2011. Vol.2, No.3, 241-247
Copyright © 2011 SciRes. DOI:10.4236/psych.2011.23038
Psychometric Properties of the Greek Version of the Test
Georgia Papantoniou1, Despina Moraitou2, Dimitra Filippidou1
1University of Ioannina, Ioannina, Greece;
2Aristotle University of Thessaloniki, Thessaloniki, Greece.
Email: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Received January 30th, 2011; revised March 15th, 2011; accepted April 20th, 2011.
The present study examined the psychometric properties of the Greek version of Spielberger (1980) self-report
measure of test anxiety, the Test Anxiety Inventory (TAI). The total sample consisted of 231 undergraduate stu-
dents (124 male, 107 female). The results verified the well established two-factor structure for the TAI. The two
factors represented the Worry (TAI-W) and Emotionality (TAI-E) subscales, respectively. Furthermore, on the
bases of the confirmatory factor analyses, using either the set of 20 items or the set of 16 items, we found con-
vincing support for the existing relationship between the two subscales of the Test Anxiety Inventory. The in-
ternal consistency of the twenty-item TAI-T scale and for the eight-item Worry and Emotionality subscales
ranged from Cronbach’s α = .81 to .94. The G-TAI and its subscales showed differential statistically significant
relationships with a self-report measure of cognitive interference.
Keywords: Psychometrics, Test Anxiety, Greece
Education is vital for every country in the world, and Greece
is not an exception as a strong and effective education can help
boost the development of the country. As testing is a common
practice in contemporary society, like Greek society, for mak-
ing important decisions about an individual’s status in school,
college, and work (Lowe, Lee, Witteborg, Prichard, Luhr,
Cullinan, Mildren, Raad, Cornelius, & Janik, 2008; Zeidner,
1998), it is no wonder that test anxiety is a significant educa-
tional problem affecting many of students in our schools and
Students with test anxiety feel tense, fearful and worried in
evaluative situations (Spielberger, Gonzalez, Taylor, Anton,
Algaze, Ross, & Westberry, 1979; Spielberger & Vagg, 1995).
Research relating test anxiety to academic performance has
established that high levels of test anxiety are associated with
lower levels of students’ learning and performance (Sub &
Prabha, 2003). Often test-anxious students at all levels of edu-
cation perform more poorly on standardized tests (Everson,
Millsap, & Rodriguez, 1991) and receive poorer grades
(Chapell, Blanding, Silverstein, Takahashi, Newman, Gubi, &
McCann, 2005) than they ought to because anxiety and other
test-taking deficiencies interfere with their performance, either
directly or indirectly (Efklides, Papadaki, Papantoniou, & Ki-
osseoglou, 1997, 1999; Lowe et al., 2008; Metallidou & Vla-
In order to assess individual differences in test anxiety,
Spielberger developed the Test Anxiety Inventory (TAI; Spiel-
berger, 1980) which is a self-report instrument. The TAI is one
of the most widely used of the test anxiety inventories as it has
been translated or adapted for many populations (see Ware,
Galassi, & Dew, 1990).
Spielberger had two major goals in developing the TAI: (1)
to construct a brief, valid self-report measure of the test anxiety
which was highly correlated with other measures of the con-
struct and (2) to use factor analytic procedures to measure the
emotionality and worry components of the test anxiety identi-
fied by Liebert and Morris (1967). Emotionality refers to per-
ceived autonomic reactions (physiological arousal) evoked by
evaluative stress (Spielberger & Vagg, 1995), whereas worry
refers to cognitive concerns about the consequences of failure
(Morris & Liebert, 1969). Worry tends to be associated with
performance decrements on cognitive and intellectual tasks, but
emotionality is not (see Hembree, 1988; Hong, 1998; Spielber-
ger et al., 1979; Van der Ploeg, 1984).
For the TAI’s original development, exploratory factors ana-
lytic procedures (principal axis factoring with varimax rotation)
have been used by its constructors. According to them, the TAI
consists of 20 items, and contains two subscales measuring
worry and emotionality. Each subscale is defined by eight items,
with the remaining four (1, 12, 13, & 19) items not ordinarily
included in subscale scores as the results did not indicate clear
patterns favoring either subscale (Spielberger et al., 1979).
There are conflicting views of the factor structure of the TAI
as regards (a) the necessity of the 20-item TAI, and (b) the
interrelations of the two components of test anxiety. Despite
Spielberger and colleagues (Spielberger, Gonzalez, Taylor,
Algaze, & Anton, 1978; Spielberger et al., 1979) having re-
ported correlations between the Worry and Emotionality sub-
scales of .71 for males and .64 for females, most studies of the
factorial structure of the TAI using exploratory factor analysis,
have employed orthogonal rotations (e.g. varimax) following a
principal factor extraction (Hedl, 1984; Schwarzer & Kim,
1984; Spielberger et al., 1978, 1979; Van der Ploeg, 1983). On
the other hand, most researchers that used confirmatory factor
analysis have established a two-factor oblique model of the
16-item TAI, composed of correlated Worry and Emotionality
G. PAPANTONIOU ET AL.
factors (Benson & Tippets, 1990; Everson, Millsap, & Rodri-
guez, 1991; Gierl & Rogers, 1996; Ware, Galassi, & Dew, 1990).
