a proposal for the establishment of an eap...

Title A Proposal for the Establishment of an EAP Vocabulary Listand an Analysis of Its Appropriateness

Author(s) Mizoguchi, Setsuko; Shiina, Kiyoko; Sano, Masako; Thrasher,Randolph; Yoshioka, Motoko

Citation 大学英語教育学会紀要(23): 77-96

Issue Date 1992-08-25

URL http://hdl.handle.net/20.500.12001/10148

Rights 大学英語教育学会

The Japan Association of College English Teachers (JACET)

77

A Proposal for the Establishment of an EAP Vocabulary List andan Analysis of Its Appropriateness

Setsuko MizoguchiMasako SanoMotoko Yoshioka

International Christian

Kiyoko, ShiinaRandolph Thrasher

University

I. INTRODUCTION

Among the many research priorities in English education at the university level in

Japan is to empirically define what constitutes English for Academic Purposes

(EAP). There is wide agreement on the importance of such a task. The EAP

vocabulary list proposed here and the research on which it is based are our anempt to

provide meaningful criteria in this area. It is hoped that this study will pr~vide

significant insights into EAP vocabulary and encourage future empirical studies in

the field of EAP.

EAP comprises many sub-fields and, as a first step, we have focused our research

specifically on vocabulary. Our interest in vocabulary research originated from

previous research conducted by some members of our group, in which an interplay

bet~een reading comprehension and vocabulary control in English as a

second/foreign language was studied. The purposes of our present study are to

create an EAP vocabulary corpus, to analyze this corpus and the pans that compose

it, and ultimately to establish an empirically based vocabulary list useful for pursuing

academic activities in English at the university level.

Our first task was to produce a database by inputting a sizqble sample of text from

introductory university textbooks in ten academic disciplines. Out of this database

was created the EAP Vocabulary Corpus, which served as the basis of our analysis.

We examined this corpus to discover the pattern of vocabulary overlap in the ten

texts we surveyed. A number of studies on vocabulary published so far have been

conducted on the basis of word frequency, but what distinguishes our present

research from these is, that we have focused on the pattern of distribution of

vocabulary over ten academic subjects. We have taken this approach because our

interest was in determining vocabulary which would be 'of service to a number of

different fields and in creating a list of vocabulary common to the majority of

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78

academic disciplines. This list will be called the EAr Vocabulary. The present

report is concerned with an explanation of how that list was arrived at and an

evaluation of its appropriateness. The evaluation was done by comparing our list

with those prepared by Zeneiren and JACET and by analyzing our data from various

aspects such as token-entry ratio, frequency, and level of difficulty.

During the analysis of the EAP Vocabulary Corpus that we created, a number of

interesting additional findings emerged. The major ones relate to characteristics of

the vocabulary in the Physical Science (PS) and Social Science' (SS) areas, the

characteristics of different academic subjects in these two areas, and the percentage

of Junior High School basic vocabulary in the whole corpus. The first two findings

were reported in detail in leu Language Research Bulletin, Vol. 4, 1989.

II. PROCEDURES USED FOR TIlE CREAnON OF TIiE EAP VOCABULARY

CORPUS

ll-l. Selection of the Materials to be Surveyed

The flfSt task of this study was to select the academic fields lobe included.. The ten

academic fields examined in our research were selected from among those offered at

Inten:ational Christian University (lCU), the institution with which all members of

the research team are associated. lCU is a liberal arts college with five divisions at

the time this research was started: Natural Sciences, Social Sciences, Languages,

Humanities, and Education. Four disciplines from the Natural Sciences Division and

six from the remaining four divisions were selected.

Table 1: PS and SS Disciplines Surveyed

Physical Science Social ScienceDisciplines Disciplines

Biology (NS) Anthropology (SS)

Chemistry (NS) Economics (SS)

Mathematics (NS) Education (Ed)

Physics (NS) Linguistics (LjPhilosophy (H)

Psychology (Ed)



79

Following the usual custom in Japan, we have labelled the four disciplines selected

from the Natural Sciences Division as Physical Science (Ri-kei, PS) and the six

selected from the other divisions as Social Science (Bun-kei, SS).

II-2. Inputting of the pata

The professors in each of the above-mentioned ten disciplines at ICU were asked

to recommend an introductory textbook they thought was appropriate for college

students beginning work in their ~ajor field (see Appendix A). In order to obtain

the database, approximately 200 pages from each of the recommended textbooks

were inputted into the ICU Computer Center IBM 4341. This data is called the ICU

Database. Since the text was inputted as it appeared in the textbooks. it was

convened to alphabeticallisls.

1I-3. Exclusion, Elimination, and Modification Procedures

Step 1: Exclusion of Junior High School Vocabulary

To establish the basic English vocabulary needed at the university level, it was

first necessary to remove from the database the words that are designated as Junior

High School Vocabulary Or High Voc). We have defined this Jr High Voc as those

words appearing in the Zeneiren list with an asterisk, that is, those marked as junior

high school vocabulary, and their inflectional forms. We also added those words in

the appendix to the Zeneiren list which are designated as junior high school

vocabulary in the Course of Study issued by the Ministry of Education in 1977.

