International Journal of Testing, 11: 71–92, 2011
C Taylor & Francis Group, LLC
Copyright 
ISSN: 1530-5058 print / 1532-7574 online
DOI: 10.1080/15305058.2010.516379

Factor Structure of Japanese Versionsof Two Emotional Intelligence Scales
Eriko Fukuda and Donald H. Saklofske
University of Calgary, Canada

Katsuo Tamaoka
Graduate School of Languages and Cultures, Nagoya University, Japan

Tak Shing Fung
University of Calgary, Canada

Yayoi Miyaoka
Hiroshima University of Economic, Japan

Sachiko Kiyama
Reitaku University, Japan

This article reports the psychometric properties of two emotional intelligence measures translated into Japanese. Confirmatory factor analysis (CFA) was conducted to
examine the factor structure of a Japanese version of the Wong and Law Emotional
Intelligence Scale (WLEIS) completed by 310 Japanese university students. A second study employed CFA to examine the factor structure of a Japanese version of the
Schutte Emotional Intelligence Scale (SEIS) completed by 200 Japanese university
students drawn from the first study. A four-factor model was replicated for both the
WLEIS and for the SEIS. Structural equation modeling indicated that higher WLEISand SEIS scores were related to higher self-reported satisfaction with life.
Keywords: emotional intelligence, factorial structure, life satisfaction, SEIS, validity, WLEIS

Correspondence should be sent to Eriko Fukuda, EDT #302, Division of Applied Psychology,
Faculty of Education, University of Calgary, 2500 University Drive, NW, Calgary, Alberta, Canada
T2N 1N4. E-mail: efukuda@ucalgary.ca

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INTRODUCTION
The construct of emotional intelligence (EI) has drawn considerable interest in
many different countries following its introduction by Salovey and Mayer (1990)
and Goleman (1995). From both a research and measurement perspective, twodistinct models of EI are currently described in the literature (e.g., Mayer, Caruso,& Salovey, 2000; Parker, Saklofske, & Stough, 2009; Petrides & Furnham, 2001;
Roberts, Zeidner, & Matthews, 2001; Zeidner, Matthews, & Roberts, 2009). The
ability model defines “emotional intelligence as the ability to perceive and express
emotion, assimilate emotion in thought, understand and reason with emotion,and regulate emotion in the self and others” (Mayer & Salovey, 1997, p. 5). The
trait model defines EI as “a constellation of behavioural dispositions and selfperceptions concerning one’s ability to recognize, process, and utilize emotionladen information” (Petrides & Furnham, 2003, p. 40). The differences in these
two models are evident; the ability model views EI as an intelligence or cognitive
ability, and trait EI is viewed more as a personality characteristic.
Ability EI models are measured by objective performance tests while trait EI
models are most often assessed by self-report tests. While there are advantages tothe use of the objective EI performance tests, the challenge is to determine what toconsider as a correct response. This is a critical point in conducting cross-cultural
studies because culture impacts societal norms and standards related to emotional
expressions and recognitions (Matsumoto & Ekman, 1989). On the other hand,self-report EI measures indicate typical attributes of the individual’s thoughts,
feelings, and behaviors in certain situations. Although the self-report measures
have disadvantages such as response bias, these issues can be better managed thanfinding solutions to the fundamental scoring issue of the performance measures.
Therefore, using self-report tests in cross-cultural settings appears to be a sensible
approach.
The central focus of contemporary EI research includes efforts to establish
consistent theories and models, establish EI as a universal construct, and develop
psychometrically sound measures. Cross-cultural studies are required to support
all three purposes. In cross-cultural research, a measurement developed in one
culture is studied in another. This process does not simply involve translations of
the items of the scale. Researchers must examine whether the measurement of a
psychological construct that was originally formulated in a single culture is applicable and meaningful in another culture (i.e., cross-culturally valid). This requires
several prerequisites including a culturally sensitive adaptation of the instrument,
and then demonstrating the same “structure” across cultures (Matsumoto & Juang,
2004).
There is a growing number of EI measures developed in Western countries,
but there are very few created specifically in Eastern countries. With this as a
background, it is noteworthy that the Wong and Law Emotional Intelligence Scale

