Jurnal Kurikulum BPK 2020

In addition, the inconsistent results of either VB or VD contributing more to reading
comprehension has spurred the interest to determine whether VB or VD has more predictive
power in the MUET reading comprehension component. The inconsistency in the findings of
the various studies might have been caused by the different types of reading comprehension
tests employed. For instance, Akbarian and Alavi (2013) found that the Test of English as a
Foreign Language (TOEFL) reading subtest is more associated with VB than the International
English Language Testing System (IELTS) test. They reported that the explained variance of
VB was 0.27 in the IELTS reading comprehension test and 0.298 in the TOEFL reading
comprehension test and the overall results show that VB has predictive power in both reading
tests. However, they did not include VD as one of the predictors. Therefore, it would be
interesting to include VD into the study to determine the link between VB and VD and the
MUET reading comprehension component among Malaysian pre-university students.
Since the VB is also analysed in the present study, it would be noteworthy to explore
the VB needed for reading comprehension (referred to as vocabulary threshold for reading). To
date, only a few studies has explored the correlation between VB and MUET reading
comprehension component (e.g. Arifur Rahman, 2017; Sarimah Shamsudin & Nor Hazwani
Munirah Lateh Anie Attan, 2016; Tengku Shahraniza Tengku Abdul Jalal, Raeidah Ariff, Isma
Suhaila Ismail, & Al-Mansor Abu Said, 2015)

THEORETICAL PERSPECTIVES OF BREADTH AND DEPTH OF VOCABULARY
KNOWLEDGE

The dimensions of breadth and depth were initially mentioned by Anderson and
Freebody (1981) but VD was loosely described as knowing and understanding a word
sufficiently when encountering it through listening or reading. Meanwhile, Anderson and
Freebody (1981) state that VB means a learner would have at least some significant aspects of
meaning of the vocabulary. In the attempt to define aspects and components of vocabulary
knowledge, a number of prolific L2 researchers (e.g. Haastrup & Henriksen, 2000; Meara,
1996; Qian, 1998, 1999, 2002; Read, 1998, 2004; Wesche & Paribakht, 1996) recognize two
dimensions of it, namely breadth and depth.
Nation (1990, 2001) has proposed eight types of word knowledge which are word parts,
written form, spoken form, form and meaning, concepts and referents, grammar, collocation
and constraints on use. However, only form and meaning are considered as VB. On the other
hand, Qian (2002) developed a framework of vocabulary knowledge which comprises four
dimensions, i.e. vocabulary size, depth of vocabulary knowledge, lexical organization, and
automaticity of receptive–productive knowledge. Another framework which has breadth and
depth as lexical space has been proposed by Daller, Milton, and Treffers-Daller (2007). In
lexical space, a learner's vocabulary knowledge is described as a three-dimensional space,
where each dimension represents an aspect of knowing a word. Thus, in all the frameworks
mentioned, there is an obvious agreement that vocabulary knowledge should have at least two
dimensions which are breadth and depth.
Breadth of vocabulary knowledge is also known as vocabulary size (Tan & Goh, 2017)
and it is easier to measure compared to depth of vocabulary knowledge. As opposed to
vocabulary breadth, depth of vocabulary knowledge is the quality of lexical knowledge, or how
well a learner knows a word (Meara, 1996; Milton, 2009; Read, 1993, 2000).
There are various components in VD such as pronunciation, spelling, register, as well
as stylistic and morphological features (Richards, 1976; Nation, 1990; Meara, 1996; Haastrup
& Henriksen, 2000). It even extends to the knowledge of the word’s syntactic and semantic
relationship with other words in the language including collocational meanings and knowledge
of antonymy, synonymy, and hyponymy (Chapelle, 1994; Henriksen, 1999; Read, 2000). Of


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these, meanings and collocations are important components in vocabulary depth (Qian, 1998,
1999, 2000, 2002; Qian & Schedl, 2004; Wang, 2014).

PAST RESEARCHES ON VB AND VD IN READING

Qian’s studies are worth mentioning because he is one of the pioneers in acknowledging
the significance of VB and VD in reading comprehension. Qian (1999) conducted an
investigation on the association between VB and depth and reading comprehension among 33
Chinese and 41 Korean L2 English learners. The findings show association between depth and
reading was the highest (r = 0.82, p = 0.05). Besides, VD added a unique portion of 11% of
explained variance in reading comprehension when VB was held constant with 60% of
explained variance in reading comprehension.
Qian (2002) carried out another study among 217 students attending ESL programme
at Toronto University. The study has found that correlation coefficients among TOEFL-
reading, TOEFL–vocabulary item measure and breadth and depth of vocabulary knowledge
were lower, ranging between 0.68 and 0.80. The correlation between depth and reading (r =
0.77, p<0.01) was again higher than breadth and reading (r = 0.74, p<0.01). In regression
analyses, even though depth was entered at second step, it still provided an additional 13%-
14% of the criterion variance over and above VB measure and TOEFL–vocabulary item
measure respectively.
Yusun et al. (2012) have found a similar result to Qian’s findings where VD made a
unique contribution (11%) to reading over and above listening comprehension and VB. Even
in reversed order, VB failed to add a significant variance to reading (Yusun et al., 2012).
Besides, Mehrpour et al. (2011) analysed the Beta indices of the two predictors and have found
that every unit increase in the level of VD, the reading comprehension score would increase by
0.46 which was higher compared to VB with a Beta index of 0.32.
The significant relation of VD and reading comprehension is substantiated by
Mohammadi and Shakouri's (2014) finding which compared the effects of the two vocabulary
teaching methods in line with breadth and depth on reading ability (TOEFL test) of 70 students
studying at Islamic Azad University of Tonekabon, Iran. Mohammadi and Shakouri’s (2014)
study has shown teaching VD is better than teaching VB as it could help better understanding
in reading. In addition, Choi's (2013) finding also has discovered the importance of VD’s role
in reading and its greater impact on reading comprehension compared to VB.
Nonetheless, studies conducted by Li and Kirby (2014) and Wang (2014) have revealed
that VB significantly predicts reading. VD as measured in Li and Kirby’s (2014) study were
knowledge precision, polysemy and word formation. The sample comprised 246 younger
participants who were from Grade 8 English immersion classes in middle school in China. Li
and Kirby (2014) claim that both VB and depth contributed to reading but breadth contributed
more variance (i.e. 9%, p < 0.01) in reading comprehension even after controlling for depth
than vice versa. Meanwhile, depth only added 1%, which is statistically insignificant to reading
comprehension after VB was entered into the regression.
Wang (2014) believes that VD and breadth of ESL learners are related to reading
comprehension and linguistic competence. Wang’s (2014) study shows that VB alone could
predict larger variance in reading comprehension which was 28.3%. However, the variance of
reading comprehension accounted for by VB alone in Elmasry’s (2012) study was higher than
that in Wang’s (2014). Elmasry (2012) reported VB alone explained 40% of variance in reading
comprehension but VD alone accounted for only 31.9% of variance in reading comprehension.
Moinzadeh and Moslehpour's (2012) study of 81 students majoring in English
Literature at Shiraz University, Iran, aged between 21 to 25 years old found that VB contributed
more to reading comprehension (test taken from Longman TOEFL) as the standardized