Step 2: Elimination Procedures

Since the text was inputted as it appeared in the textbooks, the corpus contained

proper names, foreign words, and other entries that would obscure our purpose of

preparing a list of the most useful vocabulary for EAP. Therefore, the database was

examined and the following eight categories of words were eliminated from the

database.

1. Personal and place names and their derivations. Exceptions were those proper

nouns that have come to be used as common nouns such as Christian, Buddhist, etc.

2. Foreign words, that is, those entries in italics in the Kenkyusha's New English

Japanese Dictionary. (e.g. conte, milieu, nouveau)

3. Hyphenated words. (e.g. ill-disposed, left-handed, pay-off)


The Japan Association .of College English Teachers (JACET)

80

4. Compound words with -like,' -shape, and -wise. (e.g. leajlike, 'eggshape,

clockwise)

5. Entries that occurred in the original database as one word but were listed in the

above mentioned dictionary as multiple words. (e.g. check list, p~ roll, side step)

6. Interjections. (e.g. ah, alas, 10 )

7. Archaic or rare words. (e.g. betwix, chi/de, gobbet)

8. Abbreviations. (e.g. a.d, a.m., mt., st.)

Step 3: Modification Procedures

The following modifications were made.

1. The various inflected forms 0"[ nouns, verbs, and adjectives were combined into a

single citation form. (e.g. gran:fying, gratified, and gratifies --> gratify )

2. Adjectives occurring with both -ic and -ical endings were checked to detennine

if they had an entry for each spelling both in the K enkyusha's New English

Japanese Dictiomiry and th"e'Shogakukan Random House Dictionary o/the English

Language. If they were listed separately in the dictionaries, both fonns were

retained. If they were not listed separately, the fonn listed first was selected as the

.entry in our corpus. (e.g. graphic, graphical --> graphic; theoretical, theoretic -->theoretical; but economic and economical were both retained.)

3. Words with the adverbial suffix -ward or -wards were listed only in the form

without the -so (e.g. cowards --> coward)

4. Words spelled differently in British and American English were listed under the

American spelling. (e.g. behaviour --> behavior, analyse --> analyze; sulphur

--> sulfur). However, the British spelling was retained when it was listed before

the American spelling. (dialogue, dialog --> dialogue)

II-4. The EAP Vocabulary Corpus

As the result of Steps 1-3 (See II-3), a corpus consisting of 255,495 tokens

(12,935 entries) was obtained. The term 'token' is used in the same sense as in The

American Heritage Word Frequency Book, that is, 'tokens' are the total number of

words in the input data. However, where The American Heritage Word Frequency

. Book uses the term 'type', we use the term 'entries' to denote the distinct words

among the 'tokens'. This terminology is adopted because Our entries are citation. .

forms. The corpus thus obtained, on which our whole analysis is based, is named

the EAP Vocabulary Corpus (EAP Voc Corpus).



81

Table 2: EAP Voc Corpus, Jr High Voe, and Combined Corpus

EAP Vocabulary Corpus Junior High Combined C.

Subject Tokens Enoies Tokens Tokens

Biology 27343 4108 45936 73279

Chemistry 27137 2222 38835 65972

Mathematics 19040 1458 46626 65666

Physics 27130 2069 56258 83388

Economics 24981 3719 53310 78291

Linguistics 25897 3211 56247 82144

Philosophy 22151 2708 64327 86478

Psychology 31616 4403 60794 92410

Anthropology 26510 5603 47793 74303

Education 23690 4405 53253 76943

Totals 255495 523379 778874

The columns 2 and 3 of Table 2 present the EAP Voc Corpus in the ten subjects.

Column 2 is the number of tokens and column 3 the number of entries. Column 4

shows the number of Jr High Voc tokens in the original corpuses and column 5 the

combination of columns 2 and 4. It should be noted that the numerical figures in

column 5 do not include the words eliminated in Step 2 (see II-3). The bottom line

reports the totals of columns 2, 4, and 5.

IT-5. Statistical Adjustment of the Data

Since the size of the corpuses of the 10 subjects differed, the frequency of each

entry was adjusted to reflect its occurrence in a corpus of 100,000 tokens and then

the ennies of the ten corpuses were combined and arranged in alphabetical order (see

Appendix B).

II-6. Ranking of the Entries

The ennies in this combined list were ranked according to the total number of

adjusted occun:ences in all ten corpuses (see Appendix C).

TIl. A PROPOSAL FOR TIlE ESTABLISHMENT OF TIlE EAP VOCABULARY

III-I. Two Stages Followed for the Establishment

In order to establish the EAP Vocabulary, the analysis was done in two stages.The


The Japan Association of college English Teachers (JACET)

82

first stage was based on eight subjects, four of which represent the PS and the

remaining four represent the SS and the second stage was based on ten subJects with

two more subjects added from the SS disciplines. The reason for adopting the [Wo

stage analysis was that it was first necessary to obtain the vocabulary general enough

to be of service across both the social science and physical science areas, not

focusing on individual academic disciplines. The second stage analysis was

undertaken because there was a wider range of disciplines represented in the social

science area than in the physical science area.

llI-2. SLage 1: Area-based EAP Vocabulary in the Eight Subjects

To fairly represent both the physical and social sciences in our study to detennine

which vocabulary was common to both fields and which was common to only one

or the other, an equal number· of disciplines in each area needed to be studied. All of

the four PS disciplines were used and four of the six SS disciplines were selected.

