful for teacher and students in selecting the vocabulary that is

needed and most relevant in learning.

Problems in Learning Vocabulary

The needs of sufficient vocabulary knowledge of the students at their level have been

frequently voiced not only by the teachers but also by student teachers who had teaching

practicum at participating schools. Students who lack of vocabulary knowledge has to be reduced

because it may result students became unable to acquire important language which may affect

their English proficiency in class. Moreover, Schmitt (2000) said that students at early stages

should learn about 1000-2000 high frequency words and increase about 3000-3000 words

families to read authentic text that may include academic words. In order to enrich students

vocabulary words, vocabulary profiler can be a guide in determining suitable words to students

depend on their level.

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Vocabulary Profiler

By a development of media in English learning there is a tool called vocabulary profiler.

Vocabulary profiler itself is an on-line adaptation of Nation and Hwang’s vocabulary assessment

instrument (Cobb, 2002). Moreover, Astika (2014) stated that vocabulary profile is a program

designed by Laufer and Nation in 1995 which has a function to determine lexical richness

(lexical difficulty) of a textbook. This site will be a greatest important especially in learning and

teaching English academic vocabulary that focuses on academic vocabulary. Astika (2014)

explained that the first step to use the tool is opening the website on www.lextutor.ca/vp, next a

text have to be copied and paste into the box program and then the program will analyze and

classify where the each word belongs to by viewing the percentages of word group based on four

frequency level.

Four Types of Word Frequency

According to Nation (1990) as a long-term exponent of this approach, there are four types

of word frequency: high frequency words or known as K1 words (1-1000) and K2 words (1001-

2000), academic vocabulary known as AWL words (academic), and the last is low frequency

words (off list word).

A high frequency word is the most common words that can be found in any text (Cooper, 2002).

Table 1 shows some example of K1/the most frequent words (1-1000) from Cambridge

Advanced Learner’s Dictionary:

Table 1.K1/the most frequent words (1-1000)

end are is to

in his it on

http://www.lextutor.ca/vp

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that the for and

was our as of

Moreover, Table 2 shows some example of K2/less frequent words (1001-2000) from Cambridge

Advanced Learner’s Dictionary:

Table 2.K2/less frequent words (1001-2000)

grow ship hardly beauty

easily cities check director

marriage leader rule enemy

factors bridge gas die

Academic vocabulary (AWL words) is worth focusing on for learners wishing to study English

language. Table 3 shows some examples of academic words from Cambridge Advance Learner’s

Dictionary:

Table 3.Academic vocabulary (AWL words)

academy communicate focus positive

accompany context insert respond

benefit economy job style

comment environment locate transport

Low frequency (off list) is a word list that not commonly used in the vocabulary learning

strategies (Nobert & Diane, 2012). Table 4 shows some examples of low frequency words from

Nation (1990):

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Table 4.Low frequency (off list)

abduct aberration abrasion adventurous

afflict coffle bane bankrupt

barter bespeak caste categorical

chameleon caprice carnage cannibal

Relevant Studies Related To the Topic

There are two relevant studies which also used vocabulary profiler but they had different

purposes. The first one is study of Graves (2005) title ‘Vocabulary profile of letters and novels of

Jane Austen and her contemporaries’. This study used vocabulary profile to investigate the word

used in letters and novels of Jane Austen. Furthermore, as the method he compared the word

frequencies of two or more texts by the same author using vocabulary profiler. Then, the result

showed there was a close correlation in the frequency of words and three-word sequences in

Austen’s novels and letters.

A second study which title ‘The use of vocabulary profiles in predicting the academic and

pedagogic performance of TESL trainees’ by Morris and Cobb (2001). This study used

vocabulary profiler to investigate the potential of vocabulary profiles as the predictor of

academic performance in undergraduate Teaching English as a Second Language (TESL). As the

method they analysed 122 TESL students’ writing and scored them whether the result was

correlated with the grades they had in their program of study or not using vocabulary profiler.

The finding showed that vocabulary result was result the correlation significance with the grades.

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However vocabulary profile was proved became a useful tool in carrying out a standard to make

an appropriate assessment of language skill.

The similarity between two researchers above is they used the same tool, vocabulary

profiler. Moreover, the difference between them was in the context. Graves’s context was about

the vocabulary profile on letters and novels. Moreover, Morris and Cobb’s context was about

vocabulary profile on academic performance in undergraduate TESL. However, I used

vocabulary profiler to analyse the vocabulary profiles in ninth grade’s book title ‘Think Globally

Act Locally’ at SMPN 7 Salatiga since it has not been investigated using vocabulary profiler

before.

THE STUDY

The study used descriptive method to study a textbook. According to Rivera and Rivera

(2007) descriptive method is used to find facts that appear according on personal judgment or

theories which also contains the description, recording, analysis, and interpretation. Therefore,

this study used descriptive method to analyze types of vocabularies from a textbook, vocabulary

that were not covered, and token word recycling index by using vocabulary profiler.

Context of the Study

The study was conducted at SMP N 7 Salatiga. It is located at Setiaki 15, Salatiga and it

is one of public schools in Salatiga. The main reason why the study chose that school as the

context of the research was because the textbook has not been profiled using vocabulary profiler.

Another reason was since the researcher did the Teaching Practicum (PPL) in SMPN 7 Salatiga,

the textbook material for ninth graders was easy to be accessed.

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Material

The study focused on ninth graders book entitled ‘Think Globally Act Locally’ which

contains fourteen chapters. All chapters inside the book are used to find out in which group each

vocabularies belongs to, vocabulary that were not covered, and token word recycling index. The

study used ninth graders’ book as a material because ninth grader soon will do final examination

to continue their study to Senior High School. Therefore, they had to prepare more and enrich

their vocabulary English to strengthen their preparation before final examination.

Data Collection Instruments

The study used vocabulary profiler as a tool. This tool can be accessed through

www.lextutor.ca.vp. One of its functions is to check vocabulary profile used in a textbook. The

result can be shown in different striking color; blue for high frequency words, yellow for

academic words, and red for low frequency words.

Data Collection Procedure

To collect the data, all words from the e-book materials have to be copied from e-book

into Microsoft Word first to make it easier and practical before the words being moved to box in

Vocabulary Profiler. Every chapter would be copied in different Microsoft Word file, so that the

total was fourteen Microsoft Word files.

Data Analysis Procedure

The result that has been shown by vocabulary profiler would be analysed by grouping the

vocabularies into K1 (1-1000), K2 (1001-2000), academic words, or off list words. As a

following action the researcher described the table result in a form of paragraph to make the

http://www.lextutor.ca.vp/

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result clearer. It also analysed list of vocabulary that were not covered in textbook and the token

(word) recycling index of the textbook.

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FINDINGS AND DISCUSSION

This study showed the identification of vocabulary profile of ‘Think Globally Act Locally’ book for

grade IX. Vocabulary profiler was used to calculate and analyze every word in chapter 1 until 14 which

total 40.621 words. Furthermore, the finding was separated in three big parts. The first part of this

section presented the overall vocabulary profile of the textbook showing the proportions of the

vocabulary frequency classifications. The second part showed the negative vocabulary profiles of K-1, K-

2, and AWL with lists of the vocabulary items that were not found in the textbook, also block frequency

output of off-list words. The last part of this section showed the comparison of the chapter in the

textbook with regard to the vocabulary items that were contrast. The comparison also showed the

token recycling index that provided information about an estimate of chapter comprehensibility.

Here, before the words being analyzed some words were eliminated to reduce the result of off

list words such as number, name, to be (am, is, are, was, and were), Indonesian words, name of city, and

name of country. Therefore, the spelling mistakes were also corrected.

D. Overall Vocabulary Profile of the Textbook

The analysis began by combining 14 chapters into one then analyzed using vocabulary profiler. The

result was presented in Table 5.

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Table 5. Overall Vocabulary Profile

FREQ. LEVEL FAMILIES % TYPES

%

TOKENS

%

CUMULATIVE

TOKEN%

K-1 WORDS 689

58.24%

1192

47.76%

22770

81.22%

81.22%

K-2 WORDS 368

31.11%

498

19.95%

2492

8.89%

90.11%

AWL

[570 FAMS]

TOT 2,570

126

10.65%

157

6.29%

563

2.01%

92.12%

OFF-LIST: ?? 655

26.24%

2210

7.88%

100.00%

TOTAL

(UNROUNDED)

1183+?

2496

100%

28035

100%

100.00%

The first row in Table 5 showed three terms; family, type, and token. Word family is head word,

for example: the family or head word of maker and making is make. Type is different words, for

example: committee and comparison are different words. While discussion and discussing, or discussed

are considered as the same type. Token is words in a text; the total number of words in a text. For

example, if in a text there are submit [3], coming [1], really [3], and father [2], the number of token is 9.

Table 5 shows that the vocabulary used in the textbook was in most frequently used 1000 words

group (K-1). With the additional K-2 word coverage (8.89%) the cumulative percentage of the word

coverage was 90.11%, which is relatively close estimate for good comprehension of the texts in the

book/assumes that students are familiar with all these words. According to Hirsch (2003), an

understanding of 90-95% of the words is necessary for comprehension. Based on the data in Table 5,

this word knowledge band should include knowledge of academic words 2.01%. Academic words are

those words which are commonly used in academic texts. We need to question whether the academic

words that amounted to 563 words used in the textbook need to be introduced to the students in Junior

High Schools. Another point that is worth considering is the number of off-list words that reached 2210

words. Although this off-list category is excluded from the frequency list and may occur infrequently

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(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 and teachers have to make a selection for useful words in this

category.

E. Negative vocabulary profiles of the textbook

This section presents the description of negative vocabulary profiles of the sample 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

used in this study. The list of negative K-1 words below is useful for teachers in selecting vocabulary

items that students may find useful to develop their vocabulary knowledge.

B.1. Negative VP of K-1

This following analysis 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 word families: 964

K-1 families in input: 679 (70.44%)

K-1 families not in input: 286 (29.67%)

These percentages do not refer to tokens or number of words in the textbook but rather

number of word families. According to the summary, 70.44% of word families were found in the

textbook, which means, 29.67% of word families were ‘missing’ or not found based on the words listed

in New General Service List (NGSL). The followings are 10% of the word families that are not found in the

input textbook. The complete word list has been put in Appendix A

ACCOUNT

ACCOUNTABLE ACROSS

ACTOR ACTRESS

ADMIT ADVANCE

ADVANTAGE

ADVENTURE AFFAIR

AGAINST AGE

ALLOW ALONG

ANCIENT APPEAR

APPOINT ARISE

ARM ARMY

ARRIVE ARTICLE

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ATTACK ATTEMPT

AVERAGE BALL

BAR BATTLE, etc

B.2. Negative VP of K-2

This analysis shows all the word families (=head words) from the K-2 level that were not found

in the input 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: 367 (37.22%)

K-2 families not in input: 620 (62.88%)




in New General Service List (NGSL). The followings are 10% of the word families (K-2) that were not

found in the input textbook. The complete word list has been put in Appendix B

ABROAD ABSENT

ABSOLUTE ABSOLUTELY

ACCUSE ACCUSTOM

ADMIRE AEROPLANE

AFFORD AGRICULTURE

AIM AIRPLANE

ALIKE ALIVE

ALOUD ALTOGETHER

AMBITION AMUSE

ANGLE ANXIETY

APART APOLOGIZE

APOLOGY APPLAUD

APPROVE ARCH

ARRANGE ARREST

ARROW ASH

ASHAMED ASIDE

ASTONISH AUTUMN

AVENUE AWAKE

AWKWARD AXE

BAGGAGE BALANCE

BAND BARBER

BARE BARELY

BARGAIN BARREL

BASIN BATHE

BAY BEAK

BEAM BEARD

BEAST

BEAT BEHAVE

BELL BELT

BEND BERRY

BILLION BIND, and

etc

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B.3. Negative VP of AWL

This analysis shows all the word families (=head words) from the AWL level that were not found

in the input textbook. The summary of negative vocabulary profile for AWL level is presented below:

AWL Total word families: 569

AWL families in input: 132 (23.20%)

AWL families not in input: 438 (76.98%)




in New General Service List (NGSL). The followings are 10% of the word families that were not found in

the textbook. The complete word list has been put in Appendix C.

