T1 112012008 Full text

THE VOCABULARY PROFILE OF ENGLISH SHORT

STORY BOOKS AT THE LIBRARY OF SATYA WACANA

LABORATORY SCHOOLS

THESIS

Submitted in Partial Fulfillment of the Requirements for the Degree of

Sarjana Pendidikan

La Valerie Novianne Lie

112012008

ENGLISH LANGUAGE EDUCATION PROGRAM

FACULTY OF LANGUAGE AND LITERATURE

SATYA WACANA CHRISTIAN UNIVERSITY

SALATIGA

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COPYRIGHT STATEMENT

This thesis contains no such material as has been submitted for examination in any course or accepted for the fulfillment of any degree or diploma in any university. To the best of my knowledge and my belief, this contains no material previously published or written by any other person except where due reference is made in the text.

Copyright@ 2016. La Valerie Novianne Lie and Prof. Dr. Gusti Astika, M. A.

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 Department, Faculty of Language and Literature, Satya Wacana Christian University, Salatiga.

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TABLE OF CONTENTS

Cover Page ... i

Pernyataan Tidak Plagiat ... ii

Pernyataan Persetujuan Akses ... iii

Approval Page ...iv

Copyright Statement ... v

Table of Content...vi

ABSTRACT ... 1

INTRODUCTION ... 1

LITERATURE REVIEW ... 4

The Importance of Teaching Vocabulary ... 4

The Importance of Vocabulary for Reading ... 5

The Importance of Vocabulary for Speaking ... 6

The Importance of Vocabulary for Listening ... 6

The Importance of Vocabulary for Writing ... 6

Why Vocabulary should be required. ... 7

Vocabulary Profile ... 8

Study of Vocabulary Profile ... 9

THE STUDY ... 10

Method of Research ... 10

Context of the study ...11

Samples ... 11

Research Instrument ... 12

Data collection Procedure ... 12

Data Analysis Procedure ... 12

FINDING AND DISCUSSION ... 13

Overall Vocabulary Profile of the short story books...13

Negative vocabulary profiles of the short story books ... 15

Negative VP of K-1 ... 15

Negative VP of K-2 ... 16

Negative VP of K-3 or AWL ... 17

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Comparison of Vocabulary frequency levels of the short story books ... 19

Text comparison of the short story books:...20

1. Comparison of Book 1 and Book 2 ... 20

2. Comparison of Book 2 and Book 3 ... 21

3. Comparison of Book 1 and Book 3 ... 22

CONCLUSION ... 23

REFERENCES ... 25

ACKNOWLEDGMENT ... 27

Appendices ... 28

Appendix A ... 28

Appendix B ... 30

Appendix C ... 33

Appendix D ... 36

Appendix E ... 48

Appendix F ... 60

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VOCABULARY PROFILE OF ENGLISH SHORT STORY BOOKS AT THE LIBRARY OF SATYA WACANA LABORATORY SCHOOLS

La Valerie Novianne Lie

ABSTRACT

Vocabulary is one of the important thing if the students want to learn English as their second language or foreign language. One way to learn and develop vocabulary is read a written text. However, students will find difficulties while reading, therefore the aim of this study is to profile the vocabulary in short story books of Satya Wacana Laboratory school. This study was a descriptive reasearch and three short story books were taken as the sample of this study. The vocabulary profiler can be accessed on www.lextutor.ca. This study devided into three parts. The first, showed the cumulative percentage of vocabulary; (1) K-1 words; the first most frequently 1000 words, (2) K-2 words; the next most frequently 1000 words, (3) AWL; the Academic Words List, and (4) off-list words. Second, showed the lists of vocabulary that were not covered in the short story books; and the last was comparison among three short story books to find out the token (word) recycling index of the short story books. The findings showed the cummulative percentage of K-1 and K-2 words was 95.71%. Then, the proportion of missing words that could be found in the negative words that may be useful for the teachers and the students. The last, the comparison among three short story books that showed the cummulative percentage was not significantly different and also showed the percentage of some new words.

Keywords: Vocabulary, Vocabulary profiler, Word list, Short story books.

INTRODUCTION

Library is a place that provides a lot of books to enrich students‟ knowledge. Satya Wacana Laboratory schools have a library that has a lot of books. One of the book collections are English books. There are various kinds of English books, such as English books, novels, science books and short stories. The books are provided by the school or sometimes from the teachers. The purpose of providing the books is to provide the students with reading materials in English. The readers of the books are the students of senior and junior high school. They read those books may be to entertain their mind after reading some academic books in class, to get and

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understand new vocabulary from the book and to make their writing and reading in English more fluent. Mostly, the short story books in the school have some moral values.

Books are beneficial to practice reading comprehension. Therefore, by reading

more books, student‟s comprehension toward stories or text books would get better ,

those English books could be used as a media for practicing reading. Not only to make their comprehension better, but those English books would help the students to enrich and develop their vocabulary. Vocabulary is one important thing to learn. Lu (2013) also stated that vocabulary is basic in learning a foreign language.When the students have much vocabulary, it would be easier for the students to understand a text. According to Fisher and Frey (2014), when students knew a lot of words, they could read more complex texts.

While reading a texts, some students may have difficulties. The difficulties that the learners might have while reading books is: they difficult to acquire vocabulary from a foreign language; they can not understand the story clearly because some vocabulary is too difficult for them.

This present study focused only on English short story books in Satya Wacana Laboratory school. The books did not provide information about the level of the vocabulary or grade of the books. Therefore, the aim of doing this research is to profile the vocabulary in the short story books in the school library. The profiles could provide information about the vocabulary in short story books. The vocabulary may belong to K-1 words; the first most frequently 1000 words and the most common words that are easily found in any texts, K-2 words; the next most frequently 1000 words and less frequent used than K-1 words, AWL; the Academic

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Words List that are commonly used in academic texts, and off-list words, the words that do not belong to the three lists. The off-list words are usually names of places or people or specialized terms used in a specific discipline that students of junior and senior high school may not need to learn The students should understand these K-1 words and K-2 words at junior and senior high school level because in these groups, there are many words that might be useful for the students. Each of the classifications can also be used to provide information about the proportion (percentage) of the words used in the short story books in comparison to the well-establised word lists; the K-1, K-2, and AWL. This classifications are useful for the teachers and the students in selecting vocabulary that is needed for learning and for understanding the stories. The questions for this research were :

1. What is the vocabulary profile of English short story books in Satya Wacana School‟s library?

2. What is the proportions of vocabulary that is not covered in the short sory books?

3. What is the token (word) recycling index of the short story books?

The objectives of the research were :

a. To profile the vocabulary of the short story books in Satya Wacana school‟s library. The vocabulary profiles could provide teachers with useful information about recommendation explaining the appropriate level of the vocabulary in books. Then, the information could inform the librarian to acquire books that are appropriate to the students for outside learning. There will be the cumulative percentage that could help teachers to determine the level of students‟ comperehension of the short story books.

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b. To produce lists of vocabulary that were not covered in the short story books. These words were indentified from the comparison between the vocabulary in the General Service List and the vocabulary found in the short story books. The lists would provide the teachers with information about words that are necessary for the students to learn.

c. To produce the token (word) recycling index of the short story books. The index indicates text comperensibility of the short story books, determined from the comparison of two books and calculate the ratio between the shared vocabulary in the two books and the number of unique vocabulary in the second book. So, it would give information about the new words in each books that might be useful for the students to learn.

LITERATURE REVIEW

The Importance of Teaching Vocabulary

Vocabulary is one of the elements that students or learners have to learn if they want to master English as a second language or foreign language. According to Kordjazi (2011), vocabulary is central to language. Huyen and Nga (2003) also said that vocabulary is the collection words that individual knows. Vocabulary that students know should be appropriate to their level. The teacher could not simply teach the words that students need to learn. The teacher needs to select the words that they are going to teach to the students carefully. Moreover, Lehr, Osborn, and Hiebert (2004) also said that “vocabulary is knowledge of words and word

meanings” (p.1). The teacher could teach vocabulary from many form of words.

According to Lehr et al. (2004). there are two forms of words: oral and print. Oral vocabulary is for the words that we know in listening and speaking and print

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vocabulary is for words that we know in reading and writing. Based on what Lehr et al. (2004) said, teaching vocabulary for the students would help the student in developing some skills; reading, speaking, listening, and writing. Nation and Waring (1997) stated that knowledge of vocabulary is the one component of language skills such as reading and speaking. Sweeny and Mason (2011) give suggestion steps that could help determine which words to teach: first, read text selections in advance to determine instructional purpose; second, identify words or concepts that students need to know; third, identify connections and relationships between words or concepts that chosen for instruction; forth, select words that students must know prior to reading; fifth, decide which words that students need to know incidentally and do not require direct teaching; and the last, determine what you want the children to learn.

After the teacher teaches the vocabulary to the students, the students would know and understand the meaning of text when they read a written text. Then, vocabulary profile could be useful for the steps number 2, 4, 5, and 6, because the function of vocabulary profiler is to identify the vocabulary in written text to know 4 types of vocabulary.

The Importance of Vocabulary for Reading

Reading is one of the skills that is important for the students that will be useful for the students to learn foreign language. Mikulecky (2008) said “reading is a conscious and unconscious thinking process” (p.1). Reading will enrich and give the vocabulary of the students. Pikulski and Templeton (2004) said “ a large vocabulary is more specifically predictive and reflective of high levels of reading achievement”(p.1). The more vocabulary knowledge student, the more easily students would understand the meaning of a text. As what Hirsh and Nation (1992)

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have said “knowledge of the vocabulary in a text is one of the many factors that

affect reading” (p. 689).

The Importance of Vocabulary for Speaking

Speaking is one the important skills to master language. Idrissova, Samgulova, and Tussupbekova (2015) said that “in fact, speaking and listening are closely integrated skills in learning any foreign language” (p. 277). Idrissova et al. (2015) further added that speaking and listening are important for daily life communication. From speaking and listening, the students also can enrich their vocabulary and it will give a benefit for the students. According to Pikulski and Templeton (2004), children in their lives are in process of acquiring a meaning in oral vocabulary, so they could understand what they hear and they could use many vocabulary when they talk. In other words, the students are acquiring words, it would help students to speak fluently because they have many vocabulary.

The Importance of Vocabulary for Listening

Another skill that is also important for the students is listening. Sura (2013) said that listening is an ability to know what other people say, including know the accent or pronunciation of the speaker. In addition, if the students are good at this

skill, they are able to understand people‟s speech or conversation. Saricoban (1999)

mention that in listening to English as a foreign language, it can help the students understanding colloquial vocabulary and get the important infromation from some recording or from what they are listen.

The Importance of Vocabulary for Writing

The last skills that is also important for the students is writing. Coulmas

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our planet” (p.1). Being good in writing gives students some benefit. Pikulski and

Templeton (2004) stated that “In order to make the transition to communicating through reading and writing, they need a large meaning vocabulary and effective

decoding skills” (p.2). After understand a large meaning of vocabulary, the students

will create and make a good writing. Hanson and Padua stated that giving opportunities for students to apply what they learned about vocabulary from reading to writing will increase their understanding about the word‟s meaning. In other words, when the students use their new vocabulary in their writing, they gain a deeper understanding of the target words.