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regression coefficient index (β) was .615 compared to .360 for VD. Moinzadeh and
Moslehpour's (2012) found that VB accounted for 69.6% while VD accounted for 53.8% of the
variance in reading of the participants. Thus, the general consensus among Elmasry, 2012; Li
and Kirby, 2014; Moinzadeh and Moslehpour, 2012; and Wang, 2014 is that VB has more
power compared to VD to predict reading comprehension among ESL learners.
In the Malaysian context, there are a few researchers who explored the correlation
between VB, VD and MUET reading. The results of Tengku Shahraniza Tengku Abdul Jalal et
al.'s (2015) study with 341 respondents aged 18-32years old show that the beta value of VB
(β=.504) was higher than VD (β=.130) and both VB and VD were positively related to MUET
reading comprehension. In other words, VB is better in predicting the MUET reading
comprehension.

Determining VB for reading comprehension (Vocabulary threshold for reading)

Readers below the vocabulary threshold have difficulty in reading comprehension
because they may distort the meaning of the text. There are two ways of defining ‘threshold’
(Nation, 2001). The first is to view threshold as whether a learner has acquired the vocabulary
threshold or not. In this view, learners above the threshold are able to understand the reading
texts while learners below the threshold cannot comprehend sufficiently. Another way of
viewing threshold is called ‘probabilistic boundary’ where learners above the threshold might
have higher probability of understanding the reading text sufficiently but for learners below the
threshold, the chances of understanding a text sufficiently is low.
Earlier works of Laufer (e.g. Laufer, 1989, 1992) revealed that comprehension of
English texts required a minimum of 3000 word families from a reader. In line with Laufer
(1989, 1992), Liu Na and Nation (1985) concurred that 95% of text coverage is needed among
readers to understand a text. Scholars who agree with 5000 word families as vocabulary
threshold for reading are Hirsh and Nation (1992), Hu and Nation (2000), Liu Na and Nation
(1985), Wu, Mohamad Jafre Zainol Abidin and Lin (2013).
Nation’s (2006) comprehensive study on vocabulary threshold for reading without
assistance reveals that reader needs 5000 word families and concurrently possess 8000-9000
word families for authentic reading materials. Concurring with Nation’s (2006) finding, Laufer
and Ravenhorst-Kalovski, (2010) also suggest that 8000-9000 word families that provide 98%
text coverage is an optimal threshold for reading while 4000–5000 word families, yielding 95%
text coverage is a minimal threshold.
Tan and Goh (2017) also support that the average VB needed by the students for
adequate reading is 8000 word families and propose a larger VB (i.e. about 10000 word
families) for proficient reading. However, Tan and Goh's (2017) study which investigated 53
second year Diploma in Mass Communication students at a private University College in
Malaysia could not find a vocabulary threshold because most of the students are modest readers
although the students’ VB exceeded 8000 word families.
In contrast, Schmitt et al. (2011) stipulate that there is no specific vocabulary threshold
for readers to comprehend more in a text but overall their results show that vocabulary
knowledge and reading comprehension have positive linear relationship. Schmitt et al. (2011)
also have found that readers with high vocabulary knowledge could not assist in reading
because participants with 90% of vocabulary coverage only scored 50% in reading
comprehension. Nevertheless, their study comprises many participants who scored beyond 90%
of vocabulary coverage and thus, has created a strong ceiling effect for vocabulary coverage.
So, it can be surmised that getting more participants with lower vocabulary knowledge could
help to determine a vocabulary threshold, for instance, more participants in Stæhr's (2008)
study with low vocabulary knowledge yields a lower vocabulary threshold.


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In Stæhr’s (2008) study, 68 participants did not master 2000 word families in VB while
only 20 participants managed to obtain more than 2000 word families in VB. These 88 Danish
students, aged between 15 and 16 years, have learnt English as a foreign language for seven
years, i.e. a total of 570 hours of formal English lessons. Therefore, they are gauged to have
lower levels of receptive vocabulary knowledge. The results showed that about half of the
participants (i.e. 46) managed to obtain scores above average in reading comprehension. Out
of 68 participants who did not master 2000 word families, only 26 participants scored 14 marks
or more (above average) while the rest (i.e. 42 participants) obtained below average scores
(below 14) in reading comprehension. Participants who managed to master beyond 2000 word
levels, obtained above average scores in reading comprehension. Stæhr (2008) found the
difference in the means of the two groups to be statistically significant for reading skills. Thus,
he concludes that knowing the first 2000 word families leads to a significantly better
performance in reading comprehension.
With regard to Stæhr’s (2008) conclusive statement, a current study by Engku Haliza
Engku Ibrahim, Isarji Sarudin, & Ainon Jariah Muhamad (2016) has lent credence to the
findings because they have found that the highest correlation between reading comprehension
and vocabulary mastery was at 2000 words (r=.637; p<0.01). Somehow, 10000 words had a
weak correlation with the reading test (r=.291; p<0.01)
Different types of reading comprehension tests will have different vocabulary
thresholds (e.g. Chujo and Oghigian, 2009) based on the level of difficulty in the texts. In the
Chujo and Oghigian's (2009) study, the text coverage was set at 95%. TOEIC requires a learner
to have at least 3000-4000 word families whereas TOEFL needs higher word families that is
around 3500–4500.



