Kamiya Language Testing in Asia (2017) 7:15
DOI 10.1186/s40468-017-0046-z

RESEARCH

Open Access

Can the National Center Test in Japan be
replaced by commercially available private
English tests of four skills? In the case of
TOEFL Junior Comprehensive
Nobuhiro Kamiya
Correspondence:
kamiya@fic.gpwu.ac.jp
Department of International
Communication, Gunma Prefectural
Women’s University, 1395-1
Kaminote, Tamamura-machi,
Sawa-gun, Gunma 370-1193, Japan

Abstract
Background: The National Center Test is a test that high school graduates take inorder to be matriculated into the majority of universities in Japan. Its English section(NCT), though, is scheduled to be replaced by commercially available private English
tests of four skills (reading, listening, speaking, writing) due to the fact that NCT
currently measures only the receptive skills of reading and listening. A concern has
been raised, however, regarding the score comparability between NCT and thosetests of four skills. Thus, this study was conducted in order to examine to what
extent the score of NCT and TOEFL Junior Comprehensive (JC)—one of thecommercially available private English tests of four skills—correlate with eachother and to what extent their test constructs overlap from the viewpoint of L2
competence structure.
Methods: One hundred forty-four twelfth graders in Japan took NCT and JC.
Pearson’s correlations and an exploratory factor analysis (maximum likelihood)were conducted. Moreover, confirmatory factor analyses and chi-square difference
tests were performed in order to compare several models.
Results: The results show that the scores of the two tests are highly correlated witheach other, indicating that JC can be a proper candidate to replace NCT. Moreover, an
exploratory factor analysis revealed that all of the six scores can be subsumed under asingle factor. According to the results of a series of confirmatory factor analyses andchi-square difference tests, the correlated skill model was shown to be the best fit
model, but the unitary model was almost equally a good fit as well.
Conclusions: The results overall indicate that, regardless of tests and skills to measure,
English proficiency as a general construct may determine the major portion of thesescores. This poses the question of whether, in order to assess learners’ English proficiency,
all four skills need to be measured separately.
Keywords: National Center Test, TOEFL Junior Comprehensive, Language testing,L2 competence structures© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.

Kamiya Language Testing in Asia (2017) 7:15

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Background
In Japan, most high school students who wish to be matriculated in universities are required to take a common examination titled the National Center Test for university entrance examinations. Commencing in 1990, its English section (NCT; henceforth)started with a reading section only, to which the listening section was added in 2006.
After 20 years of implementation, however, NCT is scheduled to be replaced by commercially available private tests due to the criticism of its incapability to measure four
skills (reading, listening, speaking, writing) despite the fact that the ministry’s curriculum guidelines stipulate that these skills are expected to be acquired in a balanced manner in English education in school (Ministry of Education 2010).
Traditionally, there were only a handful of English tests in which four skills could bemeasured; however, following the abovementioned trend, several new tests have recently emerged, and as far as the author knows, there are now over 10 kinds extant as
can be seen in Table 1.
Among these, TOEFL Junior Comprehensive (JC; henceforth) was listed as one of theprospective candidates as a replacement for NCT (Eigo yonginou shikaku kentei kondankai 2016). However, a concern has been raised regarding the degree ofTable 1 Commercially available private tests of four skills
Official names

Common names

Organizations

Business Language Testing Service

BULATS

Eiken Foundation of Japan (n.d.-a)Cambridge English exams

Cambridge
EnglishCambridge English Language Assessment (2017)

Eiken Test in Practical English
Proficiency

Eiken

Eiken Foundation of Japan (n.d.-b)

Global Test of English Communication GTEC

Benesse (1997–2017a)

Global Test of English Communication GTEC Academic
Academic

Benesse (n.d.-a)

Global Test of English Communication GTEC CBTComputer Based Testing

Benesse (2000-2017)

Global Test of English Communication GTEC CTE
Corporate Test Edition

Benesse (1997–2017b)

Global Test of English Communication GTEC Junior
JuniorBenesse (n.d.-b)

International English Language Testing IELTS
SystemBritish Council, IDP: IELTS Australia, and Cambridge
English Language Assessment (2017)

Pearson Test of English Academic

PTE Academic

Pearson (2014)

Progress

Progress

Pearson (n.d.)

Test of English for Academic Purposes

TEAP

Eiken Foundation of Japan (n.d.-c)

Test of English for Academic Purposes
Computer Based Testing

TEAP CBT

Eiken Foundation of Japan (n.d.-d)

Test of English as a Foreign Language
Internet-Based Testing

TOEFL iBT

Educational Testing Service (2017a)

Test of English as a Foreign Language
Junior Comprehensive

TOEFL JuniorComprehensive

Educational Testing Service (2017b)a

Test of English for International
Communication

TOEIC (LR & SW)

Educational Testing Service (2017c)

United Nations Associations Test ofEnglish

Kokuren Eiken

United Nations Associations of Japan (n.d.)

Note. In alphabetical order. No intention to be an exhaustive lista
TOEFL Junior Comprehensive has already been excluded from this page

Kamiya Language Testing in Asia (2017) 7:15

Fig. 1 A sample of unitary model

correspondence in scores between these tests and NCT for three reasons. Firstly,
whereas NCT measures only the receptive skills, others additionally measure productive skills. Secondly, whereas NCT strictly follows the guidelines on high school curriculum published by the Ministry of Education, Culture, Sports, Science, and Technology(MEXT; henceforth), others have no such constraints (see In’nami et al. 2016). GTEC
CBT, TEAP, and TEAP CBT are developed specifically geared toward Japanese highschool students, so their test constructs loosely follow the guidelines; however, GTEC
CBT also asks questions beyond them (Benesse 2000–2017), and TEAP (CBT) does notrefer to this issue at all (Eiken Foundation of Japan n.d.-c, n.d.-d), implying that theirrestrictions are not as rigorous as that on NCT. Finally, the aim of these two tests significantly differ from each other: JC aims to “measure the full range of language usesthat students encounter in English-medium school settings” (So et al. 2015, p. 4)
whereas that of NCT is to “improve the selection of candidates for admission to Japanese universities, and thereby to promote education at universities, high schools, andother educational institutions” (National Center for University Entrance Examinations
2015, p. 1). These three issues pose a question of whether not only the total scores but
also the test constructs are comparable between them. Therefore, the current study
was conducted with the aim to examine the concurrent validity of NCT and JC, namely,

Fig. 2 A sample of uncorrelated model

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Kamiya Language Testing in Asia (2017) 7:15

Fig. 3 A sample of correlated model

to what extent the scores of these two tests correlate with each other and to what extent the test constructs of these tests overlap from the viewpoint of L2 competence
structure.