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES73(WLEIS; Wong & Law, 2002) was developed in Hong Kong followed by several
published studies investigating the reliability and validity of this scale. On the other
hand, even though some EI measures are already well-known, the psychometricproperties of these measures have mainly been established in Western cultures.
For example, the Schutte Emotional Intelligence Scale (SEIS; Schutte, Malouff,& Bhullar, 2009; Schutte et al., 1998) is one of the best-known EI tests; however,published studies examining its psychometric properties are based primarily on
samples from Western countries. Consequently, the applicability of this measurein Eastern culture is not well examined and such findings are awaited.
In this study, the WLEIS and SEIS, described in more detail next, were selectedfrom available EI measures because they are both accessible, brief self-reportmeasures, and are comprised of four subscales that also yield a total EI score. In
addition, both measures are based either on the EI conceptualizations of Salovey
and Mayer’s original theoretical model (1990) or Mayer and Salovey’s revised theoretical model (1997). The original model consists of three categories of abilities:
appraisal and expression of emotion, regulation of emotion, and utilization of emotions in solving problems. The revised model emphasizes the conceptualization ofpotential intellectual and emotional growth in its four branch model of emotional
intelligence and consists of the following branches: (1) perception, appraisal, andexpression of emotion; (2) emotional facilitation of thinking; (3) understanding
and analyzing emotions and employing emotional knowledge; and (4) reflective
regulation of emotions to promote emotional and intellectual growth. The domainsof the WLEIS were developed following the four-branch model while the SEIS
was based on the original theoretical model.
The WLEIS is a 16-item scale employing a 7-point Likert type response format.
A total EI measure and four subscales are tapped by the WLEIS. Self-emotional
appraisal (SEA) assesses the individual’s self-perceived ability to understand hisor her emotions. Others’ emotional appraisal (OEA) measures the self-perceived
ability to recognize and understand other people’s emotions. The use of emotion(UOE) scale measures the self-perceived tendency to motivate oneself to enhance
performance while regulation of emotion (ROE) focuses on the self-perceived
ability to regulate one’s emotions. Validation studies of the WLEIS have been
conducted with Chinese participants in Hong Kong and two cities (i.e., Beijing
and Shandong province) in China (Law, Wong, & Song, 2004; Li, Saklofske,
Fung, & Yan, 2011; Shi & Wang, 2007; Wong & Law, 2002), Greek participants(Kafetsios & Zampetakis, 2008), and international students in the United States(Ng, Wong, Zalaquett, & Bodenhorn, 2007). General findings support the fourdimensional definition of the WLEIS among these cultural groups. In addition, an
acceptable model fit has been reported for the second-order factor structure of the
WLEIS, which consists of a general EI factor and the four subscales (Kafetsios &Zampetakis, 2008; Law et al., 2004; Wong & Law, 2002). Moreover, in response to
the criticism that trait EI models are essentially proxies for conventional personality

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constructs, these studies showed that the WLEIS is related but distinct from the
Big Five personality dimensions.
The SEIS is a 33-item scale assessing trait EI using a 5-point Likert typeresponse format. The SEIS composite score has demonstrated good reliability(Schutte, Thorsteinsson, Hine, Foster, Cauchi, & Binns, 2010); however, evidence
for the scale’s construct validity is somewhat less clear. Schutte et al. (1998) recommended using a total score to reflect a single factor or composite EI score, which
has been supported in other studies (Brackett & Mayer, 2003). However, further
studies have focused on the primary factors argued to comprise this measure. A
four-factor solution has been reported (Ciarrochi, Chan, & Bajgar, 2001; Ciarrochi, Deane, & Anderson, 2002; Petrides & Furnham, 2000; Saklofske, Austin,& Minski, 2003). For instance, Ciarrochi et al. (2001) described the four factorsof the SEIS as comprising perception of emotions, managing emotions in the self,social skills, or managing others’ emotions, and utilizing emotions. Petrides andFurnham (2000) described a somewhat similar four-factor structure comprisedof optimism/mood regulation, appraisal of emotions, social skills, and utilizing
emotions. However, other studies have reported a three-factor structure (Austin,
Saklofske, Huang, & McKenney, 2004), consisting of regulating/using emotions,
optimism/positivity, and appraisal of emotions. While the number of specific factors found in the SEIS is still a point of contention, Gardner and Qualter (2010)
compared three commonly used trait EI measures and concluded that SEIS appears
to be a valid measure of global trait EI.
Although the factor structure of the SEIS is somewhat inconsistent amongstudies conducted in Western countries, studies from Eastern cultures to support
either the three-, four-, or other-factor structure are lacking with the exception of
one study of Chinese secondary school teachers from Hong Kong. EFA and CFA
results supported the four factor structure of the SEIS but only with a reduced
12-item set (Chan, 2004). This raises the question of applicability of the SEIS in
Eastern cultures: Does the factor structure approximate or differ from the original
factor structure reported in Western cultures, and do some SEIS items need to be
removed or modified as they do not meaningfully contribute to the EI constructin Eastern cultures? Therefore, cross-cultural studies examining the psychometricproperties of the SEIS are required to demonstrate its universal applicability as
well as the robustness of the EI construct.
Life satisfaction is frequently presented as a key indicator of EI criterion validity(Austin, Saklofske, & Mastoras, 2010; Gignac, 2006; Law et al., 2004; Schutte
et al., 2010). Life satisfaction is defined as “a global assessment of a person’squality of life according to his chosen criteria” (Shin & Johnson, 1978, p. 478).
As Tatarkiewicz (1976) indicated, happiness requires satisfaction with life as awhole. Consequently, it is essential to examine the overall evaluation of one’s life
to understand the degree of satisfaction and happiness in life. The Satisfaction withLife Scale (SWLS) is one of the most well-examined scales to measure global life