ABSTRACT

ACADEMY

ACCESS

ACCOMMODATE

ACCUMULATE

ACCURATEACK

NOWLEDGE

ACQUIRE

ADEQUATE

ADJACENT

ADMINISTRATE

ADVOCATE

AFFECT

AGGREGATE

AID

ALBEIT

ALLOCATE

ALTER

ALTERNATIVE

AMBIGUOUS

AMEND

ANALOGY

ANNUAL

ANTICIPATE

APPARENT

APPEND

APPRECIATE

APPROXIMATE

ARBITRARY

ASPECT

ASSEMBLE

ASSESS

ASSIGN

ASSIST

ASSUME

ASSURE

ATTACH

ATTAIN

ATTRIBUTE

AUTHOR

AUTHORITY

AWARE

BEHAL

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B.4. Block frequency output of off-list words.

Description of block frequency output of off-list words in the textbook will be presented

here. Using the tool in the Vocabulary Profiler, these words have been frequency-blocked per ten

words and have been arranged from high to low frequency. This list of words may provide teachers

with useful information as to what words are necessary for teaching. In other words, teachers may

select words from the list that are relevant to teaching students at Junior High Schools. Below is the

list of ‘Off-list’ words, with 1704 tokens and 543 types. Note that RANK is ranking of word, FREQ is

frequency of word occurrence, COVERAGE is the percentage of word occurrences, individual or

cumulative, and the last column is the vocabulary item. The complete list of blocked frequency has

been put in Appendix D.

RANK FREQ COVERAGE

individu cumulative

WORD

1. 66 3.87% 3.87% PUNCTUATION

2. 37 2.17% 6.04% ORALLY

3. 36 2.11% 8.15% BUFFALO

4. 28 1.64% 9.79% ORPHAN

5. 26 1.53% 11.32% INGREDIENTS

6. 23 1.35% 12.67% DRUG

7. 21 1.23% 13.90% ORPHANAGE

8. 20 1.17% 15.07% DIALOGUE

9. 19 1.12% 16.19% MAGAZINE

10. 19 1.12% 17.31% RECIPE

11. 18 1.06% 18.37% BATS

12. 16 0.94% 19.31% HOMEWORK

13. 16 0.94% 20.25% SERA

14. 15 0.88% 21.13% CLASSROOM

15. 15 0.88% 22.01% COCONUT

16. 15 0.88% 22.89% NOVEL

17. 15 0.88% 23.77% UNDERLINE

18. 13 0.76% 24.53% ENCYCLOPEDIA

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19. 13 0.76% 25.29% FABRICS

20. 13 0.76% 26.05% HANDWRITE

C. Comparison of Vocabulary frequency levels every chapter in the textbook

C.1. Comparison of Vocabulary frequency levels in every chapter

Table 6 compares the frequency of K-1, K-2, AWL, and Off-list among the chapter textbook. It

provides a general picture of the extent to which the chapter differed with regards to their

frequency groups.

Table 6. Comparison of word frequency levels

Chapters K1 words

%

K2 words

%

AWL words

%

Off list words

%

Chapter 1

cumulatives %

84.88

84.88%

10.22

95.1%

1.37

96.47%

3.52

100%

Chapter 2

cumulatives %

89.1

89.1%

6.55

95.65%

1.27

96.92%

3.08

100%

Chapter 3

cumulatives %

85.9

85.9%

8.88

94.78%

0.85

95.63

4.37

100%

Chapter 4

cumulatives %

74.42

74.42%

10.44

84.86%

4.65

89.51%

10.49

100%

Chapter 5

cumulatives %

69.37

69.37%

16.28

85.65%

3.28

88.93%

11.07

100%

Chapter 6

cumulatives %

87.42

87.42%

8.77

96.19%

0.76

96.95%

3.06

100%

Chapter 7

cumulatives %

89.29

89.29%

3.97

93.26%

0.86

94.12%

5.87

100%

Chapter 8

cumulatives %

88.55

88.55%

6.65

95.2%

1.03

96.23%

3.76

27

100%

Chapter 9

cumulatives %

88.26

88.26%

6.54

94.8%

1.44

96.24%

3.76

100%

Chapter 10

cumulatives %

88.8

88.8%

6.7

95.5%

1.62

97,12%

2.88

100%

Chapter 11

cumulatives %

76.6

76.6%

10.84

87.44%

2.42

89.86%

8.14

100%

Chapter 12

cumulatives %

78.85

78.85%

7.31

86.16%

3.65

89.81%

10.19

100%

Chapter 13

cumulatives %

75.92

75.92%

10.94

86.86%

3.28

90.14%

9.85

100%

Chapter 14

cumulatives %

92.9

92.9%

4.77

97.67%

0.39

98.06

1.94

100%

Based on the Table 6, it shows that the differences of K-1, K-2, AWL, and Off-list words

among the chapters were not very significant. In other words, the proportion of vocabulary items in

each chapter was relatively similar. Contrast precentage was seen in the result of K-1 chapter 4, 5,

11, 12, and 13 which have lower (close to 80%) presentage rather than the other chapters. One

more contrast percentage was in the AWL result on chapter 4, 5, 11, 12, and 13 which got result

close to 90%. That was happened because total words in the textbook in chapter 4, 5, 11, 12, and 13

were lower rather than the other chapters which have higher total words.

However, AWL group show the smallest percentage rather than the other group. It means

that teachers should pay attention to those words since the AWL words are academic words that

need more attention rather than other groups. The cumulative percentages of the K-1 and K-2 words

also provide information about difficulty of understanding the textbooks since the percentages were

below 97%, a necessary requirement for good understanding of a text.

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C.2. Text comparison of the textbook:

C.2.1. Text comparison Chapter 5 vs. Chapter 14

This section presents the comparison of text between two chapters. The comparison shows

the token recycling index of the chapters being compared. Recycling index is the ratio between

words that are shared by two chapters and the total number of words in the second chapter. This

index provides useful information about the words that similar or shared in both chapters and words

which are new or unique in the second chapter. For teaching purposes, teachers need to pay

attention to those words that are unique in the second chapter.

The first comparison is between Chapter 5 and Chapter 14. The reason behind focusing

compare the chapters were because the distance of K1 words between Chapter 5 and 14 were

23.53% which was farther rather than the other chapter. The analysis of comparison shows that the

token recycling index was 68.50% indicating that as much as 524 of words in Chapter 5 and Chapter

14 were similar (shared). Thus, new or unique words in chapter 14 were as much as 765 words. The

shared as well as unique words are displayed in the Table 7 below. The figure next to a word is the

occurrences or frequency of that word in the book.

Table 7. Shared and Unique words in Chapter 5 vs. Chapter 14.

TOKEN Recycling Index: (524 repeated tokens : 765 tokens in new text) = 68.50%

FAMILIES Recycling Index: (105 repeated families : 200 families in new text) = 52.50%

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The complete table has been put in Appendix E.

C.2.2. Text comparison Chapter 4 vs. Chapter 14

The second comparison was between chapter 4 and chapter 14. The reason behind focusing

compare the chapters were because the distance of AWL words between chapter 4 and chapter 14

were 4.26% which was farther rather than the other chapter. The analysis of comparison shows that

the token recycling index the amount was 65.75%, indicating that as much as 503 of words in

chapter 4 and 14 were similar (shared). Thus, new or unique words in chapter 14 were 766 words.

The shared as well as unique words are displayed in the Table 8 below. The figure next to a word is

the occurrences or frequency of that word in the book.

Table 8.Comparison of Chapter 4 vs. Chapter 14

TOKEN Recycling Index: (503 repeated tokens : 765 tokens in new text) = 65.75%

FAMILIES Recycling Index: (100 repeated families : 200 families in new text) = 50.00%

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The complete table has been put in Appendix F.

CONCLUSION

The findings have presented identification of vocabulary profile from ‘Act Locally Think

Globally’ book. The study disclosed three results of what is vocabulary profile of the ninth grade

Englsh textbook, what is the proportion of vocabulary that is not covered in the textbook, and what

is the token (words) recycling index of the textbook. The result of overall vocabulary profile in the

textbook showed the proportions of the vocabulary frequency classifications. For K-1 words (1-1000)

was as much as 81.22%, K2 words (1001-2000) was as much as 8.89%, AWL words was as much as

2.01%, and the amount of Off List words was 7.88%.

The second result revealed negative vocabulary profiles of K-1, K-2, and AWL with lists of the

vocabulary items that were not found in the textbook, also block frequency output of off-list words.

For K-1total word families as much as 964 words, only 268 words were not in input. Then, K-2 total

word families as many as 968 words, and 620 words were not in input. Following by AWL total word

31

families 569 words, which 132 words were not in input. The last but not least there were 1704

tokens words and 543 types of Off-list words.

The last result disclosed vocabulary frequency levels every chapter and two comparison of

chapters which was contrast. For chapter 5 and chapter 14 which focus on K-1 contrast revealed that

there was 68.50% token recycling index, 524 words were similar, and 765 new words were found in

the chapter 14. Followed by comparison of chapter 4 and 14 which focus on AWL contrast revealed

there was 65.75% token recycling index, 503 words were similar, and 766 new words were found in

the chapter 14.

Besides, the generality of these findings are limited since this study only analyzed IX graders

book only. It will be advantageous for further research to analyze books grade VII and VIII in order

getting richer data of the study which is used in SMPN 7 Salatiga. The implication of the study is I

hope the result of vocabulary profiler may helpful for teacher to use as guidance and a reference to

check the suitable words from textbook that they will teach to the students in the class. However, it

also will be helpful for teacher to give assignment, task, or homework for the students by

recognizing the level of difficulty of the vocabulary.

32

REFERENCES

Astika, G. (2014). Profiling the vocabulary of news texts as capacity for language teachers. Indonesian Journal of Applied Linguistics,4 (2), 257-266.

Bardel, C & Lindqvist, C. (2011). Developing a lexical profiler for spoken French L2 and Italian L2. John Benjamins Publishing Company. Stockholm University / Uppsala University.

Cahyono & Widiati (2008). The teaching of EFL vocabulary in the Indonesian context.

Indonesian Journal of Applied Linguistics.

Chung, T, M, & Nation, P. (2004). Identifying technical vocabulary school of linguistics and applied language studies. Victoria University, 32, 251–263.

Cobb, T. (2010). Learning about language and learner from computer programs. Reading in a Foreign Language, 22.

Graves, D. (2005).Vocabulary profiles of letters and novels of Jane Austen and her contemporaries. Jane Austen Society of North America, 26 (1).

I.S.P. Nation. (1990). Teaching and learning vocabulary. Victoria : Victoria University Wellington.

Morris, L, & Cobb, T. (2004).Vocabulary profiles as predictors of the academic performance of teaching English as a second language trainees: System 32, 75-78.

Nation, I. S. P. (2002). Best practice in vocabulary teaching and learning.In Richards, J. C. & Renandya, W. A. (Eds.), Methodology in Language teaching: An Anthology of Current Practice (pp. 258-266). Cambridge: Cambridge University Press.

Nobert & Diane, S. (2012). Plenary speech a reassessment of frequency and vocabulary size in L2 vocabulary teaching, Cambridge: Cambridge University Press.

