i
THE VOCABULARY PROFILE
OF STUDENTS’ TEXTBOOK USED IN
THE MANAGEMENT AND GLOBAL BUSINESS STRATEGY COURSE
THESIS
Submitted in Partial Fulfillment of the Requirements for the Degree of
Sarjana Pendidikan
RATIH CHANDRA BIMANDARI 112012058
ENGLISH LANGUAGE EDUCATION PROGRAM FACULTY OF LANGUAGE AND LITERATURE
SATYA WACANA CHRISTIAN UNIVERSITY SALATIGA
i
THE VOCABULARY PROFILE
OF STUDENTS’ TEXTBOOK USED IN
THE MANAGEMENT AND GLOBAL BUSINESS STRATEGY COURSE
THESIS
Submitted in Partial Fulfillment of the Requirements for the Degree of
Sarjana Pendidikan
RATIH CHANDRA BIMANDARI 112012058
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. Ratih Chandra Bimandari 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 Language Education Program, 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
Publication Agreement Declaration………. vi
Table of Contents ……… vii
Introduction ……… 1
Literature Review The Importance of Vocabulary ……… 3
Strategy in Learning Vocabulary ……… 4
Strategy in Teaching Vocabulary ……… 6
Previous Study ……… 8
The Study Method of Research ……… 9
Sample of the Study ……… 9
Research Instrument ……… 11
Data Collection Procedures ……… 11
viii Findings and Discussions
Overall Vocabulary Profile ……… 13
Negative Vocabulary Profiles of the Textbook ……… 14
Comparison of Vocabulary Profile Across Chapter ………. 18
Comparison Between Chapter I and V ……… 19
Comparison Between Chapter I and VI ……… 20
Conclusion ……… 21
References ……… 22
Acknowledgements ……… 25
Appendices Appendix A ……… 26
Appendix B ……… 29
Appendix C ……… 37
Appendix D ……… 39
Appendix E ……… 74
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THE VOCABULARY PROFILE OF STUDENTS’ TEXTBOOK USED IN THE MANAGEMENT AND GLOBAL BUSINESS STRATEGY COURSE
RATIH CHANDRA BIMANDARI
ABSTRACT
This study was conducted to identify vocabulary profile of students’ textbook in the Management and Global Business Strategy Course at Management Program, the Faculty of Economics and Business of Satya Wacana Christian University, Salatiga. Sample of the study was a book entitled International Business: Environment and Operations Fifteenth Edition published by Pearson Education Limited 2015. Forty-one pages of 877 pages of the textbook were taken for the sample. The analysis of the data used a descriptive analysis and an electronic tool named vocabulary profiler that can be accessed on www.lextutor.ca. The study revealed some results. First, the overall words coverage of students’ textbook sample were 73.15% for K-1, 5.88% for K-2, 12.53% for AWL, and 8.44% for Off-List word. Second, some missing words could be seen in Negative Words of Vocabulary Profile, which showed the comparison between words in New General Service List (NGSL) and input text. These missing words may be useful for the students to comprehend the textbook. Third, token recycling index of students’ textbook showed comparison between two chapters, which was Chapter I and V also Chapter I and VI. In Chapter I and V showed 62.53% shared words, and 22.04% unshared words. However, in Chapter I and VI showed 65.29% shared words and 28.31% unshared words. It means that different chapter has different percentages of unique and shared words. By comparing these words category, lecturers can help students to comprehend the textbook easily. Keywords: Vocabulary Profiles, Learning Vocabulary, Teaching Vocabulary
INTRODUCTION
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Management regular class, not in an International class, once a year. This program has a vision to create innovative leaders who have entrepreneurial mind.
This study wanted to profile the students’ textbook used in Management and Global Business Strategy course since this course requires the students to understand the material discussion which is written in English. Theresia and Wicaksono (2015), the students who took this concentration program in the last two semesters, stated that although Indonesia was the language of instruction in the class, but the students’ textbook was written in English. In addition, Ankaa and Pardede (2015), the students who took this course in the last two semesters, stated that the lecturer asked the students to read the textbook before attending the class so that they could involve actively in the class discussion. Therefore, sufficient vocabulary knowledge is needed in order to understand the material discussion (Hansen, 2009). Thus, the lecturer needs to teach some terms used in management area to enrich the students’ words knowledge.
Since vocabulary plays an important role in reading, a research needs to be conducted. This research aimed to analyze the vocabulary of students’ textbook based on four word categories, which is K-1, K-2, AWL, and Off-List Words. Based on this purpose, the research questions for this research were:
1) What is the vocabulary profile of the students’ textbook used in Management and Global Business Strategy Course at Management Program in FEB-SWCU?
2) What is the proportion of vocabulary that is not covered in the textbook? 3) What is the token (word) recycling index of the textbooks?
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Negative Vocabulary Profile. It is useful for the lecturers to teach these missing words to increase the students’ reading skill. Third, token recycling index provides information about text comprehensibility by analyzing shared and unique / unshared words. Students can understand material easily by learning some unshared words appeared in second input text.
LITERATURE REVIEW
A. The Importance of Vocabulary
Vocabulary plays an important role in reading because it can affect students’
comprehension skill in understanding a textbook. Without sufficient vocabulary, students may face many difficulties in comprehending a reading text. Nation (2001) mentioned that vocabulary was divided into four categories. First, high frequency words that are included in general service list of English words contain 2000 words family, which is divided into two categories; the first 1000 (K-1 words) and second 1000 words (K-2 words). The example of K-1 words commonly used are agreement, agent, dealing, demand, etc. Meanwhile the example of K-2 words commonly used are advertising, attract, audience, management, etc. Second is academic word list (AWL), which is the important words that usually show up in an academic textbook, such as area s, assembling, instance, insufficient, etc. Third is technical word, which commonly appears in specific areas of the study, such as economist, organization, financial support, etc. The last is low-frequency words (Off-List Words), which are not included in the previous three categories, e.g. Johnson, eponymous, approximately, etc. Based on this explanation, it is essential for the lecturers and students to know the importance of vocabulary that is to help students to increase their comprehension skill.
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for the students to learn K-1 words. By having sufficient words knowledge, the learners can be more engaged with the topic discussion on the textbook. In addition, it can increase the students’ reading skill.
The more reading texts the learners read, the more vocabulary exposure they gain. Thus, reading can help the students to enrich their words knowledge. In reading, the students are expected to understand 95% of various kind of vocabulary because it can increase the students’ comprehension skill (McKenna, 2004; Nation, 2001; Laufer & Kalovski, 2010; Ferreir, 2007). If students are lack of words knowledge, it can lead them in many troubles, such as not being able to understand the material well, facing difficulties to engage with material discussion, etc.
Vocabulary can develop students’ communication skills in spoken language. The oral production of the target language depends on the input received by the learners (Clapsaddle, 2009). Without sufficient grammar, the students are still able to communicate well with others (Wagner, 2007). However, the learners will suffer to express their feeling and share their ideas or even exchange information with others when they do not have sufficient vocabulary knowledge (Ferreira, 2007). Thus, every single person should learn vocabulary in order to increase his or her communication skills. It is like a foundation to build the students’
skills. They need to know its forms and meanings (Nash & Snowling, 2006). As a result, the learners will be able to make a phrase or a sentence.
5 B. Strategy in Learning Vocabulary
In learning vocabulary, students are responsible for the success of their own learning. The students’ vocabulary coverage also becomes one of the factors that can determine their success. The more vocabulary they gain the better (Pikulski & Templeton, 2004; Lauferr & Kalovski, 2010). While some learners may learn the target language easily, some may face difficulty in the learning process. Schmitt (1997), cited in Asgari and Mustapha (2011), suggests five kinds of vocabulary learning strategies, which are determination, social, memory, cognitive, and metacognitive strategies.
The first strategy proposed is determination strategies in which the learners are learning vocabulary individually. According to Schmitt (1997, cited in Asgari & Mustapha, 2011), determination strategies are usually happened when students find a difficult vocabulary in reading passage. These strategies are usually done by guessing, using other reference works, or identifying words forms. In guessing, students can apply it by using their structural language knowledge, Greek language or original language, and context of the word in a sentence.
The second way in finding a new meaning is by using social strategy. Schmitt (1993, cited in Sener, 2015) defined social strategies as learning new words from other people by asking its meaning. Commonly teacher is the one who the students rely on. Then, the teacher will automatically answer his or her students by using grammar translation method, synonym, defining the word in phrase, applying the word in a sentence, etc. Besides, the learners also can ask their classmates or even family members to find out the word meaning.
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to link an unknown word via mental processing which was by connecting their background knowledge to the new word. According to Schmitt (1993, cited in Sener, 2015), this strategy is usually done in three sub-strategies, which are the use of visual aids, the use of applied words into context (related words, unrelated words, paraphrase, etc), and the use of structural analysis.