Why Vocabulary should be required

As we know that vocabulary is one of the important thing if the learners want to learn a foreign language, therefore vocabulary is required to be learnt. Some experts gave suggestion for the students about how many words that the students should be required. Fisher and Frey (2014) also said that if the students read 1 hour per day, five days in a week, then they would read more than 2,250,000 words per year. Schmitt, Jiang, and Grabe (2011) also said that around 95% to 98% vocabulary words that are needed to understand by the learners in written texts. Matsuoka and Hirsh (2010) also added that it required to get 95% of recognizable words to make the learning process to occur. Fisher and Frey (2014) have noted that students need to learn words per year depending on their level That is why vocabulary should be required and it is an important thing.

Vocabulary itself also has an important role in second language learning because it is the essence of a language. Yusheng Lie/ Marko (n.d.) stated that without vocabulary, there are no sentence, no text and no language. Therefore, to master the language, learners need to learn vocabulary. As people know that if

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someone wants to understand the text or speak well, they have to know a lot of vocabulary. The reason why learners need to learn vocabulary is because nowadays vocabulary is one of the foundations in Second language learning (Alcaraz, 2009).

Vocabulary Profile

There are a lot of tools that could be used to analyze vocabulary and one of them is Vocabulary Profiler. According to Meara (2005), vocabulary profilee or Lexical Frequency Profile (LFP) is introduced as a tool of assessment for particular text whether it is suitable for the learners to use at particular level or proficiency. Graves (2005) also added that vocabulary profile is an aggregation of word frequencies. The function of vocabulary profile is to identify the vocabulary in books and it can be divided into 4 levels; high frequency, academic word, technical word, and low frequency word.

Nation (2001) divides vocabulary into four levels; they are high frequency words, academic vocabulary, technical vocabulary, and low frequency words. The high frequency words contain around 2000 word families. This level include many content words such as government, forests, production, adoption, represent, boundary. Academic words form that from an academic textbook that consists of many words that are common in different kinds of academic texts such as policy, phase, adjusted, sustained. Technical words that are very closely related to the topic and it consists of some words that are very closely related to the topic and subject are of the text, including indigenous, regeneration, podocarp, beech, rimu (a New Zealand tree) and timber. The last, low frequency words that are not in high frequency words, academic words, and technical words. This level may include words like zoned, pioneering, perpetuity, aired and pastoral. They make up over 5% of the words in an academic text (Nation, 2001). This frequency has all the words

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that are not high frequency words, academic words and technical words for a particular subject.

Study of Vocabulary Profile

There are many researchers who have done researches realted to vocabulary profile. The first one is the study of Yoon, Bhat and Zechner (2012) on “Vocabulary Profile as a Measure of Vocabulary Sophistication”. This study shows the method that assesses second language learners in using vocabulary in spontaneous speech. They expected that the difficulties in each word of vocabulary are based on the frequency of the vocabulary. The study has found three different classes of features in spoken response. The first one is coverage-related, average word rank and the average word frequency, and the extent extent to which words influence human-assigned language proficiency scores was studied. Among those three types, the average of word frequency showed the most predictive strength.

Another study comes from Morris and Cobb (2004), with a title “Vocabulary profiles as predictors of academic performance of Teaching English as Second Language trainees”. The aim of this study was to examine the potential that are offered by the vocabulary profile as a predictor of academic performance in undergraduate teaching English as a Second Language (TESL) program. The vocabulary profile was for 122 TESL students by analyzing their 300 words writing. The result of this study showed that the vocabulary profiles of the students were significantly correlated with the grades. The vocabulary profile proved to be useful in doing the assessment in language skills of high proficiency non native speakers than oral interviews.

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There were some students who had done the research related to vocabulary profiler in Satya Wacana Christian University. There were two research that have been done related to it. The first one was “Vocabulary Profile in English textbooks

used in Satya wacana Junior High School” by Jusuf (2014). The result of this

research shows that the use of K1 words (1-1000) in grade 7 was 79.27%, grade 8 was 6.97% and grade 9 was 1.07%. K2 words (1001-2000) from grade 7 was 81.81%, grade 8 was 5.67% and grade 9 was 1.21%. for AWL words (academic) for grade 7 was 78.46%, grade 8 was7.84%, and grade 9 was 3.68%.

The second was “Vocabulary Profile of Introduction to Language Education

textbook by Ardyny (2014). The study shows three results; (1) 80,81% of the course book was understandable for university level while the rest of 19,19% need much effort to comprehend; (2) unit 1 of the course book could be relatively easy to comprehend because it had the highest proportion of 1000 word list (K1) and the lowest proportion of Academic Word List (AWL), while unit 5 could be hard to comprehend because of its lowest proportion of K1 and its highest proportion of AWL; (3) unit 7 did not show constrasting result of K1 and AWL however it still concluded as unit which was hard to comprehend.

However, the research on vocabulary profile of short story books has never been conducted. Therefore, this study aim to analyze the vocabulary on the short story books in library of Satya Wacana Laboratory school.

THE STUDY

Method of Research

This study was a descriptive research. According to Lans and Van Der Voordt (2002) descriptive research is about describing how the reality is.

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Sandelowski (2000) stated that qualitative descriptive study is a method of choice when straight descriptions of phenomena are desired. The researcher describes an experience or event that will describe. This research describes vocabulary profile in English short stories in Satya Wacana Laboratory school and it would show four kinds of vocabulary.

Context of the study

Satya Wacana Junior and Senior High School is the object of this study. The school has many English short story books but most of them are old. These books are provided by the teacher and sometimes the teachers asked the students to read some English short story books to enrich their knowldge. Then, some students also borrow that books because they want to read that books. Therefore, the reason why the reseacher choose those books was: those books are old, sometimes was used by the teachers and the students also read that books. So, it would give the students advantages to enrich their vocabulary and learn about narrative text from the short story books.

Samples

The samples of this study were three short story books from Satya Wacana Laboratory school. The title of the first book was „Lost in the USA‟ written by Rolf W. Roth. The book had 8 chapters and 38 pages, published by PT Gramedia Pustaka Utama, Jakarta in 2003. The title of the second book was „More Favourite Stories from Vietnam‟ and it had 8 chapters and 50 pages. The writer of this book is Marguerite Siek and was published in 1978. This book was printed by PT. Rosda Jayaputra Offset, Jakarta. The book was reprinted in 1980, 1982, 1983 and reprinted in Indonesia in 1985. Then, the last book was „Escape from The Tower‟ by Shirley

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Nagel. The book had 8 chapters and 51 pages. This book was publised in 1978 by Bowmar/Noble Publisher, Inc., and printed and bound in Singapore by FEP International Pte. Ltd. All pages would be analyzed, so the researcher would have 139 pages. The researcher chose those books because the books did not have the specific level.

Research Instrument

The aim of this study was to profile the vocabulary in short story books for all chapters. To know the result of the vocabulary profile, the researcher need a tool that is used to identify the vocabulary. The research instrument for this study was Vocabulary Profiler that can be accessed on www.lextutor.ca. This tool can show the four frequency levels of the vocabulary; the first 1000 most frequency used, the second 1000 frequent used, academic word list, and the last off-list words from a text.

Data collection Procedure

Data were collected from the library of Satya Wacana laboratory school. The data had chosen three short story books and type the stories in Ms. Word. Thus, there will be 3 files for 24 chapters from 3 short story books. The first file was the first book, the second file was second book and so on. In this research, names of person,

places, books‟ indentification, and numbers omitted. This aims to decrease the off

list words in the result.

Data Analysis Procedure

The first steps in the analysis was to open the website of Vocabulary Profiler in www.lextutor.ca, then select vocabulary profile and then click VP Classic v.4. Next, copy the text to be analyzed from Ms. Word and paste it to the space. After

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that, click submit_window on the yellow box under the white box. Then, the result of analysis provides the percentage of the four kinds of vocabulary groups; 1000 first words, the second 1000 words, academic words, and the off-list words.

FINDINGS AND DISCUSSION

The discussion of this chapter presents the answer of the research questions and shows the analysis of the vocabulary profile of three short story books. The discussion are divided into three parts. This first part of this section presents the overall vocabulary profile of the three short story books showing the proportions of the vocabulary frequency classifications. The second part shows the negative vocabulary profile of K-1, K-2, K-3 (AWL) with lists of the vocabulary items that were not found in the three textbooks. The last part of this section shows the comparison of the short story books with regard to the vocabulary items that were shared and unique in each book. The comparison also shows the token recycling index that provides information about an estimate of the short story books comprehensibility.

A. Overall Vocabulary Profile

Table 1. Overall Vocabulary Profile

FAMILIES TYPES TOKENS CUMULATIVE K-1 WORDS 686

62.88%

1178 56.07%

21485 88.87%

88.87%

K-2 WORD 380 34.83%

524 24.94%

1654 6.84 %

95.71 %

AWL

(570 FAMS TOT : 2.570)

25 2.29%

27 1.29%

47 0,20%

95.91%

OLW ?? 372

17.71%

990 4.09%

100%

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100% 100%

The first row in Table 1 shows three terms; family, type and token. Word family is head word, for example: the family or head word of affection and affecting

is affect. Type is different words, for example: accord and account are different words. While affection and affecting, or affected are considered as the same type. Token is words in a text or the total number of words in a text. For example, if in a text there are bee [6], easter [4], cutter [2], adoptive [1], champion [1]. The number of token is 14.

Table 1 shows that 88.87% of the vocabulary used in the short story books fall under the most frequently used 1000 words group (K-1). The additional K-2 word coverage was 6.84% and the cumulative percentage of K-1 and K-2 words coverage was 95.71%, which is relatively close estimate for good comperehension of the text in books. According to Schmitt, Jiang, and Grabe (2011) around 95% to 98% vocabulary words that are needed to understand by the learners in the written texts. The cumulative percentage for Academic Words List (AWL) coverage was 0.19%. The cumulative percentage of the Academic words was less than the cumulative percentage of K-1 and K-2 words, we need to question whether the academic words that amounted 47 words used in the sample short story books need to be intoduced to the junior and high school students. Another point that is worth considering is the number of Off-list words that reached 990 words, for example airport, forever, restroom. Although this category is excluded from the K-1; K-2; and AWL, it may have words that student of junior and senior high school need to learn and know. The teachers have to make a selection of the words that are listed in this category because

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some of words might be useful in teaching and or learning process if the teachers are going to use the books.

B. Negative vocabulary profiles of the short story books

This section presents the description of negative vocabulary profiles of the short story books. Negative vocabulary is vocabulary items that are not found in the short story books. These missing vcabulary items are derived from differences of words in the New General Word List and words in the short story books used in this study. The list of negative K-1 words below might be useful for the students to develop their vocabulary knowledge by looking the meaning of vocabulary in dictioanry or asking the meaning of vocabulary to the teachers.