Rivera, M. & Rivera, R. (2007). Practical guide to thesis and dissertation writing. Quezon City: KATHA Publishing, Inc.

Schmitt, N. (2000). Vocabulary in language teaching.Cambridge : Cambridge University Press.

Selecting vocabulary: Academic word list. (n.d). Cambridge Advance Learner’s Dictionary. Retrieved October 7, 2015, from http://www.uefap.com/vocab/select/awl.htm.

33

ACKNOWLEDGEMENT

This study cannot be completed without many supports and helps. First, I would like to

thank to Allah SWT for giving me spirit and strength to finish this study. I would like to express my

sincere gratitude to my supervisor, Anne Indrayanti Timotius, M.Ed. for her invaluable patience and

guidance during the completion of this study, and also my examiner, Prof. Dr. Gusti Astika, M.A for

his guidance for this thesis. I also would like to say thank you for teachers in SMPN 7 Salatiga who

guiding and giving me advice related to this study.

I would like to express gratitude to my beloved mom (Choiriyah), dad (Suwandi), sister

(Gias), nephew (Jorrel), and niece (Joccel) for their immeasurable love and support to finish this

study. Many thanks for my friends: Dwi, Ika, Herlina, and Melly for their support to finish this study. I

also would like to say thank you for Irfan Allan Saputra who always stays by my side. Indeed, I would

like to express my appreciation gratitude for my angkatan (Twelvers), all lectures, and staffs in

Faculty of Language and Literature, Satya Wacana Christian University for all knowledge and things

that have been shared.