Structure of L2 competence

Language tests are supposed to represent L2 competence that learners have. If a test
measures a single dimension of L2 competence, the test score (an observed variable)
should be subsumed under a single factor of L2 competence (an unobserved latent variable). For instance, a score from a speaking test may have a one-on-one connection
with a factor of speaking skills. However, if multiple tests measure a single dimension,
all of them should be subsumed under a single factor of L2 competence. For example,
the scores of two distinct listening tests may be measuring a single construct of listening skills. By examining the scores of various tests taken by the same test takers via factor analysis, researchers have sought to discover the structure of L2 competence. Thusfar, four major models have been proposed:
1. A unitary or unidimensional model: L2 learners have only a single construct of L2
competence, which can explain all of test scores, regardless of skills or knowledge
to be measured. For example, all of the test scores of four skills are subsumed undera single factor of L2 proficiency (Fig. 1; squares represent observed variables whereas
circles represent unobserved latent variables, henceforth).

Fig. 4 A sample of higher-order model

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Kamiya Language Testing in Asia (2017) 7:15

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Table 2 Structure of NCT
Sections

No. of questions

Testing time (min)Raw scores

Reading

55

80

0–200

Listening

25

30

0–50

Total

80

110

0–250

Source: National Center for University Entrance Examinations (2015)

2. An uncorrelated model: Rather than one, multiple constructs exist among L2
learners and each of them separately subsumes the test scores purported tomeasure the same construct, but those factors are uncorrelated. For example, thescores of the listening test and reading test are subsumed under a factor ofreceptive skills whereas those of the speaking test and writing test are subsumed
under a factor of productive skills, but the two factors of receptive skills andproductive skills are unrelated and, thus, not correlated with each other (Fig. 2).
This validates such a claim that receptive skills and productive skills need to bemeasured separately on the assumption that each of them develops independently.
3. A correlated model: Similar to the uncorrelated model, multiple constructs exist
among L2 learners; however, the unobserved latent factors are correlated witheach other. For example, the two factors of receptive skills and productive skills
are connected with a line with arrowheads at both ends (Fig. 3). This also validates
such a claim that receptive skills and productive skills need to be measured separately
because, although they develop in tandem rather than independently, their development
is still not perfectly synchronized.
4. A higher-order, second-order, or hierarchical model: Similar to the correlated
model, multiple constructs exist among L2 learners; however, instead of directly
connecting those factors, they are subsumed under another unobserved latent factor
placed on the higher level. For example, the two factors of receptive skills andproductive skills (first-order factors) are connected with another factor of L2
proficiency newly added on the right (Fig. 4; please note that the lines are
single arrow-headed this time). This model also validates such a claim thatreceptive skills and productive skills need to be measured separately because,
although they are both affected by another higher factor, how the factor influenceseach of them is not related to each other; thus, their development is not perfectly
synchronized either.
Although the past literature has not reached an agreement concerning which model
best explains L2 competence, at least, the unitary model and the uncorrelated model
Table 3 Structure of JC
Sections

No. of questions

Testing time (min)Raw scores

Reading3641

140–160

Listening36

36

140–160

Speaking

4

18

0–16

Writing

4

39

0–16

Total

80

134

Source: GC&T (n.d.)Kamiya Language Testing in Asia (2017) 7:15

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Table 4 Descriptive statistics of two NCTs on the national level and the number of participants
Years

Reading (/200)

Listening (/50)

Total (/250)

No. of participants

2016

112.4 (42.2)

30.8 (9.4)

143.2

64

2017

123.7 (45.0)

28.1 (10.2)

151.8

80

Figures in parentheses represent standard deviations. Source: National Center for University Entrance Examinations(2016, 2017)

have been scantly supported thus far. As one of the proponents of the former, Oller(1979, 1983) reported a series of proofs that a single trait (Oller himself does not define
what the trait is in a clear and consistent manner though) could explain the major portions (over 70%) of the total variance of various tests, including TOEFL PBT. He goesso far as to say, “the current practice of many ESL programs, textbooks, and curriculaof separating listening, speaking, and reading and writing activities is probably not justpointless but in fact detrimental” (p. 458). However, his argument has been refuted,
partly owing to the fact that he used principal component analysis (see Fouly et al.
1990, for the criticism), which prompted studies onward to adopt confirmatory factor
analysis and exploratory factor analysis with the Schmid-Leiman procedure instead,resulting in negating at least the strong version of the unitary model.
Most of the literature since the 1980s advocates adopting either the correlated modelor the higher-order model, which are statistically indistinguishable from each otherwhen the number of first-order factors is three (for examples, see Fouly et al. 1990;
Sasaki 1996; Shin 2005). Fouly et al. (1990) examined the scores of various tests, including TOEFL PBT, collected from 334 university students in the USA with diverse L1s,
and found that the three-correlated-factor model and the higher-order model wereequally well fit. Similarly, Sasaki (1996) analyzed scores of several English tests taken by
160 Japanese university students and concluded that both the higher-order model andthe correlated model equally fit. Shin (2005) investigated scores of TOEFL PBT and
SPEAK (Speaking Proficiency in English Assessment Kit) taken by 738 participants divided into three different levels based on their English proficiency, which resulted in
the higher-order model with three first-order factors of listening, speaking, and writing
modes being the best model across the three groups, i.e., regardless of their proficiency.
Investigating to what extent two distinct assessments measured the same language ability construct taken by 1224 English learners in the fourth grade in California, Llosa(2007) also found a model with three first-order factors (listening and speaking, reading, writing) under a higher-order factor (English proficiency) to be the best fit. Morerecently, In’nami et al. (2016) analyzed the factor structure of TEAP and compared it
to that of TOEFL iBT. The higher-order model was found to be the best fit for TEAP,
and also, the test constructs were similar between these two tests, both measuring academic English skills.
Table 5 Score conversions of JC
Sections

Raw scores (X)Converting equations

Converted scores

Reading

140–160

X − 140

0–20

Listening

140–160

X − 140

0–20

Speaking

0–16

X/4 × 5

0–20

Writing

0–16

X/4 × 5

0–20

Total

0–80

Kamiya Language Testing in Asia (2017) 7:15

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Table 6 Descriptive statistics of NCT and JC
NCTJCReading