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 or missing in the three short story books. The summary of negative vocabulary profile for K-1 level is presented below. These percentages refer to number of word families. Based on the summary, 70.64% of word families were found in the short story books, which means 29.46% of words families were

„missing‟ or not found based on the words listed in New General Service List

(NGSL).

K-1 Total word families: 964

K-1 families in input: 681 (70.64%) K-1 families not in input: 284 (29.46%)

The followings are some of the word families that are not found in the input short story books (book1, 2, and 3). The complete word list in Appendix A.

16 ACCORD

ACCOUNT

ACCOUNTABLE ACTIVE

ACTOR ACTRESS ACTUAL

ADDRESS ADMIT ADOPT ADVANCE ADVANTAGE ADVENTURE AFFAIR

AGENT AMOUNT ANCIENT APPLY ARISE ARTICLE ASSOCIATE

ATTEMPT AVERAGE BALL BASE BATTLE BENEATH BEYOND

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 short story books. The percentage refers to number of word families. Based on the summary, as much as 38.54% of words families were found in the short strory books, which means 61.56% of words families were „missing‟ or not found based on the words listed in NGSL.

The followings are some of the word families (K-2) that were not found in the input of short story books (book 1, 2, and 3). The complete word list has been put in Appendix B.

ABROAD ABSENCE ABSENT ABSOLUTE ABSOLUTELY ACCIDENT ACCUSTOM

ADVERTISE AEROPLANE AFFORD

AGRICULTURE AIM

AIRPLANE ALIKE

ALTOGETHER AMBITION ANGLE ANNOY ANXIETY APART APOLOGIZE

APOLOGY APPLAUD APPLAUSE APPROVE ARCH ARGUE ARREST K-2 Total word families: 986

K-2 families in input: 380 (38.54%) K-2 families not in input: 607 (61.56%)

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3. Negative VP of K-3 or AWL

This analysis shows all the word families (=head words) from the K-3 level that were not found in the input short story books. The percentage refers to number of word families. Based on the summary, 4.39% of words families were found in the

short strory books, which means 95.78% of words families were „missing‟ or not

found based on the words listed in NGSL.

K-3 Total word families: 569

K-3 families in input: 25 (4.39%) K-3 families not in input: 545 (95.78%)

The followings are some of the word families (K-3) that were not found in the input of short story books (book 1, 2, and 3). The complete word list has been put in Appendix C.

C. Block frequency output of off-list words.

This section presents words which belongs to the category of „Off-list‟; words

that are not listed in any three frequency categories. Using the tool in Vocabulary Profiler, these words have been frequency-blocked per ten words and have been arranged from high to low frequency. These words that are listed may useful for the students to learn in junior and senior high school. Below is the list „Off-list‟ words, ABANDON

ABSTRACT ACADEMY ACCESS

ACCOMMODATE ACCOMPANY ACCUMULATE

ACCURATE ACHIEVE

ACKNOWLEDGE ACQUIRE

ADAPT ADEQUATE ADJACENT

ADJUST

ADMINISTRATE ADULT

ADVOCATE AFFECT AGGREGATE ALBEIT

ALLOCATE ALTER

ALTERNATIVE AMBIGUOUS AMEND ANALOGY ANALYSE

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with 990 tokens and 372 types. To understand the table below, note that RANK is ranking of word, FREQ is frequency of word occurrence, COVERAGE is the percentage of word occcurrences, individual or cumulative, and the last column is the vocabulary item.

The example of the Rank, Freq, Coverage, and Word is in the complete list of blocked frequency in the table below. Rank is 1, Freq is 62, Coverage is 6.26% and 6.26%; then the word is monster. The complete list of blocked frequency has been put in Appendix D.

RANK FREQ COVERAGE

individ cumulative WORD 1. 62 6.26% 6.26% MONSTER 2. 46 4.64% 10.90% PRINCESS 3. 43 4.34% 15.24% JAILER 4. 23 2.32% 17.56% PUMPKIN 5. 21 2.12% 19.68% ENVOY 6. 18 1.82% 21.50% CELL 7. 18 1.82% 23.32% POUCH 8. 18 1.82% 25.14% TRUCK 9. 16 1.61% 26.75% PALACE 10. 16 1.61% 28.36% PASSPORT 11. 14 1.41% 29.77% MAGIC 12. 13 1.31% 31.08% OK 13. 13 1.31% 32.39% SAMPAN 14. 12 1.21% 33.60% BOLT 15. 10 1.01% 34.61% COCK 16. 10 1.01% 35.62% JUNK 17. 9 0.91% 36.53% AIRPORT 18. 9 0.91% 37.44% CHILI 19. 9 0.91% 38.35% MAJESTY 20. 8 0.81% 39.16% KID

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4. Comparison of Vocabulary frequency levels of the short story books

Table 2 below compares the frequency of K-1, K-2, AWL, and Off-list among three short story books. The table below provides a general picture of the extent to which the three books differed with regards to their frequency groups.

Table 2. Comparison of words frequency levels

K- 1 WORDS %

K- 2 WORDS %

AWL %

OFF-LIST %

BOOK 1

CUMULATIVE %

90.69 90.69

5.94 96.63

0.19 96.82

3.19 100

BOOK 2

CUMULATIVE %

86.91 86.91

7.23 94.14

0.24 94.38

5.62 100

BOOK 3

CUMULATIVE %

90.99 90.99

6.50 97.49

0.11 97.60

2.40 100

Table 2 above presents the differences of K-1, K-2, AWL, and Off-list words among the three short story books were not very significant which means that the cumulative percentage among three short story books was almost same. The cumulative percentage of K-1 and K-2 words provide information about difficulty in understanding the short story books. Since the percentages of K-1 and K-2 words were around 95% to 98%, it seems that the learners understand the written text.

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5. Text comparison of the short story books

The last section of these findings presents the comparison between two short story books. This comparison shows the token recycling index of the books being compared. Recycling index is the ratio between words that are shared by two books and total number of words in the second books.

1. Comparison of Book 1 and Book 2

The first comparison is between book 1 and book 2. The analysis of the comparison shows that token recycling index was 78.48%, indicating that 78.48% of words in book 1 and book 2 were similar (shared). New or unique words in book 2 was 21.6% (100-78.48). The shared as well as unique words are displayed in the Table 3 below. The figure next to a word is the occurrences or frequency of that word in the book.

Table 3.Shared and Unique words in Book 1 and Book 2. The complete table has been put in Appendix E.

Unique to first 873 tokens 266 families

001. english 27 002. police 27 003. plane 22 004. yes 21 005. truck 19 006. didn‟ 18

Shared 8126 tokens 422 families

001. the 851 002. he 573 003. and 418 004. be 339

Unique to second 2228 tokens 673 families

Freq first (then alpha) 001. king 117 002. monster 58 003. wife 48 004. princess 46 005. kill 26 006. beauty 25

VP novel items Same list Alpha first 001. aboard 1 002. accept 1 003. accuse 1 004. ache 1 005. ack 1 006. admire 1 007. adopt 1

21

Based on the result above in the table that showed as much as 673 word families were unique to the second book, it may be possible for the teacher to take some words that may be the teacher can teach to the students in classroom.

2. Comparison of Book 2 and Book 3

The next comparison is between book 2 and book 3. The analysis of this comparison shows that the token recycling index was 92.66%, indicating that 92.66% words in book 2 and book 3 was similar (shared). New or unique words in book 3 was 7.34% (100-92.66%). The shared or unique words are displayed in table 4 below. The figure next to a word is the frequency of that word in the book.

Table 4.Comparison of Book 2 and Book 3. The complete table has been put in Appendix F.

Unique to first 1880 tokens 636 families

001. monster 58 002. princess 46 003. kill 26 004. beauty 25 005. cat 25 006. pumpkin 23

Shared 6500 tokens 459 families

001. the 398 002. she 377 003. be 329 004. to 256

Unique to second 515 tokens 174 families

Freq first (then alpha)

001. lord 46 002. tower 32 003. yes 16 004. bolt 14 005. didn‟ 13 006. oh 13

VP novel items Same list Alpha first 001. ,sir 1 002. across 2 003. act 1 004. aked 1 005. alarm 1 006. almost 9 007. alright 1

The table above showed that as much as 174 of word families were unique to the second books. It showed that the new or unique words in the second books were not

22

as much as in the fist comparison which have many unique words. But then, the teacher should not easily ignored because may be some of them were important to the students.

3. Comparison of Book 1 and Book 3

The last comparison is between book 1 and book 3. The analysis of comparison shows that the token recycling index was 89.42%, indicating that 89.42% words in book 1 and book 3 was similar (shared). New or unique words in book 3 was 61.93% (100-89.42%) .The shared as well as unique words are displayes in the Table 5 below. The figure next is to a word is the occurrences or frequency of that word in the book.

Table 5. Comparison of Books 1 and Book 3. The complete table has been put in Appendix G.

The last comparison, between book 1 and book 3 showed that the unique word families in the second book was also not as much as the first comparison. But, the words in this group should not be ignored because the words may be useful for the student in their learning process.

Unique to first 996 tokens 296 families

001. english 27 002. police 27 003. car 22 004. plane 22 005. shout 19 006. truck 19 007. big 18

Shared 6273 tokens 392 families

001. the 398 002. she 377 003. be 329 004. to 256 005. he 254 006. and 137 007. a 136

Unique to second 742 tokens

241 families

Freq first (then alpha) 001. lord 46 002. jail 43 003. cell 18 004. bolt 14 005. brave 14 006. king 13 007. cry 12

VP novel items

Same list Alpha first 001. ,sir 1 002. afraid 9 003. ahead 2 004. aked 1 005. alarm 1 006. alright 1 007. ankle 2

23

CONCLUSION

The purpose of this study was to profile the vocabulary in English short story books of Satya Wacana school‟s library, the proportions of vocabulary that is not covered in the short sory books, and the token (word) recycling index of the short story books. In this study, the cumulative percentage of K-1 and K-2 words among three short story books coverage was 95.71% which means that these books are understand by the learners. According to Schmitt, Jiang, and Grabe (2011), around 95% to 98% vocabulary words that are needed to understand by the learners in the written texts.

Then, next section was found out the proportion of vocabulary that were missing in the short story books. The vocabulary words that were missing in the books could be found in the negative words. These vocabulary words in negative words might be useful for the student to develop their knowledge of vocabulary. Another thing that could help the student is the list of blocked-frequency of Off-list words. Those two list words, negative words and block-frequency of Off-list words should not be ignored by the teachers and teachers have to make a selection if the the teacher use the books in class. The last section is the comparison of those 3 short story books that showed the differences of cumulative percentage. The first comparison was between book 1 and book 2; the second was book 2 and book 3; and the last comparison was book 1 and book 3. The first comparison showed that 21.6% words in book 2 were new. Then, the second comparison showed the total of the new words in book 3 was 7.34%. The last comparison showed that as much as 61.93% words in book 3 were new. Based on the result of the cumulative percentage of those comparisons showed that the differences of list of new words were not significant or it was relatively same. It means that those three books are in the same level.

24

This study still has a limitation, it only profiles 3 short story books. There are many books in the Satya Wacana school‟s library which have not been analyzed. It would be better if there is a similar study of vocabulary profile for the rest of the books.