RESEARCH QUEESTIONS

1. Is there a relationship between VB and depth and the MUET reading comprehension
component?
2. To what extent do scores on VB and depth contribute to predicting the MUET reading
comprehension performance?
3. Is it possible to determine a VB threshold where learners are likely to perform above
average in the MUET reading comprehension?

METHODOLOGY

The study employed non-interventional and correlational design because it assessed the
relationship between independent variables or predictors (i.e. VB and VD) and the outcome of
variable (i.e. MUET reading comprehension).

Participants

The study was conducted at a Matriculation college in Peninsular Malaysia. Altogether
2026 students (N) were enrolled in the one-year-program. According to Israel (1992), the
number of participants required for performing multiple linear regression is 200 or more. In
order to control for attrition which is considered a threat to the internal validity of a study, an
additional 10% of the 200 participants were added. A total of 230 participants were randomly
selected. Attrition might occur when a study loses participants for a variety of reasons
(Creswell, 2014). Eventually, 5 cases of incomplete answers and 4 cases of outliers were
excluded from the study.



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Majority of the participants (i.e. 123) obtained Band 3 while only 9 obtained Band 5 in
the MUET. Table 1 presents the frequencies and percentages of participants for each Band.
None of the participants selected had obtained Band 6 or Band 1 in the MUET.

Table 1.
Number of Participants for each MUET band

MUET band No. of Participants Percentage
2 23 10.4
3 123 55.7
4 66 29.9
5 9 4.1
Total 221 100.00

Procedures

The students were randomly picked from the name list given. The students selected were
then grouped randomly in a counter balanced order within a single session of 90 minutes. The
orders in this study are: 115 students sat for Word Associations test (WAT) followed by
Vocabulary Size test (VST) while 115 students sat for the Vocabulary Size test (VST) followed
by Word Associations test (WAT). The time allocated for the WAT was 30 minutes and for the
VST, it was 60 minutes. Both the tests were administered to the students in single testing
sessions.

Instruments

MUET Reading Comprehension
The MUET is a standardized test of English proficiency for pre-university students
administered by the Malaysian Examinations Council. Reading component has the highest
weighting with a maximum score of 120 out of a possible 300 for the entire test.

Word Associates Test (WAT)
This is a well-established test of VD which was designed by Read in 1998. The WAT
has been widely used to measure three aspects of word knowledge in VD, namely, word
associations, semantics and collocational relationships (e.g. (Mehrpour et al., 2011; Moinzadeh
& Moslehpour, 2012; Qian, 1999, 2002; Tengku Shahraniza Tengku Abdul Jalal et al., 2015;
Wang, 2014). Meanwhile, there is no test that could measure every aspect of word knowledge
(Pignot-shahov, 2012).

Vocabulary Size Test (VST)
The Vocabulary Size Test (VST) is chosen because it is used to measure participants’
overall vocabulary size (breadth of vocabulary knowledge). In 2010, Beglar conducted a Rasch-
based validation of the monolingual test and found that the test can be used with learners from
a wide range of proficiency levels.








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Reliability of the Instruments

According to Beichner (1994), tests with KR-21 ≥ 0.70 are deemed to be reliable
measurements. For the VST, the Kuder-Richardson (KR-21) reliability coefficient is .80.
Meanwhile, Cronbach’s alpha is used to check the internal consistency of items with
polytomous scores. According to Tavakol and Dennick (2011), if the alpha value is higher than
.90, it indicates that there are redundant items in the test. The coefficient alpha for the WAT is
0.83, denoting that this test is also reliable.

Assumption Check

Before performing multiple regressions, several assumptions were checked, namely,
missing values, outliers, normality, independence, multicolinearity and homoscedasticity
(Tabachnick & Fidell, 2007). All the steps in data screening show the values within the
acceptable range.

RESULTS AND DISCUSSIONS

Table 2
Descriptive Statistics of average scores, standard deviation and score range of breadth and
depth of vocabulary tests and MUET reading comprehension (n = 221)

Variable MPS Average Standardised Standard Score
Score Mean deviation range
(Mean)
MUET Reading 120 61.60 51.33 13.18 32-96
Comprehension
VB 140 73.23 52.31 12.95 45-107
VD 160 109.79 68.62 10.84 80-135
Note. MPS = Maximum possible score

Based on Table 2, the standardised means show that among the three variables,
participants scored the lowest in the MUET reading. Overall, the participants did well in VD
as the standardised mean was the highest compared to VB and reading comprehension (i.e.
68.62%). This shows that MUET reading is more challenging compared to the other two tests,
namely the VST (VB test) and WAT (VD test). This concurs with the finding of Ong, Krishnan
Vengidasamy, Renu Kailsan and Christopher Selvaraj Jacob (2015) that the passages in the
MUET reading are very difficult as the Gunning Fog index for the passages compiled from
1999 to March 2014 are within the difficult range (i.e. 11 - 15.9) as the indices from 13 to 16
would be the reading level for college students aged between 19 and 22 in the United States.
Meanwhile, it is expected that the standardised mean of the VST would be lower than WAT
but the difference of standardised mean between the two tests was only 0.98%. This finding
contradicts with Qian's (2002) study which claims that WAT or VD is more difficult than VLT
or VB as the former one taps more indepth vocabulary knowledge while the latter merely taps
the superficial level of vocabulary knowledge. However, according to Elgort (2012) and Nation
and Beglar (2007) VST is more demanding and difficult because the distractors used have
elements of meaning that are shared with the correct answer.





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RQ1: Is there a relationship between VB and depth and the MUET reading
comprehension component? In order to determine the correlation and examine the relation
between vocabulary knowledge and learners’ reading comprehension a multiple linear
regression and hierarchical regression were performed. Firstly, a two-tailed Pearson correlation
analysis was conducted and the results are displayed in Table 2. The correlations of all measures
would address RQ 1.