34

Appendix A

K1 word families that are not found in the input textbooks

ACCOUNT

ACCOUNTABL

E ACROSS

ACTOR

ACTRESS

ADMIT

ADVANCE

ADVANTAGE

ADVENTURE

AFFAIR

AGAINST

AGE ALLOW

ALONG

ANCIENT

APPEAR

APPOINT

ARISE

ARM

ARMY

ARRIVE

ARTICLE

ATTACK

ATTEMPT

AVERAGE

BALL

BAR

BATTLE

BEGIN

BENEATH

BESIDE

BEYOND

BILL

BLOW

BLUE

BRIDGE

BROAD

BUSINESS

CAPITAL

CASE

CASTLE

CHARGE

CHIEF

CHURCH

CIRCLE

CLAIM

COAL

COAST

COIN

COLLEGE

COLONY

COMMAND

COMMITTEE

COMPANY

CONDITION

CONSIDER

CONTINUE

CONTROL

CORN

COST

COUNCIL

COUNT

CROWD

CROWN

CURRENT

DAUGHTER

DEAL

DEAR

DECLARE

DEMAND

DEPARTMENT

DESERT

DESIRE

DESTROY

DETERMINE

DEVELOP

DISCOVER

DISTINGUISH

DISTRICT

DOLLAR

DOUBT

DROP

DUTY

EARTH

EFFICIENT

EFFORT

ELECT

ELEVEN

ELSE

EMPIRE

EMPLOY

ENEMY

EQUAL

ESCAPE

EXCHANGE

EXIST

EXPERIMENT

FAIR

FAITH

FAMILIAR

FAMOUS

FEAR

FELLOW

FIGHT

FIGURE

FIT

FLOW

FORCE

FORMER

FORTH

GAIN

GAME

GARDEN

GAS

GENTLE

HARDLY

HEAVEN

HONOUR

HORSE

HUSBAND

ILL

INCH

INCREASE

INDEED

INDUSTRY

INFLUENCE

IRON

JOINT

JOY

JUDGE

JUSTICE

KING

LACK

LADY

LATTER

LAUGHTER

LAW

LETTER

LIMIT

LIP

LITERATURE

LORD

LOSS MANUFACTURE

MASS

MASTER

MEMORY

MERE

MIGHT

MINER

MINISTER

MOREOVER

MRS

NATIVE

NEITHER

NEWS

NINE

NOBLE

NUMERICAL

NUMEROUS

OFF

OFFICIAL

OPPORTUNITY

ORDINARY

ORGANIZE

OTHERWISE

OUGHT

OWE

PARTICULAR

PEACE

PERHAPS

POLITICAL

POPULATION

POSITION

POSSESS

POST

POVERTY

PRESIDENT

PRESSURE

PRIVATE

PROOF

PROVE

PROVISION

PUBLIC

QUALITY

QUANTITY

QUEEN

RANK

RATE

RECEIPT

RECEIVE

RECENT

RECOGNIZE

RECORD

REGARD

REMAIN

REMARK

REPUBLIC

RESERVE

RING

RISE

ROLL

ROUGH

ROYAL

SAIL

SALE

SCARCE

SEAT

SECRETARY

SENSE

SENSITIVE

SHADOW

SHALL

SHIP

SHOOT

SHORE

SILENCE

SILVER

SIR

SMILE

SNOW

SOCIETY

SOLDIER

SORT

SOUL

SPEED

SPEND

SPIRIT

SPITE

SPOT

SQUARE

STANDARD

STATION

STEEL

STOCK

STONE

35

STRANGE

STRENGTH

STRIKE

STRUGGLE

SUBSTANCE

SUFFER

SUMMER

SUPPLY

SUPPOSE

SURFACE

SURROUND

SWEET

SWORD

SYSTEM

TAX

TEAR

TEMPLE

TERM

THIRTEEN

THOUGH

THROW

THUS

TON

TOTAL

TOWARD

TRAVEL

TROUBLE

TRUST

TUESDAY

UNION

UNIVERSITY

UNLESS

UPON

VALLEY

VICTORY

VIRTUE

VOTE

WAGE

WAVE

WESTERN

WHETHER

WIDE

WIND

WINDOW

WINTER

WISE

WITHOUT

WORTH

YIELD

Appendix B

K2 word families that are not found in the input textbooks

ABROAD

ABSENT

ABSOLUTE

ABSOLUTELY

ACCUSE

ACCUSTOM

ADMIRE

AEROPLANE

AFFORD

AGRICULTURE

AIM

AIRPLANE

ALIKE

ALIVE

ALOUD

ALTOGETHER

AMBITION

AMUSE

ANGLE

ANXIETY

APART

APOLOGIZE

APOLOGY

APPLAUD

APPROVE

ARCH

ARRANGE

ARREST

ARROW

ASH

ASHAMED

ASIDE

ASTONISH

AUTUMN

AVENUE

AWAKE

AWKWARD

AXE

BAGGAGE

BALANCE

BAND

BARBER

BARE

BARELY

BARGAIN

BARREL

BASIN

BATHE

BAY

BEAK

BEAM

BEARD

BEAST

BEAT

BEHAVE

BELL

BELT

BEND

BERRY

BILLION

BIND

BITE

BLADE

BLAME

BLESS

BLIND

BLOCK

BOAST

BOLD

BONE

BORDER

BORROW

BOTTLE

BOTTOM

BOUND

BOUNDARY

BOW

BRAIN

BRASS

BREATH

BRIBE

BRICK

BROADCAST

BROWN

BRUSH

BUCKET

BUNCH

BUNDLE

BURIAL

BURST

BURY

BUSH

BUTTON

CALM

CAMERA

CAMP

CANAL

CAPE

CARD

CARRIAGE

CAT

CENT

CENTIMETRE

CENTURY

CHAIN

CHALK

CHARM

CHEAP

CHEESE

CHEQUE

CHEST

CHIMNEY

CHRISTMAS

CIVILISE

CLERK

CLIFF

COAT

COLLAR

COMB

COMFORT

COMMERCE

COMPANION

COMPARE

COMPETE

COMPLAIN

COMPOSE

COMPLICATE

CONFESS

CONFIDENCE

CONNECT

CONQUER

CONSCIENCE

CONSCIOUS

CONVENIENCE

COPPER

CORK

CORNER

COTTAGE

COURAGE

COWARD

CRACK

CRASH

CREEP

CRIMINAL

CRITIC

CROP

CRUEL

CULTIVATE

CURIOUS

CURL

CURVE

CUSHION

CUSTOM

DAMP

DANCE

DEAF

DEBT

DECAY

DECEIVE

DECREASE

DEFEND

DELAY

DELIGHT

DESPAIR

DEVIL

DIG

DIP

DISCIPLINE

DISGUST

DISMISS

36

DIVE

DONKEY

DOT

DOUBLE

DOZEN

DRAG

DRAWER

DROWN

DRUM

DUCK

DULL

EAGER

EARNEST

EDGE

EDUCATE

ELASTIC

ELDER

ELECTRIC

ELEPHANT

EMPTY

ENCLOSE

ENCOURAGE

ENGINE

ENTIRE

ENVELOPE

ENVY

ESSENCE

ESSENTIAL

EVIL

EXAMINING

EXCELLENT

EXCESS

EXCITE

EXPLODE

EXPLORE

EXTRAORDINA

RY

FADE

FAINT

FALSE

FAN

FANCY

FASHION

FASTEN

FATE

FAULT

FEAST

FIERCE

FILM

FLASH

FLAT

FLESH

FLOOD

FOLD

FOND

FOOL

FORBID

FORGIVE

FORK

FORWARD

FREEZE

FREQUENT

FRIGHT

FUR

GALLON

GAP

GARAGE

GAY

GOAT

GRACE

GRADUAL

GRAMMAR

GRASS

GRATEFUL

GRAVE

GREASE

GREY

GRIND

GUESS

GUEST

GUILTY

GUN HANDKERCHIEF

HARBOR

HARVEST

HASTE

HAT

HATE

HAY

HEAL

HEAP

HESITATE

HINDER

HIRE

HOLLOW

HOLY

HONEST

HOOK

HOST

HOTEL

HUMBLE

HURRAH

HURRY

HURT

HUT

IDEAL

IDLE

IMAGINE

IMITATE

IMMENSE

IMPROVE

INFORMAL

INFORMALLY

INN

INQUIRE

INSULT

INSURE

INTERFERE

INTERNATION

AL

INTERRUPT

INVENT

INWARD

ISLAND

JAW

JEALOUS

JEWEL

JOKE

JOURNEY

JUICE

KEY

KILOGRAM

KILOMETRE

KISS

KNEEL

LADDER

LAMP

LEAN LEND

LIBERTY

LIMB LITRE

LOAF

LOAN

LODGING

LOG

LONE

LOOSE

LOYAL

MAD

MAIL

MAP

MAT

MEANTIME

MEANWHILE

MELT

MEND

MERCHANT

MERCY

MERRY

MESSENGER

METRE

MILL

MILLIGRAM

MILLILITRE

MILLIMETRE

MINERAL

MISERABLE

MODEST

MONKEY

MOTION

MULTIPLY

MURDER

MYSTERY

NARROW

NEAT

NEEDLE

NEGLECT

NEST

NONSENSE

NOON

NOSE

NOUN

NUISANCE

NURSE

NUT

OAR

OBEY

OCEAN

OFFEND

OMIT

ONWARDS

OPPOSE

ORANGE

ORGAN

ORNAMENT

OUTLINE

OVERCOME

PAD

PARCEL

PARDON

PASSAGE

PASSENGER

PASTE

PATH

PATIENT

PATRIOTIC

PATTERN

PAUSE

PAW

PEARL

PECULIAR

PEN

PENCIL

PENNY

PERMANENT

PERSUADE

PET

PHOTOGRAPH

PIG

PIGEON

PILE

PIN

PINCH

PINK

PIPE

PITY

PLANE

PLASTER

PLENTY

PLURAL

POEM

POISON

POLITE

POOL

POSTPONE

POT

POWDER

PRACTICAL

PREACH

PRECIOUS

PREJUDICE

PRESERVE

PRETEND

PRIDE

PRIEST

PRINT

PRISON

PROBABLE

PROCESSION

PROFESSION

37

PROGRAMME

PROMPT

PRONOUNCE

PUNCTUAL

PUNISH

PUPIL

PURE

PURPLE

PUSH

PUZZLE

QUALIFY

QUARREL

QUART

RABBIT

RADIO

RAIL

RAKE

RAPID

RAT

RAW

RAY

RAZOR

REFRESH

REGRET

REJOICE

REMEDY

RENT

REPAIR

REPRODUCE

REPUTATION

REQUEST

RESCUE

RESIGN

RESPONSIBLE

RESTAURANT

RETIRE

REVENGE

REVIEW

REWARD

RIBBON

RISK

RIVAL

ROAR

ROAST

ROB

ROD

ROOF

ROOT

ROPE

ROT

ROW

RUDE

RUG

RUIN

RUSH

RUST

SACRED

SACRIFICE

SADDLE

SAKE

SALARY

SAMPLE

SATISFY

SAUCER

SAWS

SCATTER

SCENT

SCISSORS

SCOLD

SCORN

SCRATCH

SCREEN

SCREW

SEIZE

SELDOM

SEVERE

SHADE

SHALLOW

SHAME

SHAVE

SHEEP

SHEET

SHELL

SHIELD

SHILLING

SHOCK

SHOUT

SHUT SIGNAL

SINCERE

SLAVE

SLIDE

SMOOTH

SNAKE

SOAP

SOCK

SOIL

SOLEMN

SOLVE

SOUP

SOUR

SOW

SPADE

SPARE

SPILL

SPIN

SPIT

SPLENDID

SPLIT

SPOIL

STAIN

STAMP

STEADY

STEAL

STEER

STEM

STIFF

STING

STOCKING

STORM

STOVE

STRAW

STRETCH

STRICT

STRING

STRIP

STRIPE

STUPID

SUDDEN

SUPPER

SUSPECT

SUSPICION

SWALLOW

SWEAR

SWEAT

SWELL

SWING

SYMPATHY

TAIL

TALL

TAP

TAXI

TELEGRAPH

TEMPT

TEND

THEATRE

THIRST

THORN

THREAT

THUNDER

TICKET

TIDE

TIGHT

TIN

TOBACCO

TOE

TONGUE

TOWER

TRACK

TRAP

TRAY

TREASURE

TREMBLE

TRIBE

TRICK

TUBE

TUNE

TYPICAL

UGLY

UNIVERSE

UPPER

UPRIGHT

UPSET

UPWARDS

URGE

VAIN

VEIL

VERSE

VIOLENT

VOWEL

VOYAGE

WAIST

WANDER

WAX

WEATHER

WEED

WHEEL

WHIP

WHISPER

WICKED

WIDOW

WINE

WIPE

WIRE

WITNESS

WORM

WORSHIP

WRECK

WRIST

YELLOW

ZERO

38

Appendix C

AWL word families that are not found in the input textbooks

ABANDON

ABSTRACT

ACADEMY

ACCESS ACCOMMODATE

ACCUMULATE

ACCURATE ACKNOWLEDGE

ACQUIRE

ADEQUATE

ADJACENT ADMINISTRATE

ADVOCATE

AFFECT

AGGREGATE

AID

ALBEIT

ALLOCATE

ALTER

ALTERNATIVE

AMBIGUOUS

AMEND

ANALOGY

ANNUAL

ANTICIPATE

APPARENT

APPEND

APPRECIATE

APPROXIMATE

ARBITRARY

ASPECT

ASSEMBLE

ASSESS

ASSIGN

ASSIST

ASSUME

ASSURE

ATTACH

ATTAIN

ATTRIBUTE

AUTHOR

AUTHORITY

AWARE

BEHALF

BIAS

BOND

BRIEF

BULK

CAPABLE

CATEGORY

CEASE

CHALLENGE

CHANNEL

CHART CIRCUMSTANCE

CITE

CIVIL

CLASSIC

CLAUSE

CODE

COHERENT

COINCIDE

COLLAPSE

COLLEAGUE

COMMENCE

COMMENT

COMMISSION

COMMIT

COMMODITY

COMPATIBLE

COMPENSATE

COMPILE

COMPLEMENT

COMPONENT

COMPOUND COMPREHENSIVE

COMPRISE

CONCEIVE

CONCEPT

CONCLUDE

CONCURRENT

CONDUCT

CONFER

CONFINE

CONFIRM

CONFLICT

CONFORM

CONSENT

CONSEQUENT CONSIDERABLE CONSTANT

CONSTITUTE

CONSTRAIN

CONSTRUCT

CONSULT

CONSUME CONTEMPORARY

CONTRADICT

CONTRARY

CONTRIBUTE

CONTROVERSY

CONVENE

CONVERSE

CONVERT

CONVINCE

COOPERATE

COORDINATE

CORPORATE

COUPLE

CREDIT

CRITERIA

CRUCIAL

CURRENCY

CYCLE

DATA

DEBATE

DECLINE

DEDUCE

DEFINITE DEMONSTRATE

DENOTE

DENY

DEPRESS

DERIVE

DESPITE

DETECT

DEVIATE

DEVICE

DEVOTE

DIMENSION

DIMINISH

DISCRETE

DISCRIMINATE

DISPLACE

DISPLAY

DISPOSE

DISTORT

DISTRIBUTE

DIVERSE

DOCUMENT

DOMAIN

DOMINATE

DRAFT

DRAMA

DURATION

DYNAMIC

ECONOMY

ELIMINATE

EMERGE

EMPHASIS

EMPIRICAL

ENABLE

ENFORCE

ENORMOUS

ENTITY

EQUATE

EQUIP

ERODE

ERROR

ESTABLISH

ESTATE

ESTIMATE

ETHIC

ETHNIC

EVALUATE

EVENTUAL

EVOLVE

EXCEED

EXCLUDE

EXHIBIT

EXPAND

EXPERT

EXPLOIT

EXPORT

FACILITATE

FACTOR

FEATURE

FEDERAL

FEE

FILE

FINANCE

FINITE

FLEXIBLE

FLUCTUATE

FORMAT

FORTHCOMING

FOUNDATION

FRAMEWORK

FUND FUNDAMENTAL

FURTHERMORE

GENDER

GENERATE

GENERATION

GLOBE

GUARANTEE

GUIDELINE

HENCE

HIERARCHY

HIGHLIGHT

HYPOTHESIS

IDENTICAL

IDEOLOGY

IGNORANT

ILLUSTRATE

IMAGE

IMMIGRATE

IMPACT

IMPLEMENT

IMPLICATE

IMPLICIT

IMPLY

IMPOSE

INCENTIVE

INCIDENCE

INCLINE

INCOME

INCORPORATE

INDEX

INDUCE

INEVITABLE

INFER

INFRASTRUCT

URE

INHERENT

INHIBIT

INITIAL

INITIATE

INNOVATE

INPUT

INSERT

INSIGHT

INSPECT

INSTANCE

INSTITUTE

39

INTEGRAL

INTEGRATE

INTEGRITY

INTELLIGENCE

INTENSE INTERMEDIATE INTERNAL

INTERPRET

INTERVAL

INTERVENE

INTRINSIC

INVEST

INVESTIGATE

INVOKE

INVOLVE

ISOLATE

ISSUE

JUSTIFY

LABOUR

LAYER

LECTURE

LEGAL

LEGISLATE

LEVY

LIBERAL

LIKEWISE

LINK

LOCATE

LOGIC

MAINTAIN

MAJOR

MANIPULATE

MARGIN

MECHANISM

MEDIA

MEDIATE

MENTAL

MIGRATE

MILITARY

MINIMAL

MINIMISE

MINIMUM

MINISTRY

MODE

MODIFY

MONITOR

MOTIVE

MUTUAL

NEGATE

NETWORK

NEUTRAL

NEVERTHELES

S

NONETHELESS

NORM

NOTION

NOTWITHSTAN

DING

OBJECTIVE

OBVIOUS

OCCUPY

OCCUR

ODD

OFFSET

ONGOING

ORIENT

OUTCOME

OUTPUT

OVERALL

OVERLAP

OVERSEAS

PARADIGM

PARALLEL

PARAMETER

PARTNER

PERCEIVE

PERCENT

PERIOD

PERSIST

PERSPECTIVE

PHASE

PHILOSOPHY

PLUS

POLICY

PORTION

POSE

POSITIVE

POTENTIAL

PRACTITIONER

PRECEDE

PRECISE

PREDICT

PREDOMINANT

PRELIMINARY

PRESUME

PRIMARY

PRIME

PRINCIPLE

PRIOR

PRIORITY

PROHIBIT

PROPORTION

PROSPECT

PROTOCOL

PSYCHOLOGY

PUBLICATION

PURCHASE

PURSUE

QUALITATIVE

QUOTE

RADICAL

RANDOM

RANGE

RATIO

RATIONAL

REACT

REFINE

REGIME

REGION

REGULATE

REINFORCE

REJECT

RELAX

RELEVANT

RELUCTANCE

RELY

RESEARCH

RESIDE

RESTORE

RESTRAIN

RESTRICT

RETAIN

REVEAL

REVENUE

REVISE

REVOLUTION

RIGID

ROUTE

SCENARIO

SCHEDULE

SCHEME

SCOPE

SECTOR

SELECT

SEQUENCE

SERIES

SEX

SHIFT

SIGNIFICANT

SIMULATE

SITE

SO-CALLED

SOMEWHAT

SOURCE

SPECIFIC

SPECIFY

SPHERE

STABLE

STATISTIC

STATUS

STRAIGHTFOR

WARD

STRATEGY

STRESS

STYLE

SUBMIT

SUBORDINATE

SUBSEQUENT

SUBSIDY

SUBSTITUTE

SUCCESSOR

SUFFICIENT

SUM

SUPPLEMENT

SURVEY

SUSPEND

SUSTAIN

TAPE

TARGET

TECHNICAL

TECHNIQUE

TECHNOLOGY

THEME

THEORY

THEREBY

THESIS

TOPIC

TRACE

TRANSFER

TRANSFORM

TRANSIT

TRANSMIT

TRANSPORT

TREND

TRIGGER

ULTIMATE

UNDERGO

UNDERLIE

UNDERTAKE

UNIFY

UTILISE

VALID

VIA

VIOLATE

VIRTUAL

VISIBLE

VISION

VISUAL

VOLUME

VOLUNTARY

WELFARE

WHEREAS

WHEREBY

WIDESPREAD

40

Appendix D

Block frequency output of off-list words.