Recommendation for the teacher is: they have to make a selection for some vocabulary in negative VP and off-list words. Some of the words that are listed in that group might be useful for the students to develop their knowledge of vocabulary and also useful for the teacher in the learning process.

25

References

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27

ACKNOWLEDGMENT

I could not finish my thesis if there is no support from people around me. First of all, I would like to say thank to Jesus for His blessing and guidance during my thesis. Then, I would like to express my gratitude to my supervisor, Prof. Gusti Astika, M. A. for his guidance and help during my thesis and also my examiner, Anne Indrayanti Timotius, M. Ed, for the suggestion, guidance, and help for my thesis. I would like to express my big gratitude to my beloved parents, Yulianto and Yuliana Halim, for the endless support. Big graditute is also given to my brother, Kevin Septiano, and my sister, La Vanessa Novianne Lie, who always give their support for me.

My gratitude is also given to my friends in FLL SWCU; fifteeners, forteeners, thirteeners, and awesome twelvers for the laughters, tears, togetherness and their support in FLL SWCU. Special thanks to Joshua Branatha, Syarifah Amalia, Virna Margetan, Putri, Luky, Ratih, Ika, Restu, Hanna, Venda Vista, Edwin Rudiyanto who help me to finish this thesis. The last, my big gratitude is also given for all lecturers in FLL SWCU for the guiding and educating me through this unforgetable 4 years.

28 Appendices Appendix A ACCORD ACCOUNT ACCOUNTABLE ACTIVE ACTOR ACTRESS ACTUAL ADDRESS ADMIT ADOPT ADVANCE ADVANTAGE ADVENTURE AFFAIR AGENT AMOUNT ANCIENT APPLY ARISE ARTICLE ASSOCIATE ATTEMPT AVERAGE BALL BASE BATTLE BENEATH BEYOND BILL BROAD CASE CAUSE CHARACTER CHARGE CHIEF CHURCH COAL COIN COLLEGE COLONY COMMITTEE COMMON COMPANY CONCERN CONDITION CONSIDER CONTAIN CONTENT CORN COST COTTON COUNCIL CURRENT DATE DEAL DECLARE DEGREE DEMAND DEPARTMENT DEPEND DESERT DESIRE DETERMINE DEVELOP DISTINGUISH DISTRICT DOCTOR DOLLAR DOUBT EAST EFFECT EFFICIENT EFFORT EGG EIGHT ELECT ELEVEN EMPLOY EQUAL EXAMPLE EXIST EXPECT EXPERIENCE EXPERIMENT EXPRESS EXTEND FACTORY FAITH FAMILIAR FARM FAVOUR FIGURE FIT FIVE FLOW FOREIGN FORTH FRIDAY FUTURE GAIN GAME GAS GLASS HEAT HUNDRED INCH INCLUDE INCREASE INDEPENDENT INDUSTRY INFLUENCE INTRODUCE JOINT JUDGE JUSTICE LACK LAKE LANGUAGE LATTER LAUGHTER LAW LENGTH LETTER LEVEL LIBRARY LIKELY LINE LITERATURE LOCAL LOSS MACHINE MANUFACTURE MASS MATERIAL MEASURE MEMBER MEMORY MENTION MERE MIDDLE MILLION MINER MISTER MODERN MONDAY MOON MORAL MOREOVER MOTOR MRS NATIVE NECESSARY NECESSITY NEWSPAPER NINE NONE NUMERICAL NUMEROUS OBJECT OBSERVE OCCASION OIL OPERATE OPINION OPPORTUNITY ORGANIZE OUGHT OWE PAINT PARTICULAR PER PERMIT POLITICAL POPULAR POPULATION POVERTY PRESENT PRESIDENT PRESS PRESSURE PREVENT PRICE PRODUCE PRODUCT PROFIT PROOF PROPERTY PROVE PROVIDE PROVISION PUBLIC QUALITY QUANTITY QUARTER QUITE RANK RATE RATHER REASON RECEIPT RECENT RECORD REDUCE REGARD

29

RELATION RELATIVE RELIGION REMAIN REMARK REPRESENT REPUBLIC RESERVE ROUGH SALE SALT SATURDAY SCARCE SCENE SCIENCE SEASON SECRETARY SENSE SENSITIVE SERVICE SEVEN SIMPLE SITUATION SIZE

SOCIAL SOCIETY SORT SPACE SPECIAL SPITE SPREAD STAGE STANDARD STEEL STOCK STORE SUBSTANCE SUGGEST SUMMER SUNDAY

SUPPLY SUPPORT SURROUND SYSTEM TAX TEN TERM THIRTEEN THIRTY THOUSAND THURSDAY TON

TOTAL TRAVEL TRUST TUESDAY TWELVE TWENTY TYPE UNION UNITE UNIVERSITY UNLESS VALLEY VARIETY VARIOUS VESSEL VIEW VIRTUE VOTE WAGE WEALTH WEDNESDAY WESTERN WHETHER WITHIN WRITE YESTERDAY YIELD YOUTH

30 Appendix B ABROAD ABSENCE ABSENT ABSOLUTE ABSOLUTELY ACCIDENT ACCUSTOM ADVERTISE AEROPLANE AFFORD AGRICULTURE AIM AIRPLANE ALIKE ALTOGETHER AMBITION ANGLE ANNOY ANXIETY APART APOLOGIZE APOLOGY APPLAUD APPLAUSE APPROVE ARCH ARGUE ARREST ARTIFICIAL ASH ASHAMED ASIDE ASTONISH AUDIENCE AUTUMN AVENUE AVOID AWAKE AWKWARD BABY BAGGAGE BALANCE BAND BARBER BARE BARELY BARGAIN BARREL BASIN BATHE BAY BEAM BEAN BEAST BEHAVIOUR BELT BERRY BICYCLE BILLION BIND BITE BITTER BOAST BONE BORDER BORROW BOTTLE BOUND BOUNDARY BOWL BRAIN BRASS BRIBE BROADCAST BROWN BUCKET BUNCH BURIAL BURST BUTTER BUTTON CALCULATE CAMERA CANAL CAP CAPE CARD CARRIAGE CART CAUTION CENT CENTIMETRE CENTURY CHAIN CHALK CHEAP CHEAT CHECK CHEER CHEESE CHEQUE CHICKEN CHIMNEY CHRISTMAS CIVILISE CLAY CLERK CLIFF CLUB COARSE COLLAR COMBINE COMMERCE COMPARE COMPETE COMPETITION COMPLAIN COMPLICATE COMPOSE CONFESS CONFUSE CONGRATULAT E CONNECT CONQUER CONSCIENCE CONSCIOUS CONVENIENCE CONVERSATIO N COOL COPPER COPY CORK CORRECT COUGH COURAGE COUSIN COWARD CRASH CREAM CRIME CRIMINAL CRITIC CROP CRUSH CULTIVATE CUPBOARDS CURIOUS CURL CURVE CUSHION CUSTOM CUSTOMER DAMAGE DAMP DEAF DECAY DECEIVE DECREASE DEED DEER DEFEND DELAY DELICATE DESCEND DESERVE DEVIL DICTIONARY DIP DISCIPLINE DISCUSS DISEASE DISGUST DISMISS DITCH DONKEY DOT DOUBLE DOZEN DRAWER DRUM DUCK DULL DUST EARNEST EASE EDUCATE ELASTIC ELDER ELECTRIC ELEPHANT ENCLOSE ENCOURAGE ENTIRE ENVELOPE ENVY ESSENCE ESSENTIAL EXCESS EXCUSE EXPLODE EXPLORE EXTRA EXTRAORDINA RY EXTREME FALSE FANCY

31 FASHION FASTEN FATE FAULT FEMALE FEVER FIERCE FLAG FLAME FLASH FLAVOUR FLESH FOLD FORBID FORGIVE FORK FORMAL FORWARD FRAME FREQUENT FRY FUNERAL GALLON GAP GLORY GOAT GOVERN GRACE GRAIN GRAM GRAMMAR GRAND GRASS GRAVE GREASE GREY GRIND HAMMER HANDKERCHIE F HARVEST HASTE HAY HEAL HESITATE HINDER HIRE HOLLOW HOLY HONEST HORIZON HOSPITAL HOST HUMBLE HURRAH ICE IDEAL IDLE IMITATE IMMENSE INFORM INFORMAL INFORMALLY INN INQUIRE INSECT INSTRUMENT INSURE INTEND INTERFERE INTERNATIONA L INTERRUPT INVENT INWARD JAW JEALOUS JOKE JUICE KEY KILOGRAM KILOMETRE KNEE KNEEL KNOT LADDER LEAF LIBERTY LID LIMB LIQUID LIST LITRE LOAF LOAN LODGING LOG LOOSE LOYAL LUMP LUNCH LUNG MAIL MALE MANAGE MAP MATCH MEANTIME MECHANIC MEDICINE MELT MEND MERRY MESSAGE METRE MILD MILL MILLIGRAM MILLILITRE MILLIMETRE MINERAL MIX MODEL MODERATE MOTION MOUSE MUD MULTIPLY MURDER MYSTERY NAIL NEAT NEEDLE NEPHEW NEST NET NIECE NONSENSE NOUN NUISANCE NURSE NUT OAR OBEY OCEAN OFFEND OMIT ONWARDS OPPOSE OPPOSITE ORANGE ORGAN ORIGIN ORNAMENT OUTLINE PAD PAIR PAN PASSENGER PASTE PATRIOTIC PATTERN PAUSE PAW PECULIAR PEN PENCIL PENNY PERFECT PERFORM PERMANENT PERSUADE PHOTOGRAPH PIG PIGEON PIN PINCH PINT PIPE PLASTER PLENTY PLOUGH PLURAL POEM POISON POLISH POOL POSTPONE POT POUR PRACTICAL PRACTISE PRAISE PREACH PREJUDICE PRESERVE PRIDE PRIEST PRINT PRIZE PROFESSION PROGRAMME PROMPT PRONOUNCE PUMP PUNCTUAL PUPIL PURE PURPLE QUALIFY QUARREL QUART RABBIT RADIO RAKE RAPID RARE RAT RAW RAZOR RECOMMEND REFER REFLECT REFRESH