The thirds strategy is cognitive strategies. Schmitt (1997, cited in Asgari & Mustapha, 2011) stated that cognitive strategies was the same strategies applied in memory strategies, but it was more in a mechanical meanings and not related to mental processing. The most commonly used strategy in this sub-category is repetition in both written and spoken form. Other strategy used is word lists, flash cards, note taking, etc.
The last one is metacognitive strategies. According to Schmitt (1993, cited in Sener, 2015), the definition of metacognitive strategies is “a conscious overview of the learning process and making decisions about planning, monitoring, or evaluating the best ways to study”. It means that learner will be given opportunities to receive an input, produce a product, and review whether the output is good or not. The learners should study the most useful words that can advantage them in the learning process.
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vocabulary coverage. It depends on the students’ words knowledge, which means that a different student will use a different strategy in learning vocabulary.
C. Strategy in Teaching Vocabulary
A teacher’s role in teaching vocabulary is to facilitate the learners in classroom discussion through tasks in order to increase the students’ words knowledge. Rather than using intuition, it is better for the teacher to look at the vocabulary lists in the corpus (McCrostie, 2007). Pikulski and Templeton (2004) stated that paying attention to the students’ goal and vocabulary background knowledge is necessary to develop their skills. The teacher usually tends to teach vocabulary directly in classroom activities. However, the practice is not the same as its theory. It probably will take longer time than the teacher is expected, which means the teacher will not have a sufficient time to teach only about vocabulary and its meaning in classroom discussion. Actually, the teacher needs to provide opportunities for students on how to learn vocabulary independently. Thus, some strategies should be taught in the classroom discussion, such as teaching vocabulary through reading, and teaching how to use dictionaries (Nation, 2001; Webb & Chang, 2015).
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the students to have a monolingual dictionary in order to know how exactly a word is being used in a context. This strategy is aimed to encourage the learners to have wider vocabulary knowledge and increase the students’ interest in learning a new word in a particular sentence.
Reading can help the students to increase their vocabulary knowledge, therefore the teacher needs to teach vocabulary through reading. Pikulski and Templeton (2004) stated that teaching vocabulary through reading was more effective rather than asking the students to memorize the written words list. That is because the students will automatically memorize the words while reading the textbook. As the result, the students can easily follow the discussion and automatically learn new unknown words.
In short, the teacher should teach some strategies used in learning vocabulary to the students so that they can be independent learners. Reading is a good strategy to increase the students’ words knowledge because they will automatically learn new words. Besides, the teachers need to motivate the students by saying that “words are interesting and fun” so that they can easily follow the material discussion (Pikulski & Templeton, 2004). In reading, the students are expected to know 95% vocabulary in the textbook so that they can comprehend the textbook easily. Therefore, encouraging the students is necessary for the teacher to give them opportunity in enriching their words knowledge.
D. Previous Study
Some researchers have conducted studies about vocabulary profiler at Satya Wacana Christian University (SWCU). First was a research done by Trifosa (2014) that was conducted in English Teacher Education Program by profiling a students’ textbook, which is
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Book 1 and 2 achieve 81.46% and 83.93 %. Second, K-2 words in IC Book 1 and 2 had less percentage than K-1 words, which are 7.54 % and 7.12 %. Third, as much as 2.1 % and 1.72% came under AWL words in IC Book 1 and 2. The rest 8.94% and 7.11% came under Off-List Words. The results indicated that those books should be easy for the learners since the AWL word frequency was not very high.
In addition, Ardyny (2014) conducted the same research in Satya Wacana Christian University. A textbook entitled Introduction to Language Education is the sample of the research. The instrument is called The Compleat Lexical Tutor, v.4 that can be accessed on www.lextutor.ca. The finding shows three results. First, the total frequency of K-1, K-2, and AWLwords achieves 80.81%, which indicates that the book is understandable while the rest of the textbooks need more effort to comprehend. Second, the easiest chapter is found in Chapter 1 since it has low AWL word. Then, the hardest Chapter is located on Chapter 5, which contains low frequency of K-1 words, which is belong to the first 1000 words that mostly appeared in the textbook. Finally, she suggests that in creating a students’ course book, the lectures should consider the students’ vocabulary background knowledge.
The studies about vocabulary profiler have been conducted several times in some faculties at Satya Wacana Christian University. However, the same research has never been conducted yet in Faculty of Economics and Business at Satya Wacana Christian University. Therefore, the goal of this study was to analyze the vocabulary profile in students’ textbook in Management and Global Business Strategy Course at Management Program in Faculty of Economics and Business at Satya Wacana Christian University.
THE STUDY
A. Method of Research
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Lambert (2012), descriptive method can be done to analyze a particular case, person, or place. They stated that the data are analyzed descriptively to answer the research question. Based on these explanations, this study described the vocabulary profiler of economic students’ textbook in Management and Global Business Strategy Course at Management Program, Faculty of Economics and Business, Satya Wacana Christian University. The description included four categories of words, which are K-1, K-2, AWL, and Off-Lists Words (OLW) of students’ textbook entitled International Business: Environment and Operations Fifteenth Edition published by Pearson Education Limited 2015.
B. Sample of the Study
A students’ textbook entitled International Business: Environment and Operations Fifteenth Edition was used as the sample in this research, which consists of six chapters. The total number of pages were 877. The book discusses many theories related to national and international business that provides some cases to be solved, such as national environment differences, connecting countries through trade, etc. It also discusses about some policies and terms in doing a business, that is being placed in all over the worlds, such as global markets, labor demographics, etc.
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The selection of the textbook was based on the following criteria. First, the book is for university students who are expected to comprehend academic words. These words are rarely used in daily life so that it is essential to be learnt by the learners who study English for Academic Purposes. Second, the book is used in Management and Global Business Strategy Course at Management Program, which has a concentration program called an International Business in regular class. The communication between students-students and students-teacher uses Indonesia most of the time. However, the students’ textbook is written in English also the students are required to work using English for the final project, and presentation. However, it may affect the students’ performance in delivering the material discussion also understanding some specific terms used in economic and business area, especially in management, since they use Indonesia as the language of instruction.
C. Research Instrument
The instrument of the research was the Vocabulary Profiler that could be accessed at www.lextutor.ca. This program can be used to profile the vocabulary into four words, which are K-1, K-2, AWL, and Off-Lists Word. Besides, it also shows each percentage of word categories, which can give us a clear picture about the texts being analyzed. In addition, it also can categorize the words based on its appeared frequency, such as competitive shows 24 times in the input text so that it comes under first rank.
D. Data Collection Procedures
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should be erased. Second, although the tool could analyze proper name (e.g. a name of person, brand, city, company, place, etc), it would not be necessary for students learn these words since it is easy to understand them without consulting the dictionary.
E. Data Analysis Procedures
After collecting data, the texts needed to be copied in the vocabulary profiler. Next was analyzing the data from the vocabulary profiler website. The first step opened lextutor.ca website and file named All Chapter. Then, clicked Vocabprofile. After that, clicked VP-Compleat and Compleat Web VP window showed up. Next, selected classic (GWS/AWL) option so that the program could analyze the input text into K-1, K-2, AWL, and Off-Lists Word (OLW). Then, copied the text and clicked submit window. Next, saved the result table under name All Chapter Percentage.
In order to find out the negative vocabulary profile of each words category, moved the cursor down of the web page. Then, clicked VP-negative-classic-1 and Negative VP for K-1 window would appear. After that, clicked K-1 hea d-words not found to find out some words that were not included in the input text. Next, copied the result to Microsoft Office Words under file name Negative VP K-1. Then, closed the window. After that, the same steps were applied to find out the missing words of K-2 and AWL.
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website in new tab at the same search engine and clicked frequency. Next, selected Englishin the Frequency Home Page Menu. After that, copied the OWL words from previous tab to the Web Frequency Indexer window and clicked submit window. Then, copied the result in Microsoft Office Words under file name block frequency VP OWL.
Token recycling index of the textbook could be analyzed by the tool to find out the shared and unshared / unique words in the input text. First step went to lextutor.ca home page. Then, clicked Text Lex Compare and copied Chapter I and V into different column provided in the program. Gave name on each column, which were Chapter I and Chapter V. Selected families in format menu and clicked submit window. The program would automatically analyze these two chapters by comparing the words appeared in the first and second text. Then, copied the result to Microsoft Office Words under name Token Recycling Index Ch.1 and Ch.V. The same steps were applied to find out the token recycling index of Chapter I and VI.
FINDING AND DISCUSSION
This section presents the answer of the three research questions in three parts. First, the overall vocabulary profile found in the textbook will be discussed in the first part. Second, the negative words of K-1, K-2, K-3, and OWL are discussed in the second part. It presents the missing words in the input text also block frequency of OWL. Third, comparisons of the six Chapters as a foundation to analyze the token recycling index between two Chapters are presented in the last part. Besides, it also discusses shared and unique words in both texts.