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

75

satisfaction (Diener, Emmons, Larsen, & Griffin, 1985). It consists of five items
responded to on a 7-point Likert scale. In our study, this scale was used to assess
the criterion validity of EI.
The purpose of the present study was to examine the factorial structure of
the WLEIS and the SEIS with Japanese university students to determine theapplicability of these scales in the Japanese culture. In addition, this study aimed
to determine the convergent validity between these EI measures and to examine
whether EI also correlated with life satisfaction as demonstrated in a numberof studies (Austin, Saklofske, & Egan, 2005; Ciarrochi, Chan, & Caputi, 2000;
Saklofske et al., 2003; Wing, Schutte, & Byrne, 2006).

STUDY 1
MethodParticipants. A non-probability sampling method was used in this study. A
sample of 310 Japanese university students were recruited from two universities
located in the Hiroshima prefecture and from another university in the Chiba
prefecture. Information about this study was given to students in their classes, andthose who volunteered to participate received a small honorarium. The mean age
was 20.27 years (SD = 1.79). The sample included 143 males (46.1%) and 167
females (53.9%).
Measure. The WLEIS was translated into Japanese by the first author, andblindly back-translated by a bilingual native Japanese and a native English speaker.
As Berry (1980) indicated the goal of translation is to obtain instruments withconceptual equivalence—a literal translation of a measure is not sufficient for
conveying its equivalence to different cultural groups (McGorry, 2000). Therefore,
three rounds of back-translation were completed to ensure linguistic equivalenceor retention of original meaning of the WLEIS. Since this back-translation process
was conducted over a three-month period, and the translators were asked not tocheck their previous versions of translations in the second and the third translation
phase, the problem of the translators being influenced by their previous translations
was minimized.
Procedure. Participants who volunteered for this study completed the selfadministered measure in one testing session. Ethics approval was received from theConjoint Faculties Research Ethics Board at the University of Calgary in Canada
for this study because the principal investigators are registered at this university
even though the study was conducted in Japan.

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Analysis. Using SAS 9.1, missing data (<1% of data) were imputed bya stochastic regression imputation method (Rubin, 1987). Maximum-likelihood
confirmatory factor analyses (CFA) using LISREL 8.80 (Jöreskog & Sörbom,
2006) were conducted to evaluate the significance of each WLEIS factor loading
and goodness of fit of models. The following four measures of fit were used in
the evaluation of the adequacy of the models in CFA and SEM: chi-square; thenon-normed fit index (NNFI; Bentler & Bonett, 1980); the comparative fit index(CFI; Bentler, 1990); and the root mean square error of approximation (RMSEA;
Steiger, 1990). Values equal to or above .90 show an acceptable fit for the NNFIand the CFI, and values equal to or less than .08 are an acceptable fit for the
RMSEA.
Results