32 REGULAR RELIEVE REMEDY REPAIR REPEAT REPLACE REPRODUCE REPUTATION REQUEST RESIGN RESIST RESPONSIBLE RESTAURANT RETIRE REVIEW RIBBON RIPE RISK RIVAL ROAST ROB ROT RUBBER RUBBISH RUDE RUG RUIN RUST SACRED SADDLE SAKE SALARY SAMPLE SAND SAUCE SAWS SCALE SCATTER SCENT SCISSORS SCRAPE SCRATCH SCREEN SCREW SEIZE SELDOM SEVERE SEW SHADE SHALLOW SHAME SHAVE SHEEP SHEET SHELF SHELL SHELTER SHIELD SHILLING SHIRT SHOCK SHOWER SIGNAL SINCERE SINK SLAVE SLIGHT SLOPE SMOKE SMOOTH SOAP SOCK SOIL SOLEMN SOLID SOUR SPADE SPARE SPELL SPIN SPIT SPLENDID SPLIT SPOIL SPOON SPORT STAFF STAIN STAMP STEADY STEAM STEER STIFF STIR STOCKING STOMACH STOVE STRAP STRAW STRICT STRING STRIPE STUPID SUGAR SUIT SUSPICION SWALLOW SWEAR SWEAT SWELL SYMPATHY TAIL TAILOR TAME TAXI TEA TELEGRAPH TEMPER TEMPERATURE TEMPT TENDER THEATRE THIRST THORN THOROUGH THREAD THREAT THROAT THUMB THUNDER TIDE TIDY TIN TIP TOBACCO TONGUE TOOL TOUGH TOUR TOWEL TOY TRANSLATE TRAY TRIBE TRUNK TUBE TUNE TWIST TYPICAL UGLY UMBRELLA UNCLE UNIVERSE UPPER UPRIGHT UPWARDS URGE VEIL VERB VERSE VIOLENT VOWEL VOYAGE WAIST WAX WEAVE WEED WEIGH WHEAT WHEEL WIPE WIRE WITNESS WOOL WORM WORSHIP WRECK WRIST ZERO

33 Appendix C ABANDON ABSTRACT ACADEMY ACCESS ACCOMMODAT E ACCOMPANY ACCUMULATE ACCURATE ACHIEVE ACKNOWLEDG E ACQUIRE ADAPT ADEQUATE ADJACENT ADJUST ADMINISTRATE ADULT ADVOCATE AFFECT AGGREGATE ALBEIT ALLOCATE ALTER ALTERNATIVE AMBIGUOUS AMEND ANALOGY ANALYSE ANNUAL ANTICIPATE APPARENT APPEND APPRECIATE APPROACH APPROPRIATE APPROXIMATE ARBITRARY ASPECT ASSEMBLE ASSESS ASSIGN ASSIST ASSUME ASSURE ATTAIN ATTITUDE ATTRIBUTE AUTHOR AUTHORITY AUTOMATE AVAILABLE AWARE BEHALF BENEFIT BIAS BOND BRIEF BULK CAPABLE CAPACITY CATEGORY CEASE CHALLENGE CHANNEL CHAPTER CHART CHEMICAL CIRCUMSTANC E CITE CIVIL CLARIFY CLASSIC CLAUSE CODE COHERENT COINCIDE COLLAPSE COLLEAGUE COMMENCE COMMENT COMMISSION COMMIT COMMODITY COMMUNICATE COMMUNITY COMPATIBLE COMPENSATE COMPILE COMPLEMENT COMPLEX COMPONENT COMPOUND COMPREHENSI VE COMPRISE COMPUTE CONCEIVE CONCENTRATE CONCEPT CONCLUDE CONCURRENT CONDUCT CONFER CONFINE CONFIRM CONFLICT CONFORM CONSENT CONSEQUENT CONSIDERABLE CONSIST CONSTANT CONSTITUTE CONSTRAIN CONSTRUCT CONSULT CONSUME CONTEMPORA RY CONTEXT CONTRACT CONTRADICT CONTRARY CONTRAST CONTRIBUTE CONTROVERSY CONVENE CONVERSE CONVERT CONVINCE COOPERATE COORDINATE CORE CORPORATE CORRESPOND CREATE CREDIT CRITERIA CRUCIAL CULTURE CURRENCY CYCLE DATA DEBATE DECADE DECLINE DEDUCE DEFINE DEFINITE DEMONSTRATE DENOTE DENY DEPRESS DERIVE DESIGN DESPITE DETECT DEVIATE DEVICE DEVOTE DIFFERENTIATE DIMENSION DIMINISH DISCRETE DISCRIMINATE DISPLACE DISPLAY DISPOSE DISTINCT DISTORT DISTRIBUTE DIVERSE DOCUMENT DOMAIN DOMESTIC DOMINATE DRAFT DRAMA DURATION DYNAMIC ECONOMY EDIT ELEMENT ELIMINATE EMERGE EMPHASIS EMPIRICAL ENABLE ENCOUNTER ENERGY ENFORCE ENHANCE ENORMOUS ENSURE ENTITY ENVIRONMENT EQUATE EQUIP EQUIVALENT ERODE ERROR ESTABLISH ESTATE ESTIMATE ETHIC ETHNIC EVALUATE EVENTUAL

34 EVIDENT EVOLVE EXCEED EXCLUDE EXHIBIT EXPAND EXPERT EXPLICIT EXPLOIT EXPORT EXPOSE EXTERNAL EXTRACT FACILITATE FACTOR FEATURE FEDERAL FEE FILE FINANCE FINITE FLEXIBLE FLUCTUATE FOCUS FORMAT FORMULA FORTHCOMING FOUNDATION FOUNDED FRAMEWORK FUNCTION FUND FUNDAMENTAL FURTHERMORE GENDER GENERATE GENERATION GLOBE GOAL GUARANTEE GUIDELINE HENCE HIERARCHY HIGHLIGHT HYPOTHESIS IDENTICAL IDENTIFY IDEOLOGY ILLUSTRATE IMAGE IMMIGRATE IMPACT IMPLEMENT IMPLICATE IMPLICIT IMPLY IMPOSE INCENTIVE INCIDENCE INCLINE INCOME INCORPORATE INDEX INDICATE INDIVIDUAL INDUCE INEVITABLE INFER INFRASTRUCTU RE INHERENT INHIBIT INITIAL INITIATE INJURE INNOVATE INPUT INSERT INSIGHT INSTANCE INSTITUTE INTEGRAL INTEGRATE INTEGRITY INTELLIGENCE INTENSE INTERACT INTERMEDIATE INTERNAL INTERPRET INTERVAL INTERVENE INTRINSIC INVEST INVESTIGATE INVOKE INVOLVE ISOLATE ISSUE ITEM JOURNAL JUSTIFY LABEL LABOUR LAYER LECTURE LEGAL LEGISLATE LEVY LIBERAL LICENCE LIKEWISE LINK LOCATE LOGIC MAINTAIN MAJOR MANIPULATE MANUAL MARGIN MATURE MAXIMISE MECHANISM MEDIA MEDIATE MEDICAL MEDIUM MENTAL METHOD MIGRATE MILITARY MINIMAL MINIMISE MINIMUM MINISTRY MINOR MODE MODIFY MONITOR MOTIVE MUTUAL NEGATE NETWORK NEUTRAL NEVERTHELES S NONETHELESS NORM NOTION NOTWITHSTAN DING NUCLEAR OBJECTIVE OBTAIN OBVIOUS OCCUPY OCCUR ODD OFFSET ONGOING OPTION ORIENT OUTCOME OUTPUT OVERALL OVERLAP OVERSEAS PANEL PARADIGM PARAGRAPH PARALLEL PARAMETER PARTICIPATE PASSIVE PERCEIVE PERCENT PERIOD PERSIST PERSPECTIVE PHASE PHENOMENON PHILOSOPHY PHYSICAL PLUS POLICY PORTION POSE POSITIVE POTENTIAL PRACTITIONER PRECEDE PRECISE PREDOMINANT PRELIMINARY PRESUME PREVIOUS PRIMARY PRIME PRINCIPAL PRINCIPLE PRIOR PRIORITY PROCESS PROFESSIONAL PROHIBIT PROJECT PROMOTE PROPORTION PROSPECT PROTOCOL PSYCHOLOGY PUBLICATION PUBLISH PURCHASE PURSUE QUALITATIVE QUOTE RADICAL RANDOM RANGE

35

RATIO RATIONAL REACT REFINE REGIME REGISTER REGULATE REINFORCE REJECT RELAX RELEASE RELEVANT RELUCTANCE RELY

REMOVE REQUIRE RESEARCH RESIDE RESOLVE RESOURCE RESPOND RESTORE RESTRAIN RESTRICT RETAIN REVEAL REVENUE REVERSE REVISE REVOLUTION RIGID

ROLE ROUTE SCENARIO SCHEDULE SCHEME SCOPE SECTION SECTOR SELECT SEQUENCE

SERIES SEX SHIFT

SIGNIFICANT SIMILAR SIMULATE SITE SOCALLED SOLE

SOMEWHAT SOURCE SPECIFIC SPECIFY SPHERE STABLE STATISTIC STATUS STRAIGHTFOR WARD

STRATEGY STRESS STRUCTURE STYLE SUBMIT

SUBORDINATE SUBSEQUENT SUBSIDY SUBSTITUTE SUCCESSOR SUFFICIENT SUM

SUMMARY SUPPLEMENT SURVEY SURVIVE SUSPEND SUSTAIN SYMBOL TEAM TECHNICAL TECHNIQUE

TECHNOLOGY TEMPORARY TENSE TERMINATE THEME THEORY THEREBY THESIS TOPIC TRADITION TRANSFER TRANSFORM TRANSIT TRANSMIT TRANSPORT TREND TRIGGER ULTIMATE UNDERGO UNDERLIE UNDERTAKE UNIFORM UNIFY UNIQUE UTILISE VALID VARY VEHICLE VERSION VIA VIOLATE VIRTUAL VISIBLE VISION VISUAL VOLUME VOLUNTARY WELFARE WHEREAS WHEREBY WIDESPREAD

36 Appendix D

RANK FREQ COVERAGE

individ cumulative WORD 1. 62 6.26% 6.26% MONSTER 2. 46 4.64% 10.90% PRINCESS 3. 43 4.34% 15.24% JAILER 4. 23 2.32% 17.56% PUMPKIN 5. 21 2.12% 19.68% ENVOY 6. 18 1.82% 21.50% CELL 7. 18 1.82% 23.32% POUCH 8. 18 1.82% 25.14% TRUCK 9. 16 1.61% 26.75% PALACE 10. 16 1.61% 28.36% PASSPORT 11. 14 1.41% 29.77% MAGIC 12. 13 1.31% 31.08% OK 13. 13 1.31% 32.39% SAMPAN 14. 12 1.21% 33.60% BOLT 15. 10 1.01% 34.61% COCK 16. 10 1.01% 35.62% JUNK 17. 9 0.91% 36.53% AIRPORT 18. 9 0.91% 37.44% CHILI 19. 9 0.91% 38.35% MAJESTY 20. 8 0.81% 39.16% KID 21. 8 0.81% 39.97% VAN 22. 7 0.71% 40.68% OX 23. 7 0.71% 41.39% RAVENS 24. 7 0.71% 42.10% SCRAPS 25. 6 0.61% 42.71% BEE 26. 6 0.61% 43.32% DAMN 27. 6 0.61% 43.93% FLUTE 28. 6 0.61% 44.54% GRABBED 29. 6 0.61% 45.15% SCARED 30. 6 0.61% 45.76% YEAH

37 31. 5 0.50% 46.26% BANYAN 32. 5 0.50% 46.76% CHINESE 33. 5 0.50% 47.26% COACH 34. 5 0.50% 47.76% EAGLE 35. 5 0.50% 48.26% EMPEROR 36. 5 0.50% 48.76% HUGE 37. 5 0.50% 49.26% SLAMMED 38. 5 0.50% 49.76% TRAFFIC 39. 5 0.50% 50.26% TV