For the latter, one discipline was selected to represent each of the four non-physical

science divisions then at JCU: philosophy from the Humanities Division, economics

from the Social Science Division, linguistics from the Language Division, and

psychology from the Education Division.

ITI-2-1. Pattern of Overlap in the Physical Science and Social Science Areas

Table 3: Pattern of Overlap in 4 PS Subjects

4 3 2 1

No of Enlries 611 604 1019 3560Cum. TOLaI 611 1215 2234 5794Cum. % 10.5 21.0 38.6 100

Vocabulary overlap. in the four PS subjects is shown in Table 3. Column 1 gives

the number of entries 'that appeared in all four PS. subjects. Column 2, the number

that appeared in three of the four, columns 3 and 4 show the number that occurred in

two and only one of the four subjects respectively. The second line gives the

cumulative totals and the third line the cumulative percentages.

Table 4 shows the overlap in the four S5 subjects. The explanation of the columns

and lines is the same as in the previous table.



83

Table 4: Pattern of Overlap in 4 SS Subjects

4 3 2 1No of Entries 912 873 1550 4673Cum Total 912 1785 3335 8008Cum. % 11.4 22.3 41.6 100

The same data perhaps can be grasped more easily if it is presented in the fonn of

bar graphs. These graphs illustrate the great similarity in the pattern of overlap in the

PS and SS areas. This remarkable similarity in two areas usually considered quite

different is a significant finding which provides strong support for our decision to

analyze PS and SS data on an equal basis.

1 t~~lt.~~~{\~~{~w~tWqf&!.!.~~*{I&ltif~WWrmgfi%t~**~14~r.Mti~Wt~~{~~~I 3 5 6 0.o

.1000

.2000 3000 4000

Figure 1: Pattern of Overlaps in 4 PS Subjects

4 WXWMW1NI 9 1 2of-

o 1000 2000 3000 4000 5000

Figure 2: Pattern of Overlap in 4 SS Subjects

1II-2;.2. Common Vocabulary in the PS and 55 Areas

An examination of Tables 3 and 4 shows that, in each area, approximately 10

percent of the entries are common to all four subjects and another 10 percent

common to three of the four. It was decided to consider entries that occur in at least



84

three of the four subjects as essential EAP vocabulary. Those entries that occur in at

least three of the four PS subjects are designated Physical Science Common (PS

Common). Likewise, those occurring in at least three of the four SS subjects are

called Social Science Common (SS Common).' Requiring that a word appear in all

four subjects before it could be considered essential EAP vocabulary would be too

severe a restriction. Such a restriction assumes that there is more homogeneity than

our detailed analysis of the individual corpuses indicates. On the other hand,

including in our list of essential EAP vocabulary entries that occur in only two of the

four corpuses would not be restrictive enough because such vocabulary could not be

considered representative of the area. The total number of PS Common entries is

1215 and the number of SS Common entries is 1785."

III-2-3. Core Vocabulary---Vocabulary Overlap between PS and SS Common

Figure 3 shows the pattern of overlap between PS and SS Common. Some of the

same entries occur in both PS Common and SS Common. The 874 entries occurring

in both PS Common and SS Common are called Core Vocabulary, Those occurring

in only one list or the other are designated PS Non:..core Common and SS Non-core

Common respectively.

[] Core~~

J.~~J.~

till341 -- PS Non-core

D55 Non-core.:"""

Figure 3: Overlap between' PS and S5 Common

Figure 4 gives the percentages of Core and Non-core COmmon for both PS

Common and SS Common, It can be seen that Core Voc occupies 71.93 percent of

PS Common while it occupies only 48.96 percent of SS Common.



85

51.0490 48.96%

01 Core o PS Non-core

Common

m CliiJ ore o 55 Non-core

Common

Figure 4: Propo'dion of Core Vocabulary in PS and S5 Common

III-2-4. A Proposal for the Area-based EAP Vocabulary

The Core Voc (874), PS Non-core Common (341), and SS Non-core Common,

(911) have been combined and designated as the Area-based EAP Vocabulary,

which contains 2126 entries in total.

4 PS S b" td R 'd I Vb d EAP VT bl 5 Aa e , rea- ase oc an eSt ua DC In u 'Jec 5"

Entries Tokens

Biology Chern Math Physics Biology Chern . Math Physics

4108 2222 1458 2069 Total 27343 27136 19040 271301537 1312 1033 1314 AreaEAP 15809 22481 16574 2347637.41 59.05 70.85 63.51 % .57.82 82.85 87.05 86.532571 910 425 755 Residual 11534 4655 . 2466 365462.59 40.95 29.15 36.49· % 42.18 17.15 12.95 13.47

4 SS S b' td R 'd I Vb d EAP VT bl 6 Aa e " rea- ase DC an eSt ua OC In U IJec S,

Entries Tokens

Econ Ling Phil Psy Econ Ling Phil Psv

3719 3211 2708 4403 Total 24981 25897 22151. 316161710 1619 1572 1789 Area EAP .17298 . 18744 19117 2331445.98 50.42 58.05 40.63 % 69.24 72.38 86.30 73.74 (2009 1592 1136 2614 Residual 7683 7153 3034 830254.02 49.58 41.95 59.37 % 30.76 27.62 13.70 26.26



86

Tables 5 and 6 show the number and percentage of the Area-based EAP Voc and

Residual Voc in the four PS and SS subjects respectively. The term Residual Voc is

used to designate the tokens or entries that are not the Area-based Voc.