Although the 20-item TAI had been administered in Greek
samples (Hatzidimitriadou, 1995; Papantoniou & Efklides,
2004; Vasilaki & Vamvoukas, 1997) and its internal consis-
tency had been estimated, to our knowledge, neither the
20-item, nor the 16-item TAI have been tested extensively re-
garding their factor structure in Greek population. Hence, the
main objective of this study was to examine the psychometric
properties of the Greek long and short version of the TAI (fac-
tor structure, internal consistency and convergent validity) in
order to determine whether it is a useful tool for the study of
test anxiety in the Greek cultural context.
Based on previous work, we hypothesized that the G-TAI
(both the long and the short version) would have the same two-
factor structure of Worry and Emotionality. There also were
expected interrelations between these factors (Hypothesis 1).
The TAI total score and both subscales have been shown to
have high internal consistency in previous research (Benson &
Tippets, 1990; Hatzidimitriadou, 1995; Spielberger et al., 1979;
Ware, Galassi, & Dew, 1990). Thus we hypothesized that the
Greek instrument would show similar range of internal consis-
tency (Hypothesis 2).
The relationship between the TAI and its subscales with
other anxiety measures (e.g., Sarason’s Test Anxiety Scale,
TAS; Liebert & Morris’s Worry and Emotionality Question-
naire, WEQ; the STAI State and Trait Anxiety scales, and the
STAI State Anxiety scale administered under examination
stress conditions) all provide evidence of convergent validity
(Spielberger et al., 1979). In the present study, in terms of con-
vergent validity, we expected that the G-TAI would be corre-
lated positively with a self-report measure of cognitive inter-
ference, namely, the Cognitive Interference Questionnaire, CIQ
(Sarason, Sarason, Keefe, Hayes, & Shearin, 1986) (Hypothesis 3).
The total sample consisted of 231 volunteer undergraduate
students (124 male, 107 female) attending Schools of social
sciences, mathematics, physical sciences, informatics, engi-
neering and life sciences at Greek Universities.
The Test Anxiety Inventory (TAI). The TAI is a self-report
psychometric scale that was designed by Spielberger (1980) to
“measure individual differences in test anxiety as a situa-
tion-specific personality trait”. The TAI was developed for use
with adolescents and adults and consists of 20 items that ask
respondents to indicate how they generally feel in test situations
by reporting the frequency that they experience specific symp-
toms of anxiety before, during and after examinations. Re-
spondents rate their responses on a 4-point Likert-type scale.
The four response choices are: (1) almost never, (2) sometimes,
(3) often, and (4) almost always. Values of item 1 are reversed.
The TAI has two subscales that assess worry and emotionality
as major components of test anxiety. Each subscale consists of
eight items with the remaining four items not ordinarily in-
cluded in either subscale. The eight items that form the TAI
Worry subscale (TAI-W) are: 3, 4, 5, 6, 7, 14, 17, and 20. The
eight items that constitute the TAI Emotionality subscale
(TAI-E) are: 2, 8, 9, 10, 11, 15, 16, and 18. The TAI yields a
total score based on all twenty items, a score for Worry based
on the subset of eight items and a score for Emotionality based
on the other eight-item subscale.
The TAI had been translated into Greek by Hatzidimitriadou
(1995; Stogiannidou, Kiosseoglou, & Hatzidimitriadou, 1999)
and its reliability had also been assessed. For Hatzidimitriadou’s
(1995) sample (N = 296 adolescents, 161 male and 135 female),
Cronbach’s α values had been .91 for the twenty-item TAI-T
scale, .81 for the eight-item worry subscale, and .86 for the
eight-item emotionality subscale. For Vasilaki and Vamvoukas’
(1997) sample (N = 424 elementary school pupils), Cronbach’s
α value had been .80 for the twenty-item TAI-T scale. For Pa-
pantoniou and Efklides’ (2004) sample (N = 390 adolescents
and young adults, 170 male and 220 female), Cronbach’s α
values had been .90 for the twenty-item TAI-T scale, .75 for the
eight-item worry subscale, and .86 for the eight-item emotion-
ality subscale. However, since none of the aforementioned
studies was an adaptation study of the TAI in Greek population,
in this study it was preferred the original version of the TAI to
be translated into Greek by the first author and back translated
by one independent bilingual psychologist. The back-translated
questionnaire was then compared to the original and a few mi-
nor modifications were applied.
