Predicting Speaking Ability From Vocabulary 語知識能力測能性

The Japan Language Testing Association

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The 　Japan 　Language 　Testing 　Assooiation

Predicting　Speaking　Ability　From　Vocabulary　Know 且edge

　　「語彙知識によるスピーキング能力の予測可能性」

　　　　　　　　　　　Rie　KOIZUMI （小泉利恵）

Doctoral・Course，　University（）f　Tsukuba（筑波大学大学院博士課程）

　　　　　　　　　　　　　　　　　　　　　　要旨

　本研究は日本人の英語初級者（中学 3 年・高校 1 年生）を対象とし、（a）スピ

ーキング能力と語彙（発表語彙知識・話す際の語彙運用）の関係と、（b）スピー

キング能力は「発表語彙知識」によってどの程度予測が可能か、（c）スピーキ

ング能力は「語彙知識を使う能力」によってどの程度予測が可能かについて調

べた。その結果、発表語彙知識はスピーキング能力に強い影響を与え、スピ

ー

キング能力は話す際の語彙運用に強い影響を与えることが示された。スピ

ーキ

ング能力の半分以上は発表語彙知識によって予測が可能であり、またスピーキ

ング能力の約 5分の 1 は語彙知識を使う能力によって予測が可能であった。

　　　　　　　　　　　　　　　　　　　1。1皿t「oduction

　　　Vbcabulary　has　 long　been　regarded 　as　 a　vital　component 　of　communica 口ve

language　ability （e．g．，　Bachman ＆ Palmer，1996，　p，68； Ca∬ ol1，1968，　pp．54

−55）．

Since　 communicative 　 l跏 guage　 ability 　 includes　 speaking 　 ability ，　 vocabulary

owledge 　plays　an 　integral　role　in　spe 田dng 　a　language （e ．g。，　Higgs ＆ Cli丘brd，1982 ；

Levelt，1993）．　However，　there　have　been　few　studies 　that　examine 　the　degree　to　which

vocabulary 　knowledge　affects　speakdng 　ability （Noro ＆ Shimamoto，2003，p」 41）and

血e　degree　to　which 　vocabulary 　knowledge　contributes 　to　predicting　speaking 　ability．

Therefbre，　this　paper　mainly 　aims 　to　investigate　the　relationship　between　speaking

abi 正ity− amd 　vocabulary 　and 　to　examine 　how 　rnuch 　productive　vocabulary 　knowledge

can 　predict　speaking 　abMty ．　The　participants　of　this　study 　are 　Japanese　beginner　leveI

leanlers　of　English．

　　　Naturally，．it　is　unreasonable 　to　assume 　that　vocabulary 　knowledge　can 　predict　all

speakdng 　ability　since　many 　other　components 　apart 　from　vocabUlary 　knowledge　are

likely　to　exist，　so　vocabUlary 　knowledge　tests　are 　not 　enough 　to　assess　speaking 　ability，

and 　speaking 　tests　always 　seem 　to　be　indispensable．　However，　the　reason 　this　research

is　administered 　is　that　infbmlation　on 　the　strength　of 　impact　 and 　on 　the　degree　Qf

predictabihty　can 　help　understand 　the　importance　of 　vocabulary 　in　speaking 　ability ，　as

wen 　as　provide　the　basis　fbr　an　empirical 　model 　of 　speaking 　ability 　and 　for　rationales

of 　language　teaching 　and 　assessment ．

　　　　　　　　　　　　　　　　　 2．Literature　Review

2．1Re 且ationship 　Between　Speaking　Ability　and 　Vocabulary　at　a　Begjnner　Level

　　　There　 is　 a　theoretical　 and 　empirical 　 background 　 to ．the　relationship 　 between

一 1 一

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speaking and vocabulary. In the theoretical model'of Ll and L2 speaking (de Bot,1992; Leyelt, 1989, 1993), vocabulary has a central position in fbrming an utterance

with appropriate meanings and with syntactic, merphological, and phonological

structures. Levelt's model of the speaking process is one of ,the most influential from a

psycholinguistic perspective. Although Levelt's model was developed in order to

explain monolingual Ll speaking mechanisms without plaiming time, it has also been

used for L2 learners (e.g., de Bot, 1992; DOrnyei & Kormos, 1998) with some

modification when necessary, In this model, there are three stages of speech

production: (a) conceptualization (i.e., forming messages), (b) formulation (i.e., putting

the messages in a form of language), and (c) articulation (pTonouncing the form and

expressing the messages). in the formulation stage, the lexicon, which contains all the

inforrnation related to vocabulary, plays a crucial part. After rnessages are forrned,

words are searched for from a part of the lexicon and grarnmatical structures are

derived accordingly. Then morphological and phonological information is added by

using another part of the lexicon, Levelt's model suggests that yocabulary is always

required in the formulation stage and that no speech can be produced without

vo ¢ abulary. Additionally, the role of vocabulary seems rnuch greater among begiming

level learners because they tend to lack the minimum vocabulary needed.

ln addition to the theoretical importance, 'several

empirical studies have

consistently suggested that there is a substantial relationship between speaking ability

and vocabulary, especially at the beginner level (Adams, 1980; Higgs & Clifford,

1982; Ishizuka, 2000; Koizumi & Kurizaki, 2002; Takiguchi, 2003). Adams (1980)examined speaking factors that separate neighboring level groups using discriminant

analysis among workers in foreign affairs and their famiIies. It was fbund that

vocabulary is the only factor of level change from O+ to 1 on the Foreign Servicg

institute (FSI) rating scale (i.e., frorn Novice High to lntermediate Low and

intermediate Atfid on the ACTFL [American Coun¢il on the 'Ibaching

of Foreign

Languages] rating scale, Fulcher, 2003, p. 15). Although Adam's study has often been

cited (e.g., Fulcher, 2003, p. 183), the number of participants was small at the lower

levels (n = 7 fbr Novice Low to Novice High, p. 2), so precautions should be taken

when one interprets his results.