One of the most striking things about Tables 5 and 6 is that, in both the PS and SS

areas, three of the four subjects show a great similarity of distribution, while one

subject shows a different pattern than the others. The proportion of the Area-based

and Residual Voc entries in chemistry, mathematics, and physics is roughly 2 to 1..

The proportion is reversed in biology. An examination of tokens yields the similar

results. Biology is different from the other three PS subjects. In the SS area it is

philosophy that shows a different pattern. This uniqueness of biology and

philosophy is discussed at length in the Language Research Bulletin Vol; 4, 1989,

and it is mentioned here only to point out that the other three subjects in each area are

more representative of that area. The mean percentage of the Area-based EAP Voc

entries in the three typical PS subjects is 64.4 (for tokens, 85.5) and the mean

percentage of Area-based EAP Voc entries in the three typical SS subjects is 45.7

(for tokens, 71.8). These figures show that Area-based EAP Voc typically makes up

a much smaller part of the total vocabulary in the SS subjects than it does in the PS

subjects.

III-3. Stage 2: Subject-based EAP Vocabulary in the Ten Subjects

A consideration of section III-2-4 leads to the conclusion that the Area-based EAP

Voc, that is, the list based on four PS and four SS subjects, is not adequate for

reading in social sciences. If we are going to expand the list, it is obvious that the

increase should be in SS vocabulary rather than in PS. Therefore, the data from

Anthropology and Education that had been excluded from the Stage 1 analysis was

re-examined. This means that a tptal of six SS subjects were included so that,

together with the original four PS subjects; a total of .ten were considered.

TII-3-!. Pattern of Overlap in the Ten Subjects

Table 7 shows that 391 entries occur in all ten subjects, an additional 258 occur in

9 out of la, and that 271 occur in 8 of the 10 subjects and so on. The second line

shows the cumulative total of entries and the bottom line the cumulative percentage

of entries in all 10 subjects, 9 out of la, 8 out of la, and so on.



87

Table 7: Pattern of Overlap in 10 Subjects

10 9 8 7 6 5 4 3 2 1

# of Entries 391 258 271 329 394 547 824 1301 2281 6337Cum Total 39] 649 920 1249 1643 219U 3014 4315 659E 12933Cum % 3.0 5.0 7.1 9.7 12.7 16.9 23.3 33.4 51.0 100

III-3-2. Four-way Common Vocabulary in the Ten Subjects

As was mentioned above, the SS subjects were under-represented in the Area

based EAP Voc and their weight needed to be increased. To realiz.e this, the original

ten corpuses were examined to determine the number of subjects that each word

occurred in. On the basis of the pattern of distribution shown in Table 7, it was

decided to select entries that. occurred in four of the ten subjects and to designate

them as the Subject-based Four-way Common Vocabulary, which contains '3014

entries. This list of 3014 is comprised of 2041 entries which have already been

selected in the Stage 1 analysis and 973 additional new words. Our decision to use

four-way common as the criterion in the Stage 2 analysis was reached by a careful

consideration of what kinds of new wo~ds should be selected to supplement the list

already obtained in the Stage 1 analysis. Requiring that an entry occurs in four out

of ten subjects suggests three possible ways new words could enter the list. 1) They

could occur in two PS and two SS subjects. 2) They could occur in one PS subject

and three SS subjects. In this case, at least one of the SS subjects must be either

Anthropology or Education. 3) They could occur in four S5 subjects. In this case,

the word must occur in both the Anthropology and Education corpuses. These

possibilities make it clear that by using four-way common as our standard for

inclusion, the need to increase representation from the 55 area is satisfied.

1II-3-3. ,A Proposal for the Subject-based EAP Vocabulary

As was stated above, the number of ennies obtained by this second stage analysis

was 3014. In this total are included 2041 entries which also appear in the Area

based EAP Vocabulary. This leaves 973 entries which are unique to this second

corpus. These 973 entries are designated as the Subject-based EAP Vocabulary.

III-4. Establishment of the EAP Vocabulary

It is proposed that the vocabulary consisting of the 2126 entries of the Area-based

EAP Vocabulary plus the additional 973 entries of the Subject-based EAP



88

Vocabulary be established as the EAr Vocabulary (see Apprendix D). This gives a

total of 3099 entries in our final list and this number is roughly the same as the

number of entries in the Zeneiren and JACET lists.