Listening

Total

Reading

Listening

Speaking

Writing

Total

141.0536.69

177.74151.97149.40

7.97

7.27

40.41

SD

42.19

9.25

49.83

5.12

5.20

2.72

2.70

14.75

Max

200

50

248

160

160

13

13

70

Min

30

10

52

140

140

0

1

5

Skewness

−0.82

−0.70

−0.79

−0.78

−0.09

−0.55

−0.11

−0.41

Kurtosis

−0.17

−0.42

−0.23

−0.25

−0.78

0.05

−0.53

−0.47

Average

SD Standard deviation

When the number of first-order factor is as few as two, the higher-order factor is noteven susceptible to verification, and a decision is often made based on some other criteria. As one of the earliest studies that used confirmatory factor analysis, Bachman
and Palmer (1981) compared over 20 models using the data of speaking and readingcollected via three distinct methods for each from 75 Mandarin native speakers aged
between 19 and 35 residing in the USA. They found that the correlated model with two
factors and the higher-order model were equally well fit but, based on the fact that theformer is simpler with one less factor, concluded the former to be the best choice. Another study conducted by Bachman and Palmer (1982) examined the three traits ofcommunicative competence (grammatical, pragmatic, and sociolinguistic) collected via
four methods with 116 participants aged between 17 and 67 with various L1s residingin the USA and reached the conclusion that the higher-order model is superior to thecorrelated model based on the results of chi-square tests (However, see Fouly et al.
(1990), for criticism on Bachman and Palmer’s studies.). More recently, In’nami andKoizumi (2012) analyzed TOEIC scores of four listening and five reading subskills collected from 569 EFL learners, most of whom were Japanese university students. Among
several models that they tested, they found the correlated two-factor model (listening
and reading) to be the best fit. Due to the identification problem, the higher-ordermodel was not considered.
Somewhat more pertinent to the present study in terms of the same TOEFL tests offour skills, a series of large-scale studies with 2720 participants have been conducted to
explore the factor structure of TOEFL iBT (Sawaki et al. 2008, 2009; Stricker and Rock2008). They commonly adopted the higher-order model with four first-order factors ofTable 7 Results of correlation matrix
NCT
NCT

Total

Reading

Listening

Speaking

Writing

Total

Reading

.79

.99

.78

.75

.64

.68

.84

Listening

–

.86

.71

.75

.59

.70

.81

–

.79

.78

.65

.71

.86

–

.71

.58

.64

.88

–

.64

.68

.90

–

.64

.81

–

.84

Total
JC

JC

Listening

Reading
Listening
Speaking
Writing

All figures are significant at p < .001

Kamiya Language Testing in Asia (2017) 7:15

Fig. 5 Scatter plot of reading scoresfour skills, which is in line with the TOEFL score convention to report each skill-based
score along with a single composite score. Lastly, Gu (2015) is perhaps the only study
that has examined the factor structure of JC (although its pilot form), in which a totalof 436 EFL learners aged 11–15 took the test. Using item-level raw scores, three models
were compared: the unitary model, the correlated four-factor model (reading, listening,speaking, writing), and the higher-order model with those four first-order factors. The
results show that the higher-order model was the best fit with a high-order factor being
assumed as general learning ability.

Fig. 6 Scatter plot of listening scores

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Kamiya Language Testing in Asia (2017) 7:15

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Fig. 7 Scatter plot of total scores

All in all, the accumulation of the past literature leads to the hypothesis that thestructure of L2 competence is most likely to be either a correlated model with several
first-order factors or a higher-order model subsuming them.

National Center Test

All of the Japanese high school students and graduates who wish to be matriculated inpublic universities and most private universities take the same test concurrently on thesame day nationwide once a year in January. Among over 1100 four-year and juniorcolleges in Japan, around 850, including all of the national and public universities, currently require applicants to take the test (National Center for University Entrance Examinations n.d.-a), and over 550,000 applicants, comprising over a half of new highschool graduates, take the multiple-choice tests of various subjects (National Center for
University Entrance Examinations n.d.-b). The test items are made strictly in accordance with the guidelines on the high school curriculum stipulated by MEXT.
NCT consists of reading and listening sections (Table 2). Test booklets are printed
and distributed to test takers. For the listening section, IC audio recorders are distributed to test takers, each of whom listens to the audio via earphones individually. Test
takers complete multiple-choice questions by marking their answers on answer sheets.
As far as the author knows, there are only few studies that investigated the correlations of scores between NCT and other commercially available private tests (Eiken
Table 8 Results of exploratory factor analysis
NCT

JC

Reading

Listening

Reading

Listening

Speaking

Writing

Communalities

0.81

0.75

0.70

0.73

0.52

0.63

Factor loadings

0.90

0.87

0.85

0.84

0.79

0.72

Kamiya Language Testing in Asia (2017) 7:15

Fig. 8 Unitary model

Foundation of Japan 2015; Otsu 2013, 2014; Yamanishi 2001). For instance, Otsu
(2014) analyzed the relationships of scores between TOEIC, TOEFL PBT, and NCT andfound that its correlation was 0.80 between TOEIC and NCT and 0.70 between TOEFL
and NCT. However, the number of participants was as few as 63 and 28, respectively,
and their TOEIC and TOEFL scores were self-reported. Besides, the time lag between
when they took those tests and NCT was not controlled for. Thus, the reliability of thescores is not ensured. Also, the English proficiency of the participants was skewed toadvanced level, so they may not represent the typical sample. Remedying these shortcomings, Eiken Foundation of Japan (2015) analyzed the data in a more robust procedure. As many as around 1000 participants took TEAP and Eiken 3 or 4 months after
they took NCT. The study found that the correlation between NCT and TEAP is 0.80
and that between NCT and Eiken is 0.89. However, none of the tests examined in thoseabovementioned studies (i.e., TOEIC, TOEFL PBT, TEAP, and Eiken) contained speaking or writing scores even if the test had those sections; thus, it is unknown to what extent the scores derived from the accumulation of four skills correlates with NCTscores, which is currently an issue of more importance. Finally, as far as the author
knows, the factor structure of NCT has not been examined in any of the previous
studies thus far.