Table 3.
Pearson Correlations (2-tailed) between Scores on Reading Comprehension, Vocabulary Size
and VD (n = 117)

Variable Reading VB VD
Comprehension
MUET Reading - 0.94** 0.635**
Comprehension
VB 0.94** - 0.658**
VD 0.635** 0.658** -
**p<.01

Based on the information in Table 3, the answer for RQ1 is that the inter-correlations
among the three tests were positive and statistically significant. The correlation between the
reading comprehension and VB (r = 0.94) was the highest. The correlation between VB and
MUET reading comprehension in this study was higher than that found in Arifur Rahman
(2017) (i.e. r = 0.756); Sarimah Shamsudin & Nor Hazwani Munirah Lateh Anie Attan (2016)
(i.e. r = 0.84); and Tengku Shahraniza Tengku Abdul Jalal et al., (2015) (i.e. r = 0.58).
Meanwhile, the correlation coefficient between reading comprehension and VD was 0.635 and
between VB and VD was 0.658. This shows that the correlation between VD and MUET
reading comprehension in this study was moderate but statistically significant (r = 0.635). Thus,
the correlation between VB and MUET reading comprehension is more robust that of the VD
and the MUET reading comprehension.
Mochizuki (2012) and Qian (2002) claim that the close association between VB and
VD may be due to the overlap of lexical aspects measured by WAT which are synonymy and
polysemy, with breadth of vocabulary knowledge. Furthermore, when Batty (2012) conducted
a study to ascertain whether the WAT conformed better as a single construct to measure VD or
measure two lexical aspects, namely, synonyms and collocations, he found that WAT measures
synonyms and collocations, as well as general vocabulary knowledge. In this study, the
correlation coefficient between VB VD was 0.658. This indicates that WAT and VST shared
43% of their variance. Despite this, WAT is still considered a valuable test for vocabulary
research and classroom application. The correlation may also be attributed to the fact that the
more words an ESL learners know, the richer the word associations or words in the mental
lexicon, which taps into a deeper level of lexical repertoire.

RQ 2: To what extent do scores on VB and depth contribute to predicting the
MUET reading comprehension performance? Next, to answer RQ2, a regression model was
conducted to explore the extent to which the VD and breadth scores could predict the MUET
reading comprehension score. A forced entry option was chosen to enter predictor variables
into the regression equation. With forced-entry method, VB is entered first followed by VD.
This analysis was based on the theoretical assumption that VB is a learner’s basic dimension
(Gyllstad, 2007; Meara, 1996; Milton, 2010) while VD will be refined subsequently to be a




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catalyst for automaticity of lexical access in reading comprehension (Henriksen, 1999; Qian,
1999, 2000, 2002).

Table 4.
Hierarchical regression predicting English reading comprehension

MUET Reading Comprehension Component
2
2
Variable R R Adjusted R Δ F change
2
R
1. VB 0.94 0.89 0.89 0.89 1702.98***
2. VD 0.94 0.89 0.89 0.00 0.86
1A VD 0.64 0.40 0.40 0.40 147.92***
2A VB 0.94 0.89 0.89 0.48 928.43***
Note. N = 221. * p< .05, *** p< .001.

Table 4 displays the multiple regression of variance for the MUET reading
comprehension score by the VB and the VD scores. VB had stronger correlation with the
criterion variable, MUET Reading Comprehension component (r = 0.94, p < 0.001) than the
depth of vocabulary knowledge. Therefore, based on the theoretical assumptions (i.e. Gyllstad,
2007; Meara, 1996; Milton, 2010) and the high correlation between VD and VB, the predictor
variable which is VB was chosen to be entered into the regression equation first.
The first step shows that VB alone significantly accounted for 89% (R2 = 0.89) of
variance in the criterion variable, reading comprehension (F (1, 219) = 1702.98, p < 0.001).
2
2
When the variable of VD was included in step 2, R value did not show any increment i.e. R
change was 0.00 (F (1, 218) = 0.862, p > 0.05). This indicates that 89% of the variance in
MUET Reading Comprehension component was explained by only the breadth of lexical
repertoire. In other words, increasing VB will no doubt help to improve scores in the MUET
Reading Comprehension component, and the fact that 89% of the variance in MUET Reading
Comprehension was significantly explained by the VB in this study. This leaves only 11%
unexplained.
To investigate further the predictive power of VB, a fresh model was rebuilt with VD
entered initially in the regression. The second section of Table 4 (labelled as 1A and 2A)
displays the results when depth of vocabulary entered first followed by the breadth of
2
vocabulary knowledge. In this case, VD indicated 40% (R = 0.40) of the explained variance
in reading comprehension. As a predictor, VD alone had a significant amount of reading
comprehension (F (1, 219) = 147.92, p < 0.001). Subsequently, when VB was added secondly,
2
the variance of MUET reading comprehension was still uniquely described by VB as the R
change value was 0.48. Thus, VB explained an additional 48% of the MUET reading
comprehension variance 48 (F (1, 218) = 928.43, p <0.001).
Statisticians (e.g. Hair et al., 2010; Pallant, 2011) claim that beta indices enable the
assessment of the importance of each variable. Therefore, to further determine which aspect of
vocabulary knowledge could better predict reading comprehension scores, Table 5 gives
estimates for beta values (or b-values) and these values indicate each predictor’s contribution
to the model.












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Table 5.
Standardised and unstandardized b-values from hierarchical analyses for variables predicting
reading comprehension

Variables Unstandardised b Std. Error Standardised b t
(β)
Constant -10.91 3.09 -3.54
VB 0.01 0.00 0.92*** 30.47
VD 0.03 0.04 0.03 0.93
Note. In both hierarchical analyses, there was a controlled variable included (either depth or
breadth) but are not shown in this table.
***P < 0.001; N = 221