Descriptive Statistics and Correlations among the Four WLEIS
Subscales. The means and standard deviations of the four WLEIS subscales arepresented in the left half of the columns in Table 1. These descriptive statistics are
similar to those found in previous studies reported for Chinese students in Hong
Kong by Wong and Law (2002) (N = 149, Ms = 4.50–4.71, SDs = 0.91–0.97)
and Law et al. (2004) (Sample 1: N = 418, Ms = 3.78–4.15, SDs = 0.96–1.12;
Sample 2: N = 314, Ms = 4.27–4.84, SDs = 0.99–1.20). However, these results
differ slightly from those presented in other studies. Shi and Wang (2007) reported
WLEIS scores of Chinese university students in Beijing and Shandong province
in China (Male: N = 907, Ms = 4.89–5.54, SDs = 0.77–1.18; Female: N = 550,
Ms = 4.60–5.50, SDs = 0.76–1.20). Ng et al. (2007) presented WLEIS data forinternational university students studying in the United States (N = 628, Ms =5.05–5.70, SDs = 1.17–1.44), which also included Japanese international students(N = 35, Ms = 5.02–5.71, SDs = 0.85–1.18). To compare their results with theJapanese results presented here, we conducted independent samples t-tests under
TABLE 1
Descriptive Statistics and Correlations among the Four WLEIS Subscales
Subscales

M

SD

SEA

OEA

UOE

SEA
OEA
UOE
ROE

4.68
4.23
4.25
3.99

1.16
1.26
1.22
1.39

—
.37∗∗
.32∗∗
.32∗∗

—
.21∗∗
.25∗∗

—
.28∗∗

Note. SEA = Self-Emotion Appraisal, OEA = Other’s Emotion Appraisal, UOE = Use of Emotion,
and ROE = Regulation of Emotion. N = 310.
Pearson correlation coefficients. ∗∗ p < .01 (two-tailed tests).

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

77

the assumption of equal variances. The results suggested that there were significant
differences between all the scores in our study and those of Japanese students in
Ng et al. study, t.025 (341) = ±1.960, SEA: t = 5.06, OEA: t = 4.21, UOE: t =6.09, ROE: t = 4.22.
The correlations between the subscales of the WLEIS are reported in Table 1.
The small to moderate correlations among the four EI dimensions (ranging fromr = .21 to .37) are in line with the results of Wong and Law’s (2002) study (ranging
from r = .13 to .42). These results show that the four dimensions are correlated
but also tap unique aspects of the EI construct.

Internal Consistency Reliability. Internal consistency of the WLEIS was
examined with Cronbach’s coefficient alpha and was found to be satisfactory
for all the domains following Nunnally’s (1978) recommendation of .70 or higher
suggesting acceptable reliability. The reliabilities are: .790 for SEA, .867 for OEA,
.731 for UOE, and .882 for ROE. The reliability for the total WLEIS of .848 was
also satisfactory.
Factor Analysis. A series of CFA were conducted to evaluate three different
factor models of the WLEIS. The first model was a one-factor model, which has
all 16 items comprising a single factor. The second model examined was thefour-factor model of the WLEIS consisting of the four correlated factors, eachcomprised of four items. In addition, a second-order four-factor model, which
has a general EI factor and four first-order factors, was examined since somestudies reported evidence for the second-order four-factor model (Kafetsios &Zampetakis, 2008; Law et al., 2004; Wong & Law, 2002).
The results indicated a poor fit for the one-factor model of the WLEIS: NNFI =.415, CFI = .493, and RMSEA = .237 with 90% of confidence interval of.228–.247. On the other hand, the indices for the four-factor structure model
indicated a good fit to the observed data: NNFI = .961 and CFI = .969, and
RMSEA = .060 with 90% confidence interval of .049–.072. The factor loadingsof the items to factors are presented in Figure 1. Most of the items of the WLEIS
show excellent factor loadings (>.70) (Comrey & Lee, 1992) except two itemson the SEA factor, which had loadings of .649 and .432, and two items on the
UOE factor with loadings of .305 and .678. However, all the factor loadings were
statistically significant. Table 2 reports the factor loadings and variance explained
for the lower order constructs. The unstandardized and standardized factor loadings are reported in column one and two, respectively. In column three, squared
multiple-correlations for each indicator, R2, are reported. The average varianceexplained by the constructs is reported in column four.
All correlations between the underlying factors were also significant. Although
the correlation of .216 between OEA and UOE was small, all the other factor
correlations were moderate in their magnitude (see Figure 1). Thus, the assumption

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FIGURE 1
Four-factor CFA model of the WLEIS.
∗∗ p < .01.

that the WLEIS factors are correlated was supported. These results further support
a four-factor structure for the WLEIS in this Japanese university student sample,
which aligns with the findings of previous studies (Kafetsios & Zarnpetakis, 2008;
Law et al., 2004; Ng et al., 2007; Shi & Wang, 2007; Wong & Law, 2002).