RANK FREQ COVERAGE

individ cumulative WORD

1. 66 3.87% 3.87% PUNCTUATION

2. 37 2.17% 6.04% ORALLY

3. 36 2.11% 8.15% BUFFALO

4. 28 1.64% 9.79% ORPHAN

5. 26 1.53% 11.32% INGREDIENTS

6. 23 1.35% 12.67% DRUG

7. 21 1.23% 13.90% ORPHANAGE

8. 20 1.17% 15.07% DIALOGUE

9. 19 1.12% 16.19% MAGAZINE

10. 19 1.12% 17.31% RECIPE

11. 18 1.06% 18.37% BATS

12. 16 0.94% 19.31% HOMEWORK

13. 16 0.94% 20.25% SERA

14. 15 0.88% 21.13% CLASSROOM

15. 15 0.88% 22.01% COCONUT

16. 15 0.88% 22.89% NOVEL

17. 15 0.88% 23.77% UNDERLINE

18. 13 0.76% 24.53% ENCYCLOPEDIA

19. 13 0.76% 25.29% FABRICS

20. 13 0.76% 26.05% HANDWRITE

21. 12 0.70% 26.75% EXPIRATION

22. 12 0.70% 27.45% ORAL

23. 11 0.65% 28.10% ASIA

41

24. 11 0.65% 28.75% BANANA

25. 11 0.65% 29.40% COLUMN

26. 11 0.65% 30.05% CONDENSED

27. 11 0.65% 30.70% MAMMALS

28. 11 0.65% 31.35% RAVEN

29. 10 0.59% 31.94% DIARRHEA

30. 10 0.59% 32.53% DOSAGE

31. 10 0.59% 33.12% KIDS

32. 10 0.59% 33.71% SWITCH

33. 9 0.53% 34.24% CELEBRATE

34. 9 0.53% 34.77% COCKTAIL

35. 9 0.53% 35.30% FOLKTALE

36. 9 0.53% 35.83% PM

37. 8 0.47% 36.30% AFRICAN

38. 8 0.47% 36.77% CANTEEN

39. 8 0.47% 37.24% CLUES

40. 8 0.47% 37.71% FOXES

41. 8 0.47% 38.18% MATH

42. 8 0.47% 38.65% PUDDING

43. 8 0.47% 39.12% SYRUP

44. 7 0.41% 39.53% INGREDIENT

45. 7 0.41% 39.94% MOTORCYCLE

46. 7 0.41% 40.35% OLIVE

47. 7 0.41% 40.76% PLASTIC

48. 7 0.41% 41.17% RECIPES

49. 7 0.41% 41.58% TRAFFIC

50. 7 0.41% 41.99% VEGETABLES

51. 6 0.35% 42.34% BAT

52. 6 0.35% 42.69% DOUGH

53. 6 0.35% 43.04% HANDICRAFTS

54. 6 0.35% 43.39% HORNS

55. 6 0.35% 43.74% HUGE

42

56. 6 0.35% 44.09% MESSY

57. 6 0.35% 44.44% PEDESTRIANS

58. 6 0.35% 44.79% SLICED

59. 6 0.35% 45.14% TRASH

60. 5 0.29% 45.43% ATTICS

61. 5 0.29% 45.72% AWARD

62. 5 0.29% 46.01% BADMINTON

63. 5 0.29% 46.30% BATHROOM

64. 5 0.29% 46.59% BEEF

65. 5 0.29% 46.88% BLANK

66. 5 0.29% 47.17% FABRIC

67. 5 0.29% 47.46% FIBERS

68. 5 0.29% 47.75% GOODS

69. 5 0.29% 48.04% GREEK

70. 5 0.29% 48.33% HOBBY

71. 5 0.29% 48.62% HOUSEWORK

72. 5 0.29% 48.91% JAVA

73. 5 0.29% 49.20% NOVELS

74. 5 0.29% 49.49% OREGANO

75. 5 0.29% 49.78% SPECIES

76. 5 0.29% 50.07% SUNSET

77. 4 0.23% 50.30% ABSORBENT

78. 4 0.23% 50.53% AMAZING

79. 4 0.23% 50.76% AUSTRALIA

80. 4 0.23% 50.99% CHARITY

81. 4 0.23% 51.22% CHOIR

82. 4 0.23% 51.45% CONJUNCTION

83. 4 0.23% 51.68% CRYSTALS

84. 4 0.23% 51.91% DAMSELFLY

85. 4 0.23% 52.14% DECORATION

86. 4 0.23% 52.37% DIGEST

87. 4 0.23% 52.60% EMOTIONAL

43

88. 4 0.23% 52.83% ETC

89. 4 0.23% 53.06% FIBER

90. 4 0.23% 53.29% FINGERTIPS

91. 4 0.23% 53.52% HANDICRAFT

92. 4 0.23% 53.75% HAPPENINGS

93. 4 0.23% 53.98% HEATPROOF

94. 4 0.23% 54.21% HERDS

95. 4 0.23% 54.44% INDONESIA

96. 4 0.23% 54.67% JAMS

97. 4 0.23% 54.90% LAMB

98. 4 0.23% 55.13% LENGTHWISE

99. 4 0.23% 55.36% LUMPY

100. 4 0.23% 55.59% MARGARINE

101. 4 0.23% 55.82% MARINATED

102. 4 0.23% 56.05% MILILITRES

103. 4 0.23% 56.28% NEEDY

104. 4 0.23% 56.51% PACKAGING

105. 4 0.23% 56.74% PEELED

106. 4 0.23% 56.97% PROVINCE

107. 4 0.23% 57.20% RUPIAHS

108. 4 0.23% 57.43% SALAD

109. 4 0.23% 57.66% SCOOP

110. 4 0.23% 57.89% SCOUT

111. 4 0.23% 58.12% SHREDDED

112. 4 0.23% 58.35% SIFTED

113. 4 0.23% 58.58% SPICY

114. 4 0.23% 58.81% SPONGE

115. 4 0.23% 59.04% SPONGES

116. 4 0.23% 59.27% STARCH

117. 4 0.23% 59.50% SWAMPS

118. 4 0.23% 59.73% TAPIOCA

119. 4 0.23% 59.96% TERRITORY

44

120. 4 0.23% 60.19% THERMAL

121. 4 0.23% 60.42% TOFU

122. 4 0.23% 60.65% TV

123. 4 0.23% 60.88% VEGETABLE

124. 4 0.23% 61.11% WALLOW

125. 4 0.23% 61.34% WATERPROOF

126. 4 0.23% 61.57% WETTER

127. 3 0.18% 61.75% ACROBATIC

128. 3 0.18% 61.93% AFRICA

129. 3 0.18% 62.11% ALCOHOL

130. 3 0.18% 62.29% ASPIRIN

131. 3 0.18% 62.47% AWESOME

132. 3 0.18% 62.65% BAMBOO

133. 3 0.18% 62.83% BANANAS

134. 3 0.18% 63.01% BIKE

135. 3 0.18% 63.19% BOUNCE

136. 3 0.18% 63.37% BRACKETS

137. 3 0.18% 63.55% CERAMICS

138. 3 0.18% 63.73% CHASE

139. 3 0.18% 63.91% CHORES

140. 3 0.18% 64.09% CM

141. 3 0.18% 64.27% COVERINGS

142. 3 0.18% 64.45% DICTATE

143. 3 0.18% 64.63% DONNY

144. 3 0.18% 64.81% DOWNLOAD

145. 3 0.18% 64.99% EARPHONES

146. 3 0.18% 65.17% ECHOES

147. 3 0.18% 65.35% FURRY

148. 3 0.18% 65.53% GARLIC

149. 3 0.18% 65.71% GRASSHOPPERS

150. 3 0.18% 65.89% GRILL

151. 3 0.18% 66.07% GRILLED

45

152. 3 0.18% 66.25% GUITAR

153. 3 0.18% 66.43% HABITAT

154. 3 0.18% 66.61% HENS

155. 3 0.18% 66.79% INFO

156. 3 0.18% 66.97% JUNIOR

157. 3 0.18% 67.15% MAYOR

158. 3 0.18% 67.33% MEANINGFULLY

159. 3 0.18% 67.51% METER

160. 3 0.18% 67.69% ML

161. 3 0.18% 67.87% MOP

162. 3 0.18% 68.05% MOSQUITOES

163. 3 0.18% 68.23% MUSCLES

164. 3 0.18% 68.41% NOCTURNAL

165. 3 0.18% 68.59% NUTRITION

166. 3 0.18% 68.77% ORPHANS

167. 3 0.18% 68.95% OVERDOSE

168. 3 0.18% 69.13% OXYGEN

169. 3 0.18% 69.31% PALM

170. 3 0.18% 69.49% PITCHED

171. 3 0.18% 69.67% PLUG

172. 3 0.18% 69.85% POLLEN

173. 3 0.18% 70.03% POLLUTION

174. 3 0.18% 70.21% PREY

175. 3 0.18% 70.39% REDUCER

176. 3 0.18% 70.57% RELIEVER

177. 3 0.18% 70.75% RESTATE

178. 3 0.18% 70.93% RINSE

179. 3 0.18% 71.11% RINSED

180. 3 0.18% 71.29% SANDALS

181. 3 0.18% 71.47% SCHOLARSHIP

182. 3 0.18% 71.65% SKEWERED

183. 3 0.18% 71.83% SLIPPERY

46

184. 3 0.18% 72.01% SQUEALS

185. 3 0.18% 72.19% SYRUPY

186. 3 0.18% 72.37% TEASPOONFUL

187. 3 0.18% 72.55% TROPICAL

188. 3 0.18% 72.73% UNPLUG

189. 3 0.18% 72.91% YOUTUBE

190. 2 0.12% 73.03% ACETAMINOPHEN

191. 2 0.12% 73.15% AVOCADO

192. 2 0.12% 73.27% BACTERIA

193. 2 0.12% 73.39% BARLEY

194. 2 0.12% 73.51% BATIK

195. 2 0.12% 73.63% BEACH

196. 2 0.12% 73.75% BENNY

197. 2 0.12% 73.87% BIN

198. 2 0.12% 73.99% BINS

199. 2 0.12% 74.11% BOSS

200. 2 0.12% 74.23% BOYFRIEND

201. 2 0.12% 74.35% BUBBLING

202. 2 0.12% 74.47% CAMPUS

203. 2 0.12% 74.59% CARVING

204. 2 0.12% 74.71% CASUAL

205. 2 0.12% 74.83% CELSIUS

206. 2 0.12% 74.95% CHILLY

207. 2 0.12% 75.07% CHOCOLATE

208. 2 0.12% 75.19% CLASSMATES

209. 2 0.12% 75.31% COACH

210. 2 0.12% 75.43% COCKERELS

211. 2 0.12% 75.55% CONDUCTORS

212. 2 0.12% 75.67% COOKIES

213. 2 0.12% 75.79% CORD

214. 2 0.12% 75.91% CRAFT

215. 2 0.12% 76.03% CRITICIZE

47

216. 2 0.12% 76.15% CROW

217. 2 0.12% 76.27% CUBED

218. 2 0.12% 76.39% CUBES

219. 2 0.12% 76.51% DISSOLVES

220. 2 0.12% 76.63% DONATED

221. 2 0.12% 76.75% DRAGONFLIES

222. 2 0.12% 76.87% DRUGS

223. 2 0.12% 76.99% DUSTY

224. 2 0.12% 77.11% EBONY

225. 2 0.12% 77.23% ELDEST

226. 2 0.12% 77.35% EMAIL

227. 2 0.12% 77.47% ENCYCLOPEDIAS

228. 2 0.12% 77.59% ENTITLED

229. 2 0.12% 77.71% FOREHEAD

230. 2 0.12% 77.83% GARBAGE

231. 2 0.12% 77.95% GEMS

232. 2 0.12% 78.07% GENIES

233. 2 0.12% 78.19% GILLS

234. 2 0.12% 78.31% GRADUATION

235. 2 0.12% 78.43% GRASSHOPPER

236. 2 0.12% 78.55% GRATED

237. 2 0.12% 78.67% GUAVA

238. 2 0.12% 78.79% HABITATS

239. 2 0.12% 78.91% IMPERATIVE

240. 2 0.12% 79.03% INSULATORS

241. 2 0.12% 79.15% IRREFUTABLE

242. 2 0.12% 79.27% JACKFRUIT

243. 2 0.12% 79.39% JOG

244. 2 0.12% 79.51% LEAP

245. 2 0.12% 79.63% LINEN

246. 2 0.12% 79.75% LYRIC

247. 2 0.12% 79.87% MARINATE

48

248. 2 0.12% 79.99% MOTORCYCLES

249. 2 0.12% 80.11% MULLET

250. 2 0.12% 80.23% MUSCLE

251. 2 0.12% 80.35% NON

252. 2 0.12% 80.47% NOODLE

253. 2 0.12% 80.59% NYMPHS

254. 2 0.12% 80.71% OHH&#NUMBER;OHH&#NUMBER;OHH

255. 2 0.12% 80.83% ORES

256. 2 0.12% 80.95% PANTS

257. 2 0.12% 81.07% PASTRY

258. 2 0.12% 81.19% PAVEMENTS

259. 2 0.12% 81.31% PEEL

260. 2 0.12% 81.43% PHYSICIAN

261. 2 0.12% 81.55% PILLOWS

262. 2 0.12% 81.67% PLASTICS

263. 2 0.12% 81.79% POLLUTED

264. 2 0.12% 81.91% PRACTICED