Based on Adams (1980), Higgs and CliffOrd (1982) made a rnodel of the relative

iMportance of various elements (i.e., yocabulary, grammar, pronunciation, fluency, and

sociolinguistics) to overall ability. in order to validate the model, they asked 50

teachers for their opinions on how each element affects speaking proficiency at each

proficiency level. Higgs and Cliffbrd showed that their model is similar to the teachers'

perceptions and that vocabulary contributes the most to speaking proficiency at a

beginner level. One problem conceming their study is that they only examined the

teaehers' perceptions instead of having teachers rate learners' utterances from

interviews (Magnan, 1988, p. 274).

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Koizumi and Kurizaki (2002) found strong associations between speaking test

scores arid the pumber of words uttered on the speaking test (ry = .69- .80, n = 76, p.

24). ln their studM there is a question of whether their speaking test scores refiected

overal1 speaking ability

Talciguchi (2oo3) conducted principal component analysis using holistic speaking

scores based on ACTFL Proficieney Guidelines (Fulcher, 2003, pp. 233-238) and

speaking perfbrmanee measures. As a result, the holistic scores and the number of

words uttered for one minute loaded on the same factor (p. 50), which suggests a

strong association between speaking ability and vocabulary. Since Takiguchi's

participants were srnal1 in number (n = 17) as was the case with Adams (1980), the

result needs to be interpreted with caution.

ln Ishizuka (2000), there was a moderate correlation (r = .43, n = 26, pp. 15-18)

between (a) vocabulary depth tests based on Read (1993) and (b) interview tests of the

Society for 'I;esting

English Proficiency kst in Practical English Proficiency (STEP[fest; Eiken, 2003). It seems that Ishizuka failed to report enough infbrrnation on the

validity of the test used (see also Read, 2000, pp. 183-184, for the validity issue

conceming the depth test). As such, the relationship between vocabulary depth and

speaking ability seems to be less conclusive in his research.

in summary, vocabulary seems to relate to speaking ability substantially and to be

probably the strongest of many speaking ability components (e.g., pronunciation and

fluency) at the begirmer level, although each study has its own weakness. It is also

interesting to review the studies above with special fOcus on the aspects of vocabulary

they targeted. The four studies (Adams, 1980; Higgs & Clifford, 1982; Koizumi &

Kurizaki, 2002; Takiguchi, 2003) dealt with a relationship between (a) speaking ability

and (b) vocabulary used in speaking perfbTmance (vocabulary performance), and the

degree of associations were rather strong. On the other hand, Ishizuka (2000) examined

an association between (a) speaking ability and (c) vocabulary stored as knowledge

(vocabular y knowlecige), showing a moderate relationship. Therefore, the results above

can lead to the following hypothesis: The relationship between speaking ability and

vocabulary perfOrmance is stronger than the one between speaking ability and

vocabulary knowledge.

Moreover, vocabulary knowledge is often separated into receptive and productive

knowledge (e.g., Read, 2000, p. 154). Receptive knowledge is the knowledge to

understand a word, which is often used in listening and reading, whereas productiveknowledge is the knowledge to produce a word when one speaks and writes (Schmitt,2000, p. 4). Ishizuka's (2ooO) vocabulary depth can be categorized into receptiye

vocabulary knowledge since words were provided and test takers selected the right

words in the test. ln relation to speaking, productive vocabulary knowledge seerns to

affect speaking ability more than receptive vocabulary knowledge does.

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22 Purposes of the Current Study

The present research has three purposes: (a) to examine the extent of the-

relationship between speaking ability and two types of vocabulary (i.e., productiveyocabulary knowledge and vocabulary perfbrmance), (b) to inyestigate to what extent

productive vocabulary knowledge can predict speaking ability, and (c) to explore how

much the ability to use vocabulary knoWiedge is involved in speaking ability. The

participants of this study are Japanese beginner level learners of English. One

hypothesis and two research questions were addressed as follows:

Hypothesis: The relationship between speaking ability and vocabulary perforrnance is

stronger than the one between speaking abiiity and productive vocabulary

knowledge (VK).Research question 1: How much can speaking ability be predicted by vocabulary

knowledge? .

Research question 2: How much can speaking ability be predicted by the ability to use

vocabulary knowledge?

The hypothesis and research questions correspond to the three purposes of the present

paper, It was rnade based on the previous studies and tested in a confirmatory way. To

the author's knowledge, the degree to which speaking abMty can be predicted has yetto be examined, so research questions, not hypotheses, were posed.

2.3 Definitions of Key Tlerrns

Speaking ability is defined as the ability to preduce rule-based expressions as

well as formplaic phrases gSkehan. 1998). The range of ability covers only

school-based proficiency (Shohamy, 1992), which refers to the proficiency in which

vocabulary, structures, functions, and contexts are limited to what has been learned at

school, Proficiency is viewed as communicative language ability in Bachman and

Palmer (1996), which is composed of (a) language knowledge, including vocabularyknowledge, apd (b) ability to use the language knowledge (McNamara, 1996).

While vocabulary knowledge consists of various aspects (Nation, 2001, p. 27),

this study focuses only on the knowledge of written foTm and meaning at one-word

level, which is often called vocabulary size (see Koizumi, 2003b, p. 27, for detailedconstruct definitien). It is worth noting that productive vocabulary knowledge defined

here deal with written aspects, not spoken ones, because vocabulary knowledge needed

to be conceptualized as more independent from speaking ability since speaking ability

includes vocabulary knowledge theoretically.