Fig. 9 Uncorrelated skill model

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Kamiya Language Testing in Asia (2017) 7:15

Fig. 10 Correlated skill model

TOEFL Junior Comprehensive

JC was created by the Educational Testing Service (ETS) and first implemented in
2014. Primarily, the test was targeted toward a group of English learners who find
TOEFL iBT too advanced, and thus, it was “developed to address the increasing needfor objective measures of English language proficiency for young adolescent learners,
who are being introduced to English as a second or foreign language at a much younger age than ever before” (So et al. 2015, p. 1), measuring “how successfully a test takercan complete test tasks that are designed to represent the range of communicative
tasks encountered in English-medium middle schools” (p. 6). Because it was intended
to be implemented worldwide and also created by the Education Testing Service in theUSA, it had no restrictions imposed by the guidelines on the high school curriculum
stipulated by MEXT. It consisted of four sections: reading, listening, speaking, andwriting (Table 3). As can be seen in Table 3, the score bands of reading and listening are quite distinct from those of speaking and writing, which So et al. (2015)
explain as follows:
Because the speaking and writing section scores will not be equated, the scores are
not strictly comparable … To avoid any incorrect impression on the part of
stakeholders that the speaking and writing scores are comparable across forms,

Fig. 11 Uncorrelated test model

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Kamiya Language Testing in Asia (2017) 7:15

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Fig. 12 Correlated test modelas the reading and listening section scores are, it was decided that the speaking
and writing scales would be made clearly distinguishable from the reading andlistening scales. (p. 17)
Following this concept, instead of total scaled scores, overall score levels compatible with CEFR (see Tannenbaum and Baron 2015, for details) are reported in JC
ranging from 1 to 6 (see Papageorgiou et al. 2015, for details). The use of different
scoring scales for receptive skills (reading and listening) and productive skills(speaking and writing) imply that at least these two constructs need to be separately
posited in the model.
The test is conducted via computers, and all of the questions are displayed on acomputer screen. Some of the test items in the speaking and writing sections areintegrated with listening. Unfortunately, the test became defunct at the end of
2016.1,2 Although GC&T (Global Communication & Testing), the organization that
operated JC in Japan, has a single data set that explored the relationship between
JC scores and NCT scores, they are not publicized, thus inaccessible (personalcommunication, June 23, 2017).

Research questions

All in all, the present study sought to answer the following two research questions.
1. How are the scores of NCT and JC correlated with each other?
2. Which of the five models (unitary, uncorrelated skill, correlated skill, uncorrelated
test, and correlated test) best represents the test constructs of NCT and JC?

Table 9 Results of model fits
χ2

df

χ2/df

CFI

NFI

TLI

AIC

CAIC

RMSEA [CI]SRMR

Unitary

11.921

9

1.325

0.995

0.982

0.992

35.92185.5590.048 [0, 0.112]

0.0211

Correlated skill

7.882

8

0.985

1.000

0.988

1.000

33.88285.4890 [0, 0.097]

0.0154

Correlated test

11.175

8

1.397

0.995

0.983

0.991

37.17588.7830.053 [0, 0.119]

0.0201

df degree of freedom, CI 90% confidence intervalKamiya Language Testing in Asia (2017) 7:15

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Table 10 Unstandardized regression weights of unitary model
Estimate

SE

CR

p

NCT reading <− English proficiency

17.7981.428

12.461<.001

NCT listening <− English proficiency

3.770

0.317

11.896<.001

JC reading <− English proficiency

2.013

0.178

11.321<.001

JC listening <− English proficiency

2.083

0.179

11.621<.001

JC speaking <− English proficiency

0.921

0.099

9.337

<.001

JC writing <− English proficiency

1.000

SE standard error, CR critical ratio

MethodA total of 144 twelfth graders (F = 114, M = 30) in nine Japanese high schools
took part in this study. All of them were native speakers of Japanese. Efforts weremade to recruit students with diverse L2 proficiencies in order to secure a high reliability of data (Mizumoto 2014). They all officially took NCT in January of either
2016 or 2017. Their scores were validated by the official score reports sent by the
National Center for University Entrance Examinations. The total scores were calculated simply by adding the scores of reading and listening as it is the conventional
way. Their descriptive statistics on the national level along with the number of participants are shown in Table 4. Due to the relatively small discrepancies, no scoreadjustments were implemented between the scores of these 2 years. The participants also took JC in either March or May after an interval of 2–4 months following NCT. JC reports a composite score in addition to each of the four skills;
however, the composite score ranges only from 1 to 6. Such a rough scoring witha small scale inevitably incapacitates the analysis from being fine-grained, which
likely masks the precise relationship between the variables. Thus, a decision wasmade to calculate a total score that better reflects score variations. Because JC reports two different score bands for skills, in order to equalize their weights, theraw scores were converted to the scale of 0–20 (Table 5). The scale of 0–20 was
adopted in order to utilize the original range of the raw scores as much as possible(20 for reading and listening and 16 for speaking and writing) because using asmaller range will conceal the difference in the raw scores. For analyses, Pearson’s
correlations, an exploratory factor analysis (maximum likelihood), confirmatory factor analyses, and chi-square difference tests were conducted.

Table 11 Unstandardized regression weights of correlated skill model
Estimate

SE

CR

p

NCT reading <− receptive skills

8.590

0.593

14.483<.001

NCT listening <− receptive skills

1.816

0.134

13.585<.001

JC reading <− receptive skills

0.970

0.076

12.773JC listening <− receptive skills

1.000

JC speaking <− productive skills

0.916

JC writing <− productive skills

1.000

SE standard error, CR critical ratio

<.001
<.001

0.092

9.958

<.001

Kamiya Language Testing in Asia (2017) 7:15

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Table 12 Unstandardized regression weights of correlated test model
Estimate

SE

CR

p

NCT reading <− NCT

4.729

0.311

15.206<.001

NCT listening <− NCT

1.000

JC reading <− JC

2.007

0.177

11.363<.001

JC listening <− JC

2.084

0.178

11.715<.001

JC speaking <− JC

0.923

0.098

9.418

<.001

JC writing <− JC

1.000

SE standard error, CR critical ratio

Results
Descriptive statistics and correlations

Table 6 shows descriptive statistics of the results of the two tests. A wide score range wasobserved for each section of both tests. As can be seen in Table 7, the Pearson’s productmoment correlation matrix indicates that the two tests are highly correlated with eachother not only for the scores of identical skills (reading: r = .78; listening: r = .75; Figs. 5
and 6, respectively) but also for the total scores (r = .86; Fig. 7), indicating a high concurrent validity between them. Excluding total scores, the lowest figure was observed in the
combination between NCT listening and JC speaking at 0.59 and the highest was between
the two readings at 0.78.