A. Overall Vocabulary Profile
Table 1. Overall Vocabulary Profile Freq. Level Families
(%)
Types (%)
Tokens (%)
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K1 Words 747
(49.93%)
1640 (38.79%)
18750
(73.15%) 73.15%
K2 Words 318
(21.26%)
503 (11.90%)
1508
(5.88%) 79.03%
AWL Words 431 (28.81%)
899 (21.26%)
3213
(12.53%) 91.56% Off-list Words ? 1186
(28.05%)
2163
(8.44%) 100.00%
Total 1496+? 4228
(100%)
25634
(100%) -
There are three terms used in Vocabulary Profiler that shows in the Table 1. First is family, which is headword that appears in a textbook. For instance, play is the headword of the word playing, and played. The second term is called type, which shows different words in the textbook, such as account, country, chain, etc. While play, played, and playing are considered as same type. The last one is token, which is total number of the words in the textbook. For instance, if the text has [2] play, [3] country, [6] account, there are 11 token.
The cumulative percentage of K-1, K-2, and AWL words is under 95%. It can be seen in Table 1 that K-1 percentage is 73.15%, with additional 5.88% of K-2 and 12.53% of AWL, the cumulative percentage becomes 91.56%. Nation (2001) states that for comprehension, at least 95 % of a textbook should be understood by students. However, the cumulative percentage of the textbook of K-1, K-2, and AWL words is only 91.56%, which means that students need more effort to comprehend the textbook. Besides, as much as 8.44% falls under OLW that can be seen in Table 1. These words need to be learned by students since it may contain some useful words that can help them to comprehend the textbook.
B. Negative Vocabulary Profiles of Textbook
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list that was found by some experts. The used of NGSL is to make a clear picture about the words appeared in the textbook for the researchers and lecturers. Therefore, it is necessary for lecturers to know these words because it may contain some useful words for the students to enrich the students’ vocabulary knowledge, especially in academic words. This section is
divided into four parts, which are Negative Vocabulary Profiles of K-1, K-2, AWL and block frequency of OLW.
1. Negative Vocabulary Profiles of K-1 Words
This part of analysis reveals all the headwords or word families that belong to K-1 Words, which were not found in the textbook. Below is the summary of K-1 Words Negative Vocabulary Profiles. The percentage bellow refers to number of families (headwords) in the text.
K-1 Total word families : 964
K-1 families in input : 739 (76.66%) K-1 families not in input : 226 (23.44%)
Summary above shows that as much as 76.66% of K-1 word families were found in the input text, while the rest 23.44% were missing. This comparison indicates that the book is easy to comprehend by students. Below are some headwords that cannot be found in the textbook. The complete version of Negative K-1 Words is listed in Appendix A.
ACTOR APPOINT BATTLE BLOOD BRANCH
ACTRESS ARMY BED BREAD BLOW
ADVENTURE ARRIVE BELONG BLUE BRIGHT
AFFAIR ARTICLE BENEATH BOAT BROTHER
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K-2 Total word families : 986
K-2 families in input : 317 (32.15%) K-2 families not in input : 670 (67.95%)
The second part of this section presents Negative Vocabulary Profiles of K-2 Wordin which summary can be seen above. The total number of K-2 Words found by the experts was 986 word families. However, the result of the total input in the textbook only shows 317 headwords where the rest of 670 word families were missing. It indicates that the students need more effort to comprehend the textbook since the percentage of K-2 words is below one-two percent of the total words family. Below are some examples of Negative K-2 Words that may useful for the learner to increase their skills in reading the textbook. The complete list has been put in Appendix B.
ABSENCE ABSOLUTELY ACCIDENT ACCUSE
DECEIVE DEED DEER DELAY
LODGING LOG LONE LOUD
SCOLD SCORN SCRAPE SCRATCH 3. Negative Vocabulary Profiles of AWL
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AWL Total word families : 569
AWL families in input : 429 (75.40%) AWL families not in input : 141 (24.78%)
Here are some examples of AWL Negative Profiles. The complete version of those words can be seen in Appendix C.
ABSTRACT ACADEMY ACCOMPANY ACKNOWLEDGE ADJACENT
DENOTE DENY DEPRESS DETECT DEVICE
INTERMEDIATE INTERVAL INVESTIGATE INVOKE
JOURNAL
RELEASE RELUCTANCE RESTORE RESTRAIN REVEAL 4. Block Frequency of Off-List Words (OLW)
The following discussion will present OLW. These words are not included in three categories of word, which is in K-1, K-2, and AWL. However, students may need these words to increase their vocabulary knowledge and comprehension skill. Therefore, it is necessary for lecturers to select the words based on students’ need. Although the program does not provide Negative Vocabulary Profile of OLW yet, but it can analyze OLW block frequency words.
Off-List Words can be useful for the students to increase their reading skill. In the lextutor.ca web, there is frequency option in the menu. It can group the words every ten words that have been arranged based on its frequency from high to low. Therefore it is called block frequency words. Based on these information, the lecturers can select some words based on students’ need. Below are some examples of OLW from the textbook that has 2163 tokens and 1185 types.
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frequency of the words occurrences. Thirds, COVERAGE means percentage of words appeared in the textbook in both individual and cumulative. Next, WORD indicates vocabulary items seen in the textbook. E.g. the word competitive appeared 24 times in input text. Comparing to other word frequency, competitive achieved the highest percentage on block frequency words list. Therefore, the rank of competitive in OLW block frequency words list is one, which can be seen in Table 2 below. Appendix D shows the complete version of block frequency of Off-List Words.
Table 2. Block Frequency of Off-List Words
RANK FREQ COVERAGE
individ cumulative WORD 1. 24 1.11% 1.11% COMPETITIVE 2. 21 0.97% 2.08% WORLDWIDE 3. 19 0.88% 2.96% COMPETITORS 4. 18 0.83% 3.79% MONETARY 5. 17 0.79% 4.58% AIRLINES 6. 17 0.79% 5.37% SANCTIONS 7. 16 0.74% 6.11% GOODS 8. 16 0.74% 6.85% YUAN
9. 14 0.65% 7.50% DEMOCRACY 10. 14 0.65% 8.15% ELECTRONIC
C. Comparison of Vocabulary Profile across Chapters
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The textbook discusses how to be a good manager and how to manage business based on its environment in depth. It also discusses many theories related to national and international business that provides some cases to be solved, such as national environment differences, connecting countries through trade, business management across countries, etc. Therefore, AWL words appeared in the textbook was high enough.
Table 3. Comparison of Vocabulary Profile across Chapters
Chapters K1 (%) K2 (%) AWL (%) Off-Lists Words (%) I
Cumulative (%)
74.49 74.49
7.12 81.61
9.24 90.85
9.16 100.00 II
Cumulative (%)
71.74 71.74
6.32 78.06
11.83 89.89
10.10 100.00 III
Cumulative (%)
77.72 77.72
3.82 81.54
12.73 94.27
5.73 100.00 IV
Cumulative (%)
78.24 78.24
4.37 82.61
10.18 92.79
7.21 100.00 V
Cumulative (%)
70.82 70.82
6.87 77.69
13.28 90.97
9.03 100.00 VI
Cumulative (%)
73.51 73.51
5.63 79.14
12.96 92.10
7.90 100.00
Table 3 shows cumulative percentage of K-1, K-2, and AWL words on each Chapter below 95%, which is 91.81%, which is calculation of K-1, K-2, and AWL cumulative percentage on each Chapter dividing by 6. It indicates that students need more effort to comprehend the textbook since it has low cumulative percentage of K-1, K-2, and AWL. Besides, because of the limitation on the study, the researcher suggests lecturers to find out some useful words that may help students to comprehend the textbook easily.
1. Comparison between Chapter I and V
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comparing input text of Chapter I and Chapter V. Summary of the token recycling index of Chapter I and V can be seen below.
TOKEN Recycling Index: (4946 repeated tokens : 7910 tokens in new text) = 62.53%
FAMILIES Recycling Index: (328 repeated families : 1488 families in new text) = 22.04%
The result showed that as much as 62.53% of the tokens were found in the shared words in which the text has some same words. While the rest 37.47% (100% - 62.53%) belonged to new or unique words in chapter five. The unique words are divided into two categories, which are a unique to first in which words only appear in the first text, and unique to second in which the newest words are shown in second text only. For teaching purposes, it is necessary for lecturers to give close attention to unique or unshared words shown in the next chapter. It may help the students to develop their skill in reading.
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Table 4. Unique to First, Shared, unique to Second of the Input Text Unique to first
159 tokens 111 families
001. olympic 6 002. cup 5
003. professional 5 004. tournament 5 005. league 4
Shared 4946 tokens 328 families
001. the 401 002. and 257 003. of 245 004. to 220 005. in 194
Unique to second 2964 tokens 1160 families
Freq first (then alpha) 001. export 32 002. trade 29 003. divide 27 004. airline 18 005. grow 17
VP novel items Same list Alpha first 001. able 1 002. above 2 003. abroad 3 004. abrupt 1 005. absent 1
2. Comparison between Chapter I and VI
Another interesting result shows in Table 3 is the AWL comparison between Chapter I and VI, which are 9.24% : 12.96%. The clear picture about the token recycling index of these two Chapters can be seen in summary bellow. As much as 65.29% of tokens falls under shared words in input text, while 34.71 tokens (100 % - 65.29%) falls under new or unique words. It means that some additional words appeared in second input text can be used to make a clear picture on some new words appeared on a new topic discussion.