In order to reflect vocabutary used in speaking perzformance (vocabutary

peifbrmance), the nurnber of words uttered during speaking tasks was used based on

Koizumi and Kurizaki (2002) and Takiguchi (2003).

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3. Method

3.1 Participants

The participants in this research were 172 Japanese beginner level leamers from

the ages of 14 to 16, who had studied English as a foreign language fbr approximately

two or three years at school. They included third-year students at two public juniorhigh schools (n = 149) and first-year students at a prefectural senior high schoel (SHS;n = 18) and at an SHS aff/iliated .with a national university (n = 5). The participants'

speaking levels were from "less than Smattering" to

"Waystage

Plus or above" levelsi

(Koizumi, 2004a).

3.2 Materials

The two tests (a speaking test and a productive vocabulary knowledge test) were

used.2

3.2.1 Speaking 'Ilest

The speaking test was designed to assess the school-based speaking proficiency

(ShohamM 1992) of Japanese junior high school students, especially accuracy and

fluency in language competence and apprepriacy in pragmatic knowledge in Pulcher's

(2003) frarnework (p. 48). It was a face-to-face oral interview, and the speaking tasks

were created based on the contexts and functions listed in the Course of Study

(Ministry of Education, 1989, 1999) as well as North (2000). The test was composed

of three monologic tasks ([fasks 1, 4, and 5) and two interactive tasks (scripted role

plays; lhsks 2 and 3; see [fable 1). Of the five tasks, only 'Ihsk

4 provided planningtime, and this lasted one minute. The participants were not inforrned about the content

or structure of the test befbrehand. ln order to exclude the effbct of listening ability on

utterances, Japanese meanings were presented to a test taker when he or she had

dithculty in comprehendmg the interviewer.

Four analytic rating scales were developed with fbur levels (O to 3): (a) Tliskfulfillment (for Thsks 2 and 3), (b) Vbcabulary

"Ylolume,

(c) Accuracy (includingAppropriateness), and (d) Huency ((b), (c), & (d) fbr Tasks 1, 4, and 5). Koizumi

(2004a) showed some aspects of positiye validity evidence for the speaking test and

there were no misfitting or overfitting tasks in Rasch analysis.

3.2.2 Productive Vbcabulary Knowledge [Ibst

The productive vocabulary knowledge -test (VKT) was designed to assess

productive vocabulary knowledge (aspects of vocabulary size) fbr Japanese beginner

level learners of English (see Tabte 2). It was adapted from'the 1,OOO to 3,OOO word

frequency sections in the second -version of a Vbcabulary Size 'P:st

fbr Japanese

Learners of English (Mochizuki, 1998). In scoring the productive VKT, one point was

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Table 1 Examples ofthe speaking 7lest 7lrsks (All written in Japanese)7bsk 1 Please introduce yourself for 90 seconds, Please talk about many things.

When you have finished reading, please raise your head (i.e., no planning time, the

same as in 'fasks

3 and 5). 11rrlbpic Exarnples: name, grade, school, fayorites, family, and friends7lask 2 Ybu are talking with your friend, Express what you're talking about as shown

in the picture (see Koizumi, 2003a, fbr a picture). Elicited sentences: I want to go to

Maru a to bu a bookJGoodb e.fHetlo,IVP'hat time is it now.2/I am sor ,

7Zisk 3 Ybu are a reporter for your school newspaper. YOu are going to interview a boy

who transferred from another school last week, and then write a report. Look at your notes and ask him questions about himself. The teacher in front of you will play the role of the new student.

'slrNotes:

Things to ask the boy (l)Do you like this school.7

@W7;ere do you live now,7 @Where did you live befbre?7bsk 4 -T}311 me about your favorite singer, TV programs, or animal for 90 seconds.

YOu have one minute to prepare. IfrExarnple: reasons, how popular they are7Lisk 5 Ybur brother is mischievous. While you were at school, he scattered your things about your room. When you scolded him about it, he said,

"nothing has

changed at al1." Tbll him how the room has changed by comparing how it was befbre

with how it is now. Ybu have 90 seconds tos eak. (see Koizumi, 2003a, for ictures)

given when a meaning and a written fOrm matched exactly.

After Rasch analysis, some misfitting and overfiuing iterns were excluded (see[fable 2 for the number of rernaining items). Koizumi (2003b) examined sorne aspects

of yalidity of inferences and uses of the two tests and obtained favorable results.

Table 2 Examples ofthe Productive ltbcabulary Knowledge 7lest

(a) Productive ltbcabuiary Knowledge 7lest (originally 40 items; 30 items remained) Write the English word that best corresponds to the Japanese meaning on your answer

sheet. The first letter of the English word is already given. Write as much of the word

as possible even if you are unsure about the exact answer.

1. Sft (1 )[Answer:lunch] 34. =L!7FV (c- [chicken]

3.3 Procedures and Analyses

The students took the two tests in July of 2002. The speaking test was conducted

after schoel, whereas the two vocabulary knowledge tests were administered in Englishclasses except at the prefectural high school, where it was conducted after school. in

administering the 15-minute speaking test, 11 Japanese interviewers with sufficient

English speaking ability participated after attending a practice session to learn the

interview procedures. The interviewers rnoved to the next section when there was a

silence for at least 15 seconds, in order to avoid pressuring the students. During the

speaking test, al1 the utterances were tape-recorded. As for the productive VKT, a

maximum of 45 minutes was provided by which time al1 students said that they had

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finished the tests.