The Cognitive Interference Questionnaire (CIQ). The CIQ
(Sarason et al., 1986) is a 22-item questionnaire designed to
measure, following performance on a task, the degree to which
people experienced various types of thoughts while working on
it, and the degree to which these thoughts are viewed as inter-
fering with concentration. According to its constructors (see
Sarason et al., 1986), the CIQ measures two types of thoughts,
task-oriented worries and off-task thoughts. The “Task-oriented
Worries” dimension was chosen to test the convergent validity
of the G-TAI. For the purposes of previous studies, the first 10
items of the CIQ, providing post-performance reports of the
frequency of occurrence of task-oriented worries, had been
translated into Greek by the first author; the single factor struc-
ture of the Greek version of the “Task-oriented Worries” di-
mension of the CIQ was verified with CFA and its reliability
was also assessed (see Papantoniou & Efklides, 2004; Papanto-
niou, Moraitou, Dinou, & Katsadima, 2010). Participants were
asked to indicate the frequency of occurrence of task-related
thoughts that intruded while they were working on the exami-
nation in an introductory course of their School, on a 5-point
scale from 1 (never) to 5 (very often). Cronbach’s α was ac-
ceptable: .83 for this sample.
Data were collected across multiple sessions ranging in size
from 15 to 20 participants. Questionnaires were administered
during participants’ examination in cognitive ability tests. The
TAI was administered at the beginning of the examination.
Participants also provided demographic information, including
age, gender, and class level (freshman, sophomore, junior or
senior) prior to completing the questionnaires. The CIQ was
administered at the end of the examination. Participation in the
study was voluntary and participants were informed that all
results were confidential.
G. PAPANTONIOU ET AL. 243
Although exploratory factor analysis is useful in test con-
struction, it does not provide an especially convincing test of
the factorial structure of scale as it does not permit the investi-
gator to hypothesize and confirm which of a series of alterna-
tive plausible latent factor models best fits the data. Therefore
we used confirmatory factor analyses to compare the factor
structures, implied for the TAI by previous theory and empiri-
cal research, using either the 20 or the 16 items. Structural
equation models were conducted in EQS Version 6.1 and per-
formed on covariance matrix using the Maximum Likelihood
estimation procedure (Bentler, 2005). Starting from the covari-
ance matrix, the viability of a two-factor model composed of
correlated Worry and Emotionality factors, inferred from
Spielberger et al. (1979), was tested in sequential fashion
against a series of logically nested alternative models.
A non-statistical significance of the χ2-test indicates that the
implied theoretical model significantly reproduces the sample
variance-covariance relationships in the matrix. Since this test
is sensitive to sample size, model fit was also evaluated by
using the root mean squared error of approximation (RMSEA).
The RMSEA tests how well the model would fit the population
covariance matrix. A rule of thumb is that RMSEA < .05 indi-
cates close approximate fit and values between .05 and .08
suggest reasonable error of approximation (Kline, 2005). The
Comparative Fit Index (CFI) which is one of the indexes as-
sessing the relative improvement in fit of the researcher’s
model compared with a baseline model was also used. A rule of
thumb for the CFI is that values greater than .90 may indicate
reasonably good fit of the researcher’s model (Kline, 2005). In
addition, model fit was evaluated by using the standardized root
mean squared residual (SRMR). The SRMR is a measure of the
mean absolute correlation residual, the overall difference be-
tween the observed and the predicted correlations. Values of the
SRMR less than .10 are generally considered favourable (Kline,
As regards the sample size requirements, for SEM techniques,
it is recommended as a rule of thumb that there be at least five
observations per estimated parameter (Hair, Anderson, Tatham,
& Black, 1998). A total of 20 parameters were estimated in
confirmatory factor model. Hence, the sample size for path
model had to exceed 100. Thus, the sample size exceeded the
minimum recommended level for performing confirmatory
Initially, we used confirmatory factor analyses to examine
the factor structure, established by Spielberger et al. (1979) in
the TAI Manual, using the 20 items. More specifically, we
compare the following three factor structures: Model A, a
one-factor model in which all twenty items loaded on a single
latent factor; Model B, a two-factor model in which all sixteen
items of the two subscales, Worry and Emotionality, loaded on
a first-order latent factor. Both this first-order latent factor and
the four items, that are not included in the Worry and Emotion-
ality subscales, loaded on a second-order latent factor called
TAI-T; and Model C, a three-factor model in which the two
subscales, Worry and Emotionality, were first-order latent fac-
tors. Both these first-order latent factors and the four items, that
are not included in them, loaded on a second-order latent factor
For each confirmatory factor analysis (CFA) model, a single
path was freed from the relevant factor to each item. No cross
loadings were allowed. In the last model containing more than
one factor, latent factors were defined without any covariance
between them as they loaded on a second-order latent factor.