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

79

TABLE 2First-Order Factor Loadings and Variance Explained for the WLEIS

Item

Unstandardized
Parameter
Estimates

Standardized
Parameter
Estimates

Squared Multiple
Correlations for
the Indicator

Average Variance
Explained by
the Constructs

1
2
3
4

.935
1.293
1.278
.676

.649
.880
.883
.432

.421
.774
.779
.187

.540

5
6
7
8

1.039
1.336
1.174
1.167

.719
.847
.743
.854

.516
.717
.553
.729

.629

9
10
11
12

1.099
.514
1.311
1.313

.678
.305
.803
.820

.460
.093
.645
.673

.468

13
14
15
16

1.229
1.360
1.210
1.431

.746
.864
.723
.907

.557
.747
.523
.823

.662

Similarly, the second-order four-factor model also demonstrated a good fit forthe observed data (Figure 2): NNFI = .961 and CFI = .967. The RMSEA also
showed an acceptable fit: RMSEA = .061 with 90% confidence interval .050 –.073. Likewise for the four-factor model, all the factor loadings were statisticallysignificant. Similar to Table 2 for the lower order constructs, Table 3 reports factor
loadings and variance explained for the second-order construct. The second-orderfactor analysis replicated the less than .70 factor loading on two items on the SEA

TABLE 3
Second-Order Factor Loadings and Variance Explained for the WLEIS

Constructs
SEA
OEA
UOE
ROE

Unstandardized
Parameter
Estimates (Beta)

Standardized
Parameter
Estimates

Squared Multiple
Correlations for
the Indicator

Average Variance
Explained by
the Construct

1.098
.721
.553
.639

.739
.585
.484
.539

.547
.342
.234
.290

.353

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FUKUDA ET AL.

FIGURE 2
Second-order four-factor CFA model of the WLEIS.
∗∗ p < .01.

factor, which had loadings of .650 and .433 and two items on the UOE factor,
which had loadings of .307 and .678.
The chi-square difference statistic was used to test the improvement in fit aspaths were added. The minimum fit function chi-square for the four-factor model
was χ 2 (98) = 210.270, p < .001 while that of the second-order four-factor
model was χ 2 (100) = 216.188, p < .001. The result of the chi-square difference
test was not significant, χ 2 (2) = 5.918, p = .052, which suggests that bothmodels described the data equally well. As Wong and Law (2002) stated, thetheoretical expectation of the WLEIS consists of the four underlying factors of

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

81

the multidimensional EI construct, and these factors can be combined to form an
estimate of the overall EI construct. The second-order factor model is supported
in this study.
STUDY 2
MethodParticipants. Two hundred Japanese university students from the two universities in the Hiroshima prefecture who completed the WLEIS also completed
the SEIS as well as the SWLS. The mean age of the group was 20.55 years (SD =1.48). The sample included 86 males (43%) and 114 females (57%).
Measures. The SEIS was used to examine EI of the Japanese university
students and the SWLS was used to measure the level of their life satisfaction. The
scales used in Study 2 were translated into Japanese by the first author, and blindly
back-translated by a bilingual native Japanese and a native English speaker. As
in Study 1, three rounds of back-translations were conducted to ensure linguistic
equivalence.
Although researchers have proposed different factors for the SEIS, the following four factors described by Ciarrochi et al. (2001) were selected for examinationin this study: emotion perception (EP), managing self-relevant emotions (MSE),
managing other’s emotions (MOE), and utilizing emotions (UE). Optimism reported in other four factors solutions was thought to be more reflective of Westerncultural values, which might be less applicable to the Japanese culture (Heine &Lehman, 1995).
Procedure. Participants volunteered to complete the self-administered measures in one testing session. Ethics approval was received from the Conjoint
Faculties Research Ethics Board at the University of Calgary in Canada for thisstudy.
Analysis. Missing values comprised less than 1% of data. They were imputed using stochastic regression imputation in SAS 9.1. A maximum-likelihood
CFA was performed to evaluate the factorial structure of the SEIS and structural
equation modeling (SEM) was employed to examine the predictive validity of theemotional intelligence scales using LISREL8.80 (Jöreskog & Sörbom, 2006).
Results

Descriptive Statistics (SEIS and SWLS) and Correlations among theFour SEIS Subscales. The means and standard deviations of the four EI82

FUKUDA ET AL.