Tlasks 2 and 3 on the speaking test were scored by three trained raters by listening

to the tapes while looking at the transcription. For Thsks, 1, 4, and 5 on the speaking

test, utterances were scored by a rater based on transcribed and quantified results,

which were checked by three trained raters. The answers on the productive VKT were

scored by two trained raters. Both for the speaking test and productive VK[I; the

interrater reliability was very high (Both 1.00 in Rasch analysis). The data of the two

tests were analyzed separately using EACETS for Windows 3.45.2 (Linacre, 1991),

which implements the multi-faceted Rasch measurement model. Each of the logit

ability scores derived was considered to reflect a student's speaking ability and

productive vocabulary knowledge.

The number of words uttered during speaking tasks, which is a reflection of

vocabulary perforrTiance, was counted only on the three tasks that elicited extendedspeech (i.e., Tasks 1, 4, and 5) and the values were averaged to remove the effects of

tasks. Although the three measures (i.e., the nurnber of unpruned token, pruned token,

and word types) were originally considered,. only the number of word types (Type, r

hereafter) was selected fbr subsequent analysis. This was because three measures were

highly correlated (r = .91- .99) and because the relationship arnong them was strong

enough to raise the question of multicollinearity (Tabachnick & Fidell, 2001, p. 84),with which two of the three variables needed to be removed. Types were counted based

on lemma as in Daller, van Hout, and Treffers-Daller (2003). That is, base fOnn and

inflected forms were considered to be the same type. For example, the following were

considered to one type: p4ay, plays, playing; be, is, am, are, was, wene, been; 7bro.

7laro's.

in order to investigate Research question 2, five speaking pei[fbrmance measures

of lexical complexity were utilized (see fable 3). These measures were deemed to

indicate the existence of the ability to produce utterances with lexical complexity and,

by extension, the ability to use vocabulary knowledge to produce such utterances. The

measures were computed using utterances from three tasks combined (i.e., an average

of the results of 'fasks

1, 4, and 5).

The definitions of Iexical words and gramrnatical words were based on

O'Loughlin (2001) with minor modifications (see Koizumi, 2004b for details). Lexical

words were content words and gramrnatical words were function words (e.g., all forms

of be, db, have and auxiliaries). The judgment of Iexical and grammatical words from

all the three tasks was conducted by the author and another rater who were majoring in

applied linguistics. The interrater reliability was very high (r = .99, p < .Ol). When

there were disagreements, they discussed and carne to an agreement, which was

utilized as the final coding.

Hypothesis and Resegrch question 1 were answered using path analysis by

utilizing SPSS 10.0 E and Amos 4.02. Research question 2 was solyed using multiple

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regression analysis. An alpha level of .05 was used for al1 statistical tests.

Table 3 Five Speaking Peiformance Measures ofLexical Comptexit),

(LC 1) No. of different word types divided by No. of words; i.e., type token ratio)

[Code: Typelroken]

(LC2) No. of different word types divided by the square root of No. of words

[TypelV-[Ibken](LC3) No. of lexical words per word [Lexical wordsllbken]

(LC4) No. of sophisticated lexical words and No. of basic (i.e., non-sophisticated)

lexical words given half the weight divided by No. of words (Weighted lexical

density) [{Sophistcated words + O.5 ' Basic words}IIbken]

(LC5)No.ofso histicatedwordt es erword[So histicatedwordt esllbken]

Alote. See Daller, van Hout, & Treffers-Daller (2003) fbr LCI, LC2, and LC5 and

O'Loughlin (2001) for LC3 and LC4. Sophisticated words = words not in the list of

1250 words in the JACET List of 8000 Basic Wbrds [JACET Basic Word Revision

Committee, 2003] with proper nouns and Japanese words excluded. JACET 8000

LeveE Marker (Shimizu, 2004) was used fbr coding.

4. Results and Discussion

4.1 Before Constructing Models

Missing data, which can be considered missing completely at random ('Ibyoda,

2003, p. 107), were excluded (n = 28), and as a result, 144 students rernained.

As seen in Table 4, the internal consistency estimates were high on the two tests.

in the initial analysis, two assumptions about using structural equation modeling were

examined: univariate norrnality and multivariate norrnality (Kunnan, 1998, p. 313). As

for univariate normality, a value of kurtosis was rather 1arge <more than 1 ± 21; Kunnan,

p. 313) on Type, However, the histogram fOr Type was near to normal distributions.

Tbble4 DescriptiveStatisticsfortheThreeMeasures

Measure MSD Min Max Skew Kurt a

Speaking Testa (Speaking Ability)PToductive VKTa (Productive VK)

T e(Vbcabular PerfOrmance)

O.02 O.98 -2.30 3.78 O.45 1.21 .86

-1.71 2.22 -7.97 5.76 O.06 1.49 .91

.11.27 8.52 O.OO 42.67 1.38 2.04

Note. n = 144. Min = Minimum; Max = Maximum; Skew = Skewness; Kurt = Kurtosis."the

results of logit scores except for the Cronbach's alpha (ct), which was calculated

using raw scores (The averaged scores of the raters were used). O = interpretation of

the measures; VKT = Vbcabulary Knowledge 'Ibst.

Next, Mahalanobis Distance was utilized in order to detect multivariate outliers,

which are related to multivariate normality. Five cases (students with x 2 of more than

18.47, cij' = 4, p < .OOI, Tabachnick & Fidell, 2001, p. 93) were deleted until no others

showed the extrerne values.3 Befbre removal, rnultivariate normality values in the

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Amos output were 21.22, but after deletion, the value decreased to 2.78. Although both

figures were high enough to be significant, showing multivariate non-normality, the

value after exclusion became much closer to norrriality, which led to the decision that

this data of 139 students (i.e., 144 - 5) would be used for subsequent analysis (seeAppendix A, for means, SDs, and correlations, n = 139).