265. 2 0.12% 82.03% PREDATOR

266. 2 0.12% 82.15% PRESERVATIVES

267. 2 0.12% 82.27% PUPPETEER

268. 2 0.12% 82.39% PUPPETS

269. 2 0.12% 82.51% REWRITE

270. 2 0.12% 82.63% ROUTINELY

271. 2 0.12% 82.75% SANDSTONE

272. 2 0.12% 82.87% SAUCEPAN

273. 2 0.12% 82.99% SCAVENGER

274. 2 0.12% 83.11% SENSATION

275. 2 0.12% 83.23% SENSATIONAL

276. 2 0.12% 83.35% SHRED

277. 2 0.12% 83.47% SIFT

278. 2 0.12% 83.59% SKEWER

279. 2 0.12% 83.71% SOAK

49

280. 2 0.12% 83.83% SOAKS

281. 2 0.12% 83.95% SOY

282. 2 0.12% 84.07% SQUASHED

283. 2 0.12% 84.19% SUNDRIED

284. 2 0.12% 84.31% SWEETHEART

285. 2 0.12% 84.43% TAMARIND

286. 2 0.12% 84.55% TEAK

287. 2 0.12% 84.67% TEASPOON

288. 2 0.12% 84.79% TELESCOPE

289. 2 0.12% 84.91% TINY

290. 2 0.12% 85.03% TISSUES

291. 2 0.12% 85.15% TUB

292. 2 0.12% 85.27% VENDOR

293. 2 0.12% 85.39% WEDDING

294. 2 0.12% 85.51% WEDDINGS

295. 2 0.12% 85.63% WOVEN

296. 2 0.12% 85.75% WOW

297. 1 0.06% 85.81% ABSORB

298. 1 0.06% 85.87% ABSORBS

299. 1 0.06% 85.93% ACEH

300. 1 0.06% 85.99% ACHIEVER

301. 1 0.06% 86.05% ACID

302. 1 0.06% 86.11% ADDITIVES

303. 1 0.06% 86.17% AHMAD

304. 1 0.06% 86.23% AIRLESS

305. 1 0.06% 86.29% AMBON

306. 1 0.06% 86.35% AMERICA

307. 1 0.06% 86.41% ANIDAN

308. 1 0.06% 86.47% ANT

309. 1 0.06% 86.53% ANTIOXIDANT

310. 1 0.06% 86.59% ANTIQUE

311. 1 0.06% 86.65% APPLIANCE

50

312. 1 0.06% 86.71% ARABIC

313. 1 0.06% 86.77% ASCORBIC

314. 1 0.06% 86.83% AVAIBALE

315. 1 0.06% 86.89% BACK&#NUMBER;BACK

316. 1 0.06% 86.95% BAKERY

317. 1 0.06% 87.01% BALSA

318. 1 0.06% 87.07% BARBEQUE

319. 1 0.06% 87.13% BENCH

320. 1 0.06% 87.19% BLACKBOARD

321. 1 0.06% 87.25% BLENDED

322. 1 0.06% 87.31% BRAND

323. 1 0.06% 87.37% BRANDS

324. 1 0.06% 87.43% BROADWAY

325. 1 0.06% 87.49% BULL

326. 1 0.06% 87.55% BULLS

327. 1 0.06% 87.61% BUMP

328. 1 0.06% 87.67% CAFFEINE

329. 1 0.06% 87.73% CALENDAR

330. 1 0.06% 87.79% CARBOHYDRATE

331. 1 0.06% 87.85% CARVERS

332. 1 0.06% 87.91% CARVINGS

333. 1 0.06% 87.97% CASSAVA

334. 1 0.06% 88.03% CASTER

335. 1 0.06% 88.09% CELEBRATION

336. 1 0.06% 88.15% CELEBRATIONS

337. 1 0.06% 88.21% CELL

338. 1 0.06% 88.27% CHAMPION

339. 1 0.06% 88.33% CHARCOAL

340. 1 0.06% 88.39% CHAT

341. 1 0.06% 88.45% CHATTING

342. 1 0.06% 88.51% CHILIES

343. 1 0.06% 88.57% CHOPPED

51

344. 1 0.06% 88.63% CIGAR

345. 1 0.06% 88.69% CIGARETTES

346. 1 0.06% 88.75% CLOVES

347. 1 0.06% 88.81% COCKROACH

348. 1 0.06% 88.87% COCKS

349. 1 0.06% 88.93% COCONUTS

350. 1 0.06% 88.99% COMMEMORATION

351. 1 0.06% 89.05% COMPULSORY

352. 1 0.06% 89.11% CONDITIONER

353. 1 0.06% 89.17% CONGRATULATION

354. 1 0.06% 89.23% CORIANDER

355. 1 0.06% 89.29% CRESTS

356. 1 0.06% 89.35% CUISINE

357. 1 0.06% 89.41% CURRICULAR

358. 1 0.06% 89.47% DAIRY

359. 1 0.06% 89.53% DAMSELFLIES

360. 1 0.06% 89.59% DAWN

361. 1 0.06% 89.65% DAYBREAK

362. 1 0.06% 89.71% DAYDREAM

363. 1 0.06% 89.77% DEHYDRATED

364. 1 0.06% 89.83% DESEEDED

365. 1 0.06% 89.89% DESSERT

366. 1 0.06% 89.95% DISCOUNT

367. 1 0.06% 90.01% DOLL

368. 1 0.06% 90.07% DONORS

369. 1 0.06% 90.13% DOSAGES

370. 1 0.06% 90.19% DOSE

371. 1 0.06% 90.25% DOSES

372. 1 0.06% 90.31% DRAGONFLY

373. 1 0.06% 90.37% DRAIN

374. 1 0.06% 90.43% DRAPED

375. 1 0.06% 90.49% DRIZZLE

52

376. 1 0.06% 90.55% EFFICIENTLY

377. 1 0.06% 90.61% ELEVATOR

378. 1 0.06% 90.67% EMBROIDERED

379. 1 0.06% 90.73% EMULSIFIER

380. 1 0.06% 90.79% ENT

381. 1 0.06% 90.85% ETERNAL

382. 1 0.06% 90.91% EVAPORATED

383. 1 0.06% 90.97% EXAM

384. 1 0.06% 91.03% EXEMPLARY

385. 1 0.06% 91.09% EXTRACURRICULAR

386. 1 0.06% 91.15% FARMYARD

387. 1 0.06% 91.21% FAUNA

388. 1 0.06% 91.27% FERMENTED

389. 1 0.06% 91.33% FINS

390. 1 0.06% 91.39% FLAMMABLE

391. 1 0.06% 91.45% FLIED

392. 1 0.06% 91.51% FLORA

393. 1 0.06% 91.57% FLU

394. 1 0.06% 91.63% FLUTTERING

395. 1 0.06% 91.69% FOSSILS

396. 1 0.06% 91.75% FOURTY

397. 1 0.06% 91.81% FRESHWATER

398. 1 0.06% 91.87% FULFILLED

399. 1 0.06% 91.93% GAEA

400. 1 0.06% 91.99% GEAE

401. 1 0.06% 92.05% GEOGRAPHY

402. 1 0.06% 92.11% GERMS

403. 1 0.06% 92.17% GLOOMY

404. 1 0.06% 92.23% GLUTINOUS

405. 1 0.06% 92.29% GRADUATIONS

406. 1 0.06% 92.35% GRANDEST

407. 1 0.06% 92.41% GUTTERS

53

408. 1 0.06% 92.47% GUYS

409. 1 0.06% 92.53% HANDSOME

410. 1 0.06% 92.59% HATCH

411. 1 0.06% 92.65% HEADACHE

412. 1 0.06% 92.71% HEADMASTER

413. 1 0.06% 92.77% HEARTED

414. 1 0.06% 92.83% HOP

415. 1 0.06% 92.89% HORN

416. 1 0.06% 92.95% IDR

417. 1 0.06% 93.01% IMPORTED

418. 1 0.06% 93.07% INACTIVE

419. 1 0.06% 93.13% INSISTED

420. 1 0.06% 93.19% INTERPERSONAL

421. 1 0.06% 93.25% JAKARTA

422. 1 0.06% 93.31% JAPANESE

423. 1 0.06% 93.37% JOGGING

424. 1 0.06% 93.43% KAPOK

425. 1 0.06% 93.49% KID

426. 1 0.06% 93.55% KILOMETERS

427. 1 0.06% 93.61% KNITTING

428. 1 0.06% 93.67% KNOB

429. 1 0.06% 93.73% KOREAN

430. 1 0.06% 93.79% KRATON

431. 1 0.06% 93.85% LABORATORY

432. 1 0.06% 93.91% LAUNCHED

433. 1 0.06% 93.97% LAYOUT

434. 1 0.06% 94.03% LIME

435. 1 0.06% 94.09% LITER

436. 1 0.06% 94.15% LITTER

437. 1 0.06% 94.21% LUXURY

438. 1 0.06% 94.27% MAGAZINES

439. 1 0.06% 94.33% MANADO

54

440. 1 0.06% 94.39% MANGOES

441. 1 0.06% 94.45% MATES

442. 1 0.06% 94.51% MILLILITERS

443. 1 0.06% 94.57% MODAL

444. 1 0.06% 94.63% MOISTURIZED

445. 1 0.06% 94.69% MOPPING

446. 1 0.06% 94.75% MOUNT

447. 1 0.06% 94.81% MSG

448. 1 0.06% 94.87% NETT

449. 1 0.06% 94.93% NONLIVING

450. 1 0.06% 94.99% NOTEBOOK

451. 1 0.06% 95.05% OAK

452. 1 0.06% 95.11% OATMEAL

453. 1 0.06% 95.17% ODER

454. 1 0.06% 95.23% OKAY

455. 1 0.06% 95.29% OLIVES

456. 1 0.06% 95.35% OPTIMISTIC

457. 1 0.06% 95.41% ORGANIC

458. 1 0.06% 95.47% OVERCOOK

459. 1 0.06% 95.53% PANTHER

460. 1 0.06% 95.59% PEANUT

461. 1 0.06% 95.65% PECK

462. 1 0.06% 95.71% PEELING

463. 1 0.06% 95.77% PICNIC

464. 1 0.06% 95.83% PIE

465. 1 0.06% 95.89% PLAYGROUNDS

466. 1 0.06% 95.95% PLEA

467. 1 0.06% 96.01% PLUGGING

468. 1 0.06% 96.07% POLYGLOT

469. 1 0.06% 96.13% PONDS

470. 1 0.06% 96.19% PRINCESS

471. 1 0.06% 96.25% PROTEIN

55

472. 1 0.06% 96.31% PUNCTUATED

473. 1 0.06% 96.37% RAINBOWS

474. 1 0.06% 96.43% REARED

475. 1 0.06% 96.49% REWRITTEN

476. 1 0.06% 96.55% RUFF

477. 1 0.06% 96.61% SACHET

478. 1 0.06% 96.67% SACHETS

479. 1 0.06% 96.73% SALTWATER

480. 1 0.06% 96.79% SATURATES

481. 1 0.06% 96.85% SCAR

482. 1 0.06% 96.91% SCULPTURE

483. 1 0.06% 96.97% SEAL

484. 1 0.06% 97.03% SENIOR

485. 1 0.06% 97.09% SHIMMER

486. 1 0.06% 97.15% SHOALS

487. 1 0.06% 97.21% SHUTTLECOCKS

488. 1 0.06% 97.27% SINA

489. 1 0.06% 97.33% SKEWERS

490. 1 0.06% 97.39% SKILLFUL

491. 1 0.06% 97.45% SKIP

492. 1 0.06% 97.51% SLICE

493. 1 0.06% 97.57% SMART

494. 1 0.06% 97.63% SODIUM

495. 1 0.06% 97.69% SOLO

496. 1 0.06% 97.75% SORETHROAT

497. 1 0.06% 97.81% SOUNDLY

498. 1 0.06% 97.87% SOUVENIR

499. 1 0.06% 97.93% SOUVENIRS

500. 1 0.06% 97.99% SOYA

501. 1 0.06% 98.05% SPINACH

502. 1 0.06% 98.11% SPRINKLED

503. 1 0.06% 98.17% SPRITZ

56

504. 1 0.06% 98.23% SPROUTS

505. 1 0.06% 98.29% SQUEEZED

506. 1 0.06% 98.35% STARVE

507. 1 0.06% 98.41% STEAK

508. 1 0.06% 98.47% STEAMER

509. 1 0.06% 98.53% SUDANESE

510. 1 0.06% 98.59% SUPER

511. 1 0.06% 98.65% SWAYED

512. 1 0.06% 98.71% SWEETEN

513. 1 0.06% 98.77% SWOOP

514. 1 0.06% 98.83% TABLESPOON

515. 1 0.06% 98.89% TABLESPOONS

516. 1 0.06% 98.95% TABLET

517. 1 0.06% 99.01% TABLETS

518. 1 0.06% 99.07% TAMPER

519. 1 0.06% 99.13% TEASPOONS

520. 1 0.06% 99.19% TEENAGER

521. 1 0.06% 99.25% TELLER

522. 1 0.06% 99.31% TEXTBOOKS

523. 1 0.06% 99.37% TOILET

524. 1 0.06% 99.43% TOILETS