An exploratory factor analysis

Because all of the six scores were highly correlated, an exploratory factor analysis was
conducted in order to detect the number of latent factors, using raw scores. Althoughstrong correlations were observed between reading and total scores of NCT (r = .99)
and between listening and total scores of JC (r = .90), since total scores were excluded
for factor analysis, none of the variables tested in a factor analysis had a correlation ofover 0.8. Also, the determinant was 0.01. Thus, multicollinearity was not at issue. A
Bartlett’s test was significant at p < .001, indicating that it is significantly different froman identity matrix. The value of Kaiser-Meyer-Olkin statistic was 0.917, verifying thesampling adequacy. No univariate or multivariate outlier was detected using z scoresand Mahalanobis distance (max = 20.309), respectively. All of the values for skewness
and kurtosis fall within the range of |3.30| (z score at p < .01), securing univariate normality (Tabachnick and Fidell 2007). Mardia’s normalized estimate of multivariate
kurtosis was 0.871, which is well below 5, securing multivariate normality (Byrne 2016).
Maximum likelihood extracted a single factor, explaining 68.97% of total variance.
Furthermore, as can be seen in Table 8, all of the communalities were over 0.50, and allof the factor loadings were over 0.70. Judging from these figures, the reading score ofNCT represents the largest portion of the factor.

Confirmatory factor analyses

The results of the exploratory factor analysis imply that, regardless of tests and skills tomeasure, a general construct, supposedly general English ability, determined the major
portion of these scores, which supports the unitary model. Thus, in order to verify
whether the unitary model would show the best fit over others, confirmatory factor

Kamiya Language Testing in Asia (2017) 7:15

Page 15 of 22

Table 13 Variances of unitary model
English proficiency

Estimate

SE

CR

p

4.506

0.807

5.587

<.001

e6

2.691

0.359

7.500

<.001

e5

3.518

0.449

7.840

<.001

e4

7.277

1.053

6.909

<.001

e3

7.785

1.095

7.109

<.001

e2

20.878

3.124

6.684

<.001

e1

340.37856.436

6.031

<.001

SE standard error, CR critical ratio

analyses were conducted, comparing three models: unitary (Fig. 8), two uncorrelated latent factors of receptive and productive skills (Fig. 9), and two correlated latent factorsof receptive and productive skills (Fig. 10). Moreover, although high correlations wereobserved between these two tests, each of them was made for different aims and, thus,
may have divergent test constructs from each other. Thus, for the sake of comparison
with the three models above, two additional models, two uncorrelated latent factors of
NCT and JC (Fig. 11), and two correlated latent factors of NCT and JC (Fig. 12) wereanalyzed. The higher-order model was not considered due to the fact that the numberof first-order factors was two.
First of all, the two uncorrelated models (Figs. 9 and 11) were rejected as they were unidentified. Although this issue was solved by setting the weight of another regression pathas one, such a treatment is not theoretically supported, and additionally, the models were
found to be a poor fit (p < .001). Thus, these models are not considered further.
The results of the remaining three models are shown in Tables 9, 10, 11, 12, 13, 14, 15,
and 16 and Figs. 13, 14, and 15. Table 9 indicates that both the unitary and the correlated
skill models are satisfactorily well fit in terms of all of the indices (i.e., χ2/df. < 1.5,
CFI > 0.95, NFI > 0.95, TLI > 0.95, RMSEA < 0.05, SRMR < 0.05). Besides, the differencesin AIC and CAIC are quite small and the lowest value in the confidence interval of
RMSEA was 0, indicating that neither of the two models is significantly better than theother. Thus, because the unitary model is nested in the correlated skill model, a chisquare difference test was performed, whose results show that the latter was slightly better
than the former (χ2 difference = 4.039, df difference = 1, p = .044). The correlation between the two latent factors of receptive skills and productive skills was 0.93.
Table 9 also shows that the correlated test model was also as well fit as the unitarymodel for all of the indices except RMSEA at 0.053 > 0.05. A chi-square difference test
indicates that the degree of fit of these two models are not significantly different fromeach other (χ2 difference = 0.746, df difference = 1, p = .388). The correlation between
the two latent factors of NCT and JC was 0.98.

Discussion
RQ1: How are the scores of NCT and JC correlated with each other?
The correlation of scores between NCT and JC turned out to be all high. The highcorrelation of the total scores (r = .86) indicates that JC could have been a proper candidate to replace NCT for their total scores (however, see In’nami et al., 2016, for a
criticism of such a claim). This figure is somewhat compatible with those found in pastKamiya Language Testing in Asia (2017) 7:15

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Table 14 Variances of correlated skill model
Receptive skills

Estimate

SE

CR

p

19.495

3.113

6.261

<.001

Productive skills

5.037

0.874

5.762

<.001

e6

2.160

0.413

5.233

<.001

e5

3.114

0.458

6.802

<.001

e4

7.327

1.065

6.882

<.001

e3

7.684

1.090

7.047

<.001

e2

20.650

3.125

6.607

<.001

e1

329.335

56.216

5.858

<.001

SE standard error, CR critical ratio

studies that investigated correlations between NCT scores and other commerciallyavailable tests (r = .70 for TOEFL, r = .80 for TOEIC and TEAP, r = .89 for Eiken)(Eiken Foundation of Japan 2015; Otsu 2014). These results imply that whether the test
follows the restriction of the guidelines on high school curriculum published by the
MEXT exerts little impact on the test results. However, the unique contribution of thepresent study lies in the fact that this is perhaps the first study that proved that this applies to a commercially available test of not only two receptive skills but also thosemeasuring four skills. Unfortunately, JC has already stopped its operation and, thus,
cannot be a replacement for NCT. Still, the high compatibility between NCT and JCscores may be a good omen for other four-skill tests without the curriculum restriction
to be named as prime candidates to replace NCT. To be cautious, however, such verifications need to be empirically tested in future studies. That is partly because the difficulty of those tests may not be compatible from the perspective of preparation. It isrelatively easy to prepare for NCT because (a) the test content falls within the scope of
curriculum guidelines, which limits the coverage of what to study; (b) a large numberof exercise books are constantly published; and (c) the test becomes open to the public
once the test is over every year, and the accumulation of the questions used in the pastmakes it easy to anticipate the types of questions to be asked. In contrast, those othertests of four skills lack all or at least one of these three points. Perhaps the test with theleast disadvantage is Eiken because numerous exercise books are published, and it publicizes the questions used on the website; still, it is without the restriction of the curriculum guidelines, making it rather difficult to anticipate what to study for the test. All
Table 15 Variances of correlated test model
Estimate