4.2 Model of Speaking Abi]ity and Vbcabulary

ln order to make a model explaining the relationships between speaking abilitY,

vocabulary knowledge, and vocabulary perfOrmance, one path model (Model 1) was

drawn. It should be noted that, as in Figure 1, there are theoretical directions where

productive vocabulary knowledge affects speaking ability, which infiuences

vecabulary performance, but not the other way around.

FigureI. Model1(StandardizedSolution;n=139)

in the path model (see Figure 1), rectangles show measured (observed) variables,

which can be assessed directly. Circles represent measurement errors. One-way arrows

represent a direct impact, and the numbers beside them are path coefficients, indicating

the degree.of impact. The coefficients range from -1.00

to 1.00 in the standardized

solution. For model estimation, a maximum likelihood method was us ¢ d. As shown in

Table 5, Model 1 fit the data well. All the coefficients of the regression weights and

correlations were significant.

In order to investigate whether there are significant differences in strengths

between the effect of productive vocabulary knowledge on speaking ability and the

effect of speaking ability on vocabulary performance in eaeh task, the following two

procedures were followed. First, a model was made with two path coefficients in focus

fixed to the same value (see Figure 2). Then, four types of fit indices that can be used

for inodel comparison when the degrees of freedom are different CIbyoda, 2003, p. 127,

225) were utilized for a comparison between the constrained (AB fixed) model and

Model 1. FirstlM Parsimony adjustment Comparative Fit lndex (PCFD showed that the

fixed model was a little better than Model 1 (see Table 5). Secondly, Root Mean

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Square Error of Approximation (RMSEA) dernonstrated that Model 1 was better than

the AB fixed modei. Thirdly, Browne-Cudeck Criterion (BCC)4 indicated that Model 1

was betteT than the AB fixed model. Fourthly, the x 2 difference test (Tletbachnick &

Fidell, 2001, p. 703) suggested that Model 1 was significantly better than the AB fixed

model.

Figure2, CodingofPathCoefficients

Table5･ FitStatisticsforModels 2 X(d

,x21dy'

CFIPCFIRMSEA(909t6CI)BCC

x2difference

(dCriteria >,05 <2.0 >.90 Hi her< O.05Lower

Model1 O.72 O,72

(1), .40

1.00 .33 .oo(.OO-.21)17.20

ABFixed 136.89 68.44

(2), .OO

.50 .34 .70 151.31

(.60- .80)

136.17** (1)

Note. n = 139. PCFI = Parsimony adjustment Comparative Fit lndex; RMSEA = Root

Mean Square Error of Approximation; CI = Confidence interval; BCC =

Browne-Cudeck Criterion; See Arbuckle & Wothke (1995) for the criteria; Higher =

The higher, the better. **p

< .Ol .

When a model is better thaii another model, it indicates that the condition (i.e.,fixed or non-fixed) in the better model is preferable and that the path coefficients at

issue are either different or the same across the models. The results in the four indices

did not always agree (i.e., PCFI: A= B; RMSEA, BCC, and x2difference test: A ;

B). However, eonsistent results arrived at by three indices were as follows: A iE B.

Therefore, Hypothesis (i.e., "lhe

relationship between speaking ability and vocabulary

performance is stronger than the one between speaking ability and productive

vocabulary knowledge.") was supported.

A point to be noted is that although the comparison between Model 1 and the

fixed model was conducted for hypothesis testing, the model accepted in this study was

Model 1 because the fixed model was not based on the previous literature. Moreover,

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one thing to be kept in mind in interpreting these results is that results of one-way

arrows showing a direct impact do not guarantee that there are causal relationships, and

that other types of evidence are needed to clairn causality (e.g., Maeda, 2004). ln this

research, causal relationships were stated for the fo11owing two reasons in addition to

the results of path analysis. First, theoretically, productive vocabulary knowledge can

be deemed to affect speaking abilitM which influences yocabulary perfb'rmance.

Second, in terms of test administration, the opposite causal direction of speaking

ability affecting productive vocabuiary knowledge is unlikely because about three

quarters of the students took the productive VKT before the speaking test whereas the

rest took the speaking test before the productive VKT.

Model 1 shows that productive vocabulary knowledge influenced speaking ability

strongly (.78) and that speaking ability afiiected vocabulary perforrnance strongly (.81).The impact of speaking ability on vocabulary perfbrrnance was stronger than the

impact of productiye vocabulary knowledge on speaking ability. Moreover, productive

vocabulary knowledge had a substantial influence on vocabulary perfOrrTiance (.63[= .78* .81]).

Conceming the impact of productive vocabulary knowledge on speaking ability,

it was found that if students have such knowledge, it is likely that they have high

speaking ability to a large extent. Ishizuka (20oo) found a moderate effect of receptive

vocabulary depth on speaking abili.ty (r = .43), whereas the current study dealt with

productive yocabulary size and fbund a stronger effect. It seems that productive

vocabulary size affects speaking ability more than receptive vocabulary depth does

among beginning leamers.

With respect to the effect of speaking abMty on yocabulary performance, the

results indicate that if students have high speaking abilitM it is likely that they will

produce better vocabulary perfbpmance, specifically a 1arger number of word types.

This result is consistent with previous studies (Adarns, 1980; Higgs & Cliffbrd, 1982;

Koizumi & Kurizaki, 2002; Takiguchi, 2003). Additionally, the influence of productive

vocabulary knowledge on vocabulary perfOrmance is also substantial.