For all models, the metric was set by fixing factor variances to
1.0. The fits of the models compared directly using chi-square
As shown in Table 1, for all three models, the chi-square
goodness-of-fit statistic was significant (p < 0.001), thus lead-
ing to the rejection of the null hypothesis of good fit. Therefore
information from other indicators of fit was assessed. The
RMSEA fell in the marginal range of .05 - .08 (see Kline, 2005)
indicating at least adequate fit for these models based on this
criterion. Specifically, the RMSEA was below .08 for the single
and the two-factor models (Model A: RMSEA = .074, Model B:
RMSEA = .074) and below .07 for the three-factor model
(Model C: RMSEA = .069). Standardized root-mean- square
residual (SRMR) values were below .08 (ranged from .050
to .053) indicating also good fit for all models tested. All CFI
values also fell in the marginal range of .90 - .95 (see Brown,
2006). In conclusion, the fit indices of the final model (Model
C) indicated that the final model [χ2(168, Ν = 231) = 352.73, p
= .000, χ2/df = 2.10, CFI = .93, RMSEA = .069, SRMR = .050]
fits the data better than the rest models of this set of analysis
Furthermore, we compared Models A and B with Model C
using the chi-square difference test. The results showed that the
Δχ2 was significant in all cases: Model A & Model C: Δχ2(Δdf
= 2) = 30.66, p < .001; Model B & Model C: Δχ2(Δdf = 1) =
30.29, p < .001. Thus, on the basis of the chi-square difference
tests, comparisons of CFI, and the low value of its RMSEA and
SRMR, the best-fitting model was unambiguously the three-
factor model with the first-order latent factors (Worry & Emo-
tionality) and the four items, that are not included in them,
Summary of fit tests for Confirmatory Factor Analysis Models with 20 ΤΑΙ items.
Model (factor) χ2 P CFI χ2/df SRMR RMSEA
Model A (Single factor) χ2(170, N = 231) = 383.39 < .001 .914 2.26 .053 .074
Model B (Two-factors: one first-order and
one second-order latent factors) χ2(169, N = 231) = 383.02 < .001 .914 2.27 .053 .074
Model C (Three-factors: two first-order
and one second-order latent factors) χ2(168, N = 231) = 352.73 < .001 .925 2.10 .050 .069
G. PAPANTONIOU ET AL.
loaded on a second-order latent factor called TAI-T (Model C).
Moreover, this model has also the advantage of previous theo-
retical and empirical validation (see Spielberger et al., 1979).
Model C is displayed in Table 2.
Consequently, a second set of confirmatory factor analyses
of Models A - C was performed, using only the sixteen TAI
items in the Worry and Emotionality subscales. In this revised
item set, we excluded the four items (Items 1, 12, 13, & 19)
which were not used in computing scores on the Worry and
Emotionality subscales because double loadings on these items
are reported in the TAI Manual.
More specifically, the CFA models that were tested on the
revised set of 16 items were the following: Model A, a one-
factor model in which all sixteen items loaded on a single latent
factor; Model B, a two-factor model in which the two subscales,
Worry and Emotionality, were first-order latent factors. In this
model CFA was performed twice. At the first performance,
latent factors were defined without any covariance between
them (Measurement model: Model B1). At the second per-
formance, latent factors were allowed to freely intercorrelate
(Structural model: Model B2); and Model C, a three-factor
model in which the two subscales, Worry and Emotionality,
were first-order latent factors that loaded on a second-order
latent factor called TAI.
However, during our trial to test Model C, the EQS program
(Bentler, 2005) warned that a parameter estimate is not inside
the specific boundaries. More specifically, the disturbance of
the first-order Worry factor was being held at the lower bound-
ary (.000) specified for the problem. The constraint of this pa-
rameter at lower boundary indicates a solution which is not
acceptable: that the first-order Worry factor could be perfectly
predicted from the second-order latent factor called TAI. In
addition, as shown in Table 3, Measurement Model B1 would
clearly be rejected, and in the interests of saving space, these
results are not detailed further.