525. 1 0.06% 99.49% TOLL

526. 1 0.06% 99.55% TOOTHACHE

527. 1 0.06% 99.61% TOSS

528. 1 0.06% 99.67% TRANSLATORS

529. 1 0.06% 99.73% TRANSPARENT

530. 1 0.06% 99.79% TRUCKS

531. 1 0.06% 99.85% TUTORING

532. 1 0.06% 99.91% UDDER

533. 1 0.06% 99.97% UNRECYCLABLE

534. 1 0.06% 100.00% VACCINATION

535. 1 0.06% 100.00% VENDORS

57

536. 1 0.06% 100.00% VICE

537. 1 0.06% 100.00% VIETNAM

538. 1 0.06% 100.00% VINEGAR

539. 1 0.06% 100.00% VIRGIN

540. 1 0.06% 100.00% VITAMIN

541. 1 0.06% 100.00% YAWNING

542. 1 0.06% 100.00% YEAST

543. 1 0.06% 100.00% YOGYAKARTA

Appendix E

Unique to first

1164 tokens

277 families

001. oral 28

002. rice 28

003. cup 26

004. recipe 25

005. cook 24

006. ingredient

23

007. act 22

008. apple 22

009. water 20

010. place 19

011. sugar 19

012. pan 17

013. analyse 14

014. coconut 14

015. flour 14

016. ice 14

017. steam 14

018. manual 13

019. each 12

020. leave 12

021. condense

11

022. milk 11

023. add 10

024. indicate

10

025. now 10

026. other 10

027. serve 10

028. switch 10

029. version 10

030. cocktail 9

Shared

524 tokens

105 families

001. you 68

002. the 36

003. i 26

004. a 23

005. it 19

006. to 17

007. can 14

008. in 14

009. this 13

010. and 12

011. come 12

012. of 10

013. if 9

014. have 8

015. know 8

016. will 8

017. be 7

018. correct 7

019. get 7

020. go 7

021. just 7

022. here 6

023. light 6

024. do 5

025. every 5

026. problem 5

027. they 5

028. write 5

029. clear 4

030. how 4

031. learn 4

032. loud 4

033. say 4

Unique to second

241 tokens

95 families

Freq first

(then alpha)

001. always 20

002. where 9

003. beauty 8

004. oh 8

005. song 7

006. you’re 7

007. from 6

008. home 6

009. life 6

010. ready 6

011. back 5

012. alone 4

013. hand 4

014. home” 4

015. line 4

016. mile 4

017. never 4

018. sing 4

019. we 4

020. “son 4

021. far 3

022. help 3

023. horizon 3

024. love 3

025. million 3

026. people 3

027. there 3

028. absence 2

029. child 2

030. dark 2

031. deep 2

VP novel items

Same list

Alpha first

001. absence 2

002. alone 4

003. also 1

004. always 20

005. ask 1

006. back 5

007. back…back…

1

008. beauty 8

009. because 1

010. belong 1

011. blank 1

012. bright 1

013. child 2

014. dark 2

015. deep 2

016. don’ 2

017. download 1

018. easy 1

019. eye 1

020. eyes… 1

021. fact 1

022. far 3

023. father 2

024. fathers’ 1

025. fill 1

026. from 6

027. glory 1

028. guitar 1

029. hand 4

58

031. fruit 9

032. lid 9

033. minute 9

034. novel 9

035. raise 9

036. self 9

037. close 8

038. handwrite

8

039. measure 8

040. method 8

041. pudding 8

042. basket 7

043. put 7

044. syrup 7

045. table 7

046. vegetable

7

047. warm 7

048. between 6

049. body 6

050. bowl 6

051. cream 6

052. food 6

053. mixture 6

054. peel 6

055. plug 6

056. rinse 6

057. shred 6

058. sift 6

059. slice 6

060. step 6

061. • 6

062. difference

5

063. dish 5

064. dress 5

065. english 5

066. green 5

067. stir 5

068. study 5

069. two 5

070. until 5

071. up 5

072. by 4

073. consist 4

074. core 4

075. fingertip

4

076. five 4

077. gram 4

078. heat 4

079. heatproof

4

080. immediate

034. some 4

035. tell 4

036. with 4

037. word 4

038. about 3

039. but 3

040. find 3

041. first 3

042. for 3

043. good 3

044. group 3

045. meaning 3

046. over 3

047. right 3

048. use 3

049. when 3

050. active 2

051. any 2

052. class 2

053. copy 2

054.

dictionary 2

055. he 2

056. into 2

057. journal 2

058. like 2

059. look 2

060. make 2

061. more 2

062. most 2

063. necessary

2

064. no 2

065. on 2

066. or 2

067. part 2

068. person 2

069. same 2

070. sentence

2

071. spell 2

072. sure 2

073. then 2

074. together

2

075. while 2

076. work 2

077. after 1

078. another 1

079. at 1

080. below 1

081. best 1

082. book 1

083. care 1

084. chapter 1

032. don’ 2

033. father 2

034. happy 2

035. hold 2

036. inside 2

037. interest 2

038. irrefutable

2

039. may 2

040. mother 2

041. night 2

042.

ohh…ohh…ohh… 2

043. parent 2

044. protect 2

045. road 2

046. see 2

047. seem 2

048. she 2

049. slippery 2

050. slope 2

051. sun 2

052. telescope 2

053. through 2

054. worry 2

055. ‘cause 2

056. also 1

057. ask 1

058. back…back…

1

059. because 1

060. belong 1

061. blank 1

062. bright 1

063. download 1

064. easy 1

065. eye 1

066. eyes… 1

067. fact 1

068. fathers’ 1

069. fill 1

070. glory 1

071. guitar 1

072. instrument

1

073. lyric 1

074. many 1

075. message 1

076. moon 1

077. mothers’ 1

078. music 1

079. nature 1

080. open 1

081. popular 1

082. possible 1

030. happy 2

031. help 3

032. hold 2

033. home 6

034. home” 4

035. horizon 3

036. inside 2

037. instrument

1

038. interest 2

039. irrefutable

2

040. life 6

041. line 4

042. love 3

043. lyric 1

044. many 1

045. may 2

046. message 1

047. mile 4

048. million 3

049. moon 1

050. mother 2

051. mothers’ 1

052. music 1

053. nature 1

054. never 4

055. night 2

056. oh 8

057.


058. open 1

059. parent 2

060. people 3

061. popular 1

062. possible 1

063. protect 2

064. ready 6

065. return 1

066. road 2

067. see 2

068. seem 2

069. share 1

070. she 2

071. sing 4

072. sky 1

073. slippery 2

074. slope 2

075. son 1

076. song 7

077. space 1

078. sun 2

079. talk 1

080. teach 1

081. telescope 2

59

4

081. knot 4

082. lengthwise

4

083. lump 4

084. margarine

4

085. move 4

086. order 4

087. practise 4

088. rub 4

089. rule 4

090. salad 4

091. scale 4

092. scoop 4

093. should 4

094. thick 4

095. thing 4

096. thorough 4

097. tie 4

098. turn 4

099. underline

4

100. as 3

101. bake 3

102. centimetre

3

103. complete 3

104. contain 3

105. cool 3

106. cut 3

107. firm 3

108. four 3

109. goal 3

110. gold 3

111. handle 3

112. hour 3

113. hundred 3

114. level 3

115. mix 3

116. paper 3

117. piece 3

118. pour 3

119. press 3

120. proper 3

121. recommend

3

122. rest 3

123. room 3

124. speak 3

125. spice 3

126. start 3

127. taste 3

128. teaspoon 3

129. title 3

085. difficult

1

086. example 1

087. front 1

088. less 1

089. let 1

090. listen 1

091. long 1

092. mark 1

093. need 1

094. not 1

095. note 1

096. one 1

097. present 1

098. punctuate

1

099. read 1

100. reflect 1

101. repeat 1

102. state 1

103. time 1

104. too 1

105. what 1

083. return 1

084. share 1

085. sky 1

086. son 1

087. space 1

088. talk 1

089. teach 1

090. thousand 1

091. very 1

092. view 1

093. way 1

094. we’ve 1

095. youtube 1

082. there 3

083. thousand 1

084. through 2

085. very 1

086. view 1

087. way 1

088. we 4

089. we’ve 1

090. where 9

091. worry 2

092. youtube 1

093. you’re 7

094. ‘cause 2

095. “son 4

60

130. young 3

131. “cook” 3

132. “warm” 3

133. accident 2

134. adjust 2

135. all 2

136. avocado 2

137. bean 2

138. before 2

139. boil 2

140. butter 2

141. certain 2

142. closes 2

143. combine 2

144. cord 2

145. crush 2

146. cube 2

147. cubed 2

148. damage 2

149. design 2

150. dissolve 2

151. drink 2

152. entitle 2

153. etc 2

154. few 2

155. final 2

156. full 2

157. graduate 2

158. grate 2

159. half 2

160. hear 2

161. inform 2

162. jackfruit

2

163. keep 2

164. large 2

165. leaf 2

166. little 2

167. low 2

168. manner 2

169. mistake 2

170. out 2

171. power 2

172. prepare 2

173. quarter 2

174. require 2

175. ripe 2

176. saucepan 2

177. section 2

178. settle 2

179. show 2

180. slight 2

181. small 2

182. soak 2

183. states 2

61

184. structure

2

185. tablespoon

2

186.