Research question 2 of the degree to which speaking ability can be predicted by

vocabulary knowledge was answered using the value at the upper right of the rectangle,

which signifies the proponion of speaking ability accounted for (R2) by productiyevocabulary knowledge in Model 1 (.61). Therefore, more than half of speaking ability

(6 1 %) can be explained by productive vocabulary knowledge. Since the current study

assessed only one-word level vocabulary knowledge, it is rather surprising to obtain

such a high percentage of speaking abdity explained only by productive vocabulary

knowledge. One may wonder if the reason such a high percentage was gained was that

the students' utterances evaluated and interpreted as speaking ability were very shert,

so the impact of vocabulary was strong. However, this explanation may be difficult to

adyance because speaking tasks elicited utterances varying from short to long. While

-11-




two dialogic tasks (i.e., [fasks 2 and 3) mostiy extracted one-sentence level utterances,

three monoiogic tasks produced a short to rnedium length of speech (in case of the

number of pruned token, 1letsk 1: M = 18.01, SD = 14.15; 'fask

4: MF 11.40, SD =

12.69; 1fask 5: M = 16,58, SD = 18.22), Further analysis needs to be perfbrmed

regarding the relationship between the number of fbrmulaic expressions and speaking

ability. The proportions of vocabulary perfOrmance accounted fbr (R2) by speakingability (65%) suggest that about half of vocabulary perfbrmance can be predicted by

speaking ability.

43 What Explains the Remaining 39% in Speaking Ability?

The results in Section 4.2 show that 61% of spealdng ability is pTedicted by

productive vocabulary knowledge. The next question to address is what covers the

remaining 39%. Research question 2 was examined in an exploratory way by using

five perfbrmance measures of lexical complexity (see [fable 3) and productivevocabulary knowledge logit scores as independent vaiiables, and speaking ability logit

scores as a dependent variable. The reason confirmatory analysis was not used here

was that theoretically, speaking ability afliects speaking performance, and the attempt

to explain speaking ability frorn speaking perfbrrnance is in the opposite direction.

[[:wo types of multiple regression analysis were utilized: sequential and stepwise

regression analysis. ln sequential regression analysis, two steps were taken. First,

productive vocabulary knowledge was first entered into the regression equation.

Second, fiye perfbrmance measures were then entered and some measures were

selected and included into the regression equation based on statistical criteria. Stepwise

multiple regression analysis was employed for "eliminating variables that are clearly

superfluous in order to tighten up future research" (Thbachnick & Fidell, 2001, p. 138).

The multiple regression equation was found to be significantly meaningfu1, F(3, 134) =

164.65, p < .OOI, The three variables were able to predict speaking ability to a 1arge

degree (79%; see 'Ihble

6).S

Since speaking perfbrmance measures (e.g., LCI and LC2) can be considered to

imply the existence of the ability to produce such performance, speaking ability was

accounted for by productive vocabulary knowledge (60%)6 and the ability to produce

utterances with lexical complexity and, by extension, the ability to use vecabulary

knowledge to produce such utterances (19%; 15 + 4). Arnong the two perfbmiance

measures other than productive vocabulary knowledge, the percentage of LC2 (Typelffoken) was the highest. Ms result suggests that other lexical aspects than the size

of productive vocabulary knowledge may be worth further exploration in investigating

predictors of speaking ability.

-12-




[[bbte 6 Regression Analysis Summaryfor Six I,keriables Pnedicting SPeaking AbilityMeasure B SEBff R zR2

ConstantStep 1: Productive Vbcabulary Knowledge

Step 2: LC2 Type!V-[fbken

LCIT etlbken

O.50 O.38O.19** O.02 .43

O.22** O.04 .36-1.55** 033 -.25

.60**.75**.79**.15.04

?Vote. n = 138. T = Thsk. R2 signifies the percentage explained cumulatively (e.g., 74%

was explained by productive vocabulary knowledge and LC2 Type/J'Ibken.

**p

< ,Ol.

Compared with the more percentage explained. by productive vocabulary

knowledge, 19% of speaking ability was explained by the ability to use vocabulary

knowledge, the proponion of which was smaller but seems substantial. Such ability

may include the ski11 to select words from the vocabulary knowledge appropriate to

context, to construct discourse, and to pronounce formed utterances in a limited time. It

seems that this 19% has a crucial role because it makes vocabulary knowledge function

as an element of speaking ability. The role of the 19% can be illustrated by examining

two students' examples in ･this study. A male student at junier high school had high

productive vocabulary knowledge but low speaking ability. His teacher mentioned that

he could not speak at length and logically even in his first language (Japanese). He

seems to have had some vocabulary knowledge but lacked the ability to use it properly.On the other hand, a female student at junior high school had low productivevocabulary knowledge but high speaking ability. Her teacher stated that she spoke a

great deal in Japanese when she was cheerfu1 although her basic knowledge was not

'high. She appears to have had a smal1 amount of vocabulary knowledge but to have

been able to put it to use very effectively. Her characteristics were reminiscent of

"VVes"

(Schmidt, 1983), who had low linguistic knewledge but high speaking ability

and who was able to communicate effectively. These examples illustrate the

importance of the ability to use vocabulary knowledge in speaking.

ln the present research the remaining 21% (i.e., 1OO - 79) of speaking ability was

not predicted, but based on Bachman and Palmer (1996), it is likely that some

proponions are accounted for by students' indiyidual factors, such as communication

strategies (DeKeyser, 1988), motivation (Koizumi, 2002), risk-taking,extroversion-introyersion (Skehan, 1989), as well as prior experiences of language

leaming and assessment (e.g., how English was leamed and whether and how often

speaking tests were taken), whereas others are explained by measurement errors.

4.4 Further Analysis: Relationship Between Ilest Performance in the Productive

Vocabulary Knowledge Ilest and in the Speaking [fest

-13-




While all the hypothesis and research questions were examined in Sections 4.2

and 4.3, a more detailed analysis was made conceming the relationship between test

perfbrrnance in the productiye vocabulary knowledge test (VKT) and in the speaking

test.