IV. ANALYSIS OF TIlE EAP VQCABULARY

IV-1: TIle Percentage of the EAP Vocabulary in each of the Ten Subjects

Table 8: Breakdown of EAP Voc Corpus into EAP &Non-EAP (Tokens)

Subjects EAPVoc EAPVoc Non-EAP VocCorpus No % No %

Biology 27343 19466 T1.19 7877 28.81Chemisrry 27136 23391 86.20 3745 13.80Mathematics 19040 17172 90.19 1868 9.81Physics 27130 24245 89.37 2885 10.63Economics 24981 20329 81.38 4652 - 18.62 :Linguistics 25897 20326 78.49 5571 21.51Philosophy 22151 20089 90.69 2062 9.31Psychology 31616 25818 81.66 5798 18.34Anthropology 26510 19439 73.33 7071 26.67Education 23690 18268 77.11 5422 22.89

I

Table 8 shows the numbers and percentages of the EAP and Non-EAP Voc

(tokens) within what is called the EAP Vocabulary Corpus. It is clear from the

Table that the EAP Vocabularv accounts for a very high percentage within each

academic discipline. If it is reasonable to assume a close relationship between

reading comprehension and knowledge of vocabulary, the findings presented above

indicate that university level reading skill depends to a great degree on the EAP

Vocabulary that has been recommended.

Table 9 shows the breakdown of the Combined Corpus (see II-4). Column 1

presents the total number of tokens in each of the ten corpuses. Columns 2 and 3

sh·ow the number and the percentage of the Jr High Voc tokens, Columns 4 and 5

those of the EAP Voc tokens, columns 6 and 7, those of the Non-EAP Voc tokens.

It should be noted that the Jr High Voc and the EAP VocabularY together make up

90 or more percent of the tokens in all subjects. This is another demonstration of the

appropriateness of our proposed EAP Vocabulary because the students who have


The Japan Association of College English'Teachers (JACET)

89

learned this vocabulary will find a large portion of the vocabulary in their specialized

field familiar.

Table 9: Breakdown of the Combined Corpus (Tokens)

Subjects Comb'd Jr High Voe ' EAP Voc Non-EAP VocCorpus No % No % No %

Biology 73279 45936 62.69 19466 26.56 7877 1 10.75Chemistry 65972 38835 58.87 23391 35.46 3746 5.68Mathematics 65666 46626 71.00 17172 26.15 1868 12.84Physics 83388 56258 67.47 24245 29.07 2885 13.46Economics 78291 53310 68.09 20329 25.97 4652 5.94Linguistics 82144 56247 68.47 20326 24.74 5571 6.78Philosophy 86478 64327 74.39 20089 23.23 206~ 12.38Psychology 92410 60794 65.79 25818 27.94 5798 16.27Anthropology 74303 47793 64.32 19439 26.16 7071 !9.52Education 76943 53253 69.21 18268 23.74 5422 i 7.05

IV-2. Token-Entry Ratio of the EAP Vocabulary

In this section the EAP Vocabulary is analyzed i~ tenns of the Token-Entry Ratio

(TER). The TER is obtained by dividing the number of tokens by the number of

entries and shows how frequently individual words are used.

Table 10 shows the TER for the EAP and Non-EAP Voc in the ten subjects.

Table 10: TER of EAP and Non-EAP Voe in 10 Subjects

Subjects EAPTER Non-EAPTERBiology 9.4 3.9

Chemistry 15.0 5.6

Mathematics 15.1 5.8

Physics 15.8 5.4

Economics 9.3 3.G'

Linguistics 10.3 4.5

.Philosophy 10.7 2.5

Psychology 11.1 2.8

Anthropology 7.9 2.2

Education 8.1 2.5

Average 11.27 3.82



90

The Table shows that the EAr Vocabulary has a considerably higher TER value

than that of the Non-EAP Voc. The average for the EA? Vocabulary across the ten

subjects is 11.27, while the average for the Non-EAP is only 3.82. This means that

words in the EAr Vocabulary list occur repeatedly in each of the subjects and this is

a fanher demonstration of its appropriateness.

The TER also shows the characteristic difference between the PS and SS areas.

Table 11: TER for PS and 55 Areas

PS

Subjects TERBiology 6.7

Chemistry 12.2Mathematics 13.1Physics 13.1

Average 11.3

SS

Subjects TEREconomics 6.7Linguistics 8.1Philosophy 8.2Psychology 7.2

Anthropology 4.7

Education 5.4

Average 6.7

Table 11 presents the TER for each subject. The mean TER of the PS subjects is

11.3 and that of the SS is 6.7. The mean TER in the three typical PS disciplines

(chemistry, mathematics, and physics) is 12.8 which is neal}y double the mean

figure for the SS disciplines. The peculiar nature of biology is again obvious in the

TER (see Ill-2-4). The TER of biology more resembles those of SS s~bjects than it

does those of the other PS subjects.

IV-3. The EAr VocCfbulary in Terms of Frequency

As was mentioned in II-5, the raw frequency of occurrence was adjusted to reflect

frequency in a corpus of 100,000 words in order to make the corpuses of the ten

subjects comparable. Using this adjusted frequency data, a rank list was obtained.

Figure 5 shows the proportion of the EAP and Non-EAP Voc in ranks 1 to 1000,

1001 to 2000, 2001 to 3000, and above· 3001. The percentage of the EA P

Vocabulary in the first 1000 ranks shows 92.3, in 1001-2000 ranks, 75.7, and iri

2001-3000 ranks, 60.7. It drastically drops at 3001 ranks or above. (7.7%) Of the

3099 EAP Voc entries, 2337 entries (75.4%) fall in the first 3000 ranks. Only 762



91

entries (24.6%) do not fall in 1 through 3000 ranks. In other words, the

appropriateness of the EAP Vocabulary list is supported by this examination of

frequency just as it was in our examination of the TER.