As regards the rest two models, using the sixteen item set,
the chi-square goodness-of-fit tests were statistically significant
for them (for Models A and B2, p < .001), resulting in a rejec-
tion of the null hypothesis of good fit. Therefore information
from other indicators of fit was assessed. The CFI values fell in
the marginal range of .90 - .95 (see Brown, 2006) and the
RMSEA fell in the marginal range of .05 - .08 (see Kline, 2005)
indicating adequate fit for these models based on these criteria
(Model A: RMSEA = .080, Model B1: RMSEA = .072). Stan-
dardized root-mean-square residual (SRMR) values were be-
low .08 (ranged from .050 to .054) indicating also good fit for
the models tested. In conclusion, the fit indices of Model B2
indicated that this model [χ2(103, Ν = 231) = 224.69, p = .000,
χ2/df = 2.18, CFI = .94, RMSEA = .072, SRMR = 0.050] fits
the data better than Model A of this set of analysis (Brown,
Furthermore, we compared Model A with Model B2 using
the chi-square difference test. The results showed that the Δχ2
was significant: Model A & Model B2: Δχ2(Δdf = 1) = 30.83, p
< .001. Thus, on the basis of the chi-square difference tests,
comparisons of CFI, and the low value of its RMSEA and
SRMR, the best-fitting model was unambiguously the two-
factor (Worry & Emotionality) model with interrelations be-
tween them (Structural Model B2). Similarly to Model C of the
first set of confirmatory factor analyses, this model also has the
advantage of previous theoretical and empirical validation (see
Benson & Tippets, 1990; Ware, Galassi, & Dew, 1990). Model
B2 is displayed in Table 4.
In conclusion, the comparison between the best-fitting model
of the first set of confirmatory factor analyses, which were
performed using the 20 TAI items (Model C: Three-factors:
two first-order and one second-order latent factors), and the
best-fitting model of the second set of confirmatory factor
The best-fitting model (Model C) in the structure of the 20-item version of the Greek Test Anxiety Inventory (standardized solution).
Items TAI-W (F1) TAI-E (F2) TAI-T (F3) E / D R2
TAI 3 .570 .821 .325
TAI 4 .766 .643 .586
TAI 5 .361 .933 .130
TAI 6 .342 .940 .117
TAI 7 .681 .732 .464
TAI 14 .691 .722 .478
TAI 17 .629 .777 .396
TAI 20 .615 .788 .379
TAI 2 .741 .672 .549
TAI 8 .844 .536 .713
TAI 9 .760 .650 .577
TAI 10 .727 .686 .529
TAI 11 .754 .657 .568
TAI 15 .819 .574 .671
TAI 16 .784 .621 .614
TAI 18 .799 .601 .639
TAI 1 .607 .795 .368
TAI 12 .744 .668 .554
TAI 13 .748 .664 .559
TAI 19 .533 .846 .284
F1 (TAI-W) .942 .337 .887
F2 (TAI-E) .966 .260 .933
Note: TAI-W = The TAI-Worry factor; TAI-E = The TAI-Emotionality factor; TAI-T = The TAI-Total factor.
G. PAPANTONIOU ET AL. 245
Summary of fit tests for Confirmatory Factor Analysis Models with 16 ΤΑΙ items.
Model (factor) χ2 P CFI χ2/df SRMR RMSEA
Model A (Single factor) χ2(104, N = 231) = 255.52 < .001 .922 2.46 .054 .080
Model B1 (Two-factors: two first-order latent
factors) χ2(104, N = 231) = 450.68 < .001 .820 4.33 .292 .120
Model B2 (Two factors: two first-order latent
factors + interrelation between factors) χ2(103, N = 231) = 224.69 < .001 .937 2.18 .050 .072
Model C (Three-factors: two first-order and
one second-order latent factors)
The best-fitting model (Model B2) in the structure of the 16-item version of the Greek Test Anxiety Inventory (standardized solution).
Items TAI-W (F1) TAI-E (F2) E R2
TAI 3 .576 .817 .332
TAI 4 .775 .632 .601
TAI 5 .342 .940 .117
TAI 6 .335 .942 .112
TAI 7 .694 .720 .481
TAI 14 .675 .738 .456
TAI 17 .625 .781 .391
TAI 20 .611 .791 .374
TAI 2 .737 .676 .542
TAI 8 .853 .522 .727
TAI 9 .771 .637 .595
TAI 10 .713 .701 .509
TAI 11 .749 .662 .561
TAI 15 .812 .584 .659
TAI 16 .792 .610 .628
TAI 18 .798 .602 .637
F2 (TAI-E) – F1 (TAI-W) .906
Note: TAI-W = The TAI-Worry factor; TAI-E = The TAI-Emotionality factor.
analyses, which were performed using only the 16 TAI items
(Model B2: Two factors: two first-order latent factors + inter-
relation between factors), on the basis of their NC (χ2/df)
(Model C: NC = 2.10, Model B2: NC = 2.18), CFI (Model C:
CFI = .925, Model B2: CFI = .937), RMSEA (Model C:
RMSEA = .069, Model B2: RMSEA = .072), and SRMR
(Model C: SRMR = .050, Model B2: SRMR = .050), suggests a
good fit for both of the two models.
Internal consistency was estimated, for the twenty-item TAI-T
scale and for the eight-item worry and emotionality subscales,
using Cronbach’s α coefficient. The values were .94, .81,
and .92, respectively.