In order to answer RQ 2 specifically, Table 5 displays that VB was statistically
significant where it recorded the highest beta value (β = 0.92, p < 0.001) compared to depth of
vocabulary knowledge (β =0.03, p > 0.01) which is not significant. As a result, the inclusion of
ESL learners’ VB and depth in the model of regression shows a large contribution of breadth
in comparison to the contribution of VD. In other words, for one standard deviation of change
in breadth of vocabulary knowledge, there will be 0.92 of standard deviation change in MUET
reading comprehension scores. Meanwhile, for depth of vocabulary knowledge, one standard
deviation of change in it will change 0.03 of a standard deviation in the MUET reading
comprehension scores or vice versa but that figure is insignificant.
This finding is in line with (Elmasry, 2012; Li & Kirby, 2014; Moinzadeh &
Moslehpour, 2012; Tengku Shahraniza Tengku Abdul Jalal et al., 2015; Wang, 2014) where
breadth has more predictive power on reading performance. Nonetheless, it does not indicate
that depth is unimportant for ESL learners for other skills. In addition, this finding is similar to
the finding of Moinzadeh and Moslehpour (2012) where the standardized regression coefficient
(β) showed that VB contributed significantly and more to reading comprehension. However,
Moinzadeh and Moslehpour (2012) found that VD also contributed significantly in reading
comprehension whereas this current study did not find depth as a significant predictor.
VD is hypothesized as a significant predictor in reading comprehension because it has
more items and points (160 points) compared to VB test (utilised in the study was Vocabulary
Level Test or VLT) (90 points) (Qian, 1999). Therefore, Qian’s studies (1998, 1999, 2002),
found that VD is more powerful than VB. However, the current study has found that VB is
more powerful as it was measured using VST which has more items or more points (140 points)
compared to VLT which has less points or items (90 points). It is conjectured that VB would
perform better in the regression analysis of this current study because VST measured words
from 1000 word-family to 14,000 word-family while VD test (WAT) was only based on words
at 2000, 3000 and 5000 levels. Otherwise, if WAT was joined with another instrument to gauge
VD, the contribution in predicting reading comprehension variance might be higher or
equivalent to the contribution of VB in reading comprehension. For example, in Choi's (2013)
study, when Vocabulary Knowledge Scale (VKS) was added to WAT, this VD was more highly
correlated with reading comprehension (0.790), exceeding that of VLT with reading
comprehension (0.765).

RQ 3: Is it possible to determine a VB threshold where learners are likely to
perform above average in the MUET reading comprehension? In order to answer RQ 3,
cross tabulation was performed (see Table 6). The possible VB threshold for reading can be
obtained through examining the results for MUET reading scores above or equivalent to 56
marks and below 56 marks (i.e. average score as 21/45 x 120) against VB. From Table 7,


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participants who scored more than or equal to 7000 word families in VB had the tendency to
obtain more than or equal to 56 (average score) in the MUET reading comprehension
component (M=70.54, SE=0.83), than those who scored less than 7000 word families in
vocabulary knowledge (M=50.38, SE=0.78). This difference of -20.17, BCa 95%CI [-22.45, -
17.88] was significant at t (221) = -17.04, p = 0.001. In other words, since p <0.0001 is less
than our chosen significance level α = 0.05, we can reject the null hypothesis, and conclude
that the that the mean scores for reading comprehension among participants who scored more
than or equal to 7000 word families and participants who scored less than 7000 word families
is significantly different.
It is noticed that in Table 6, there were 29 participants with 6000 word families who did
score above average (scores ≥56) in reading comprehension. Hence, learners who are below
the vocabulary threshold might perform well in reading comprehension or learners who pass
the vocabulary threshold might not do well in reading comprehension but the probability of
both the conditions is low. This is also based on evidence from empirical studies which show
that readers with large vocabulary do not necessarily achieve high scores in reading
comprehension (e.g. Schmitt et al., 2011; Stæhr, 2008) because other skills could contribute to
reading (Grabe, 2004; Perfetti, Landi, & Oakhill, 2005). Likewise in Tan and Goh's (2017)
study’s outcome, students could not achieve adequate reading comprehension though their VB
(exceeded 8000 word families) was sufficient for the reading comprehension. Nevertheless,
vocabulary threshold viewed as probabilistic boundary in the present study does not indicate
that vocabulary is unimportant in reading. So, the vocabulary threshold mentioned in the
present study is not only useful, but essential to assist other processing skills in reading. As a
result, this clearly shows that the definition of ‘threshold’ in this study, i.e. ‘probabilistic
boundary’ (Nation, 2001) is similar to Laufer’s position.
Thus, the answer for RQ3 is that the level of 7000 word families seems to be a
reasonable vocabulary threshold for MUET reading comprehension as it is near to the
vocabulary threshold for comprehending written text highlighted by scholars (e.g. Laufer &
Ravenhorst-Kalovski, 2010; Nation, 2006; N. Schmitt, 2008).

Table 6.
VB levels obtained by the two groups of participants i.e. above average and below average in
reading comprehension

reading scores
<56 ≥56 Total
VSL 4000 Count 6 0 6
% within VSL 100.0% 0.0% 100.0%
% within reading <56and ≥56 9.7% 0.0% 2.7%
% of Total 2.7% 0.0% 2.7%
5000 Count 27 0 27
% within VSL 100.0% 0.0% 100.0%
% within reading<56 and ≥56 43.5% 0.0% 12.2%
% of Total 12.2% 0.0% 12.2%
6000 Count 29 36 65
% within VSL 44.6% 55.4% 100.0%
% within reading<56 and ≥56 46.8% 22.6% 29.4%
% of Total 13.1% 16.3% 29.4%
7000 Count 0 51 51
% within VSL 0.0% 100.0% 100.0%
% within reading <56 and ≥56 0.0% 32.1% 23.1%


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% of Total 0.0% 23.1% 23.1%
8000 Count 0 45 45
% within VSL 0.0% 100.0% 100.0%
% within reading<56 and ≥56 0.0% 28.3% 20.4%
% of Total 0.0% 20.4% 20.4%
9000 Count 0 22 22
% within VSL 0.0% 100.0% 100.0%
% within reading<56 and ≥56 0.0% 13.8% 10.0%
% of Total 0.0% 10.0% 10.0%
10000 Count 0 5 5
% within VSL 0.0% 100.0% 100.0%
% within reading<56 and ≥56 0.0% 3.1% 2.3%
% of Total 0.0% 2.3% 2.3%
Total Count 62 159 221
% within VSL 28.1% 71.9% 100.0%
% within reading<56 and ≥56 100.0% 100.0% 100.0%
% of Total 28.1% 71.9% 100.0%


Table 7.