TOKEN Recycling Index: (3312 repeated tokens : 5073 tokens in new text) = 65.29%
FAMILIES Recycling Index: (291 repeated families : 1028 families in new text) = 28.31%
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Table 5. Unique to First, Shared, unique to Second of the Input Text Unique to first
224 tokens 148 families
001. play 14 002. fan 7 003. olympic 6 004. cup 5 005. event 5
Shared 3312 tokens 291 families
001. the 264 002. and 191 003. in 174 004. of 157 005. to 156
Unique to second 1761 tokens 737 families
Freq first (then alpha) 001. he 22
002. manufacture 21 003. work 20
004. need 15 005. state 14
VP novel items Same list Alpha first 001. abandon 1 002. able 6 003. abroad 6 004. absolute 2 005. accept 3
CONCLUSION
The purpose of the study was to find out the vocabulary profiles, some missing words, and token recycling index of students’ textbook in Management and Global Business Strategy
Course at Management Program in Faculty of Economics and Business, Satya Wacana Christian University, Salatiga. Therefore, the study had three research questions. They were “What is the vocabulary profile of the students’ textbook in Management and Global
Business Strategy Course at Management Program in Faculty of Economics and Business, Satya Wacana Christian University?”, “What is the proportion of vocabulary that is not
covered in the textbook?”, and “What is the token (word) recycling index of the textbook?”.
23
Vocabulary Profile. These words are necessary to be taught since it can help students to increase their skills in using English. The other finding shows that block frequencyof OLW can also be useful in teaching-learning vocabulary. It may contain some useful words that students’ need to comprehend the textbook. It also can help students to increase their words
knowledge. However, due to the limitation of the study, lecturers are suggested to review this category of words and select the words based on students’ need. The next result shows that
the comparison between Chapter I and V has 62.53% shared words and 37.47% unshared words. On the other hand, the comparison between Chapter I and VI shows 65.29% shared words and 34.71% unshared words in the input texts. These comparisons can give a clear picture on some new words appeared in a new chapter. It is useful for the lecturers to teach vocabulary based on students’ need.
24
REFERENCES
Ardyny, A. D. G. K. (2014). Vocabulary Profile of Introduction to Language Education Textbook. Retrieved February 14, 2016, from http://repository.uksw.edu/bitstream/123456789/5478/3/T1_112010082_Full%20text.pdf
Asgari , A., & Mustapha, G. B. (2011). The Type of Vocabulary Strategies Used by ESL Students in University Putra Malaysia. English Language Teaching, 4 (2), 84-90.
Cahyono, B. Y., & Widiati. U. (2008). The Teaching of EFL Vocabulary in the Indonesian Context: The State of The Art. TEFLIN Journal, 19 (1), 1-17.
Clapsaddle, K. (2009). Literacy for Students with Significant Disabilities. Retrieved February 14, 2016, from http://www4.esc13.net/uploads/seln/docs/09_10materials/Literacy.pdf. Ferreira, L. H. F. (2007). How to Teach Vocabulary Effectively: An Analysis of the Course
Book Eyes and Spies. Retrieved November 7, 2015, from http://www.portaldoconhecimento.gov.cv/bitstream/10961/2431/1/lastversion.pdf.
Hansen, K.M. (2009). Vocabulary Instruction, Reading Comprehension, and Students Retention: A Review of Literature. Retrieved November 1, 2015, from
https://www.nmu.edu/sites/DrupalEducation/files/UserFiles/Files/Pre-Drupal/SiteSections/Students/GradPapers/Projects/Hansen_Kristina_MP.pdf.
Kusumarasdyati. (2006). Verbal Report as a Method to Elicit Lexical Processing Strategies. English Department, Faculty of Letters, Putra Christian University, 8 (2), 101-103. Lambert, V.A. & Lambert, C.E. (2012). Qualitative Descriptive Research : An Acceptable
Design. Pacific Rim International Journal of Nursing Research, 16 (4), 255-256.
Laufer, B., & Kalovski, G. C. (2010). Lexical Threshold Revisited: Lexical Text Coverage, Learners’ Vocabular Size and Reading Comprehension. Reading in a Foreign Language, 22 (1), 15-30.
McCrostie, J. (2007). Investigating the Accuracy of Teachers’ Word Frequency. Regional Language Centre Journal, 38 (1), 53-66.
McKenna, M.C. (2004). Teaching Vocabulary to Struggling Older Readers. Perspectives, 30 (1), 13-16.
Nash, H., & Snowling, M. (2006). Teaching New Words to Children with Poor Existing Vocabulary Knowledge: A Controlled Evaluation of the Definition and Context Methods. International Journal of Language & Communication Disorders, 41, 335-354. Nation, I. S. P. (2001). Learning vocabulary in another language. New York: Cambridge
University Press.
25
Nation, I. S. P. (2008). Teaching Vocabulary Strategies and Techniques. Boston : Heinle Cengange Learning.
Pikulski, J. J., & Templeton, S. (2004). Teaching and Developing Vocabulary: Key to Long Term Reading Success. Retrieved February 10, 2016, from https://www.eduplace.com/marketing/nc/pdf/author_pages.pdf.
Sener, S. (2015). Vocabulary Learning Strategy Preferences and Vocabulary Size of Pre-service English Teachers. iJERs, 6 (3), 15-33.
Trifosa, O. (2014). Vocabulary Profile of IC (Integrated Course) Books Used in English Teacher Education Program of Satya Wacana Christian University. Retrieved February
10, 2016, from
http://repository.uksw.edu/bitstream/123456789/5433/3/T1_112010014_Full%20text. pdf
Vaughan, L. (2003). Statistical Methods for the Information Professional: A Practical, Painless Approach to Understanding, Using, and Interpreting Statistic. Medford, New Jersey: Information Today, Inc.
Wagner, R. K., Mue, A. E., & Tannenbaum, K. R. (Eds). (2007). Vocabulary Acquisition: Implication for Reading Comprehension. New York: The Guilford Press.