Above 3001@1 I

2 0 0 1 . 3 0 0 0 :§R~Mt~f.Witt.1l~WfWWI I

1 001 - 2 000 ~ltia~.(*~~~{~W?t}~(~I 1

1 - 1 0 0 0 [email protected],~X~'wm1~~~~'*ti~1~N~~~_1.in 1

0% 20% 40% 60% 80% 100

%

o Non-EAP

Figure 5: Percent:lge of EAP and Non-EAP Voe In the Stratified Rank·List

The characteristic difference between the PS and SS areas is also observed in the

disnibution of high and low frequency words.

Table 12: Percent:lges of High and Low' Frequency Words

Bio Chern Math Phy Eco Ling Phil Psy Anth Ed

1-9 83.2 77.9 74.8 . 76.8 86.0 81.3 82.4 83.8 89.2 87.3

10+ 16.8 22.1 25.2 23.2 14.0 18.7 17.6 16.2 10.8 12.7

Table 12 shows the percentage of words that occur from one to nine times and the

percentage of those occurring 10 or more rimes in the ten subjects. If the PS and SS

areas are compared, the percentages of words occurring from 1 to 9 times (low

frequency words) in the S5 is higher than those in PS disciplines except for biology.

V-4. Level of Difficulty of the EAP VocabulID

For the purpose of analyzing the EAP Vocabulary in tenns of diffic;ulty, the three

level difficulty distinction, A, B, and C, which was validated in our previous

research (lCU Language Division Anllual Reporcs, Vols. 2 and 3) was adopted.

The A level words are those included in the Zene.iren list, the B level words are those

occurring with a single asterisk in Kenkyusha's New Collegiace English-Japanese

Dictionary (4th ed.). Words not appearing in A or B are classified as C level. For

purposes of comparison, the aneiren list and the list derived from the dictionary



92

were modified by using the exclusion, elimination, and modification procedures

described in Section II-3.

18.10% 61.31 %

[0 A level

D B level

Eill C level

Figure 6: Breakdown of EAP Voc According to Levels of Difficulty

Figure 6 shows the division of the EA P Vocabularv into the three levels of

difficulty. Out of 3099 entries, 1900 (61.31 %) are A level, 561 (18.10%) are B

level, and 638 (20.59%) are C level. An observation of the lCU Placement Test

Vocabulary Subtest results indicate that the High-A level vocabulary (see ICU

Language Division Annual Report, Vol. 5) has not been fully acquired yet by April

ent.eri~g freshmen. This shows the appropriateness of having a large percentage of

the A level words in the EAP Vocabulary list. The inclusion of 18.10% ofB level

and 20.59% of C level vocabulary is also appropriate because A level alone is not

adequate for university reading.

V. THE EAP VOCABULARY IN COMPARISON WIlli TIIE ZENElREN ANDJACETLISTS

In this section the EAP Vocabulary list is compared with the lists recommended

by Zeneiren and JACET. In dealing with the JACET list, we used the same

exclusion, elimination and modification procedures described in Section II-3.

Table 13: Overlap between Proposed EAP Voc List and Zeneiren andJACET

Zeneiren JACET either Z or J UniqueEAP

3099 1900 1720 2051 1048

EAPVoc 61.3% 55.5% 66.2% 33.8%



93

Table 13 shows the pattern of overlap between our proposed EAP Vocabulary list

and those of Zeneiren and JACET. It can be seen that the percentages of overlap

between our list and those of Zeneiren and JACET is 61.3% and 55.5%

respectively. The percentage of items on our proposed list to occur in either one or

the other of these lists is 66.2%, which is a sizeable overlap. The remaining 33.8%

is unique to our list.

VI. CONCLUSION

This research can be deemed successful if the EA? VQcabulary list is useful tQ

students in a variety of disciplines. It has commonly been thought that the most

effective language teaching would take place if students were divided according to

their specialization. However, the vocabulary overlap among all of the ten

disciplines we surveyed is large enough to make it reasonable for students in all

disciplines to study the EA? VQcabulary rather than only the vocabulary specific to

their subject area. The empirically based vocabulary list which we propose in this

paper supports the notion that there exists such a category as essential English

vocabulary for academic purposes. The appropriateness of the list is well validated

by the result Qf comparison with other already published lists and of analyzing Qur

data from the standpoints Qf the TER, frequency, and level of difficulty.

It should also be mentioned that, in spite of the overlap described above, some

characteristic patternings of vocabulary distributiQn in the Physical and Social

Sciences were also observed. Although the popular image of the physical science

area is that there is a,great number of vocabulary unique to each discipline, we found

that the number of specialized words is smaller than in SS but that the frequency of

. Qccurrence Qf these words is higher than that of specialized words in SS. However,

'biology and philosophy are exceptions to the typical characteristics of the PS and SS

disciplines respectively.

It is aUf hope that this list will serve as a standard in determining appropriate

vQcabulary for various academic fields as well as becoming the basis for further

research in the area of English for Academic Purposes.