40. 5 0.50% 50.76% VEGETABLES 41. 4 0.40% 51.16% AIRLINE 42. 4 0.40% 51.56% AMERICA 43. 4 0.40% 51.96% BASEBALL 44. 4 0.40% 52.36% CHASED 45. 4 0.40% 52.76% CIVILIZATION 46. 4 0.40% 53.16% EASTER

47. 4 0.40% 53.56% FAIRY 48. 4 0.40% 53.96% GHOST 49. 4 0.40% 54.36% IDIOT 50. 4 0.40% 54.76% PRAIRIE 51. 4 0.40% 55.16% SLIT

52. 4 0.40% 55.56% TOOTHACHE 53. 4 0.40% 55.96% UNFAITHFUL 54. 4 0.40% 56.36% VICTIM 55. 4 0.40% 56.76% VINE

56. 3 0.30% 57.06% AMBASSADOR 57. 3 0.30% 57.36% BEHEADED 58. 3 0.30% 57.66% BETRAYED 59. 3 0.30% 57.96% BLOSSOMED 60. 3 0.30% 58.26% BOXCAR 61. 3 0.30% 58.56% CABIN 62. 3 0.30% 58.86% CRAWLED 63. 3 0.30% 59.16% DECK

38 64. 3 0.30% 59.46% EYED 65. 3 0.30% 59.76% FOG 66. 3 0.30% 60.06% HENS 67. 3 0.30% 60.36% HOPPED 68. 3 0.30% 60.66% JAIL 69. 3 0.30% 60.96% MANSION 70. 3 0.30% 61.26% MONSTERS 71. 3 0.30% 61.56% MOSQUITO 72. 3 0.30% 61.86% ORPHANS 73. 3 0.30% 62.16% OVERJOYED 74. 3 0.30% 62.46% POUNDING 75. 3 0.30% 62.76% REED 76. 3 0.30% 63.06% SEASHORE 77. 3 0.30% 63.36% SHRANK 78. 3 0.30% 63.66% SLAPPING 79. 3 0.30% 63.96% TORTOISE 80. 3 0.30% 64.26% TRESS 81. 3 0.30% 64.56% WOW

82. 2 0.20% 64.76% ALONGSIDE 83. 2 0.20% 64.96% AMAZED 84. 2 0.20% 65.16% ANCHOR 85. 2 0.20% 65.36% ANKLE 86. 2 0.20% 65.56% ASHORE 87. 2 0.20% 65.76% BOLTED 88. 2 0.20% 65.96% BOSS 89. 2 0.20% 66.16% BOTHER 90. 2 0.20% 66.36% BRIDE 91. 2 0.20% 66.56% BUDDHA 92. 2 0.20% 66.76% BUSHED 93. 2 0.20% 66.96% CHASING 94. 2 0.20% 67.16% CLAWS 95. 2 0.20% 67.36% CUTTER 96. 2 0.20% 67.56% DAZZLED

39 97. 2 0.20% 67.76% DOVER 98. 2 0.20% 67.96% DRIPPING 99. 2 0.20% 68.16% DUSTY 100. 2 0.20% 68.36% FOGS 101. 2 0.20% 68.56% FUEL 102. 2 0.20% 68.76% GIANT 103. 2 0.20% 68.96% GUYS

104. 2 0.20% 69.16% HANDSOME 105. 2 0.20% 69.36% HERO

106. 2 0.20% 69.56% HONEY 107. 2 0.20% 69.76% HORBOUR 108. 2 0.20% 69.96% HUGGED 109. 2 0.20% 70.16% IMPERIAL 110. 2 0.20% 70.36% JACKET 111. 2 0.20% 70.56% JEANS 112. 2 0.20% 70.76% LANTERN 113. 2 0.20% 70.96% LUXURIOUS 114. 2 0.20% 71.16% MIGHTY 115. 2 0.20% 71.36% MOANED 116. 2 0.20% 71.56% MOORED 117. 2 0.20% 71.76% MOSS 118. 2 0.20% 71.96% NERVOUS 119. 2 0.20% 72.16% NODDED 120. 2 0.20% 72.36% ORPHAN 121. 2 0.20% 72.56% PAIL 122. 2 0.20% 72.76% PRICKED 123. 2 0.20% 72.96% PRINCE 124. 2 0.20% 73.16% RAGGED 125. 2 0.20% 73.36% RAINY 126. 2 0.20% 73.56% RAVEN 127. 2 0.20% 73.76% REPAY 128. 2 0.20% 73.96% REVIVED 129. 2 0.20% 74.16% SHED

40 130. 2 0.20% 74.36% SHRINK

131. 2 0.20% 74.56% SKYSCRAPERS 132. 2 0.20% 74.76% STARING 133. 2 0.20% 74.96% SWORDSMAN 134. 2 0.20% 75.16% THIRSTY 135. 2 0.20% 75.36% TINY

136. 2 0.20% 75.56% TRAPDOOR 137. 2 0.20% 75.76% VICTIMS 138. 2 0.20% 75.96% WEDDING 139. 2 0.20% 76.16% WIG

140. 2 0.20% 76.36% WILDERNESS 141. 1 0.10% 76.46% ABOARD 142. 1 0.10% 76.56% ACCENT 143. 1 0.10% 76.66% ACK 144. 1 0.10% 76.76% AD

145. 1 0.10% 76.86% ADOPTIVE 146. 1 0.10% 76.96% ADORED 147. 1 0.10% 77.06% AHH 148. 1 0.10% 77.16% AKED 149. 1 0.10% 77.26% ALARM 150. 1 0.10% 77.36% ALRIGHT 151. 1 0.10% 77.46% AMERICAN 152. 1 0.10% 77.56% ANNOUNCE 153. 1 0.10% 77.66% APARTMENT 154. 1 0.10% 77.76% ARMOUR 155. 1 0.10% 77.86% ATTENDANTS 156. 1 0.10% 77.96% AWAITS 157. 1 0.10% 78.06% AWARDED 158. 1 0.10% 78.16% BANG 159. 1 0.10% 78.26% BARK 160. 1 0.10% 78.36% BARKED 161. 1 0.10% 78.46% BCAK 162. 1 0.10% 78.56% BEACH

41 163. 1 0.10% 78.66% BILLIARD 164. 1 0.10% 78.76% BLANKET 165. 1 0.10% 78.86% BLAST 166. 1 0.10% 78.96% BLOODS 167. 1 0.10% 79.06% BORED 168. 1 0.10% 79.16% BOTHERED 169. 1 0.10% 79.26% BUBBLES 170. 1 0.10% 79.36% BUZZING 171. 1 0.10% 79.46% BYE 172. 1 0.10% 79.56% CAFE 173. 1 0.10% 79.66% CANDLE 174. 1 0.10% 79.76% CAPTURE 175. 1 0.10% 79.86% CAPTURED 176. 1 0.10% 79.96% CARVING 177. 1 0.10% 80.06% CELEBRATED 178. 1 0.10% 80.16% CELLS

179. 1 0.10% 80.26% CHAMPION 180. 1 0.10% 80.36% CHARITABLE 181. 1 0.10% 80.46% CHASE

182. 1 0.10% 80.56% CHEEK 183. 1 0.10% 80.66% CHEEKS 184. 1 0.10% 80.76% CHICH 185. 1 0.10% 80.86% CHILL 186. 1 0.10% 80.96% CHOP 187. 1 0.10% 81.06% CLAPPED 188. 1 0.10% 81.16% COACHMAN 189. 1 0.10% 81.26% COCKPIT 190. 1 0.10% 81.36% COILED 191. 1 0.10% 81.46% COMIC 192. 1 0.10% 81.56% CONVEY 193. 1 0.10% 81.66% CORAL 194. 1 0.10% 81.76% CORPSE

42 196. 1 0.10% 81.96% COUNTYSIDE 197. 1 0.10% 82.06% CRAWL

198. 1 0.10% 82.16% CRAWLING 199. 1 0.10% 82.26% CRAZY

200. 1 0.10% 82.36% CROSSROADS 201. 1 0.10% 82.46% CROWED 202. 1 0.10% 82.56% CRUMBLING 203. 1 0.10% 82.66% CUNNING 204. 1 0.10% 82.76% DEARLY 205. 1 0.10% 82.86% DEL 206. 1 0.10% 82.96% DESSERT 207. 1 0.10% 83.06% DILIGENT 208. 1 0.10% 83.16% DILIGENTLY 209. 1 0.10% 83.26% DISASTER 210. 1 0.10% 83.36% DISGRACE 211. 1 0.10% 83.46% DISGUISED 212. 1 0.10% 83.56% DIVORCED 213. 1 0.10% 83.66% DREESED 214. 1 0.10% 83.76% DUMB 215. 1 0.10% 83.86% ECHOED 216. 1 0.10% 83.96% ELIXIR 217. 1 0.10% 84.06% ENVIOUSLY 218. 1 0.10% 84.16% ER

219. 1 0.10% 84.26% ESCORT 220. 1 0.10% 84.36% ET

221. 1 0.10% 84.46% EXCLAIMED 222. 1 0.10% 84.56% EXECUTED 223. 1 0.10% 84.66% EXOTIC

224. 1 0.10% 84.76% EXPECTANTLY 225. 1 0.10% 84.86% FESTIVITIES 226. 1 0.10% 84.96% FETCH 227. 1 0.10% 85.06% FID

43 229. 1 0.10% 85.26% FLAPPING 230. 1 0.10% 85.36% FOGGY 231. 1 0.10% 85.46% FOREHEAD 232. 1 0.10% 85.56% FOREVER 233. 1 0.10% 85.66% FREIGHT 234. 1 0.10% 85.76% FROG 235. 1 0.10% 85.86% FROWNED 236. 1 0.10% 85.96% GALLOP 237. 1 0.10% 86.06% GALLOPING 238. 1 0.10% 86.16% GAMBLE 239. 1 0.10% 86.26% GAMBLED 240. 1 0.10% 86.36% GAMBLING 241. 1 0.10% 86.46% GASPED 242. 1 0.10% 86.56% GHOSTS 243. 1 0.10% 86.66% GILDED 244. 1 0.10% 86.76% GLEAMED 245. 1 0.10% 86.86% GLEAMING 246. 1 0.10% 86.96% GLOSSY 247. 1 0.10% 87.06% GOODS 248. 1 0.10% 87.16% GRAB

249. 1 0.10% 87.26% GRUMBLING 250. 1 0.10% 87.36% GUST

251. 1 0.10% 87.46% GUSTS 252. 1 0.10% 87.56% GUY 253. 1 0.10% 87.66% HAIRPIN 254. 1 0.10% 87.76% HANDCUFFED 255. 1 0.10% 87.86% HAUNT

256. 1 0.10% 87.96% HEADLIGHTS 257. 1 0.10% 88.06% HEADPHONES 258. 1 0.10% 88.16% HEARTED 259. 1 0.10% 88.26% HELL 260. 1 0.10% 88.36% HEN 261. 1 0.10% 88.46% HEROES