26
ACKNOWLEDGEMENTS
27
APPENDICES
APPENDIX A
Negative Vocabulary Profile of K-1 Words
29 APPENDIX B
34 CURSE
CURTAIN CURVE CUSHION DAMP DANCE DARE DEAF DECAY DECEIVE
LEAN LEG LID LIMB LIQUID LITRE LOAF LOCK LODGING LOG
RUST SACRED SAD SADDLE SAKE SAUCE SAUCER SAWS SCENT SCISSORS
35 APPENDIX C
Negative Vocabulary Profile of AWL Words
36 CONCLUDE
CONFER CONFINE CONSTRUCT CONTRADICT CONTRARY
INFER INHIBIT INJURE INSERT INSTRUCT INTEGRAL
PRACTITIONER PRECEDE PRECISE
PRELIMINARY PRINCIPAL PROTOCOL
37 APPENDIX D
Block Frequency of Off-List Words
RANK FREQ COVERAGE
individ cumulative WORD
1. 24 1.11% 1.11% COMPETITIVE 2. 21 0.97% 2.08% WORLDWIDE 3. 19 0.88% 2.96% COMPETITORS 4. 18 0.83% 3.79% MONETARY 5. 17 0.79% 4.58% AIRLINES 6. 17 0.79% 5.37% SANCTIONS 7. 16 0.74% 6.11% GOODS 8. 16 0.74% 6.85% YUAN
9. 14 0.65% 7.50% DEMOCRACY 10. 14 0.65% 8.15% ELECTRONIC 11. 14 0.65% 8.80% YEN
12. 12 0.55% 9.35% IMPORT
13. 12 0.55% 9.90% INTELLECTUAL 14. 11 0.51% 10.41% CRISIS
15. 10 0.46% 10.87% VERSUS 16. 9 0.42% 11.29% CHOCOLATE 17. 9 0.42% 11.71% IMPORTS 18. 9 0.42% 12.13% OBJECTIVES
19. 9 0.42% 12.55% STANDARDIZATION 20. 8 0.37% 12.92% BARRIERS
21. 8 0.37% 13.29% COLLABORATION 22. 8 0.37% 13.66% COMPETENCE 23. 8 0.37% 14.03% COMPETENCIES 24. 8 0.37% 14.40% DEMOCRATIC 25. 8 0.37% 14.77% MOBILITY 26. 8 0.37% 15.14% NON
38 28. 7 0.32% 15.83% BRAND
29. 7 0.32% 16.15% CONVERGENCE 30. 7 0.32% 16.47% EURO
31. 7 0.32% 16.79% EXPATRIATE 32. 7 0.32% 17.11% FAVORABLE 33. 7 0.32% 17.43% LOGISTICS 34. 7 0.32% 17.75% NICHE 35. 7 0.32% 18.07% SEGMENT 36. 6 0.28% 18.35% CANDIDATES 37. 6 0.28% 18.63% EFFICIENCIES 38. 6 0.28% 18.91% EXECUTIVE 39. 6 0.28% 19.19% EXECUTIVES 40. 6 0.28% 19.47% HEADQUARTERS 41. 6 0.28% 19.75% IMPORTING 42. 6 0.28% 20.03% OLYMPICS 43. 6 0.28% 20.31% ONLINE 44. 6 0.28% 20.59% RETAIL
45. 6 0.28% 20.87% STANDARDIZED 46. 6 0.28% 21.15% TRANSACTIONS 47. 6 0.28% 21.43% VERTICAL 48. 6 0.28% 21.71% WEB
49. 5 0.23% 21.94% ADAPTIVENESS 50. 5 0.23% 22.17% AIRCRAFT 51. 5 0.23% 22.40% BRANDS 52. 5 0.23% 22.63% CAPITA
39 60. 5 0.23% 24.47% LUXURY 61. 5 0.23% 24.70% MOBILE
62. 5 0.23% 24.93% MODERNIZATION 63. 5 0.23% 25.16% OPTIMISTIC
64. 5 0.23% 25.39% ORGANIZATIONAL 65. 5 0.23% 25.62% POLYSILICON 66. 5 0.23% 25.85% PORTFOLIO 67. 5 0.23% 26.08% PROFITABILITY 68. 5 0.23% 26.31% QUEST
69. 5 0.23% 26.54% RECALL
70. 5 0.23% 26.77% RECONCILIATION 71. 5 0.23% 27.00% RECYCLING 72. 5 0.23% 27.23% TALENT
73. 5 0.23% 27.46% TELECOMMUNICATIONS 74. 4 0.18% 27.64% ABUNDANT
75. 4 0.18% 27.82% ANNOUNCED 76. 4 0.18% 28.00% ASSETS
77. 4 0.18% 28.18% AUTHORITARIAN 78. 4 0.18% 28.36% AUTOCRATIC 79. 4 0.18% 28.54% CAMPAIGNS 80. 4 0.18% 28.72% CAREER
81. 4 0.18% 28.90% COLLABORATIVE 82. 4 0.18% 29.08% CONFIGURATION 83. 4 0.18% 29.26% CONFIGURING 84. 4 0.18% 29.44% CONTINENT 85. 4 0.18% 29.62% CONTINENTAL 86. 4 0.18% 29.80% COSMETICS 87. 4 0.18% 29.98% COUNTERPARTS 88. 4 0.18% 30.16% COUNTERPOINT 89. 4 0.18% 30.34% ECOMAGINATION 90. 4 0.18% 30.52% GROSS
40 92. 4 0.18% 30.88% INFLATION 93. 4 0.18% 31.06% INTERNET 94. 4 0.18% 31.24% ISLAM 95. 4 0.18% 31.42% LEAGUE 96. 4 0.18% 31.60% LIABILITY 97. 4 0.18% 31.78% LUCRATIVE 98. 4 0.18% 31.96% LUGGAGE
99. 4 0.18% 32.14% MULTIDOMESTIC 100. 4 0.18% 32.32% OBSTACLES 101. 4 0.18% 32.50% OUTRIGHT 102. 4 0.18% 32.68% PHOTO 103. 4 0.18% 32.86% SENIOR 104. 4 0.18% 33.04% SOCCER 105. 4 0.18% 33.22% SOLAR 106. 4 0.18% 33.40% SOVEREIGN 107. 4 0.18% 33.58% SUBSIDIARIES 108. 4 0.18% 33.76% TENNIS
109. 4 0.18% 33.94% TOURNAMENTS 110. 4 0.18% 34.12% TRANSPARENCY 111. 4 0.18% 34.30% VENTURE
112. 4 0.18% 34.48% ZONE 113. 3 0.14% 34.62% ADS
114. 3 0.14% 34.76% AGGRESSIVE 115. 3 0.14% 34.90% AIRLINE 116. 3 0.14% 35.04% AIRPORT 117. 3 0.14% 35.18% ALLIANCES 118. 3 0.14% 35.32% APPEAL 119. 3 0.14% 35.46% ASSET
120. 3 0.14% 35.60% AUTOMOBILES 121. 3 0.14% 35.74% BEEF
41 124. 3 0.14% 36.16% BROKER 125. 3 0.14% 36.30% BURGERS 126. 3 0.14% 36.44% CARRIERS 127. 3 0.14% 36.58% CASH
128. 3 0.14% 36.72% COLLECTIVIST 129. 3 0.14% 36.86% COMPACT 130. 3 0.14% 37.00% COMPELLING 131. 3 0.14% 37.14% COMPETITIVENESS 132. 3 0.14% 37.28% COMPLIANCE 133. 3 0.14% 37.42% COMPLY
134. 3 0.14% 37.56% CONSERVATIVE 135. 3 0.14% 37.70% CONSOLIDATED 136. 3 0.14% 37.84% CONTEND
137. 3 0.14% 37.98% COUNTER 138. 3 0.14% 38.12% CREDIBILITY 139. 3 0.14% 38.26% DEFICIT
140. 3 0.14% 38.40% DEMOCRACIES 141. 3 0.14% 38.54% DISPUTE
142. 3 0.14% 38.68% ENGAGEMENT 143. 3 0.14% 38.82% EXECUTING 144. 3 0.14% 38.96% EXIT
145. 3 0.14% 39.10% EXPATRIATES 146. 3 0.14% 39.24% FORECASTING 147. 3 0.14% 39.38% FORECASTS 148. 3 0.14% 39.52% FRANC
149. 3 0.14% 39.66% FRANCHISEE 150. 3 0.14% 39.80% HAMBURGERS 151. 3 0.14% 39.94% HAZARDOUS 152. 3 0.14% 40.08% HEDGE 153. 3 0.14% 40.22% HEDGES
42 156. 3 0.14% 40.64% IMPORTER
157. 3 0.14% 40.78% INDIVIDUALIZED 158. 3 0.14% 40.92% INTANGIBLE 159. 3 0.14% 41.06% JAPANESE 160. 3 0.14% 41.20% JURISDICTION 161. 3 0.14% 41.34% LAUNCHED 162. 3 0.14% 41.48% LEVERAGE 163. 3 0.14% 41.62% LITERACY 164. 3 0.14% 41.76% MENU
165. 3 0.14% 41.90% MULTIPARTY 166. 3 0.14% 42.04% OPPOSITION 167. 3 0.14% 42.18% OUTLETS 168. 3 0.14% 42.32% PARITY 169. 3 0.14% 42.46% PEG 170. 3 0.14% 42.60% PITFALLS 171. 3 0.14% 42.74% PROSPERITY 172. 3 0.14% 42.88% RETAILING 173. 3 0.14% 43.02% REUSE