Comparison of Vocabulary Size more than or equal to 7000 word families and less than 7000
word families on Reading Comprehension

Variable N M SD SE t df p
Reading Comprehension -17.40 219 0.00
vocabulary size <7000 98 50.38 7.74 0.78
vocabulary size ≥7000 123 70.54 9.16 0.83
Note: Test of the assumption of equal variances assumed,
Bootstrap results were based on 1000 bootstrap samples,
Mean difference -20.17,
Bias-corrected and accelerated (BCa) 95% Confidence Interval (CI) [-22.45, -17.88],
N = number of participants, M = mean, SD = Standard Deviation, SE = Standard Error Mean,
t = t-statistic, df = degrees of freedom, p = Significance value

CONCLUSION AND IMPLICATIONS

Based on this study, vocabulary knowledge is a significant contributor to MUET
reading comprehension of the Malaysian ESL Matriculation College students. It was found that
VB was a stronger predictor of MUET reading comprehension, a written receptive text. The
findings of this study have pedagogical implications for the Malaysian ESL classroom,
including helping ESL instructors to decide if VB is indeed an issue in the poor performance
of reading comprehension among their ESL learners. Besides, the findings could raise students’
awareness of the importance of vocabulary knowledge and reading comprehension.
Generally, VB and VD are obviously not mutually exclusive. In other words, an ESL
learner with large vocabulary will have more in-depth knowledge of the word. As shown in the
outcome of this study, ESL learners’ VB was highly and positively correlated with their depth
of vocabulary knowledge and both types of vocabulary knowledge accounted for 80% of the
variance in the MUET Reading Comprehension component. Based on the findings in this
present study, it is suggested that VB should get more attention in the ESL classroom if the


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objective is to produce learners who read well. So, this suggestion contradicts with the finding
of Mohammadi and Shakouri (2014) which states teaching VD is better for understanding
reading comprehension. Other than rote memorization of word spelling or memorization of
word definitions, ESL teachers may also implement other instructions in the ESL classroom
with the aim to produce richer word knowledge.
Due to a high correlation of VB and reading comprehension, reading without some
vocabulary acquisition instructions would hamper their understanding of the texts. So, activities
such as extensive reading should be supplemented with some vocabulary exercises to assist the
students in vocabulary acquisition and make reading worth the effort. Since learning VB is vital
for understanding a reading text, pre-teaching vocabulary in every reading activity can lessen
the students’ struggles with vocabulary. Other than reducing the number of unfamiliar words,
pre-teaching can also boost vocabulary acquisition which helps learners to comprehend better
in future.
The present study has found that learners need at least 7000 word families to
comprehend adequately in MUET reading comprehension. Thus, needless to say that learners
should realise the importance of learning VB as this skill would assist them to acquire more
vocabulary independently for input-based learning throughout their academic life and beyond.
As reading is a vital aid learning a second language, the benefits are manifold if one has
mastered 8000-9000 word families which is recommended by Nation (2006) and Laufer
Ravenhorst-Kalovski (2010) for adequate reading authentic materials. Tan & Goh, (2017)
suggest a larger VB (i.e. 10000 word families) if a learner wants to be a proficient reader. So,
with large VB, reading would not be difficult, understanding texts may help to increase the
learner’s knowledge and thus creating positive attitude towards reading.
Indeed, it can be claimed that reading comprehension might require a large vocabulary
(VB) instead of in-depth word meaning. However, this does not mean that ESL learners do not
need to learn words well. Perhaps, VD is more inclined to be correlated with writing as has
been stated by Li and Kirby (2014) and Wang (2014). Thus, it would be interesting for future
correlational studies to include the MUET writing component. Besides that, it should be noted
that the VD in this study is only partially operationalised where it covers word associations,
semantics and collocational relationships. Undeniably, VD includes other lexical aspects such
as spoken and written forms, grammatical knowledge, knowledge of word parts, word’s register
and frequency knowledge (Nation, 1990, 2001). It can be envisioned that the study of VD could
be extended to other lexical aspects which provides more extensive findings of VD.
The Malaysian Examinations Council is responsible for the administration of the
MUET since the commencement of the exam in 1999. After eight years, the MUET syllabus
was revised in 2006 to ensure that it maintains its relevancy as a standardized language test in
the country. It is hoped that the findings of the study may also be beneficial to the Malaysian
Examinations Council in their effort to revise the MUET syllabus in the near future especially
the reading component seeing that the MOE are planning to calibrate the MUET with the
CEFR. Perhaps, the Malaysian Examinations Council could take CEFR vocabulary into
consideration for MUET test development in future.



REFERENCES

Arifur Rahman. (2017). Association among vocabulary size, academic achievement, English
language proficiency, and language dominance. (Unpublished Master Thesis), Universiti
Putra Malaysia.
Akbarian, I. & Alavi, S. M. (2013). Comparing the contribution of vocabulary breadth to IELTS
and TOEFL reading subtests. Porta Linguarum, 20, 135–151.



203

Anderson, R. C. & Freebody, P. (1981). Vocabulary Knowledge. In J. T. Guthrie (Ed.),
Comprehension and Teaching: Research Reviews (pp. 77–117). Newark: International
Reading Association. Retrieved from http://files.eric.ed.gov/fulltext/ED203299.pdf
Batty, A. O. (2012). Identifying dimensions of vocabulary knowledge in the word associates
test. Vocabulary Learning and Instruction, 1(1), 70–77.
http://doi.org/10.7820/vli.v01.1.batty
Beglar, D. (2010). A Rasch-based validation of the vocabulary size test. Language Testing,
27(1), 101–118. http://doi.org/10.1177/0265532209340194
Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American
Association of Physics Teachers, 62(8), 750–761.
Choi, H. (2013). Effects of depth and breadth of vocabulary knowledge on English reading
comprehension among Korean high school students. Language Research, 49(2), 419–452.
Chujo, K. & Oghigian, K. (2009). How many words do you need to know to understand TOEIC,
TOEFL and EIKEN? An examination of text coverage and high frequency vocabulary.
The Journal of ASIA TEFL, 6(2), 121–148.
Creswell, J. W. (2014). Educational Research: Planning, Conducting and Evaluating
Quantitative and Qualitative Research (4th ed.). Essex: Pearson.
Daller, H., Milton, J. & Treffers-Daller, J. (2007). Modelling and Assessing Vocabulary
Knowledge. In H. Daller, J. Milton & J. Treffers-Daller (Eds.). Cambridge: Cambridge
University Press.
Elgort, I. (2012). Effects of L1 definitions and cognate status of test items on the vocabulary
size test. language testing, 30(2), 253–272. http://doi.org/10.1177/0265532212459028
Elmasry, H. I. (2012). Depth and breadth of vocabulary knowledge: Assessing their roles in
reading comprehension of high-school EFL Learners in the UAE. The British University,
Dubai.
Engku Haliza Engku Ibrahim, Isarji Sarudin & Ainon Jariah Muhamad. (2016). The
relationship between vocabulary size and reading comprehension of ESL learners. English
Language Teaching, 9(2), 116. http://doi.org/10.5539/elt.v9n2p116
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: SAGE Publications.
Folse, K. (2010). Is explicit vocabulary focus the reading teacher’s job? Reading in a Foreign
Language, 22(1), 139–160. Retrieved from
http://connection.ebscohost.com/c/articles/50229245
Grabe, W. (2004). Research on Teaching Reading. Annual Review of Applied Linguistics, 24,
44–69. http://doi.org/10.1017/S0267190504000030
Gyllstad, H. (2007). Testing English Collocations - Developing Receptive Tests for the Use
with Advanced Swedish Learners. Media-Tryck, Lund University, Sweden.
Haastrup, K. & Henriksen, B. (2000). Vocabulary acquisition: Acquiring depth of knowledge
through network building. International Journal of Applied Linguistics, 10(2), 221–240.
http://doi.org/10.1111/j.1473-4192.2000.tb00149.x
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate Data Analysis
(7th ed.). Pearson Prentice Hall.
Henriksen, B. (1999). Three dimensions of vocabulary development. Studies in Second
Language Acquisition, 21(2), 303–317.
Hirsh, D. & Nation, I. S. P. (1992). What vocabulary size is needed to read unsimplified texts
for pleasure? Reading in a Foreign Language, 8(2), 689–696. Retrieved from
http://nflrc.hawaii.edu/rfl/PastIssues/rfl82hirsh.pdf
Hu, M. & Nation, I. S. P. (2000). Vocab Density and Reading Comprehension. Reading in a
Foreign Language, 23(1), 403–430. Retrieved from
http://nflrc.hawaii.edu/rfl/PastIssues/rfl131hsuehchao.pdf
Laufer, B. (1989). What percentage of text-lexis is essential for comprehension? In C. Lauren