TABLE 4
Descriptive Statistics and Correlations among the Four SEIS Subscales
Subscales

M

SD

EP

UE

MSE

EP
UE
MSE
MOE

3.17
3.71
3.35
3.37

.55
.60
.59
.58

—
.38∗∗
.47∗∗
.45∗∗

—
.54∗∗
.51∗∗

—
.65∗∗

Note. EP = Emotion Perception, UE = Utilizing Emotions, MSE = Managing Self-Relevant
Emotions, and MOE = Managing Other’s Emotions. N = 200.
Pearson correlation coefficients. ∗∗ p < .01 (two-tailed tests).

subscales are presented in the left half of the columns in Table 4. The moderatecorrelations, ranging between .38 and .65, suggest that the four dimensions werecorrelated but also represent unique aspects of the EI construct.
The means of the SWLS items ranged from 2.92 to 4.34 and standard deviations
ranged from 1.52 to 1.71. The total SWLS mean was 18.56 (SD = 6.10) anddiffers slightly from those reported in other studies. For example, Schimmack,
Radhakrishnan, Oishi, Dzokoto, and Ahadi (2002) reported a SWLS mean scoreof 20.08 (SD = 6.18) for the Japanese university students (N = 147) in their study.

Internal Consistency Reliability. Internal consistency for the SEIS was
satisfactory for three domains: .836 for EP, .749 for MSE, and .723 for UOE. Basedon Nunnally’s recommendation, the reliability for UE was lower than desired, .681;
however, according to DeVellis (1991), reliability estimates between .65 and .70
are minimally acceptable. The reliability of .894 for the total SEIS score was also
very acceptable. The reliability .818 for the SWLS was also satisfactory.
Factor Analysis. Schutte et al. (1998) recommended using the general EI
score while other researchers suggest either a three- or four-factor interpretation.
Therefore, three CFA models were examined: a one-factor model, a four-factor
model, and the second-order factor model. The third model is of interest since it
reflects both the general and subfactors. However, because the factor structure of
the SEIS for the Japanese sample is not known, the one- and four-factor models
were examined first.
The results suggest a poor to mediocre fit for the one-factor model of the SEIS:NNFI = .794, CFI = .807, and RMSEA = .099 with 90% confidence interval .093
– .105. In contrast, the fit indices indicated an acceptable fit for the four-factor
model of the SEIS: NNFI = .903, CFI = .910, and RMSEA = .066 with 90%
confidence interval .059 – .072. The factor loadings of the items on each factor of
the CFA analysis are presented in Figure 3. Although all loadings met statistical

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALESFIGURE 3
Four-factor CFA model of the SEIS.
∗∗ p < .01.

83

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FUKUDA ET AL.

TABLE 5First-Order Factor Loadings and Variance Explained for the SEIS

Item

Unstandardized
Parameter
Estimates

Standardized
Parameter
Estimates

Squared Multiple
Correlations for
the Indicator

Average Variance
Explained by
the Constructs

5
9
15
18
19
22
25
29
32
33

.594
.451
.605
.748
.344
.568
.821
.614
.629
.579

.518
.459
.636
.727
.338
.539
.808
.589
.633
.579

.269
.211
.404
.529
.114
.290
.654
.347
.401
.335

.355

6
7
8
17
20
27

.411
.549
.420
.461
.588
.592

.409
.592
.449
.476
.608
.584

.167
.351
.201
.226
.369
.342

.276

2
3
10
12
14
21
23
28
31

.313
.551
.600
.544
.337
.385
.644
.698
.623

.292
.522
.597
.558
.402
.335
.628
.617
.642

.085
.272
.356
.311
.161
.112
.395
.380
.412

.276

1
4
11
13
16
24
26
30

.550
.460
.555
.626
.403
.441
.450
.491

.559
.448
.561
.554
.415
.497
.391
.589

.312
.200
.315
.307
.172
.247
.153
.347

.257

significance, only 18 items show excellent to good factor loadings (> .55). Six
items show fair (>.45) and nine items had poor factor loadings (>.32). In addition,an item on the MSE factor presented as a non-interpretable factor loading, .292
(Comrey & Lee, 1992). Table 5 reports factor loadings and the explained variancefor the lower order constructs of the SEIS. The unstandardized and standardized