Because of limitation of space, only samples of the EA? Vocabulary list, the

Adjusted Frequency list, and the Rank list are given in this paper.



9t1

References

Carroll, John B., et al. (1971) The American Heritage Word Freguency Book.New York: American Heritage Publishing Co., Inc.

. Carter, Ronald. (1987) Vocabulary: Applied Linguistic Perspectives. London:Allen & Unwin.

Dollerup, Cay, et aI. (1989) "Vocabularies in the Reading Process," ATLAReview. Vol. 6, 21-33.

Linde, Richard, et al. (1977) uAn Analysis of the English Vocabulary ItemsAttained by High School Graduates in Japan--An Interim Report," AnnualReports, Vol. 2. ICU Division of Languages, 61-89.

Linde, Richard, et al. (1978) "An Analysis of the English Vocabulary ItemsAttained by High School Graduates in Japan--The Second Interim Report,"Annual Reports, Vol. 3. ICU Division of Languages, 85-114.

Linde, Richard, et al. (1980) "Factors Contributing to the English Reading Abilityof Japanese University Students--Vocabulary," Annual Reports. Vol. 5.ICUDivision of Languages, 87-109.

Mizoguchi, Setsuko, et a1. (1989) "Patterns of Vocabulary Overlap In EightPhysical and Social Science Subjects," lCULanguage Research Bulletin,Vol. 4. No. 1,39-61.

Ta)<efuta, Yoshio. (1981) Computer no Mira Gendai Eigo-:-Vocabularv no Kagaku[Computer Analy:ds of Contemporary English--An Empirical Study ofVocabulary). Tokyo: Ejuka Publishing Co.

The: compilation of the ICU EAP Vocabulary Corpus was partially funded by a .Mombusho Grant for Scientific Research, Grant No. 590101011.

Appendix A: The Author's ~nd the Titiles of the Textbooks Used

Biological Systems by Shelby D. Gerking.Chemistry by Michell J. Sienko.Calculus and Analvtic GeometrY by George B. Thomas, Jr.Fundamentals of Physics by David Halliday and Robert Resnick.Economics by Paul A. Samuelson.General Linguistics: An Introductory Survey by R. H. Robins.The Cenrral Questions of Philosophy by A. J. Ayer.Psychology by Guy R. Lefrancois.Social Anrhropologv in Perspective by 1. M. Lewis.A Hislon' of the Problems of Education by John S. Brubacher.



95

Appendix B: The Ajusted Frequency of Occurrences and Distributionby Areas and Subjects

PS(.4) SS(4) (oth.,..) Toh\. RankBio eh. Mat Phy Eco Lin Phi Pay I Ant Ed'"

absance e 2 0 121 6 19 1 26: 8 6 96 848abso\.ute 1 24 40 6\ 15 ,5 3 :I : 8 16 120 611abso\.uh\.y 0 2 14 01 '" 1 0 12 : 0 0 33 1780.bsorb 56 9 2 11 1 0 0 0: 1 6 76 943.bsorption 49 6 0 0 0 0 0 2: 1 0 66 1190",baorptive 1 0 0 0 0 0 0 0: 0 0 1 9163abstain 0 0 0 0 6 0 0 0: 1 1 7 4669.bstention 0 0 0 0 0 0 I 0: 0 0 1 9163abstinanca 0 0 0 0 1 0 0 0: 0 0 1 9163abstract 4 3 2 0 ,3 39 32 6: 11 9 108 678abst,..ctab\.e 0 0 0 0 0 4 0 0: 0 0

'"6979

abst,..cUon 0 0 0 0 1 46 1 I : 0 0 49 1316abstr.ct\.y 0 0 0 0 0 0 1 0: 1 0 2 7970abal,.usa 0 0 0 0 0 0 0 0: 1 0 1 9163

~bsu,.d 0 0 0 0 3 2 6 0: " 0 '" 3081

.bsurdity 0 0 0 0 0 0 5 0: 0 0 6 6341

.bsurdly 0 0 0 (I 0 0 1 0: 0 0 1 9163

abundance 15 Ii 0 0 0 0 0 0: 7 0 28 2002

.bundant Ii 2 0 0 3 0 1 I : 7 1 20 2476

.bundant\y 0 0 0 0 0 0 0 0: 0 1 1 9153

.bu•• 0 0 0 0 1 11 0 2: 6 Ii 13 3204

Appendix C:, The Rn'nk List

ran\( fraq rank fraa ,.an\( frao

1 forca 1914 17 flgura 991 33 a'lamant 714

,2 ax IUIlp\.a 1679 18 rasu\.t 990 34 typa 713

3 eystam 1381 19 a\.actr,on 968 35 mo\.acu\.a 611.4

" anargy 1376 20 how.ver 960 36 aqua\. 676

6 function 1:1.43 21 charg. 106 37 •• n•• 672

6 aquation 11811 22 prob'lam 897 38 condi tlon 66.4

7 tartft 1170' 23 ar.a 863 39 prooaa. 6'62

8 faot 1100 24 incr•••• 863 <40 g.nar.\' 649

9 thus 1017 2!i con.tant 849 <41 lIIe thod 646

10 atom 1076 26 "'... BAli . 42 invo'lv. 640

11 ca"'\, 1070 27 objact 830 43 consldar S21

12 pa,.Uc\'a 1066 28 IllO ti on 791 44 dam and 619

13 curv. 1035 29 \.ava\ 741 45 raqui'ra 618

14 va\.ua 1026 '30 uni t 734 046 tamperatura 616

16 thaory 1020, 31 socia\. 729 47 \1m1t- 607

16 education 992 32 individua\. 716 48 poal tion 699



96

Appendix ·D: The EAP Vocabulary

ill!.l (Freq, Rank)Recommended EAP Vocabu\ary:Occurrencee in eatabloi.hed List.