temperature 2

187. therefore

2

188. unit 2

189. well 2

190. according

1

191. achieve 1

192. appliance

1

193. audience 1

194. automatic

1

195. avoid 1

196. banana 1

197. both 1

198. box 1

199. capacity 1

200. cassava 1

201. caster 1

202. celsius 1

203. chilies 1

204. choose 1

205. chop 1

206. clove 1

207. coarse 1

208.

conjunction 1

209. coriander

1

210. degree 1

211. deseeded 1

212. dessert 1

213. divide 1

214. drain 1

215. drizzle 1

216. efficient

1

217. eight 1

218. evaporate

1

219. except 1

220. fine 1

221. finish 1

222. follow 1

223. fresh 1

224. garlic 1

225. generous 1

226. give 1

227. ground 1

62

228. harm 1

229. kind 1

230. knob 1

231. lead 1

232. length 1

233. lime 1

234. litre 1

235. machine 1

236. material 1

237. maximum 1

238. meat 1

239. mention 1

240. ml 1

241. model 1

242. next 1

243. object 1

244. operate 1

245. option 1

246. overcook 1

247. own 1

248. palm 1

249. parts: 1

250. perform 1

251. pie 1

252. plan 1

253. prevent 1

254. proceed 1

255. process 1

256. red 1

257. result 1

258. salt 1

259. scrape 1

260. separate 1

261. size 1

262. so 1

263. spinach 1

264. spoon 1

265. sprout 1

266. steamer 1

267. sweet 1

268. text 1

269. than 1

270. tip 1

271. tool 1

272. top 1

273. toss 1

274. vary 1

275. waste 1

276. wrap 1

277. yet 1

Appendix F

63

Unique to first

797 tokens

279 families

001. direction 29

002. product 25

003. label 22

004. drug 21

005. date 15

006. store 15

007. tea 15

008. dose 13

009. inform 13

010. expire 12

011. pack 12

012. content 11

013. free 11

014. amount 10

015. available 10

016. describe 10

017. ingredient

10

018. keep 9

019. analyse 8

020. contain 8

021. guide 8

022. oil 8

023. olive 8

024. oral 8

025. cough 7

026. yes 7

027. bag 6

028. by 6

029. four 6

030. name 6

031. • 6

032. as 5

033. flour 5

034. greece 5

035. medicine 5

036. oregano 5

037. out 5

038. pain 5

039. seven 5

040. before 4

041. cup 4

042. direct 4

043. each 4

044. fever 4

045. five 4

046. food 4

047. mililitres 4

048. only 4

Shared

503 tokens

100 families

001. you 68

002. the 36

003. i 26

004. a 23

005. it 19

006. to 17

007. in 14

008. this 13

009. and 12

010. come 12

011. of 10

012. if 9

013. have 8

014. know 8

015. will 8

016. be 7

017. correct 7

018. get 7

019. go 7

020. just 7

021. from 6

022. here 6

023. do 5

024. every 5

025. problem 5

026. they 5

027. write 5

028. clear 4

029. hand 4

030. how 4

031. learn 4

032. loud 4

033. say 4

034. we 4

035. with 4

036. word 4

037. about 3

038. but 3

039. find 3

040. first 3

041. for 3

042. group 3

043. meaning 3

044. over 3

045. there 3

046. use 3

047. when 3

048. active 2

049. any 2

Unique to second

262 tokens

100 families

Freq first

(then alpha)

001. always 20

002. can 14

003. where 9

004. beauty 8

005. oh 8

006. song 7

007. you’re 7

008. home 6

009. life 6

010. light 6

011. ready 6

012. back 5

013. alone 4

014. home” 4

015. line 4

016. mile 4

017. never 4

018. sing 4

019. some 4

020. tell 4

021. “son 4

022. far 3

023. good 3

024. help 3

025. horizon 3

026. love 3

027. million 3

028. people 3

029. right 3

030. absence 2

031. dark 2

032. deep 2

033. don’ 2

034. father 2

035. he 2

036. hold 2

037. inside 2

038. interest 2

039. into 2

040. irrefutable

2

041. mother 2

042. necessary 2

043. night 2

044.


045. parent 2

VP novel items

Same list

Alpha first

001. absence 2

002. alone 4

003. always 20

004. another 1

005. ask 1

006. back 5

007. back…back…

1

008. beauty 8

009. because 1

010. belong 1

011. blank 1

012. book 1

013. bright 1

014. can 14

015. dark 2

016. deep 2

017. don’ 2

018. download 1

019. easy 1

020. eye 1

021. eyes… 1

022. far 3

023. father 2

024. fathers’ 1

025. fill 1

026. glory 1

027. good 3

028. guitar 1

029. he 2

030. help 3

031. hold 2

032. home 6

033. home” 4

034. horizon 3

035. inside 2

036. instrument

1

037. interest 2

038. into 2

039. irrefutable

2

040. let 1

041. life 6

042. light 6

043. line 4

044. listen 1

64

049. paper 4

050. piece 4

051. practise 4

052. sugar 4

053. syrup 4

054. three 4

055. two 4

056. : 3

057. alcohol 3

058. aspirin 3

059. away 3

060. difference 3

061. drink 3

062. english 3

063. formula 3

064. fresh 3

065. identify 3

066. liquid 3

067. mixture 3

068. now 3

069. nutrition 3

070. other 3

071. overdose 3

072. pound 3

073. reach 3

074. reducer 3

075. reliever 3

076. start 3

077. table 3

078. task 3

079. teaspoon 3

080. temperature

3

081. than 3

082. twenty 3

083. water 3

084. wheat 3

085. year 3

086. according 2

087.

acetaminophen 2

088. artificial 2

089. attend 2

090. barley 2

091. both 2

092. bread 2

093. bubble 2

094. cans 2

095. close 2

096. column 2

097. complete 2

098. drug: 2

099. fat 2

100. flavour 2

101. focus 2

050. child 2

051. class 2

052. copy 2

053.

dictionary 2

054. happy 2

055. journal 2

056. like 2

057. look 2

058. make 2

059. may 2

060. more 2

061. most 2

062. no 2

063. on 2

064. or 2

065. same 2

066. see 2

067. sentence

2

068. spell 2

069. sun 2

070. sure 2

071. then 2

072. while 2

073. work 2

074. after 1

075. also 1

076. at 1

077. below 1

078. best 1

079. care 1

080. chapter 1

081. difficult

1

082. example 1

083. fact 1

084. front 1

085. less 1

086. many 1

087. mark 1

088. nature 1

089. need 1

090. not 1

091. one 1

092. present 1

093. punctuate

1

094. read 1

095. reflect 1

096. repeat 1

097. state 1

098. too 1

099. very 1

100. what 1

046. part 2

047. person 2

048. protect 2

049. road 2

050. seem 2

051. she 2

052. slippery 2

053. slope 2

054. telescope 2

055. through 2

056. together 2

057. worry 2

058. ‘cause 2

059. another 1

060. ask 1

061. back…back…

1

062. because 1

063. belong 1

064. blank 1

065. book 1

066. bright 1

067. download 1

068. easy 1

069. eye 1

070. eyes… 1

071. fathers’ 1

072. fill 1

073. glory 1

074. guitar 1

075. instrument

1

076. let 1

077. listen 1

078. long 1

079. lyric 1

080. message 1

081. moon 1

082. mothers’ 1

083. music 1

084. note 1

085. open 1

086. popular 1

087. possible 1

088. return 1

089. share 1

090. sky 1

091. son 1

092. space 1

093. talk 1

094. teach 1

095. thousand 1

096. time 1

097. view 1

098. way 1

045. long 1

046. love 3

047. lyric 1

048. message 1

049. mile 4

050. million 3

051. moon 1

052. mother 2

053. mothers’ 1

054. music 1

055. necessary 2

056. never 4

057. night 2

058. note 1

059. oh 8

060.


061. open 1

062. parent 2

063. part 2

064. people 3

065. person 2

066. popular 1

067. possible 1

068. protect 2

069. ready 6

070. return 1

071. right 3

072. road 2

073. seem 2

074. share 1

075. she 2

076. sing 4

077. sky 1

078. slippery 2

079. slope 2

080. some 4

081. son 1

082. song 7

083. space 1

084. talk 1

085. teach 1

086. telescope 2

087. tell 4

088. thousand 1

089. through 2

090. time 1

091. together 2

092. view 1

093. way 1

094. we’ve 1

095. where 9

096. worry 2

097. youtube 1

098. you’re 7

65

102. give 2

103. half 2

104. health 2

105. hour 2

106. important 2

107. instant 2

108. instruct 2

109. kill 2

110. kind 2

111. minute 2

112. ml 2

113. moderate 2

114. must 2

115. net 2

116. perfect 2

117. physician 2

118. pour 2

119. product: 2

120. put 2

121. remove 2

122. rule 2

123. safe 2

124. salt 2

125. study 2

126. such 2

127. sundried 2

128. tablet 2

129. take 2

130. thirty 2

131. under 2

132. vegetable 2

133. version 2

134. warn 2

135. weigh 2

136. well 2

137. which 2

138. wild 2

139. ‘available’

2

140. ache 1

141. acid 1

142. add 1

143. advice 1

144. agent 1

145. air 1

146. all 1

147. anidan 1

148. antioxidant

1

149. around 1

150. ascorbic 1

151. avaibale 1

152. available’ 1

153. avoid 1

154. bake 1

099. we’ve 1

100. youtube 1

099. ‘cause 2

100. “son 4

66

155. become 1

156. between 1

157. blend 1

158. box 1

159. break 1

160. buy 1

161. caffeine 1

162. call 1

163. cap 1

164. carbohydrate

1

165. caution 1

166. cell 1

167. celsius 1

168. certain 1

169. chemical 1

170. choose 1

171. cloud 1

172. coffee 1

173. cold 1

174. colour 1

175. common 1

176. cool 1

177. correspond 1

178. cure 1

179. danger 1

180. doctor 1

181. dry 1

182. due 1

183. effect 1

184. emulsifier 1

185. energy 1

186. enhance 1

187. ensure 1

188. equivalent 1

189. even 1

190. event 1

191. evident 1

192. expose 1

193. external 1

194. extra 1

195. extract 1

196. farm 1

197. feel 1

198. ferment 1

199. fire 1

200. flame 1

201. flammable 1

202. flu 1

203. follow 1

204. fourty 1

205. fruit 1

206. full 1

207. gaea 1

208. geae 1

67

209. harm 1

210. headache 1

211. house 1

212. hundred 1

213. immediate 1

214. kraton 1

215. language 1

216. lead 1

217. litre 1

218. market 1

219. mechanic 1

220. medical 1

221. medium 1

222. minor 1

223. miss 1

224. moisture 1

225. new 1

226. noodle 1

227. novel 1

228. oatmeal 1

229. obtain 1

230. opposite 1

231. organic 1

232. panel 1

233. per 1

234. pint 1

235. place 1

236. preserve 1

237. price 1

238. project 1

239. protein 1

240. pump 1

241. reduce 1

242. relief 1

243. relieve 1

244. result 1

245. room 1

246. saturate 1

247. seal 1

248. seconds 1

249. seek 1

250. shake 1

251. should 1

252. sight 1

253. sina 1

254. six 1

255. slice 1

256. sodium 1

257. sole 1

258. sorethroat 1

259. soya 1

260. speak 1

261. spritz 1

262. steep 1

263. step 1

68

264. tamper 1

265. temporary 1

266. ten 1

267. therefore 1

268. toll 1

269. toothache 1

270. treat 1

271. unit 1

272. vinegar 1

273. virgin 1

274. vitamin 1

275. yeast 1

276. you: 1

277. ‘not 1

278. ‘no’ 1

279. ‘yes’ 1

vocabulary profile of the ninth grade english … · vocabulary profile of the ninth grade english...

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