174. 3 0.14% 43.16% SAFEGUARD 175. 3 0.14% 43.30% SEGMENTS 176. 3 0.14% 43.44% SHORTAGES
177. 3 0.14% 43.58% SIMULTANEOUSLY 178. 3 0.14% 43.72% SPURRED
43 188. 3 0.14% 45.12% WEBSITES 189. 3 0.14% 45.26% WHOLESALE 190. 3 0.14% 45.40% WORKPLACE 191. 2 0.09% 45.49% ACTUALIZATION 192. 2 0.09% 45.58% ADHERENCE 193. 2 0.09% 45.67% ADVERSE 194. 2 0.09% 45.76% ALLIANCE 195. 2 0.09% 45.85% ARGUABLY 196. 2 0.09% 45.94% ASSERT 197. 2 0.09% 46.03% ATHLETES
198. 2 0.09% 46.12% AUTHORITARIANISM 199. 2 0.09% 46.21% AUTOMOBILE
200. 2 0.09% 46.30% BAN 201. 2 0.09% 46.39% BANANAS 202. 2 0.09% 46.48% BASEBALL 203. 2 0.09% 46.57% BATTERY 204. 2 0.09% 46.66% BOOSTS 205. 2 0.09% 46.75% BRAINPOWER 206. 2 0.09% 46.84% BROADEN 207. 2 0.09% 46.93% BROKING 208. 2 0.09% 47.02% CAMPAIGN 209. 2 0.09% 47.11% CANDIDATE 210. 2 0.09% 47.20% CENTRALIZED 211. 2 0.09% 47.29% CLIENTS 212. 2 0.09% 47.38% CLOUT 213. 2 0.09% 47.47% COMBAT
214. 2 0.09% 47.56% COMPARABILITY 215. 2 0.09% 47.65% COMPETITOR 216. 2 0.09% 47.74% CONFIGURATIONS 217. 2 0.09% 47.83% COPYRIGHT
44 220. 2 0.09% 48.10% DAUNTING 221. 2 0.09% 48.19% DE
222. 2 0.09% 48.28% DELIST 223. 2 0.09% 48.37% DELISTING
224. 2 0.09% 48.46% DEMOCRATIZATION 225. 2 0.09% 48.55% DEMOGRAPHICS 226. 2 0.09% 48.64% DENOMINATED 227. 2 0.09% 48.73% DEPENDENCE 228. 2 0.09% 48.82% DEPOSIT 229. 2 0.09% 48.91% DIGITAL
230. 2 0.09% 49.00% DISCREPANCIES 231. 2 0.09% 49.09% DIVIDENDS 232. 2 0.09% 49.18% DIVISIONAL 233. 2 0.09% 49.27% EMBARGO 234. 2 0.09% 49.36% EMBARGOES 235. 2 0.09% 49.45% EMISSIONS 236. 2 0.09% 49.54% EMOTIONAL 237. 2 0.09% 49.63% EN
238. 2 0.09% 49.72% ENDORSE 239. 2 0.09% 49.81% ENDURING 240. 2 0.09% 49.90% ENGAGE 241. 2 0.09% 49.99% ENGAGING 242. 2 0.09% 50.08% ENTERPRISES 243. 2 0.09% 50.17% ENTREPRENEUR 244. 2 0.09% 50.26% ESCALATING 245. 2 0.09% 50.35% EXECUTED 246. 2 0.09% 50.44% EXECUTION 247. 2 0.09% 50.53% EXPATS
248. 2 0.09% 50.62% EXPENDITURES 249. 2 0.09% 50.71% FISCAL
45 252. 2 0.09% 50.98% FRAUGHT
253. 2 0.09% 51.07% GEOGRAPHICALLY 254. 2 0.09% 51.16% GEOGRAPHY
255. 2 0.09% 51.25% GLOBALIZED 256. 2 0.09% 51.34% GOVERNANCE 257. 2 0.09% 51.43% GRADUATES 258. 2 0.09% 51.52% HAPPYNOMICS 259. 2 0.09% 51.61% HARMONIZATION 260. 2 0.09% 51.70% HOMOGENEOUS 261. 2 0.09% 51.79% HOPEFULLY 262. 2 0.09% 51.88% HORIZONTALLY 263. 2 0.09% 51.97% HUGE
264. 2 0.09% 52.06% IMPERATIVE 265. 2 0.09% 52.15% IMPERATIVES 266. 2 0.09% 52.24% IMPORTERS 267. 2 0.09% 52.33% INALIENABLE 268. 2 0.09% 52.42% INHABITANTS 269. 2 0.09% 52.51% INTERBANK
270. 2 0.09% 52.60% INTERNATIONALIZATION 271. 2 0.09% 52.69% INTERVIEWS
272. 2 0.09% 52.78% ISLAMIC 273. 2 0.09% 52.87% JEWELRY 274. 2 0.09% 52.96% LATIN 275. 2 0.09% 53.05% LAUNCH 276. 2 0.09% 53.14% LEISURE 277. 2 0.09% 53.23% LEVERAGING 278. 2 0.09% 53.32% LIQUIDITY 279. 2 0.09% 53.41% LOCALE 280. 2 0.09% 53.50% LOGO
281. 2 0.09% 53.59% MAINSTREAM 282. 2 0.09% 53.68% MASCOT
46 284. 2 0.09% 53.86% MERGED 285. 2 0.09% 53.95% MORALE 286. 2 0.09% 54.04% MOSQUE 287. 2 0.09% 54.13% MOTIVATOR 288. 2 0.09% 54.22% MULTIBANK 289. 2 0.09% 54.31% NEGOTIATE 290. 2 0.09% 54.40% OPTED 291. 2 0.09% 54.49% OVERFLIES 292. 2 0.09% 54.58% PACKAGING 293. 2 0.09% 54.67% PARADOX 294. 2 0.09% 54.76% PATENTS 295. 2 0.09% 54.85% PENSION
296. 2 0.09% 54.94% PERSONALIZED 297. 2 0.09% 55.03% PERSONNEL 298. 2 0.09% 55.12% PERVASIVENESS 299. 2 0.09% 55.21% PHARMACEUTICALS 300. 2 0.09% 55.30% PLANET
301. 2 0.09% 55.39% PLATFORM 302. 2 0.09% 55.48% PLATFORMS 303. 2 0.09% 55.57% POLICYMAKERS 304. 2 0.09% 55.66% POLLUTION
305. 2 0.09% 55.75% POLYCRYSTALLINE 306. 2 0.09% 55.84% PRECONDITION 307. 2 0.09% 55.93% PREEMPT
308. 2 0.09% 56.02% PRESTIGE 309. 2 0.09% 56.11% PRO
310. 2 0.09% 56.20% PROACTIVELY 311. 2 0.09% 56.29% PROFILES 312. 2 0.09% 56.38% PROPOSITION 313. 2 0.09% 56.47% PROPRIETARY 314. 2 0.09% 56.56% PROS
47 316. 2 0.09% 56.74% RANKINGS 317. 2 0.09% 56.83% RATIFIED 318. 2 0.09% 56.92% RESET 319. 2 0.09% 57.01% RESETS
320. 2 0.09% 57.10% RESOURCEFULNESS 321. 2 0.09% 57.19% RESPONSIBLY
322. 2 0.09% 57.28% RETAILERS 323. 2 0.09% 57.37% RETHINKING 324. 2 0.09% 57.46% ROUTINELY 325. 2 0.09% 57.55% SANCTION 326. 2 0.09% 57.64% SANCTIONED 327. 2 0.09% 57.73% SATELLITE 328. 2 0.09% 57.82% SCORES 329. 2 0.09% 57.91% SEEMINGLY 330. 2 0.09% 58.00% SEGMENTATION 331. 2 0.09% 58.09% SESSIONS
332. 2 0.09% 58.18% SHAREHOLDERS 333. 2 0.09% 58.27% SHRINKING 334. 2 0.09% 58.36% SIGNATORY 335. 2 0.09% 58.45% SILICON 336. 2 0.09% 58.54% SMART
337. 2 0.09% 58.63% SOVEREIGNTY 338. 2 0.09% 58.72% SPONSORED 339. 2 0.09% 58.81% SPONSORS 340. 2 0.09% 58.90% SPURS 341. 2 0.09% 58.99% STADIUM
48 348. 2 0.09% 59.62% TARIFF
349. 2 0.09% 59.71% TECHNOLOGIES 350. 2 0.09% 59.80% TELEVISION 351. 2 0.09% 59.89% TIERS
352. 2 0.09% 59.98% TOTALITARIANISM 353. 2 0.09% 60.07% TOXIC
354. 2 0.09% 60.16% TRADEMARK 355. 2 0.09% 60.25% TRADEOFFS 356. 2 0.09% 60.34% TRANSCENDS 357. 2 0.09% 60.43% TRANSIENT
358. 2 0.09% 60.52% TRANSNATIONAL 359. 2 0.09% 60.61% TRANSPARENT 360. 2 0.09% 60.70% TREATY
361. 2 0.09% 60.79% TRIAD
362. 2 0.09% 60.88% TRUSTWORTHY 363. 2 0.09% 60.97% TURNOVER 364. 2 0.09% 61.06% UNWIND
365. 2 0.09% 61.15% URBANIZATION 366. 2 0.09% 61.24% VENTURES 367. 2 0.09% 61.33% VICE