SE

CR

p

NCT

64.765

9.987

6.485

<.001

JC

4.543

0.810

5.606

<.001

e6

2.654

0.360

7.378

<.001

e5

3.469

0.447

7.764

<.001

e4

7.098

1.067

6.653

<.001

e3

7.733

1.115

6.938

<.001

e2

20.158

3.199

6.301

<.001

e1

319.62260.869

5.251

<.001

SE standard error, CR critical ratio

Kamiya Language Testing in Asia (2017) 7:15

in all, more studies are expected to be conducted to see the compatibilities of thescores of those tests and NCT.
RQ2: Which of the five models (unitary, uncorrelated skill, correlated skill, uncorrelated test, and correlated test) best represents the test constructs of NCT and JC?
In the present study, the unitary model and the correlated test model were equallywell fit according to the chi-square difference test. However, when two models areequally well fit, the model should be chosen based upon the principle of parsimony.
That is to say, the simpler model with more degrees of freedom (unitary model) should
be preferred over a more saturated model with fewer degrees of freedom (correlated
test model). Together with the fact that the RMSEA value was over 0.05 in the correlated test model, this model should be rejected. This indicates that these two tests are
not only similar but also even close to being identical in terms of their test constructs;
thus, they do not warrant being distinguished from each other by test types. The highcorrelation between the two factors of tests (r = .98) in the model also corroborates thisdecision because previous studies adopted the correlation of over 0.9 as a criterion to
detect extremely highly correlated factors as they are statistically indistinguishable. In
fact, based on this notion, Gu (2015) discarded the correlated four-factor model.
Furthermore, the two models, the unitary model and the correlated skill model, wereequally well fit. The chi-square difference test showed the latter was marginally better
than the former if the conventional alpha level of 0.05 is adopted. Actually, some of the
figures in Table 9 indicate the superiority of the latter (e.g., CFI = 0.995 vs 1, TLI = 0.992
vs 1, RMSEA = 0.048 vs 0). However, because the P value was only slightly lower than
0.05 (p = .044), if the more restricted alpha level is adopted (even at 0.04), both models
fit equally well. Then, following the principle of parsimony, the unitary model should
be chosen over the correlated skill model. Additionally, the two latent factors are estimated to be correlated at as high as 0.93, and for the abovementioned reason, these
two factors are statistically indistinguishable.
It should be noted that, in In’nami et al. (2016), the unitary model was found to be
the best fit both for TEAP and TOEFL iBT when the endogenous (observed) variables
were skill-based scores: reading, listening, speaking, and writing of TEAP and TOEFL
iBT, and claimed that this evidence indirectly supported the higher-order structures.
However, an important difference should be noted between their study and the current
one. At least for TEAP, In’nami et al. rejected the correlated model with two first-orderfactors of receptive skills and productive skills due to the low TLI value (0.718). On thecontrary, in the current study, it was exactly this model that was found to be the best
choice (TLI = 1), which indirectly negates the possibility of the higher-order model.
Thus, depending upon what perspective to take, either the unitary model or the correlated skill model can be the best candidate. If the correlated skill model is to be
adopted in line with numerous previous studies that examined the factor structure oflanguage competence, this study suggests that at least receptive skills and productive
skills warrant being measured separately. (Due to the fact that a single variable was obtained for speaking and writing, this study does not avail to claim that each of these
two skills should be measured separately.) However, if the unitary model is to be
adopted, this is quite an unexpected result because the model has been almost uniformly rejected in most of the past literature on empirical grounds. This study is most
likely the first study to analyze the factor structure of NCT, so there is no study to refer

Page 17 of 22

Kamiya Language Testing in Asia (2017) 7:15

Page 18 of 22

Table 16 Squared multiple correlations
Unitary

Correlated skill

Correlated test

NCT reading

0.807

0.814

0.819

NCT listening

0.754

0.757

0.763

JC reading

0.701

0.705

0.703

JC listening

0.729

0.727

0.735

JC speaking

0.521

0.576

0.527

JC writing

0.626

0.700

0.631

to; however, at least one study, Gu (2015), examined that of JC, so it warrants comparing with the current study.
Table 17 shows some contrastive features between these two studies. Regarding participants’ ages, Gu’s study is perhaps the study targeting one of the youngest cohorts inthis line of studies, but the results were in accordance with other studies that targetedolder learners, typically university students. Furthermore, ages are often intertwinedwith proficiency, but Shin (2005) negated its effects on the model choice. Thus, neither
age nor proficiency can account for the discrepancy. Next, although the participants inboth Gu (2015) and the present study were learning English as a foreign language, theformer come from 15 different countries, over a half of them were Koreans, whereas allof the learners in the present study were Japanese, which could potentially be a culprit.
However, Sasaki (1996), targeting only Japanese university students, also rejected theunitary model outright as it was poorly fit. Finally, according to Gu’s (2015) descriptions, the pilot version and the authentic version of JC have some differences; however,assumingly, which are rather minor. To summarize, all of these abovementioned scenarios must be ruled out.
Gu (2015) speculated that those test items that require integrative skills to tackle may
load on multiple factors, tested a model that allowed cross-loadings, but found that thismodel was not tenable as all of the loadings on the secondary factors turned out to be
non-significant. Such a verification was possible because the analysis was done on theitem level. Here, although speculatively, lies the reason for the discrepancy in themodels to be adopted in the two studies. Perhaps, on each item level, the loading on
the secondary factor is too weak to be significant, but as a composite score of a few

Fig. 13 Standardized regression weights of unitary modelKamiya Language Testing in Asia (2017) 7:15

Fig. 14 Standardized regression weights of correlated skill model

items on multiple factors, their effects may have become significant, which may explain
why the correlations among the first-order factors of the four skills were all highly correlated at around 0.9, and thus, the correlated model was consequently discarded inGu’s study. It should be noted that, in contrast to Gu’s study, the present study adopted
even broader categories of two factors for the first order: receptive skills and productive
skills, and this practice even likely facilitates blurring the boundaries between firstorder factors. In JC, some of the test items in the speaking and writing sections are integrated with listening. Thus, the receptive skill (of listening) is also measured in productive skills (of speaking and writing). Unfortunately, the uncorrelated/correlated
models in which receptive skills are also loaded on JC speaking and writing were unidentified in this study. However, setting the regression weights from productive skills
to both JC speaking and writing at 1 in the uncorrelated model, the estimated value
from receptive skills to JC speaking and to JC writing turned out to be significant at
0.435 (p < .001) and 0.475 (p < .001), respectively. Thus, although tentatively, theabovementioned hypothesis is supported.
As a final note, Alderson (1991) claims that, when exploring the nature of language
proficiency, a homogeneous group should be recruited because test takers with a variety of background of ethnics, cultures, languages, and education may yield different
outcomes, leading to confounding results. Sasaki (1996) also posits that learning contexts may exert on disproportionate development of each skill. The present study