204

& M. Nordman (Eds.). Special Language: From Humans to Thinking Machines (pp. 316–
323). Clevedon/ Philadelphia: Multilingual Matters Ltd.
Laufer, B. (1992). How much lexis is necessary for reading comprehension? In P. J. L. Arnaud
& H. Béjoint (Eds.). Vocabulary and Applied Linguistics (First, pp. 126–132). London:
Macmillan.
Laufer, B. & Ravenhorst-Kalovski, G. C. (2010). Lexical threshold revisited: Lexical text
coverage, learners’ vocabulary size and reading comprehension. Reading in a Foreign
Language, 22(1), 15–30.
Li, M. & Kirby, J. R. (2014). The effects of vocabulary breadth and depth on English reading.
Applied Linguistics, 1–25. http://doi.org/10.1093/applin/amu007
Liu Na & Nation, I. S. P. (1985). Factors affecting guessing vocabulary in context. RELC
Journal, 16(1), 33–42. http://doi.org/10.1177/003368828501600103
Meara, P. (1996). The dimensions of lexical competence. In G. Brown, K. Malmkjaer & W. J
(Eds.), Performance and Competence in Second Language Acquisition (pp. 35–53).
Cambridge: Cambridge University Press.
Mehrpour, S., Razmjoo, S. A. & Kian, P. (2011). The relationship between depth and breadth
of vocabulary knowledge and reading comprehension among Iranian EFL learners.
Journal of English Language Teaching and Learning, 222(Year 53), 97–127.
Milton, J. (2009). Measuring Second Language Vocabulary Acquisition. In D. Singleton (Ed.).
Bristol: Multilingual Matters.
Milton, J. (2010). The development of vocabulary breadth across the CEFR levels. In I.
Bartning, M. Martin, & I. Vedder (Eds.), Communicative proficiency and linguistic
development: Intersections between SLA and language testing research. Eurosla
Monograph (pp. 211–232). Second Language A Eurosla Monograph 1. Retrieved from
http://eurosla.org/monographs/EM01/211-232Milton.pdf
Mochizuki, M. (2012). Four empirical vocabulary test studies in the three dimensional
framework. Vocabulary Learning and Instruction, 01(1), 44–52.
http://doi.org/10.7820/vli.v01.1.mochizuki
Mohammadi, J. & Shakouri, A. (2014). The effect of teaching learners’ breadth and depth of
vocabulary on reading comprehension via online classroom. International Journal of
Multidisciplinary and Current Research, 2, 1166–1170. Retrieved from http://ijmcr.com
Moinzadeh, A. & Moslehpour, R. (2012). Depth and breadth of vocabulary knowledge: Which
really matters in reading comprehension of Iranian EFL Learners? Journal of Language
Teaching and Research, 3(5), 1015–1026. http://doi.org/10.4304/jltr.3.5.1015-1026
Nation, I. S. P. (1990). Teaching and Learning Vocabulary. Teaching and Learning
Vocabulary. Boston, MA: Heinle and Heinle. Retrieved from
http://www.savingthewahyanites.net/wp-
content/uploads/2012/01/TeachingAndLearningVocabulary-Nation1990.pdf
Nation, I. S. P. (2001). Learning Vocabulary in Another Language. In C. A. Chapelle & S.
Hunston (Eds.). (13th ed.). New York: Cambridge University Press.
Nation, I. S. P. (2006). How large a vocabulary is needed for reading and listening? The
Canadian Modern Language Review, 63(1), 59–82. Retrieved from
http://eric.ed.gov/?id=EJ750537
Nation, I. S. P. & Beglar, D. (2007). A vocabulary size test. The Japan Association for
Language Teaching (JALT), 31(7), 9–13. Retrieved from http://jalt-
publications.org/tlt/issues/2007-07_31.7
Nor Ashikin Ab Manan, Noraziah Azizan & Nur Fatima Wahida Mohd Nasir. (2016).
Measuring the receptive and productive vocabulary level of first year undergraduates from
a public university in Malaysia. Journal of Applied Environmental and Biological
Sciences, 7, 53–59. http://doi.org/10.1038/353622a0