44 262. 1 0.10% 88.56% HOBS 263. 1 0.10% 88.66% HOOVES 264. 1 0.10% 88.76% HORE 265. 1 0.10% 88.86% HORRIBLE 266. 1 0.10% 88.96% HOWLING 267. 1 0.10% 89.06% HUGGING 268. 1 0.10% 89.16% HUSH 269. 1 0.10% 89.26% I;LL 270. 1 0.10% 89.36% IDIOTS 271. 1 0.10% 89.46% INCENSE 272. 1 0.10% 89.56% INHERITED 273. 1 0.10% 89.66% JOSS

274. 1 0.10% 89.76% KETTLE 275. 1 0.10% 89.86% KIDS 276. 1 0.10% 89.96% KNELT 277. 1 0.10% 90.06% LAD 278. 1 0.10% 90.16% LADI 279. 1 0.10% 90.26% LASS 280. 1 0.10% 90.36% LEAKING 281. 1 0.10% 90.46% MAGICAL 282. 1 0.10% 90.56% MAGNIFICENT 283. 1 0.10% 90.66% MANGIFICENT 284. 1 0.10% 90.76% MATE

285. 1 0.10% 90.86% MIDDAY 286. 1 0.10% 90.96% MIRROR 287. 1 0.10% 91.06% MOBILE 288. 1 0.10% 91.16% MOTHS 289. 1 0.10% 91.26% OPENINGS 290. 1 0.10% 91.36% OREDERED 291. 1 0.10% 91.46% OUTSTRETCHED 292. 1 0.10% 91.56% OVERHANGING 293. 1 0.10% 91.66% OW

45 295. 1 0.10% 91.86% PASSAGEWAY 296. 1 0.10% 91.96% PECKED

297. 1 0.10% 92.06% PEEP 298. 1 0.10% 92.16% PIE 299. 1 0.10% 92.26% PLACER 300. 1 0.10% 92.36% PLEADED 301. 1 0.10% 92.46% POCKED 302. 1 0.10% 92.56% PORCH 303. 1 0.10% 92.66% PRICK

304. 1 0.10% 92.76% PROSPERITY 305. 1 0.10% 92.86% PROVINCE 306. 1 0.10% 92.96% PUSSED 307. 1 0.10% 93.06% RADIANT 308. 1 0.10% 93.16% RAGS 309. 1 0.10% 93.26% RAILING 310. 1 0.10% 93.36% REMOTE 311. 1 0.10% 93.46% RESTROOM 312. 1 0.10% 93.56% REUNION 313. 1 0.10% 93.66% REVIVE 314. 1 0.10% 93.76% SCREAM 315. 1 0.10% 93.86% SCREAMED 316. 1 0.10% 93.96% SETA

317. 1 0.10% 94.06% SHRIEK 318. 1 0.10% 94.16% SHRIEKED 319. 1 0.10% 94.26% SIDEWALK 320. 1 0.10% 94.36% SIDEWALKS 321. 1 0.10% 94.46% SIGHED 322. 1 0.10% 94.56% SILL

323. 1 0.10% 94.66% SLAMMING 324. 1 0.10% 94.76% SLITS 325. 1 0.10% 94.86% SMART 326. 1 0.10% 94.96% SMOOTHER 327. 1 0.10% 95.06% SNORES

46 328. 1 0.10% 95.16% SOBBED 329. 1 0.10% 95.26% SOL

330. 1 0.10% 95.36% SOLDIRES 331. 1 0.10% 95.46% SPAIN 332. 1 0.10% 95.56% SPIED 333. 1 0.10% 95.66% SPOILT 334. 1 0.10% 95.76% SPROUT 335. 1 0.10% 95.86% SPROUTED 336. 1 0.10% 95.96% STARED 337. 1 0.10% 96.06% STARES 338. 1 0.10% 96.16% STATUE 339. 1 0.10% 96.26% STROLLED 340. 1 0.10% 96.36% STUMBLE 341. 1 0.10% 96.46% STUNNED 342. 1 0.10% 96.56% SUPREME 343. 1 0.10% 96.66% SWEATSHIRT 344. 1 0.10% 96.76% SWORDSMEN 345. 1 0.10% 96.86% TALENTED 346. 1 0.10% 96.96% TALISMAN 347. 1 0.10% 97.06% TATTERED 348. 1 0.10% 97.16% TEMPED 349. 1 0.10% 97.26% TERRIFIED 350. 1 0.10% 97.36% THRONE 351. 1 0.10% 97.46% TONES 352. 1 0.10% 97.56% TRAMPLED 353. 1 0.10% 97.66% TROUGH 354. 1 0.10% 97.76% TRUCKS 355. 1 0.10% 97.86% TUGGED 356. 1 0.10% 97.96% UNGRATEFUL 357. 1 0.10% 98.06% UNKIND 358. 1 0.10% 98.16% USELESSLY 359. 1 0.10% 98.26% VACATIONS 360. 1 0.10% 98.36% VANISHED

47 361. 1 0.10% 98.46% VER

362. 1 0.10% 98.56% VIETNAMESE 363. 1 0.10% 98.66% WANDERER 364. 1 0.10% 98.76% WATERHOLE 365. 1 0.10% 98.86% WEEP

366. 1 0.10% 98.96% WEPT 367. 1 0.10% 99.06% WITHER 368. 1 0.10% 99.16% WITHERS 369. 1 0.10% 99.26% WITTY 370. 1 0.10% 99.36% WORIED 371. 1 0.10% 99.46% WS 372. 1 0.10% 99.56% YELLED

75

051. reward 5 052. school 5 053. sun 5 054. television 5

055. there’ 5 056. ticket 5 057. traffic 5 058. airline 4 059. animal 4 060. baseball 4 061. black 4 062. civilise 4 063. class 4 064. clear 4 065. complete 4 066. easter 4 067. family 4 068. fur 4 069. mile 4 070. neck 4 071. park 4 072. prairie 4 073. puzzle 4 074. seat 4 075. she’ 4 076. tree 4 077. wake 4 078. you’ve 4 079. ago 3 080. arrive 3 081. boxcar 3 082. fat 3 083. father 3 084. fight 3 085. ground 3 086. hotel 3 087. hug 3 088. idea 3 089. it’ll 3 090. lean 3 091. music 3 092. north 3 093. office 3 094. rent 3 095. second 3 096. sister 3 097. speed 3 098. tooth 3 099. track 3 100. tress 3 101. we’ve 3 102. wow 3 103. you’ll 3 104. above 2 105. age 2 106. below 2 107. book 2 108. boss 2

052. small 24 053. time 24 054. from 23 055. turn 23 056. so 22 057. take 22 058. when 22 059. about 21 060. away 21 061. make 21 062. must 21 063. find 20 064. step 20 065. here 19 066. stop 19 067. tell 19 068. call 18 069. if 18 070. run 18 071. well 18 072. against 17 073. can 17 074. girl 17 075. street 17 076. wait 17 077. all 16 078. ask 16 079. some 16 080. yes 16 081. hand 15 082. quick 15 083. start 15 084. again 14 085. any 14 086. before 14 087. face 14 088. into 14 089. like 14 090. no 14 091. one 14 092. pull 14 093. sure 14 094. too 14 095. try 14 096. by 13 097. didn’ 13 098. happen 13 099. oh 13 100. soon 13 101. walk 13 102. i’ll 12 103. man 12 104. plan 12 105. right 12 106. stand 12 107. voice 12 108. want 12 109. every 11 110. fine 11

050. basket 3 051. behead 3 052. believe 3 053. blow 3 054. body 3 055. breath 3 056. bright 3 057. busy 3 058. dress 3 059. ear 3 060. fear 3 061. fright 3 062. note 3 063. pass 3 064. pile 3 065. pounding 3 066. sail 3 067. send 3 068. serve 3 069. slap 3 070. slide 3 071. soft 3 072. spill 3 073. stare 3 074. than 3 075. trap 3 076. wet 3 077. white 3 078. wife 3 079. ahead 2 080. ankle 2 081. bar 2 082. bear 2 083. bird 2 084. blood 2 085. bold 2 086. breathe 2 087. cake 2 088. captain 2 089. carry 2 090. cover 2 091. crawl 2 092. crowd 2 093. cruel 2 094. daughter 2 095. dinner 2 096. dover 2 097. edge 2 098. fellow 2 099. finger 2 100. fire 2 101. flower 2 102. gallop 2 103. half 2 104. heavy 2 105. hot 2 106. husband 2 107. jailer’ 2 108. kitchen 2

048. crawl 2 049. crowd 2 050. cruel 2 051. crumble 1 052. cry 12 053. dare 1 054. daughter 2 055. day’ 1 056. deep 1 057. del 1 058. detail 1 059. die 4 060. dig 1 061. dinner 2 062. distance 4 063. dover 2 064. drag 1 065. dress 3 066. drip 1 067. drown 1 068. duty 1 069. ear 3 070. easy 1 071. edge 2 072. either 1 073. er 1 074. escape 4 075. execute 1 076. fact 1 077. fail 1 078. fairy 1 079. fear 3 080. feed 1 081. fellow 2 082. fid 1 083. field 1 084. finger 2 085. fire 2 086. fix 1 087. flower 2 088. fog 1 089. freeze 1 090. fresh 1 091. fright 3 092. frown 1 093. gallop 2 094. gasp 1 095. gate 8 096. green 1 097. grow 1 098. grumble 1 099. guard 11 100. guards’ 1 101. half 2 102. hall 11 103. hardly 4 104. health 1 105. heart 5 106. heavy 2

76

109. bush 2 110. bushed 2 111. cabin 2 112. calm 2 113. cross 2 114. difficult 2 115. direction 2 116. during 2 117. dust 2 118. engine 2 119. enjoy 2 120. enough 2 121. excite 2 122. film 2 123. fuel 2 124. guy 2 125. he’ll 2 126. hill 2 127. jacket 2 128. jeans 2 129. kick 2 130. lay 2 131. less 2 132. lesson 2 133. mistake 2 134. nervous 2 135. order 2 136. partner 2 137. picture 2 138. question 2 139. rid 2 140. search 2 141. sidewalk 2 142. sing 2 143. skyscraper 2

144. spend 2 145. they’re 2 146. thirst 2 147. trouble 2 148. west 2 149. wilderness 2

150. wouldn’ 2 151. accent 1 152. add 1

153. advertise 1 154. ahh 1

155. aid 1 156. although 1 157. anger 1 158. apartment 1 159. apple 1 160. area 1 161. arrange 1 162. asleep 1 163. bark 1 164. bed 1 165. billiard 1

111. head 11 112. help 11 113. it’ 11 114. long 11 115. never 11 116. reach 11 117. sit 11 118. very 11 119. wall 11 120. dark 10 121. friend 10 122. good 10 123. house 10 124. how 10 125. hurry 10 126. laugh 10 127. more 10 128. old 10 129. people 10 130. sound 10 131. where 10 132. woman 10 133. almost 9 134. angry 9 135. behind 9 136. bell 9 137. care 9 138. don’ 9 139. fall 9 140. foot 9 141. inside 9 142. let 9 143. light 9 144. smile 9 145. still 9 146. sudden 9 147. thing 9 148. watch 9 149. work 9 150. after 8 151. always 8 152. begin 8 153. day 8 154. ever 8 155. eye 8 156. few 8 157. give 8 158. glad 8 159. hard 8 160. i’ 8 161. late 8 162. lock 8 163. minute 8 164. much 8 165. over 8 166. speak 8 167. talk 8 168. air 7 169. answer 7