Fig. 15 Standardized regression weights of correlated test model

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Kamiya Language Testing in Asia (2017) 7:15

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Table 17 Comparison between Gu (2015) and the present study
Gu (2015)

Present study

Numbers

436

144

Ages

11–15

17–19

Learning environments

EFL

EFL

Native countries

15 different countries

Japan

Participants

Tests formats

Pilot

Authentic

Analysis levels

Item raw scores

Total skill scores

followed Alderson's (1991) advice, but this practice, of course, comes at the expense ofgeneralizability; thus, the results of the present study cannot be generalized beyond Japanese high school students. Then, it is somewhat surprising that the unitary model
could be adopted for them because, generally speaking, English education in Japantends to emphasize receptive skills over productive skills partly owing to the fact that
the NCT measures only the former. However, the results of the present study indicatethat even productive skills develop relatively evenly with receptive skills among Japanese high school students, which is in line with Sasaki (1996). All in all, this study
showed that what Sasaki found among Japanese university students may be applicableeven among Japanese high school students.

Conclusions
Assuming that all of the six sections in the two tests are measuring a single construct,a question one could ask is, in order to assess learners’ English proficiency, do receptive
skills and productive skills need to be measured separately? Since the communality of
NCT reading was 0.81, 81% of NCT reading scores can be explained by a single factor.
JC speaking had the lowest communality, but still, it was over a half (0.52). Then, it ispossible to claim that, by assessing learners only through NCT reading, we can quite
confidently surmise their English proficiency.
Nevertheless, this paper was written without any intention to propose that only reading should be assessed for university entrance examinations in Japan. Such a practicewill naturally lead English classes to heavily focus on reading only, possibly via the
Grammar Translation Method. In order to foster communicative skills in a balancedmanner among students, including productive skills, tasks in which students utilize all
four skills should be implemented. For that sake, the washback effect imposed by sucha high-stake test as NCT is useful, if not indispensable. It is sincerely hoped that,through implementing the system to require all students to take the tests of four skills
for university entrance examination, English education in Japan will cultivate all four
skills among students more than ever.
Endnotes
1
Readers may wonder why a defunct test was chosen and claim that a test still in use
should be targeted instead. The abolishment of the test was announced after a large
portion of data were already collected, all of a sudden by ETS. So this incident unpredictably happened.

Kamiya Language Testing in Asia (2017) 7:15

2

The deadline was postponed until the end of March 2017 for some exceptional
cases, including the present study.
Acknowledgements
First of all, I would like to express my deepest gratitude to Atsushi Mizumoto and Yo In’nami who gave me valuablecomments on statistical procedures and also to Neal Snape for proofreading the paper. Second, this study was not
feasible without the technical help of Kazuma Shigeno for his technical help and the help of the five staffers at GC&T,
Sota Hori, Kotaro Kurihara, Naoko Ohno, Hiromi Ando, and especially Mikihiko Takawa; so I greatly appreciate theircontinuous support. Lastly, I thank all of the participants and the high school principals who kindly allowed me torecruit students at their schools and, not to mention, the editor and anonymous reviewers for their constructive
feedback and patience.
Funding
This study was supported by KAKENHI (JP15K02789).
Authors’ contributions
NK singly designed the study, collected the data, performed the statistical analysis, drafted the manuscript, andapproved the final manuscript.
Competing interests
The author declares that he has no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Received: 10 July 2017 Accepted: 8 September 2017

References
Alderson, C. J. (1991). Language testing in the 1990s: how far have we come? How much further have we to go? In S.
Anivan (Ed.), Current developments in language testing (pp. 1–26). Singapore: SEAMEO Regional Language Centre.
Bachman, L. F., & Palmer, A. S. (1981). The construct validation of the FSI oral interview. Language Learning, 31(1), 67–86.
Bachman, L. F., & Palmer, A. S. (1982). The construct validation of some components of communicative proficiency.
TESOL Quarterly, 16(4), 449. https://doi.org/10.2307/3586464.
Benesse. (1997a-2017a). GTEC. Retrieved from http://www.benesse.co.jp/gtec/
Benesse. (1997b-2017b). GTEC CTE. Retrieved from http://www.benesse.co.jp/gtec/cte_company/index.html
Benesse. (2000–2017). GETC CBT. Retrieved from http://www.benesse-gtec.com/cbt/.
Benesse. (n.d.-a). GTEC daigakuban 4-ginouka start no oshirase flier. Tokyo, Japan.
Benesse. (n.d.-b). GTEC Junior. Retrieved from http://www.benesse-gtec.com/jr/
British Council, IDP: IELTS Australia, & Cambridge English Language Assessment. (2017). IELTS. Retrieved from https://www.ielts.org/
Byrne, B. M. (2016). Structural equation modeling with AMOS: basic concepts, applications, and programming (3rd ed.).
New York: Routledge.
Cambridge English Language Assessment. (2017). Cambridge English. Retrieved from http://www.
cambridgeenglish.org/
Educational Testing Service. (2017a). TOEFL iBT. Retrieved from https://www.ets.org/toefl
Educational Testing Service. (2017b). TOEFL Junior. Retrieved from https://www.ets.org/toefl_junior/
Educational Testing Service. (2017c). TOEIC. Retrieved from https://www.ets.org/toeic/
Eigo yonginou shikaku kentei kondankai. (2016). Eigo yonginou shiken jyouhou site. Retrieved from http://4skills.jp/
qualification/comparison.html
Eiken Foundation of Japan. (2015). Daigakunyushi centersiken tono soukanchousa: Jitsuyoueigo ginoukentei to TEAPde jissi. Retrieved from Tokyo, Japan: https://www.eiken.or.jp/teap/info/2015/pdf/20151007_pressrelease_
testresearch.pdf
Eiken Foundation of Japan. (n.d.-a). BULATS. Retrieved from http://www.eiken.or.jp/bulats/
Eiken Foundation of Japan. (n.d.-b). Eiken. Retrieved from http://www.eiken.or.jp/eiken/
Eiken Foundation of Japan. (n.d.-c). TEAP. Retrieved from http://www.eiken.or.jp/teap/
Eiken Foundation of Japan. (n.d.-d). TEAP CBT. Retrieved from http://www.eiken.or.jp/teap/
Fouly, K. A., Bachman, L. F., & Cziko, G. A. (1990). The divisibility of language competence: a confirmatory approach.
Language Learning, 40(1), 1–21. dx.doi.org/10.1111/j.1467-1770.1990.tb00952.x.
GC&T. (n.d.). TOEFL Junior Comprehensive Test naiyou. Retrieved from https://gc-t.jp/toefljunior/comprehensive/
test/detail/
Gu, L. (2015). Language ability of young English language learners: definition, configuration, and implications. Language
Testing, 32(1), 21–38. https://doi.org/10.1177/0265532214542670.
In’nami, Y., Koizumi, R., & Nakamura, K. (2016). Factor structure of the Test of English for Academic Purposes (TEAP®) test
in relation to the TOEFL iBT® test. Language Testing in Asia, 6(1). https://doi.org/10.1186/s40468-016-0025-9.
In'nami, Y., & Koizumi, R. (2012). Factor structure of the revised TOEIC test: a multiple-sample analysis. Language Testing,29(1), 131–152. https://doi.org/10.1177/0265532211413444.
Llosa, L. (2007). Validating a standards-based classroom assessment of English proficiency: a multitrait-multimethod
approach. Language Testing, 24(4), 489–515. https://doi.org/10.1177/0265532207080770.