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

85

TABLE 6
Second-Order Factor Loadings and Variance Explained for the SEIS

Constructs
EP
UE
MSE
MOE

Unstandardized
Parameter
Estimates (Beta)

Standardized
Parameter
Estimates

Squared Multiple
Correlations for
the Indicator

Average Variance
Explained by
the Construct

.735
1.148
1.828
2.971

.592
.754
.877
.948

.351
.569
.770
.898

.647

factor loadings are reported in column one and two, respectively. In column three,squared multiple-correlations for each indicator, R2, are reported. The averagevariance explained by the constructs is reported in column four. Of note is that thecorrelations between the four factors were statistically significant.
The second-order factor model was then examined. The fit indices suggestthat the second-order four-factor model of the SEIS was acceptable (Figure 4):
NNFI = .904, CFI = .910, and RMSEA = .066 with the 90% confidence interval.059 – .072. The four parameter estimates from the EI construct were statisticallysignificant as shown in Figure 4. However, similar to the results of the first-order
CFA, only 16 items show excellent to good factor loadings (>.55), 10 items show
fair (>.45), and 6 items show poor factor loadings (>.32). One item on the MSE
factor still showed a non-interpretable factor loading, .289 (Comrey & Lee, 1992).
Similar to Table 5 for the lower order constructs, Table 6 reports factor loadings
and variance explained for the second-order construct.
The chi-square difference statistic was used to test the improvement in fit aspaths were added. The result of the four-factor model indicated minimum fit
function χ 2 (489) = 937.452, p < .001 while that of the second-order four-factor
model was χ 2 (491) = 939.961, p < .001. The result of the chi square difference
test was not significant, χ 2 (2) = 2.509, p = .285, which suggests that bothmodels describe the observed data equally well. However, based on the assertion
by Schutte et al. (1998) for using the general EI score derived from the SEIS,
the second-order factor model is supported in this study of Japanese university
students.

Convergent Validity. The correlation between the total scores of the WLEISand SEIS was calculated to determine the convergent validity of these two EI
measures (Kerlinger & Lee, 2000). The results show these two measures to behighly correlated (r = .758, p < .01).

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FIGURE 4
Second-order four-factor CFA model of the SEIS.
∗ p < .05. ∗∗ p < .01.

FACTOR STRUCTURE OF JAPANESE VERSIONS OF EI SCALES

87

Y1
SEA
OEA

.895**.670**.441**.848**
WLEIS.474**

SWLS.577**
UOE.578**.863**.471**.386**

Y2
Y3
Y4

ROE
Y5
FIGURE 5
SEM model for WLEIS and life satisfaction.
∗∗ p < .01.

Predictive Validity. EI has been shown to be related to life satisfaction(Austin et al., 2005; Ciarrochi et al., 2000; Extremera & Fernandez-Berrocal,
2005; Saklofske et al., 2003; Wing et al., 2006). Prior to examining the predictive
validity of the two EI measures using SEM, a CFA was performed to confirm
the factor structure of the model for the Japanese university student data. The
first model tested with CFA included the WLEIS correlated with a single factor
model of SWLS (Lewis, Shevlin, Bunting, & Joseph, 1995; Shevlin & Bunting,1994). The four parcels of the WLEIS are mean scores of the following subscales:
SEA, OEA, UOE, and ROE. The five items (i.e., Y1, Y2, Y3, Y4, and Y5) areindicators of the factor labelled SWLS. The fit indices indicated a good fit to theobserved data: NNFI = .930 and CFI = .950. The fit index of the RMSEA showed
a mediocre fit: RMSEA = .087 with 90% confidence interval .061–.113.
SEM was then employed to examine the relationship between the latent variables and their indicators (i.e., observed variables). As shown in Figure 5, themagnitude of the standardized direct effect of EI, measured with the WLEIS, on
satisfaction with life showed a medium effect, .474.
Similarly, a CFA was performed to confirm the factor structure of the second
model, which included the SEIS correlated to a single factor model of SWLS.
The four parcels of the SEIS are mean scores of the following subscales: EP, UE,
MSE, and MOE. The fit indices indicated a good fit to the observed data: NNFI =.955 and CFI = .967. The RMSEA also showed an acceptable fit: RMSEA = .078
with 90% confidence interval .051–.106. As presented in Figure 6, the results of
the SEM showed the magnitude of the standardized direct effect of EI measured
with SEIS on satisfaction with life (SWLS) to have a medium effect, .406.
Thus, these SEM results indicate that higher EI, assessed by both the WLEIS
and the SEIS, was related to higher self-reported satisfaction with life. This isconsistent with the findings in other studies indicating that EI is associated with88