368. 2 0.09% 61.42% VIEWPOINTS 369. 2 0.09% 61.51% WAHABI 370. 2 0.09% 61.60% WARNINGS 371. 2 0.09% 61.69% WEAKENED 372. 2 0.09% 61.78% WEAKENING 373. 2 0.09% 61.87% WELLBEING 374. 2 0.09% 61.96% WORKFORCE 375. 1 0.05% 62.01% ABAYAS 376. 1 0.05% 62.06% ABIDING 377. 1 0.05% 62.11% ABRUPT
49 380. 1 0.05% 62.26% ABSORBED
381. 1 0.05% 62.31% ACCOMPLISHING 382. 1 0.05% 62.36% ACCOMPLISHMENT 383. 1 0.05% 62.41% ADAMANT
384. 1 0.05% 62.46% ADEPT 385. 1 0.05% 62.51% ADVENT 386. 1 0.05% 62.56% ADVERSELY 387. 1 0.05% 62.61% ADVERTISERS 388. 1 0.05% 62.66% AFFILIATION 389. 1 0.05% 62.71% AFFINITIVE 390. 1 0.05% 62.76% AFFLUENT 391. 1 0.05% 62.81% AFIELD 392. 1 0.05% 62.86% AFTERWARD 393. 1 0.05% 62.91% AGGRAVATED 394. 1 0.05% 62.96% AIRPORTS 395. 1 0.05% 63.01% AIRSPACE 396. 1 0.05% 63.06% ALCOHOL 397. 1 0.05% 63.11% ALIGN
398. 1 0.05% 63.16% ALLEGEDLY 399. 1 0.05% 63.21% ALLY
50 412. 1 0.05% 63.86% APP
413. 1 0.05% 63.91% APPALLINGLY 414. 1 0.05% 63.96% APPAREL 415. 1 0.05% 64.01% APPEASE 416. 1 0.05% 64.06% APPROVALS 417. 1 0.05% 64.11% ARBITRATION 418. 1 0.05% 64.16% ARCHETYPES 419. 1 0.05% 64.21% ARCHITECTURE 420. 1 0.05% 64.26% ARRAY
421. 1 0.05% 64.31% ASCENT 422. 1 0.05% 64.36% ASPIRE 423. 1 0.05% 64.41% ATHLETIC 424. 1 0.05% 64.46% AUDITS 425. 1 0.05% 64.51% AUGMENTED 426. 1 0.05% 64.56% AUSTERITY 427. 1 0.05% 64.61% AUTO
428. 1 0.05% 64.66% AUTOMAKER 429. 1 0.05% 64.71% AVON
430. 1 0.05% 64.76% AWAITING 431. 1 0.05% 64.81% BACKLASH 432. 1 0.05% 64.86% BAKERIES 433. 1 0.05% 64.91% BANANA 434. 1 0.05% 64.96% BANNER 435. 1 0.05% 65.01% BARRIER 436. 1 0.05% 65.06% BASKETBALL 437. 1 0.05% 65.11% BEHOLDER 438. 1 0.05% 65.16% BERNE 439. 1 0.05% 65.21% BET 440. 1 0.05% 65.26% BETTING 441. 1 0.05% 65.31% BID 442. 1 0.05% 65.36% BIO
51
444. 1 0.05% 65.46% BIOENGINEERED 445. 1 0.05% 65.51% BIOLOGICAL 446. 1 0.05% 65.56% BIREGIONAL 447. 1 0.05% 65.61% BIRTHPLACE 448. 1 0.05% 65.66% BLOC
449. 1 0.05% 65.71% BLOCKADES 450. 1 0.05% 65.76% BONUSES 451. 1 0.05% 65.81% BOOMING 452. 1 0.05% 65.86% BOSSES 453. 1 0.05% 65.91% BOUNTIFUL 454. 1 0.05% 65.96% BOUTIQUE 455. 1 0.05% 66.01% BOYCOTT 456. 1 0.05% 66.06% BOYCOTTING 457. 1 0.05% 66.11% BOYCOTTS 458. 1 0.05% 66.16% BRANDING
459. 1 0.05% 66.21% BREAKTHROUGH 460. 1 0.05% 66.26% BROADBAND 461. 1 0.05% 66.31% BROADENED 462. 1 0.05% 66.36% BROILED 463. 1 0.05% 66.41% BROKERAGE 464. 1 0.05% 66.46% BROKERS 465. 1 0.05% 66.51% BUDAPEST 466. 1 0.05% 66.56% BULBS
52 476. 1 0.05% 67.06% CARRIER 477. 1 0.05% 67.11% CASUAL 478. 1 0.05% 67.16% CASUALLY 479. 1 0.05% 67.21% CELEBRATED 480. 1 0.05% 67.26% CELL
481. 1 0.05% 67.31% CELLULAR 482. 1 0.05% 67.36% CENSUSES 483. 1 0.05% 67.41% CENTRALIZING 484. 1 0.05% 67.46% CENTRIFUGE 485. 1 0.05% 67.51% CEO
486. 1 0.05% 67.56% CERTIFICATE 487. 1 0.05% 67.61% CERTIFY 488. 1 0.05% 67.66% CHAMPAGNE 489. 1 0.05% 67.71% CHAMPIONED 490. 1 0.05% 67.76% CHAMPIONS 491. 1 0.05% 67.81% CHANEL 492. 1 0.05% 67.86% CHERRY 493. 1 0.05% 67.91% CHINESE 494. 1 0.05% 67.96% CHRONICLES 495. 1 0.05% 68.01% CIGARETTE 496. 1 0.05% 68.06% CITIZENRY 497. 1 0.05% 68.11% CLAD 498. 1 0.05% 68.16% CLICK 499. 1 0.05% 68.21% CLIMATE 500. 1 0.05% 68.26% CLIMATIC 501. 1 0.05% 68.31% CLUSTERING 502. 1 0.05% 68.36% COHESIVENESS 503. 1 0.05% 68.41% COLLECTIVISTS 504. 1 0.05% 68.46% COLLUDE
505. 1 0.05% 68.51% COLLUSION
53
508. 1 0.05% 68.66% COMMONPLACE 509. 1 0.05% 68.71% COMPETENCY 510. 1 0.05% 68.76% COMPETENT 511. 1 0.05% 68.81% COMPETITIVELY 512. 1 0.05% 68.86% COMPLIED
513. 1 0.05% 68.91% COMPOSTED 514. 1 0.05% 68.96% COMPREHEND 515. 1 0.05% 69.01% COMPROMISE 516. 1 0.05% 69.06% CONCOMITANTLY 517. 1 0.05% 69.11% CONDUCIVE
518. 1 0.05% 69.16% CONSEQUENTIAL 519. 1 0.05% 69.21% CONSERVATISM 520. 1 0.05% 69.26% CONSERVE 521. 1 0.05% 69.31% CONSERVING 522. 1 0.05% 69.36% CONSORTIUM 523. 1 0.05% 69.41% CONSUMMATED 524. 1 0.05% 69.46% CONTAINERIZATION 525. 1 0.05% 69.51% CONTINUALLY 526. 1 0.05% 69.56% CONTRABAND 527. 1 0.05% 69.61% CONTRACEPTIVES 528. 1 0.05% 69.66% CONVERGE
529. 1 0.05% 69.71% CONVERTIBILITY 530. 1 0.05% 69.76% CONVEY
531. 1 0.05% 69.81% CONVICTIONS 532. 1 0.05% 69.86% COPYRIGHTS 533. 1 0.05% 69.91% CORRODE 534. 1 0.05% 69.96% CORROSIVE 535. 1 0.05% 70.01% CORRUPT 536. 1 0.05% 70.06% COTONOU 537. 1 0.05% 70.11% COUNSELING 538. 1 0.05% 70.16% COUNTERFEIT
54 540. 1 0.05% 70.26% CRAYONS 541. 1 0.05% 70.31% CREDIBLE 542. 1 0.05% 70.36% CRITICISM 543. 1 0.05% 70.41% CROSSCHECK 544. 1 0.05% 70.46% CROSSROADS 545. 1 0.05% 70.51% CRUDE
546. 1 0.05% 70.56% CUMBERSOME 547. 1 0.05% 70.61% CURBING 548. 1 0.05% 70.66% CURBS 549. 1 0.05% 70.71% CURTAIL 550. 1 0.05% 70.76% CURTAILED 551. 1 0.05% 70.81% CUSTODIAN 552. 1 0.05% 70.86% CUTBACKS 553. 1 0.05% 70.91% DECISIVELY 554. 1 0.05% 70.96% DEDICATED 555. 1 0.05% 71.01% DEEMED 556. 1 0.05% 71.06% DEFAULT 557. 1 0.05% 71.11% DEFENSE 558. 1 0.05% 71.16% DEFRAUDED 559. 1 0.05% 71.21% DELEGATE 560. 1 0.05% 71.26% DELEGATION 561. 1 0.05% 71.31% DEMISE
55 572. 1 0.05% 71.86% DESPOTS 573. 1 0.05% 71.91% DESSERTS 574. 1 0.05% 71.96% DETERIORATES 575. 1 0.05% 72.01% DETERIORATING 576. 1 0.05% 72.06% DETERMINANTS 577. 1 0.05% 72.11% DETERMINISM 578. 1 0.05% 72.16% DETESTED 579. 1 0.05% 72.21% DEVALUATION 580. 1 0.05% 72.26% DEVALUATIONS 581. 1 0.05% 72.31% DEVISING
582. 1 0.05% 72.36% DICTATE 583. 1 0.05% 72.41% DICTATES 584. 1 0.05% 72.46% DICTATORSHIP 585. 1 0.05% 72.51% DIFFUSION 586. 1 0.05% 72.56% DIGIT