There were two words shared between the productive VKT and the speaking test:

box and popular. Responses from test takers in the two tests in relation to the two

words were cornpared in this section. ln the speaking test, Thsk S contained a picture of

a box, whereas in the prompt of Tleisk 4, popularity was one of the discussion subjects

(see Z[letble 1). Howeyer, it was not obligatory to use the two words, and obligatory

contexts will be required in future studies.

[fable 7 demonstrates that most students fo11owed the expected pattems but that

there were some students (n = 2 and 6 fOr each word) who responded in an unexpected

way (i.e., they did not use a spelling simi1ar to the pronunciation but uttered the word

successfu11y) fbr three probable reasons. First, they had learned the sound but had not

yet acquired the spelling. The fact that both box and popuiar are loanwords from

English te Japanese may be related to this phenomenon. Second, they were not able to

recall a word from a prompt in Japanese (as in the productive VKT) but were able to

do it from a picture (as in Thsk 5 in the speaking test). The second reason can be

justhied with the claim that word knowledge inc!udes various aspects (e.g., Nation,

2001), Third, the amount of pressure in the two tests was different. Some students may

have given up writing a word or written a different word sharing the same starting

letter (e.g., book) when they were not very sure, even wheh an instruction said, "Write

as much of the word as possible even if you are unsure about the exact answer." ln

contrast, they faced interlocutors in the speaking test, so they may have felt more

pressured and tried harder to utter the word.

Among these three reasons, the first reveals a future direction for research. When

a relationship between speaking and productive vocabulary knowledge is analyzed in

detai1, the knowledge to pronounce words, rather than knowledge of spellings, needs to

be assessed in a spoken version of a productive vocabulary knowledge test. lf a written

version using the same fbrrnat as the current study was employed (maybe because ofpracticality). the scoring methods may require modification in order to take knowledge

of pronunciation into account. Such modification would involve giving credit fbr

incorrect spelling with seeming knowledge of correct pronunciation. ln fact, the qualityof this method was investigated in Koizumi (2003b), but the conclusion was that the

method was less valid than exact word scoring .in terms of correlational analysis.

However, in the analysis of a relationship with speaking ability, the consideration of

pronunciation seems crucial, and scoring methods may need to be flexible according to

the test purpose.

-14-




[fable 7 lixpected and Actual Response Patterns inRelation to the 7Svo Yilords Shared

Between the Productive 1,bcabulary Knowledge 7lest and the Speaking 71est

Expectedpatterns bex(n -

165)popular(n - 152)

Corrects Mn Ex ectable Ex ectable*7082 30 15

lncorrect spelling with an

indication of knowledge ofcorrect ronunciation

Expectable Expectable'1 2 24 23

incorrect spellifig with liuleindication of knowledge of

pronunciation OR no

answer

Little Expectableexpectable

2a 8 6 54

Note, Used [Not used] = The word was [not] used. * = This pattern can occur due to a

lack of time to use the word, lack of ability to use the knowledge, lack of confidence to

use the word, or due to not noticing the prompt in the picture. aResponses

in the

productive VKT: no answer (n = 1), book (n = 2); bResponses: no answer (n = 4),po (n= 1), pluer (n = 1).

5. Conclusion

S.1 Main Findings in the Current Stitdy

The current study investigated the relationship between speaking ability,

vocabulary knowledge, and vocabulary perft)miance of Japanese beginner level

learners of English. The results indicate the existence of (a) a substantial effect of

productive vocabulary knowledge on speaking abilitM and (b) a substantial effect of

speaking ability on vocabulary perfbrrnance, and that the impact of (b) is significantlystronger than that of (a) (i.e., a support for Hypothesis). It is also demonstrated that

more than half (60-61%) of speaking ability is explained by productive vocabulary

knowledge (i.e., a response to Research question 1) whereas oneLfifth (19%) is

pr¢ dicted by the ability to use vocabulary knowledge (i.e., a response to Research

question 2).

5.2 Practica] Implications

The findings in the current study suggest three implications for practical purposes.

First, for instruction, the importance of productive vocabulary knowledge carmot be

overstated. Thus, increasing such knowledge is essential to enhancing high speaking

ability.7 However, the ability to put the vocabulary knowledge into use may also be

important although the proponion of speaking ability covered by it is smalier (19%). Second, fbr assessment, speaking tests that elicit speech from students are

necessary because speaking ability involves not only knowledge but also the ability to

use it, which was empirically tested in this study. In addition, rating categories of

-15-




speaking tests should include lexical aspects (e.g,, vocabulary volume and lexical

complexity), which can also be weighted in scoring, because of the essential role of

vocabulary in speaking ability.

Third, since a substantial amount of speaking ability can be predicted by

productive vocabulary knowledge, this finding can make the administration of

speaking tests more practical by predicting speaking ability with approximate precisionfrom vocabulary knowledge, and by selecting tasks in $peaking tests that are more

appropriate to students' speaking ability.8 It should be noted, however, that only a smal1

portion of tasks in a speaking test should be selected using this method since this

procedure involves errors of estimates, and that content aspect of validity needs to be

considered in task selection.

5.3 Further Research

The current study only examined the relationship between speaking ability and

vocabulary of Japanese beginner leyel learners of English. Further investigation should

focus on interrnediate and advanced learners. An anticipated result based on Adams

(1980) and Higgs & Cliffbrd (1982) is that there is a decrease in the degree to which

speaking ability can be explained by vocabulary knowledge, which may lead to the

modification of models.