Pearson correlations between the G-TAI-T and its subscales
TAI-W and TAI-E, on the one hand, and the CIQ, on the other,
were computed. Moderate positive correlations were found.
The values were .46, .40, and .46, respectively (p < .01).
The main aim of this study was to examine the psychometric
properties of the Greek long and short version of the TAI;
namely, the factor structure, internal consistency, and concur-
Overall, the results are promising and verified the well estab-
lished two-factor structure for the TAI. Furthermore, on the
bases of the confirmatory factor analyses, using either the set of
20 items, or the set of 16 items, we found convincing support
for the existing relationship between the two subscales of the
Test Anxiety Inventory. More specifically, the best-fitting
model of the first set of confirmatory factor analyses, which
were performed using the 20 TAI items (Model C: Three-fac-
tors: two first-order and one second-order latent factors), as-
sumed that the two subscales, Worry and Emotionality, were
G. PAPANTONIOU ET AL.
first-order latent factors. Both these first-order latent factors
and the four items, that are not included in them, loaded on a
second-order latent factor called TAI-T. Similarly, the best
fitting model of the second set of confirmatory factor analyses,
which were performed using only the 16 TAI items (Structural
Model B2: Two-factors: two first-order latent factors + interre-
lation between factors), assumed that the two subscales, Worry
and Emotionality, were first-order latent factors with covari-
ance between them. These findings confirm our Hypothesis 1,
and they are consistent to previous work either on the original
and revised English version (Benson & Tippets, 1990; Hedl,
1984; Spielberger et al., 1978, 1979; Ware, Galassi, & Dew,
1990) or on the original Canadian and German versions (Gierl
& Rogers, 1996; Schwarzer, 1984) that assumed a positive
correlation between the two subscales.
However, the results from this study indicate that the first-
order latent factors loadings (.94 for the Worry and .97 for the
Emotionality factor) on the second-order latent factor called
TAI-T (Model C: Three-factors: two first-order and one sec-
ond-order latent factors), as well as their interrelation (.91)
(Structural Model B2: Two-factors: two first-order latent fac-
tors + interrelation between factors) were higher than those
typically reported in the literature (Everson, Millsap, & Rodri-
guez, 1991; Schwarzer, 1984; Spielberger et al., 1978, 1979;
Van der Ploeg, 1983). This magnitude of the covariance be-
tween the latent Worry and Emotionality factors is comparable
to that reported by Benson and Tippets (1990) and Ware, Ga-
lassi, and Dew (1990). According to them, the higher interrela-
tion might be attributed to the relatively higher age and greater
heterogeneity of their samples as compared with other studies.
Similarly to the aforementioned studies (Benson & Tippets,
1990; Ware, Galassi, & Dew, 1990), the current sample, which
included undergraduate students from a variety of academic
disciplines and class levels, was considerably older and more
diverse than those used in the studies where moderate interrela-
tion between the Worry and Emotionality factors was reported.
Hypothesis 2 regarded the internal consistency of the Greek
version of the TAI. The results were consistent to previous
work either on the original and revised English version (Benson
& Tippets, 1990; Spielberger et al., 1979; Ware, Galassi, &
Dew, 1990) or on the 20-item Greek version (Hatzidimitriadou,
1995; Papantoniou & Efklides, 2004). In this study, as well as
in previous studies, the internal consistency of the TAI total
score and both subscales tend to be high (Cronbach’s α coeffi-
cients were ranged from .81 to .94).
Hypothesis 3 regarded the concurrent validity of the G-TAI.
The results were in the predicted direction, with the TAI-T, the
Worry and the Emotionality factors associating positively with
the measure of cognitive interference. This finding suggests
that persons with high TAI Total, Worry and Emotionality
scores are more disposed to report that they incurred task-ori-
ented worries during performance of a recently completed task
than persons with low scores on this measure. This is in accor-
dance with research on cognitive interference (Sarason, Pierce,
& Sarason, 1996) and test anxiety (Spielberger et al., 1979; see
Zeidner, 1998). In conclusion, our study showed that the
G-TAI-T, and its subscales TAI-W and TAI-E are moderately
related to cognitive interference. Future studies aiming to ex-
amine psychological properties of the G-TAI should investigate
further its convergent and divergent validity with other meas-
ures that are, or have already been, in the procedure of adapta-
tion in Greek population, like STAI (Fountoulakis, Papadopou-
lou, Kleanthous, Papadopoulou, Bizeli, Nimatoudis, Iakovides, &
Kaprinis, 2006). Finally, given the inconsistencies with regard
to gender differences in previous factor studies of the TAI
(Benson & Tippets, 1990; Everson, Millsap, & Rodriguez,
1991; Gierl & Rogers, 1996; Hedl, 1984; Schwarzer, 1984;
Spielberger et al., 1978, 1979; Van der Ploeg, 1984; Ware,
Galassi, & Dew, 1990), the question arises as to whether the
TAI measures the same construct to the same degree for both
male and female participants. Therefore, future studies should
also determine whether the two verified factor structures from
this study are invariant across gender.