205

Nor Hazwani Munirah Lateh, Sarimah Shamsudin & Abdul Halim Abdul Raof. (2018).
Receptive vocabulary levels of Malaysian university students. LSP International Journal,
5(1), 105–113. Retrieved from
https://lspinternationaljournal.utm.my/index.php/lspij/article/view/68/64
Nur Ainil Sulaiman, Salehuddin, K. & Rozainee Khairuddin. (2018). Academic word list
knowledge of Malaysian ESL undergraduates. GEMA Online Journal of Language
Studies, 18(4), 1–14. http://doi.org/10.17576/gema-2018-1804-01
Ong, S. B., Krishnan Vengidasamy, Renu Kailsan & Christopher Selvaraj Jacob. (2015).
Readability of MUET reading passages. In Language Studies Evolution and Revolution:
The Past, Present and Fuuture. Bangi, Malaysia: Universiti Kebangsaan Malaysia.
Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS (4th
ed.). Crows Nest: Allen and Unwin.
Perfetti, C., Landi, N. & Oakhill, J. (2005). The acqusition of reading comprehension skill. In
M. J. Snowling & C. Hulme (Eds.). The Science of Reading: A Handbook (pp. 227–247).
Malden: Blackwell. http://doi.org/10.1111/b.9781405114882.2005.x
Pignot-shahov, V. (2012). Measuring L2 receptive and productive vocabulary knowledge.
Language Studies Working Papers, 4, 37–45. Retrieved from
http://www.readingconnect.net/web/FILES/english-language-and-
literature/elal_LSWP_Vol_4_Pignot_Shahov.pdf
Qian, D. D. (1998). Depth of Vocabulary Knowledge: Assessing its Role in Adults’ Reading
Comprehension in English as a Second Language. University of Toronto. Retrieved from
https://tspace.library.utoronto.ca/bitstream/1807/12079/1/NQ33914.pdf
Qian, D. D. (1999). Assessing the roles of depth and breadth of vocabulary knowledge in
reading comprehension. The Canadian Modern Language Review, 56(2), 283–307.
Retrieved from
http://ezproxy.upm.edu.my:2088/ehost/pdfviewer/pdfviewer?sid=240b19c8-d65c-4c6d-
a445-85825c87a89a@sessionmgr4004&vid=9&hid=4206
Qian, D. D. (2000). Validating the role of depth of vocabulary knowledge in assessing reading
for basic comprehension tasks in TOEFL 2000. Princeton: Educational Testing Service.
Qian, D. D. (2002). Investigating the relationship between vocabulary knowledge and academic
reading performance: An assessment perspective. Language Learning, 52(3), 513–536.
http://doi.org/10.1111/1467-9922.00193
Qian, D. D. & Schedl, M. (2004). Evaluation of an in-depth vocabulary knowledge measure
for assessing reading performance. Language Testing, 21(1), 28–52.
http://doi.org/10.1191/0265532204lt273oa
Read, J. (1993). The development of a new measure of L2 vocabulary knowledge. Language
Testing, 10(3), 355–371. http://doi.org/10.1177/026553229301000308
Read, J. (1998). Validating a test to measure depth of vocabulary. In A. J. Kunnan (Ed.),
Validation in Language Assessment (pp. 41–60). New Jersey: Lawrence Erlbaum
Associates.
Read, J. (2000). Assessing Vocabulary. In J. C. Alderson & L. F. Bachman (Eds.). Cambridge:
Cambridge University Press.
Read, J. (2004). Plumbing the depths: How should the construct of vocabulary knowledge be
defined. In P. Bogaards & B. Laufer (Eds.). Vocabulary in a Second Language (pp. 209–
227). Amsterdam/Philadelphia: John Benjamins Publishing Company.
Sarimah Shamsudin & Nor Hazwani Munirah Lateh Anie Attan. (2016). Relationship between
vocabulary size and MUET score of Malaysian ESL undergraduates. Retrieved April 27,
2019, from http://eprints.usm.my/38175/
Schmitt, N. (2008). Review Article: Instructed Second Language Vocabulary Learning.
Language Teaching Research, 12(3), 329–363.


206

http://doi.org/10.1177/1362168808089921
Schmitt, N., Jiang, X. & Grabe, W. (2011). The percentage of words known in a text and
reading comprehension. The Modern Language Journal, 95(1), 26–43.
http://doi.org/10.1111/j.1540-4781.2011.01146.x
Sidek, H. M. & Rahim, H. A. (2015). The role of vocabulary knowledge in reading
comprehension: A cross-linguistic study. Procedia - Social and Behavioral Sciences,
197(February), 50–56. http://doi.org/10.1016/j.sbspro.2015.07.046
Stæhr, L. S. (2008). Vocabulary size and the skills of listening, reading and writing. Language
Learning Journal, 36(2), 139–152. http://doi.org/10.1080/09571730802389975
Tabachnick, B. G. & Fidell, L. S. (2007). Using multivariate statistics. Boston: Pearson.
http://doi.org/10.1037/022267
Tan, A. W. L. & Goh, L. H. (2017). Relationship between vocabulary size and reading
comprehension levels of Malaysian tertiary students. International Journal of English
Language and Translation Studies, 5(4), 149–155.
Tavakol, M. & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal
of Medical Education, 2, 53–55. http://doi.org/10.5116/ijme.4dfb.8dfd
Tengku Shahraniza Tengku Abdul Jalal, Raeidah Ariff, Isma Suhaila Ismail & Al-Mansor Abu
Said. (2015). Do breadth and depth of vocabulary knowledge aid reading comprehension?
Australian Journal of Basic and Applied Sciences, 9(25), 50–55.
Varatharajoo, C. (2017). A perspective into Malaysian ESL Learners’ vocabulary acquisition
in the 21st century. Bahagian Perancangan Dan Penyelidikan Dasar Pendidikan,
Kementerian Pendidikan Malaysia, 18, 83–91.
Wang, Z. (2014). A correlation analysis on the depth and breadth of ESL Learners’ vocabulary
knowledge and their overall linguistic competence. Theory and Practice in Language
Studies, 4(12), 2460–2465. http://doi.org/10.4304/tpls.4.12.2460-2465
Wesche, M. B. & Paribakht, T. S. (1996). Assessing second language vocabulary knowledge:
depth versus breadth. Canadian Modern Language Review, 53, 13–40. Retrieved from
http://eric.ed.gov/?id=EJ535872
Wu, X. J., Mohamad Jafre Zainol Abidin & Lin, S. E. (2013). A study on the relationship
between English vocabulary threshold and word guessing strategy for pre-university
Chinese students in Malaysia. English Language Teaching, 6(9), 168–176.
http://doi.org/10.5539/elt.v6n9p168
Yusun, K., Hey, S. K. & Jieun, P. (2012). Is it vocabulary breadth or depth that better predict
Korean EFL learners’ reading comprehension? English Teaching, 67(4), 149–172.
















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