Page 21 of 22

Kamiya Language Testing in Asia (2017) 7:15

Ministry of Education, C., Sports, Science and Technology. (2010). Koutou gakkou gakushuu shidou yoryo eiyakuban(kariyaku). Retrieved from http://www.mext.go.jp/a_menu/shotou/new-cs/youryou/eiyaku/__icsFiles/afieldfile/2011/04/11/1298353_9.pdf
Mizumoto, A. (2014). Sokuteino datouseito shinraisei -yuidatano hissujyouken towa. In O. Takeuchi & A. Mizumoto (Eds.),
Gaikokugo kyouiku kenkyu handbook -kaiteiban (pp. 17–31). Tokyo: Shohakusha.
National Center for University Entrance Examinations. (2015). National Center for University Entrance Examinations
Annual Report. Retrieved from Tokyo, Japan: http://www.dnc.ac.jp/albums/abm.php?f=abm00006725.pdf&n=2015
大学入試センター英版.pdf
National Center for University Entrance Examinations. (2016). Heisei 28 nendo daigaku nyushi center shiken jisshi kekka
no gaiyou. Retrieved from http://www.dnc.ac.jp/albums/abm.php?f=abm00006898.pdf&n=別添2:[試験情報]平成28
年度大学入試センター試験実施結果の概要.pdf
National Center for University Entrance Examinations. (2017). Heisei 29 nendo daigaku nyushi center shiken jisshi kekka
no gaiyou. Retrieved from http://www.dnc.ac.jp/albums/abm.php?f=abm00009105.pdf&n=別添2:【修正 試験情報】平成29年度大学入試センター試験実施結果の概要%2B%2B-%2Bコピー.pdf
National Center for University Entrance Examinations. (n.d.-a). Center shiken sanka daigaku jyouhou. Retrieved fromhttp://www.dnc.ac.jp/center/daigaku_jouhou.html
National Center for University Entrance Examinations. (n.d.-b). Shiganshasuu jyukenshasuu touno suii. Retrieved fromhttp://www.dnc.ac.jp/data/suii/suii.html
Oller, J. W. J. (1979). Language tests at school: A pragmatic approach. London: Longman.
Oller, J. W. J. (1983). Evidence for a general language factor: an expectancy grammar. In J. W. J. Oller (Ed.), Issues inlanguage testing research (pp. 3–10). Rowley: Newbury House.
Otsu, T. (2013). Monitor chousasha jyukensha ni okeru eigohyoujyunka test no seiseki. Heisei 24 nendo listening test no
jisshikekka ya seikatou wo kensyoushi sono kaizenwo hakarutameno chousakenkyuu ni kannsuru houkokusho, 89–95.
Otsu, T. (2014). Hyoujyunka eigoshiken to center shiken eigokamoku tokuten tono kankei bunseki. Retrieved from
Tokyo, Japan: http://antlers.rd.dnc.ac.jp/~otsu/doc/jart2014_p66-69.pdf
Papageorgiou, S., Xi, X., Morgan, R., & So, Y. (2015). Developing and validating band levels and descriptors for reporting
overall examinee performance. Language Assessment Quarterly, 12(2), 153–177. https://doi.org/10.1080/15434303.
2015.1008480.
Pearson. (2014). PTE Academic. Retrieved from http://pearsonpte.com/
Pearson. (n.d.). Progress. Retrieved from http://product.pearsonelt.com/progress/
Sasaki, M. (1996). Second language proficiency, foreign language aptitude, and intelligence: quantitative and qualitative
analyses (Vol. 6): Peter Lang Pub Incorporated.
Sawaki, Y., Stricker, L. J., & Oranje, A. H. (2008). Factor structure of the TOEFL Internet-based test (iBT): exploration in afield trial sample. Retrieved from Princeton, NJ: http://onlinelibrary.wiley.com/doi/10.1002/j.2333-8504.2008.tb02095.
x/epdf
Sawaki, Y., Stricker, L. J., & Oranje, A. H. (2009). Factor structure of the TOEFL Internet-based test. Language Testing, 26(1),005–030. https://doi.org/10.1177/0265532208097335.
Shin, S.-K. (2005). Did they take the same test? Examinee language proficiency and the structure of language tests.
Language Testing, 22(1), 31–57. https://doi.org/10.1191/0265532205lt296oa.
So, Y., Wolf, M. K., Hauck, M. C., Mollaun, P., Rybinski, P., Tumposky, D., & Wang, L. (2015). TOEFL Junior design framework(RR-15-13). Retrieved from Princeton, NJ: http://onlinelibrary.wiley.com/doi/10.1002/ets2.12058/epdf
Stricker, L. J., & Rock, D. A. (2008). Factor structure of the TOEFL Internet-based test across subgroups. Retrieved from
Princeton, NJ: https://www.ets.org/Media/Research/pdf/RR-08-66.pdf
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Needham Heights: Allyn and Bacon.
Tannenbaum, R. J., & Baron, P. A. (2015). Mapping scores from the TOEFL Junior Comprehensive test onto the Common
European Framework of Reference (CEFR). Retrieved from Princeton, NJ: https://www.ets.org/Media/Research/pdf/
RM-15-13.pdf
United Nations Associations of Japan. (n.d.). Kokuren Eiken. Retrieved from http://www.kokureneiken.jp/
Yamanishi, T. (2001). Eiken syutokukyuu to daigaku nyuushi center shiken eigokamoku no tensuu tono soukankankei.
STEP Bulletin, 13, 26–42.

Page 22 of 22