Moreover, the results of the present study may be related to the following

experirnental aspects: the elicitation method, the speaking tasks, the rating scales, and

the vocabulary tests used. Therefore, replication studies are needed for generalization.ln relation to the speaking test, the speaking ability assessed in the current research

may be rather limited in that the ability covered was not proficiency overal1 but

school-based proficiency and that it did not include a component of pronunciation.Therefore, a wider range of speaking ability needs to be assessed fbr the purpose of

generalization. Regarding the vocabulary tests used, one-word level vocabulary size

was the target, but multi-word level vocabulary knowledge size as well as vocabulary

depth may also be crucial in speaking (e.g., Read, 2000). A funher rnodel may also be

necessary that includes other faetors explaining speaking ability, while test method

effects (i.e., paper-and-pencil tests vs. perfbrmance tests) should be controlled and

latent yar iables introduced utilizing structural equation modeling.

Acknowledgements

I gratefully acknowledge the financial support of the Japan Language Tbsting

Association (JIJITA), An earlier version of this paper was presented at the 7th JL[[:AArmual Conference on October 25, 2003, at Kumarnoto University and at the 24th

Tsukuba So6iety of English Language Tleaching on June 27, 2004. I arn deeply

indebted to Professor Akihiko Mochizuki for his usefu1 suggestions and

encouragement in conducting the experirnent and analysis. I would also wish to thank

-16-




Professor Tamaki Hattori, Mr. Hitoshi Takiguchi, Mr. Yb In'narni, Mr. Koya Suzuki,

Ms. Maki Shimizu, and many others who gave me invaluable comments.

Notes

1. An example of proficiency descriptors at the Smattering level is.℃̀ an use some

basic greetings; gan say thank you, sorr y" and an example at the Waystage Plus level

is "Can

interact with reasonable ease in structured situations and short

conversations, provided the other person helps if necessary" (Nomh, 2000, pp. 274-275), North's proficiency scale was used as the basis of the Common Reference

Levels of Common European Framework (Council of Europe, 2001). ln the

Common Reference Levels, most of the participants in this study belonged to the

level of basic users or below.

2. AIthough two other paper-and-pencil tests, a receptive vocabulary knowledge test

and a grammar test, were also conducted (Koizumi, 2003b), the two tests were not

used in the current study because the correlation between productive vocabulary

knowledge and receptive vocabulary knowledge was high (r = ,84, n = 139), and

because a grammar test had rather low reliability ( a = .67, n = 144).

3. Ari analysis of the relationship between variables reyealed that the five outliers had

exceptionally weak (n = 2) or strong (n = 3) relationships between speaking ability,

vocabulary knowledge and yocabulary perfbrniance.4. The reason why BCC was used instead ofAIC in this study was because

"BCC

. imposes a slightly greater penalty fbr model complexity than does AIC" (Arbuckle & Wbthke, 1995, p. 404), which seemed more appropriate for the model

comparlson.

5. R2 was the stime when productive vocabulary knowledge and five lexical complexity

measures were entered on at a time.

6. The values are very slightly different between Figure 1 and Thble 6, probably

because of the smal1 decline in nurnbers in [[hble 6 (n = 136).

7. See Aizawa, Ochiai, & Osaki (2003), Nation (2001), Schmitt (2000) for teaching

principles of vocabulary.

8. This method seems to be able to reduce not only the time it takes to administer an

adaptive speaking test and to iearn how to administer it, but also an interviewer's

on-line burden of selecting tasks during the test (see Koizumi, 2003a, for detailed

analysis based on Bachman & Palmer, 1996). Moreover, efficient task selection can

"enhance

measurernent precision" (Lord, 1971, p. 228), leading to a decrease in the

number of tasks (Heming, 1987, p, 140). This method is based on the principle of

two-stage testing, a type of adaptive or tailored test (e,g., Weiss, 1983, p. 6). The use

of vocabulary and other knowledge for selecting later tasks can be found in two

existing tests: the DIALANG assessment system (Council of Europe, 2001, pp.

226-243) and 'fest

Ybur English (Chapelle, Jamieson, & Hegelheimer, 2003), which

-17-




do not include speaking sections. Although this approach is less ethcient than a

computer-adaptive speaking test (Kenyon, Stauffer, Louguit, & van Duzer, 2002),

the format presented in the current study seems to have an advantage in that it is

useful when there is limited access to computer programs that enable

computer-adaptive task selection, and in that approximate speaking ability can be

obtained before administration of the speaking test.

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AppendixA IntercorrelationsandDescriptiveStatisricsforThreeMeasuresMeasure 1 2 M SDMinMax Skew Kurt

1. Speaking Ability2. Productive Vbcabulary

Knowledge3.T e

.78

.81.66

-O.05 O.91, -2.30 2.68 O.11 O.43-1.80 .2.09 -7.97 3.96 -O.16 1.44

10.81 7.77O.OO 37.33 1.131.08

Note. n = 139. All Pearson product-moment correlation coefficients were significant at

P<.Ol.

Appendix B Intercorrelations and Descriptive Statisticsfor Seven Measures

Measure 1 2 3 4 5 6 7 M sw

1 . Speaking Ability

2. Productive Nlocabulary ,77*'

Knowiedge

3. LCI [Typelroken] -.69'" -.50'"

4.LC2[TypelV-Ibken] .78'* .62"" -,64'*

--

5.LC3 -.4S**-.46** .49** -.38** --

[Lexical wordslroken]

6. LC4 [{Sophisticated -.37** -.42'* .38** -.29"'

.91*'

words + O.5 * Basic

words}IToken]

7.LC5[Sophisticated -.02

-.10

-.02 .00 .16

word typeslroken]

cf.Te ,80'*

.54*

-O.03 O.90-1.76 2.03

O.80 O.14

4.69 1.45

O.57 O.11

O.33 O.06

O.08 O.05

.65** -.74** ,94** -.46**-,39** -.07 10.89 7.75

Note. n = 138. *p < ,05.*'ip < .Ol.

-20-

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Predicting Speaking Ability From Vocabulary 語 知識 能力 測 能性

Predicting Speaking Ability From Vocabulary 語知識能力測能性