587. 1 0.05% 72.61% DIGITIZATION 588. 1 0.05% 72.66% DILEMMA 589. 1 0.05% 72.71% DIM
56
604. 1 0.05% 73.46% DISSIMILARITY 605. 1 0.05% 73.51% DIVESTED 606. 1 0.05% 73.56% DODGING 607. 1 0.05% 73.61% DOMICILED 608. 1 0.05% 73.66% DOWNTOWN 609. 1 0.05% 73.71% DOWNTURN 610. 1 0.05% 73.76% DRAB
611. 1 0.05% 73.81% DRASTICALLY 612. 1 0.05% 73.86% DUBIOUS 613. 1 0.05% 73.91% DUPLICATION 614. 1 0.05% 73.96% DYNAMISM 615. 1 0.05% 74.01% EARMARKED 616. 1 0.05% 74.06% ECOTOURISM 617. 1 0.05% 74.11% ECSTATIC
618. 1 0.05% 74.16% EGALITARIANISM 619. 1 0.05% 74.21% ELECTRONICS 620. 1 0.05% 74.26% ELEVATOR 621. 1 0.05% 74.31% ELITE 622. 1 0.05% 74.36% EMAIL
623. 1 0.05% 74.41% EMBARKING 624. 1 0.05% 74.46% EMULATE 625. 1 0.05% 74.51% ENCOMPASS 626. 1 0.05% 74.56% ENCOMPASSING 627. 1 0.05% 74.61% ENDORSES 628. 1 0.05% 74.66% ENFORCEABLE 629. 1 0.05% 74.71% ENFORCER 630. 1 0.05% 74.76% ENGAGED 631. 1 0.05% 74.81% ENROLLED 632. 1 0.05% 74.86% ENROLLMENT 633. 1 0.05% 74.91% ENTAIL
57
636. 1 0.05% 75.06% ENTREPRENEURIAL 637. 1 0.05% 75.11% EQUITABLE
638. 1 0.05% 75.16% ERADICATE 639. 1 0.05% 75.21% ERAS
640. 1 0.05% 75.26% ESPOUSED 641. 1 0.05% 75.31% ESTEEMED 642. 1 0.05% 75.36% ESTIMATORS 643. 1 0.05% 75.41% ETHNOCENTRIC 644. 1 0.05% 75.46% ETHNOGRAPHIC 645. 1 0.05% 75.51% EUROAND
646. 1 0.05% 75.56% EUROMONEY 647. 1 0.05% 75.61% EX
648. 1 0.05% 75.66% EXCEL 649. 1 0.05% 75.71% EXCELS 650. 1 0.05% 75.76% EXERT 651. 1 0.05% 75.81% EXHAUST 652. 1 0.05% 75.86% EXPEDITED 653. 1 0.05% 75.91% EXTRAPOLATE 654. 1 0.05% 75.96% FABRICATED 655. 1 0.05% 76.01% FABRICATION 656. 1 0.05% 76.06% FACET
657. 1 0.05% 76.11% FACETS
658. 1 0.05% 76.16% FAMILIARIZING 659. 1 0.05% 76.21% FARE
660. 1 0.05% 76.26% FAVORABLY 661. 1 0.05% 76.31% FAVORED 662. 1 0.05% 76.36% FAVORS 663. 1 0.05% 76.41% FEASIBLE 664. 1 0.05% 76.46% FERTILE 665. 1 0.05% 76.51% FESTIVAL 666. 1 0.05% 76.56% FINNISH
58 668. 1 0.05% 76.66% FLASHPOINT 669. 1 0.05% 76.71% FLAWED 670. 1 0.05% 76.76% FLEDGLING 671. 1 0.05% 76.81% FLOURISHED 672. 1 0.05% 76.86% FLUX
673. 1 0.05% 76.91% FOES 674. 1 0.05% 76.96% FORAYS 675. 1 0.05% 77.01% FORE
676. 1 0.05% 77.06% FORECASTED 677. 1 0.05% 77.11% FORESEEABLE 678. 1 0.05% 77.16% FORGED
59 700. 1 0.05% 78.26% GEARED 701. 1 0.05% 78.31% GIANTS 702. 1 0.05% 78.36% GIRDS 703. 1 0.05% 78.41% GLUT 704. 1 0.05% 78.46% GOINGS 705. 1 0.05% 78.51% GOODWILL 706. 1 0.05% 78.56% GOVERNORS 707. 1 0.05% 78.61% GREEK 708. 1 0.05% 78.66% GRILLING 709. 1 0.05% 78.71% GROCERY 710. 1 0.05% 78.76% GRUESOME 711. 1 0.05% 78.81% GUIDANCE 712. 1 0.05% 78.86% GUT
713. 1 0.05% 78.91% HALT
714. 1 0.05% 78.96% HAMBURGER 715. 1 0.05% 79.01% HANDLERS 716. 1 0.05% 79.06% HARDWARE 717. 1 0.05% 79.11% HARMONIZE 718. 1 0.05% 79.16% HARSH 719. 1 0.05% 79.21% HAUL
720. 1 0.05% 79.26% HEADACHES 721. 1 0.05% 79.31% HEADCOUNT 722. 1 0.05% 79.36% HEALTHCARE 723. 1 0.05% 79.41% HEATHROW 724. 1 0.05% 79.46% HIGHS
60 732. 1 0.05% 79.86% IMPEDIMENT 733. 1 0.05% 79.91% IMPORTED 734. 1 0.05% 79.96% IMPRESS 735. 1 0.05% 80.01% IMPRESSION 736. 1 0.05% 80.06% INCINERATED 737. 1 0.05% 80.11% INCREMENTAL 738. 1 0.05% 80.16% INCULCATE 739. 1 0.05% 80.21% INCUR
740. 1 0.05% 80.26% INCURSIONS 741. 1 0.05% 80.31% INDELIBLE 742. 1 0.05% 80.36% INDIA 743. 1 0.05% 80.41% INDICES 744. 1 0.05% 80.46% INDONESIA
745. 1 0.05% 80.51% INDUSTRIOUSNESS 746. 1 0.05% 80.56% INEFFICIENTLY 747. 1 0.05% 80.61% INFLATIONARY 748. 1 0.05% 80.66% INFRINGING 749. 1 0.05% 80.71% INGREDIENTS 750. 1 0.05% 80.76% INJECT
751. 1 0.05% 80.81% INLAND 752. 1 0.05% 80.86% INNOCENT
753. 1 0.05% 80.91% INNOVATIVENESS 754. 1 0.05% 80.96% INSISTED
61
764. 1 0.05% 81.46% INTERMEDIARIES 765. 1 0.05% 81.51% INTERNATIONALIST 766. 1 0.05% 81.56% INTERPERSONAL 767. 1 0.05% 81.61% INTRA
768. 1 0.05% 81.66% INTRAZONAL 769. 1 0.05% 81.71% INTRICACIES 770. 1 0.05% 81.76% INTRICATE 771. 1 0.05% 81.81% INTRIGUING 772. 1 0.05% 81.86% INVADERS 773. 1 0.05% 81.91% INVASION 774. 1 0.05% 81.96% INVENTIVE 775. 1 0.05% 82.01% INVENTORY 776. 1 0.05% 82.06% IRANIAN 777. 1 0.05% 82.11% ISSUERS 778. 1 0.05% 82.16% JAMMING 779. 1 0.05% 82.21% JEDDAH 780. 1 0.05% 82.26% JUMBLE 781. 1 0.05% 82.31% KEEN 782. 1 0.05% 82.36% KINSHIP 783. 1 0.05% 82.41% KNOCKOFF 784. 1 0.05% 82.46% KORAN 785. 1 0.05% 82.51% KORANIC 786. 1 0.05% 82.56% LABORERS 787. 1 0.05% 82.61% LAHORE 788. 1 0.05% 82.66% LANDLINES 789. 1 0.05% 82.71% LAWSUIT 790. 1 0.05% 82.76% LEASING 791. 1 0.05% 82.81% LEGACIES 792. 1 0.05% 82.86% LEGITIMATE 793. 1 0.05% 82.91% LEGITIMIZED 794. 1 0.05% 82.96% LEST
62 796. 1 0.05% 83.06% LIMBO 797. 1 0.05% 83.11% LIQUOR 798. 1 0.05% 83.16% LITERAL 799. 1 0.05% 83.21% LITIGATION 800. 1 0.05% 83.26% LOCALES
801. 1 0.05% 83.31% LOCALIZATION 802. 1 0.05% 83.36% LOCALLYALL 803. 1 0.05% 83.41% LONGEVITY 804. 1 0.05% 83.46% LOWERS 805. 1 0.05% 83.51% LOWS 806. 1 0.05% 83.56% MACRO 807. 1 0.05% 83.61% MAGAZINE 808. 1 0.05% 83.66% MAINSTAY 809. 1 0.05% 83.71% MALAISE 810. 1 0.05% 83.76% MANDATE 811. 1 0.05% 83.81% MANIPULATOR 812. 1 0.05% 83.86% MANNEQUINS 813. 1 0.05% 83.91% MAPPED 814. 1 0.05% 83.96% MAPPING
815. 1 0.05% 84.01% MARGINALIZED 816. 1 0.05% 84.06% MARKEDLY 817. 1 0.05% 84.11% MARKETPLACE 818. 1 0.05% 84.16% MASSIVE
819. 1 0.05% 84.21% MATERIALIZE 820. 1 0.05% 84.26% MATES