B .oc••• ( 29, 1951·) F(JACET & Zen Eir.n)B acoa.alb\a ( .28, ?0~2) S FAdju.ted Fraquancy and Rank

J A ecoidant ( 48, 1333) C p S FDi.tribution by EAP SUbcategori.eC aocidenhl. \~ ( 9, 3997) F

A acoolMlodata ( 33, 1780) FOccurrence. in EetabUeh.d Li.tsJ A acoompany ( 106, 895) C P S FJ A acoo"'pl.i.h ( 75, 988) S FJ JACET 1720

B aocord ( 101, 726) S FA Zen Eir.n .. 1900B accordanca ( 12, 3383) S F

J'A aocording ( 228, 290) C P S FLevalo. of Difficul.ty a acoordingl.y ( 43, 1453) S FJ A account ( 481, 89) C P S FA \ev.l. ........... 1900

B aooumu\at. ( 37, 16014) Fa l.eve\ ............ 561B acoumul.a ti on ( 15, 2961 ) S FC \eve\ ........... 638

A acouracy ( 47, 1367) S FJ A accurat. ( 74, 968) C P S F

B accurata\y ( 44, 1437) C P S FEAP SubcategoriesJ A accu.a ( 21, 7392) 9 FJ A accuatom ( 16, 2837) S FC Ar.a-ba••d Cora J A achi.va ( 172. 412) C P S FP Ar.a-ba.ed PS COlMlon A achiavamant ( 66, 1068) C P 9 FS Ar.a-ba.ed 99 Common j B acid ( 161. 473) P FF '" . SUbjact-ba.ad 4-way Common, A acknow\.dga ( 31 ; 1869) p. F

B acknowl.adgmant ( 6, 4960) S FC P S F ........... 874A acquaint ( 8. 427"5") FP F ••••• ,.. It •• 301 A acquaintanca ( 18, 2636) S l=S F ........... 866 J A acquira ( 118, 621) S FP •• It ••••••• 40 a acqulaiUon ( 20, 2476) S F5 ........... 45 J A acra ( 24, 2200) FF ........... 973 J A act ( 628, 72) CPS F

J A· action ( 210. 322) CPS FJ A activa ( 66. 1064) 9 FLave\ ( Fraq, Rank) J A activity ( 367, 160) 5 F'J A abandon ( 21, 2392) S F J A actual. ( 242, 268) C P,S FC abandonm.nt ( 4, 6979) F J A actua\\y ( 243, 266) C P S FB abbraviate ( 10, 3783) P F A acuta ( 23, 2261) S FB abide e II, 3547) F J A adapt ( 43. 1463) 5 FJ A ab1\1ty e 191, 363) C P S F

B adaptation ( 48, 1333) 5 FB abnormal. ( 27, 2048) F J A add e 446, 110) C P 5 FB abo\i.h ( 4. 6979) F J A addi tion ( 238, 276) CPS FJ A abroad e 21! 2392) F J A addi tional. ( 91, 799) CPS FJ A ab••nc. ( 86, 848) C P 9 F J A ad.quate ( 41, 1617) S FJ A ab.o\ut. ( 120. 611> C P S F

C adaquatel.y ( 19, 2660) C P S FJ A absoloutel.y ( 33, 1780) S F a adhara ( 12. 3383) 5 FJ A absorb ( 76, 943) P F C adh.ranca e 9, 3997) S Fa absorption ( 66, 1190) F C adheaion ( 8, 4276) FJ A abstract ( 108, 678) C P S FB adjacant ( 61. 1129) C P 5 FC abstraction ( 49, 1316) S F J B adj acth,a ( 107, 686) S FA absurd ( 14. 3081) S F J A adju.t ( 35, . 1713) C P S F

,abundant ( 20, 2476) S F

( 47, 1367) S FA

B adju.t",.ntA abus. ( 13. 3204) F B adminIst.r ( 28. 2002) FJ B acadamic ( 62, 111 e) S F J A admlnlst,.aUcin ( 40, 16-42) S FA academy ( 16, 2837) FC admis.lb\a ( Ii,' 6341) SC acc.\erata ( 108, 678) P F J A admission ( 8, 4276) S FC acca\.raUon ( 378, 142) P F J A admi t ( 72. 992) S FJ A accent ( 9" 3997) F J A adopt ( 113. 64~) C P S FJ A accept ( 178', 390) C P S F B adop~ion ( 14. 3081) FJ C acc.pt~b\. ( 49. 1315) S F J A adu\t ( 77, 935) S FJ B accaptanca ( 76, 943) S FC adu\thood ( 22, 2326) 5 F


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