109. lantern 2 110. lazy 2 111. leather 2 112. market 2 113. most 2 114. nod 2 115. pail 2 116. post 2 117. pray 2 118. pretend 2 119. rope 2 120. self 2 121. shadow 2 122. shall 2 123. shed 2 124. sick 2 125. sight 2 126. soup 2 127. straight 2 128. tap 2 129. tight 2 130. trapdoor 2 131. trip 2 132. wash 2 133. week 2 134. wig 2 135. winding 2 136. ,sir 1 137. aked 1 138. alarm 1 139. alright 1 140. bang 1 141. bcak 1 142. blast 1 143. box 1 144. breakfast 1

145. brush 1 146. candle 1 147. clean 1 148. coachman 1 149. continue 1 150. count 1 151. crumble 1 152. dare 1 153. day’ 1 154. deep 1 155. del 1 156. detail 1 157. dig 1 158. drag 1 159. drip 1 160. drown 1 161. duty 1 162. easy 1 163. either 1 164. er 1 165. execute 1 166. fact 1

107. high 4 108. hob 1 109. hole 1 110. hoof 1 111. hop 1 112. horse 4 113. horses’ 1 114. hot 2 115. hurt 4 116. husband 2 117. i;ll 1 118. imagine 1 119. iron 1 120. jail 43 121. jailer’ 2 122. journey 1 123. kettle 1 124. king 13 125. kitchen 2 126. ladi 1 127. lantern 2 128. lass 1 129. last 11 130. lazy 2 131. lead 7 132. leather 2 133. lie 1 134. lift 1 135. lip 1 136. lord 46 137. marched 1 138. market 2 139. may 5 140. meal 1 141. milk 4 142. most 2 143. narrow 1 144. nod 2 145. note 3 146. offer 1 147. pail 2 148. pardon 4 149. pass 3 150. passage 11 151. passageway 1

152. pie 1 153. pile 3 154. pock 1 155. post 2 156. pounding 3 157. pray 2 158. pretend 2 159. prison 10 160. pussed 1 161. railing 1 162. rain 8 163. raven 9 164. read 1

77

166. bottom 1 167. brother 1 168. business 1 169. cafe 1 170. centre 1 171. certain 1 172. champion 1 173. change 1 174. chich 1 175. circle 1 176. clock 1 177. cockpit 1 178. coffee 1 179. comic 1 180. contact 1 181. control 1 182. countyside 1

183. cow’ 1 184. crazy 1 185. dead 1 186. desk 1 187. divorce 1 188. drink 1 189. driver’ 1 190. empire 1 191. et 1

192. everything’ 1

193. exact 1 194. exchange 1 195. exercise 1 196. exotic 1 197. fence 1 198. films: 1 199. flap 1 200. flat 1 201. flight 1 202. form 1 203. four 1 204. freight 1 205. further 1 206. grade 1 207. guess 1 208. gun 1 209. hair 1 210. handcuff 1 211. handle 1 212. hasn’ 1 213. headlight 1 214. headphone 1 215. hell 1 216. history 1 217. honey 1 218. how’ 1 219. hunt 1 220. hush 1 221. island 1 222. join 1

170. around 7 171. fast 7 172. hang 7 173. hit 7 174. little 7 175. live 7 176. might 7 177. morning 7 178. toward 7 179. wish 7 180. won’ 7 181. eat 6 182. even 6 183. forget 6 184. leave 6 185. life 6 186. loud 6 187. pick 6 188. place 6 189. push 6 190. save 6 191. should 6 192. slow 6 193. stone 6 194. word 6 195. along 5 196. child 5 197. couldn’ 5 198. each 5 199. final 5 200. food 5 201. front 5 202. home 5 203. hope 5 204. keep 5 205. kind 5 206. left 5 207. lose 5 208. meet 5 209. move 5 210. near 5 211. off 5 212. once 5 213. other 5 214. put 5 215. remember 5 216. shake 5 217. tall 5 218. who 5 219. window 5 220. able 4 221. attend 4 222. best 4 223. blue 4 224. bring 4 225. can’ 4 226. catch 4 227. close 4 228. closes 4

167. fail 1 168. fairy 1 169. feed 1 170. fid 1 171. field 1 172. fix 1 173. fog 1 174. freeze 1 175. fresh 1 176. frown 1 177. gasp 1 178. green 1 179. grow 1 180. grumble 1 181. guards’ 1 182. health 1 183. hob 1 184. hole 1 185. hoof 1 186. hop 1 187. horses’ 1 188. i;ll 1 189. imagine 1 190. iron 1 191. journey 1 192. kettle 1 193. ladi 1 194. lass 1 195. lie 1 196. lift 1 197. lip 1 198. marched 1 199. meal 1 200. narrow 1 201. offer 1 202. passageway 1

203. pie 1 204. pock 1 205. pussed 1 206. railing 1 207. read 1 208. realise 1 209. refuse 1 210. rocked 1 211. rub 1 212. rush 1 213. scream 1 214. set 1 215. several 1 216. ship’ 1 217. skirt 1 218. smell 1 219. son 1 220. spot 1 221. square 1 222. star 1 223. steal 1 224. strip 1

165. realise 1 166. refuse 1 167. rocked 1 168. rope 2 169. round 5 170. rub 1 171. rush 1 172. sail 3 173. scream 1 174. secret 7 175. self 2 176. send 3 177. serve 3 178. set 1 179. several 1 180. shadow 2 181. shall 2 182. shed 2 183. ship 6 184. ship’ 1 185. shop 7 186. sick 2 187. sight 2 188. sir 12 189. skirt 1 190. slap 3 191. slide 3 192. slit 5 193. smell 1 194. soft 3 195. son 1 196. soup 2 197. spill 3 198. spot 1 199. square 1 200. stairs 7 201. star 1 202. stare 3 203. steal 1 204. stick 10 205. straight 2 206. strip 1 207. supper 10 208. suppose 1 209. swing 1 210. tap 2 211. tape 1 212. taste 1 213. temped 1 214. tend 1 215. than 3 216. though 6 217. tight 2 218. tonight 1 219. trap 3 220. trapdoor 2 221. treat 1 222. trip 2 223. true 5

78

223. kiss 1 224. large 1 225. limit 1 226. love 1 227. mad 1 228. main 1 229. marry 1 230. men: 1 231. metal 1 232. mobile 1 233. mom’ 1 234. month 1 235. moth 1 236. mother’ 1 237. mountain 1 238. nature 1 239. neighbour 1 240. nice 1 241. noon 1 242. normal 1 243. number 1 244. pack 1 245. page 1 246. panic 1 247. peace 1 248. porch 1 249. possible 1 250. private 1 251. rail 1 252. recognize 1 253. remote 1 254. restroom 1 255. reunion 1 256. ride 1 257. row 1 258. sentence 1 259. seta 1 260. shape 1 261. share 1 262. shoe 1 263. shoulder 1 264. sill 1 265. six 1 266. skin 1 267. slam 1 268. smart 1 269. solution 1 270. song 1 271. south 1 272. spain 1 273. state 1 274. station’ 1 275. stretch 1 276. stuff 1 277. suspect 1 278. sweatshirt 1

279. teach 1 280. text 1

229. cloth 4 230. corner 4 231. danger 4 232. drop 4 233. follow 4 234. moment 4 235. noise 4 236. only 4 237. or 4 238. part 4 239. pay 4 240. please 4 241. point 4 242. side 4 243. stay 4 244. table 4 245. through 4 246. while 4 247. whole 4 248. why 4 249. without 4 250. alone 3 251. another 3 252. bend 3 253. cow 3 254. drive 3 255. explain 3 256. far 3 257. floor 3 258. fly 3 259. free 3 260. hat 3 261. hide 3 262. hour 3 263. mean 3 264. next 3 265. night 3 266. piece 3 267. probable 3 268. quiet 3 269. room 3 270. seem 3 271. shine 3 272. short 3 273. show 3 274. sorry 3 275. strange 3 276. strong 3 277. surprise 3 278. thank 3 279. that’ 3 280. together 3 281. top 3 282. visit 3 283. water 3 284. wind 3 285. wood 3 286. across 2 287. already 2

225. suppose 1 226. swing 1 227. tape 1 228. taste 1 229. temped 1 230. tend 1 231. tonight 1 232. treat 1 233. weather 1 234. whip 1 235. whisper 1 236. winter 1 237. women’ 1 238. woried 1 239. yell 1 240. yet 1 241. you’ 1

224. warm 5 225. wash 2 226. weather 1 227. week 2 228. wet 3 229. whip 1 230. whisper 1 231. white 3 232. wife 3 233. wig 2 234. winding 2 235. winter 1 236. women’ 1 237. wonder 4 238. woried 1 239. yell 1 240. yet 1 241. you’ 1

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281. that: 1 282. there’ll 1 283. they’ll 1 284. they’ve 1 285. trade 1 286. vacation 1 287. village 1 288. war 1 289. was: 1 290. waterhole 1 291. weren’ 1 292. where’ 1 293. whistle 1 294. who’ 1 295. worse 1 296. ’clock 1

288. bad 2 289. become 2 290. beside 2 291. between 2 292. bit 2 293. boy 2 294. chance 2 295. climb 2 296. cut 2 297. difference 2

298. dirty 2 299. empty 2 300. fill 2 301. fun 2 302. happy 2 303. hunger 2 304. job 2 305. leg 2 306. listen 2 307. luck 2 308. matter 2 309. miss 2 310. mouth 2 311. name 2 312. new 2 313. news 2 314. notice 2 315. own 2 316. raise 2 317. ready 2 318. really 2 319. rest 2 320. same 2 321. scare 2 322. since 2 323. sky 2 324. story 2 325. tear 2 326. tie 2 327. tire 2 328. tomorrow 2 329. touch 2 330. until 2 331. use 2 332. what’ 2 333. worry 2 334. young 2 335. act 1 336. also 1 337. bag 1 338. because 1 339. board 1 340. both 1 341. bread 1 342. bundle 1 343. country 1 344. course 1 345. decide 1

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357. jump 1 358. knock 1 359. land 1 360. low 1 361. many 1 362. mark 1 363. meaning 1 364. meat 1 365. mind 1 366. money 1 367. nose 1 368. past 1 369. perhaps 1 370. person 1 371. play 1 372. poor 1 373. problem 1 374. race 1 375. real 1 376. red 1 377. ring 1 378. shut 1 379. silver 1 380. sleep 1 381. tiny 1 382. under 1 383. understand 1

384. wasn’ 1 385. wave 1 386. wear 1 387. which 1 388. wide 1 389. woman’ 1 390. world 1 391. worth 1 392. year 1