Although more research is needed to further validate the
G-TAI and to replicate our current findings, the results of our
study show that both the 20-item version and the 16-item ver-
sion of the G-TAI are efficient instruments for measuring test
anxiety in the Greek cultural context, as they are of equal psy-
chometric strength in confirmatory factor analyses. As such, the
two versions of the G-TAI provide an initial base in examining
cross-cultural differences in test anxiety which, as being one of
the negative activating test emotions, contributes to the under-
standing of academic performance and self-regulated learning
(see Efklides, 2011). Consequently, we recommend the 16-item
version of the G-TAI, as an economical measure of worry and
emotionality, and the 20-item version of the G-TAI, as a meas-
ure that can further provide with a total test anxiety score. In
conclusion, the short version of the G-TAI can be used for
checking the impact of each test anxiety dimension on various
cognitive, metacognitive, and volitional aspects of learning and
achievement. In the same logic, the long version of the G-TAI
can be used for providing more information on this specific
emotion of a learner’s affective profile.
Benson, J., & Tippets, E. (1990). Confirmatory factor analysis of the
Test Anxiety Inventory. In C. D. Spielberger, & R. Diaz-Guerrero
(Eds.), Cross-cultural anxiety (pp. 149-156). New York: Hemi-
Bentler, P. M. (2005). EQS 6.1., Encino, CA: Multivariate Software,
Brown, T. A. (2006). Confirmatory factor analysis for applied research.
New York: Guilford.
Chapell, M. S., Blanding, Z. B., Silverstein, M. E., Takahashi, M.,
Newman, B., Gubi, A., & McCann, N. (2005). Test anxiety and aca-
demic performance in undergraduate and graduate students. Journal
of Educational Psychology, 97, 268-274.
Efklides, A. (2011). Interactions of metacognition with motivation and
affect in self-regulated learning: The MASRL model. Educational
Psychologist, 46, 6-25. doi:10.1080/00461520.2011.538645
Efklides, A., Papadaki, M., Papantoniou, G., & Kiosseoglou, G. (1997).
Effects of cognitive ability and affect on school mathematics per-
formance and feelings of difficulty. The American Journal of Psy-
chology, 110, 225-258. doi:10.2307/1423716
Efklides, A., Papadaki, M., Papantoniou, G., & Kiosseoglou, G. (1999).
Individual differences in school mathematics performance and feel-
ings of difficulty: The effects of cognitive ability, affect, age, and
gender. European Journal of Psychology of Education, XIV, 57-69.
Everson, H. T., Millsap, R. E., & Rodriguez, C. M. (1991). Isolating
gender differences in test anxiety: A confirmatory factor analysis of
the Test Anxiety Inventory. Educational and Psychological Meas-
G. PAPANTONIOU ET AL. 247
urement, 51, 243-251. doi:10.1177/0013164491511024
Fountoulakis, K. N., Papadopoulou, M., Kleanthous, S., Papadopoulou,
A., Bizeli, V., Nimatoudis, I., Iakovides, A., & Kaprinis, G. S. (2006).
Reliability and psychometric properties of the Greek translation of
the State-Trait Anxiety Inventory form Y: Preliminary data. Annals
of General Psychiatry, 5, 2. doi:10.1186/1744-859X-5-2
Gierl, M. J., & Rogers, W. T. (1996). A confirmatory factor analysis of
the Test Anxiety Inventory using Canadian high school students.
Educational and Psychological Measurement, 56, 315-324.
Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate
data analysis. NJ: Prentice Hall.
Hatzidimitriadou, E. (1995). Self-esteem, test anxiety and school
achievement: Interactions during adolescence. Unpublished MSc
Thesis, Thessaloniki: Aristotle University of Thessaloniki [In Greek].
Hedl, J. J. Jr. (1984). A factor analytic study of the Test Anxiety In-
ventory. International Review of Applied Psychology, 33, 267-283.
Hembree, R. (1988). Correlates, causes, effects, and treatment of test
anxiety. Review of Educational Research, 58, 47-77.
Hong, E. (1998). Differential stability of individual differences in state
and trait test anxiety. Learning and Individual Differences, 10, 51-69.
Kline, R. (2005). Principles and practice of structural equation model-
ing. New York: The Guilford Press.
Liebert, R. M., & Morris, L. W. (1967). Cognitive and emotional com-
ponents of test anxiety: A distinction and some initial data. Psycho-
logical Reports, 20, 975-978.
Lowe, P. A., Lee, S. W., Witteborg, K. M., Prichard, K. W., Luhr, M.