FUKUDA ET AL.

Y1
EP
UE

.897**.562**.649**.849**
EIS.406**

SWLS.829**
MSE.784**.859**.478**.389**

Y2
Y3
Y4

MOE
Y5
FIGURE 6
SEM model for SEIS and life satisfaction.
∗∗ p < .01.

life satisfaction (Brackett & Mayer, 2003; Carmeli, Yitzhak-Halevy, & Weisberg,
2009; Gignac, 2006; Palmer, Donaldson, & Stough, 2002).

DISCUSSION
The purpose of the present study was to evaluate the applicability of the WLEIS
and the SEIS with Japanese university students in Japanese culture. The factorial
structure of both scales and the convergent validity between these EI measuresand life satisfaction were examined in two studies.
In Study 1, the CFA results for a sample of Japanese students supported thesecond-order four factor structure of the WLEIS described by Wong and Law(2002). In addition, the results from Study 2 supported the second-order factor
structure for the SEIS described by Schutte et al. (1998) and the four first-orderfactors described by Ciarrochi et al. (2001). Some researchers may argue the pointof examining the factor structure of the SEIS because other studies have alreadyreported inconsistent factor structures (Austin et al., 2004). However, considering
the fact that a study from Hong Kong did not support the factor structure of 33
items of the SEIS, the current study did demonstrate the potential use of the SEIS
in Japanese culture.
Further examination of the psychometric properties of the WLEIS added support for the reliability of the scale. Internal consistency reliabilities for each domainof the WLEIS and the total scale were satisfactory. Although internal consistency
reliabilities for total scale and three domains of the SEIS were satisfactory, theUE subscale had somewhat lower than desired reliability. Thus, caution is recommended when using the subscales of SEIS in Japan pending further study. In
addition, further examination of this measure at the item level is required due
to lower than desirable factor loadings on about half of the 33 SEIS items. This

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89

implies that substantial revisions to the SEIS may be necessary if it is to be usedin culturally different settings such as Japan.
Another contribution of this study is the demonstration of convergent validity
evidence based on the total scores of the two EI measurements. While the highcorrelation of .758 between the two EI scales may, in part, be attributed to thesemeasurements both having been developed based on the EI theory proposed by
Salovey and Mayer, this finding offers some further support for the robustness of
the EI construct across cultures and language groups, in this case with a sampleof Japanese university students.
Another important finding is the predictive validity evidence for the EI scales
in relation to self-reported life satisfaction. Both EI measurements significantly
predicted the level of life satisfaction. Consequently, the higher one’s EI score
measured by the WLEIS or the SEIS, the higher one’s level of life satisfaction
is in this Japanese university sample. This mirrored the trend described in otherstudies that found EI is positively correlated with life satisfaction (Austin et al.,2005; Ciarrochi et al., 2000; Saklofske et al., 2003; Wing et al., 2006).
The overall findings in this study present preliminary evidence for the applicability of the WLEIS and the SEIS for use in Japan or at least with Japanese
university students. Considering the fact that the WLEIS was developed in South
East Asia, this study conducted in Japan does add some further evidence of thegeneralizability of the measure in non-Western cultures. Further cross-culturaldevelopment work needs to be done on the SEIS, and such studies using item level
analyses would further contribute to our knowledge of EI as a universal construct.
Moreover, since this study did not examine Japanese cultural impact on the EI
construct, further studies are necessary to examine whether there are some EI
attributes and skills unique to Japanese culture and whether they are captured byexisting EI measures or require the development of more culturally sensitive andrelevant measures.
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