E., Cullinan, C. M., Mildren, B. A., Raad, J. M., Cornelius, R. A., &
Janik, M. (2008). The Test Anxiety Inventory for Children and Ado-
lescents (TAICA): Examination of the psychometric properties of a
new multidimensional measure of test anxiety among elementary and
secondary school students. Journal of Psychoeducational Assessment,
26, 215-230. doi:10.1177/0734282907303760
Metallidou, P., & Vlachou, A. (2007). Motivational beliefs, cognitive
engagement, and achievement in language and mathematics in ele-
mentary school children. International Journal of Psychology, 42,
Morris, L. W., & Liebert, R. M. (1969). The effects of anxiety on timed
and untimed intelligence tests: Another look. Journal of Consulting
and Clinical Psychology, 33, 240-244. doi:10.1037/h0027164
Papantoniou, G., & Efklides, A. (2004). Affective and cognitive effects
on action control. Psychology: The Journal of the Hellenic Psycho-
logical Society, 11, 285-302 [Ιn Greek].
Papantoniou, G., Moraitou, D., Dinou, M., & Katsadima, E. (2010).
Psychometric properties of the Greek version of the Action Control
Scale. The International Journal of Educational and Psychological
Assessment, 5, 45-60.
Sarason, I. G., Pierce, G. R., & Sarason, B. R. (1996). Domains of
cognitive interference. In I. G. Sarason, G. R. Pierce, & B. R. Sara-
son (Eds.), Cognitive interference: Theories, methods, and findings
(pp. 139-152). Mahwah, NJ: Erlbaum.
Sarason, I. G., Sarason, B. R., Keefe, D. E., Hayes, B. E., & Shearin, E.
N. (1986). Cognitive interference: Situational determinants and trait-
like characteristics. Journal of Personality and Social Psychology, 51,
Schwarzer, R. (1984). Worry and emotionality as separate components
in test anxiety. International Review of Applied Psychology, 33,
Schwarzer, C., & Kim, M-J. (1984). Adaptation of the Korean form of
the Test Anxiety Inventory: A research note. In H. M. Van der Ploeg,
R. Schwarzer, & C. D. Spielberger (Eds.), Advances in test anxiety
research (pp. 277-285). Hillsdale, NJ: Erlbaum.
Spielberger, C. D. (1980). Test Anxiety Inventory. Palo Alto, CA: Con-
sulting Psychologists Press.
Spielberger, C. D., Gonzalez, H. P., Taylor, D. J., Algaze, B., & Anton,
W. D. (1978). Examination stress and test anxiety. In C. D. Spiel-
berger, & I. G. Sarason (Eds.), Stress and anxiety (pp. 167-191).
New York, NY: Hemisphere/Wiley.
Spielberger, C. D., Gonzalez, E. P., Taylor, C. J., Anton, W. D., Algaze,
B., Ross, G. R., & Westberry, L. G. (1979). Preliminary manual for
the Test Anxiety Inventory. Palo Alto, CA: Consulting Psychologists
Spielberger, C. D., & Vagg, P. R. (1995). Test anxiety: A transactional
process model. In C. D. Spielberger & P. R. Vagg (Eds.), Test anxi-
ety: Theory, assessment and treatment (pp. 3-14). Washington, DC:
Taylor & Francis.
Stogiannidou, A., Kiosseoglou, G., & Hatzidimitriadou, E. (1999).
Relation between “rationality”, self-esteem and school achievement
in Greek high school students. Psychology: The Journal of the Hel-
lenic Psychological Society, 6, 72-87 [In Greek].
Sub, A., & Prabha, C. (2003). Academic performance in relation to
perfectionism, test procrastination and test anxiety of high school
children. Psychological Studies, 48, 7-81.
Van der Ploeg, H. M. (1983). The validation of the Dutch form of the
Test Anxiety Inventory. In H. M. Van der Ploeg, R. Schwarzer, & C.
D. Spielberger (Eds.), Advances in test anxiety research (pp.
191-202). Hillsdale, NJ: Erlbaum.
Van der Ploeg, H. M. (1984). Worry, emotionality, intelligence, and
academic performance in male and female Dutch secondary school
children. In H. M. Van der Ploeg, R. Schwarzer, & C. D. Spielberger
(Eds.), Advances in test anxiety research (pp. 201-210). Hillsdale, NJ:
Vasilaki, E., & Vamvoukas, M. (1997). Test anxiety and coping strate-
gies employed by school children aged 11 - 12 years old. Pedagogi-
cal Inspection, 25, 1-17 [In Greek].
Ware, W. B., Galassi, J. P., & Dew, K. M. H. (1990). The Test Anxiety
Inventory: A confirmatory factor analysis. Anxiety Research, 3,
Zeidner, M. (1998). Test anxiety: The state of art. New York, NY: