2013. Vol.4, No.6A2, 27-35
Published Online June 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.46A2005
Copyright © 2013 SciRes. 27
Mathematics Anxiety and Its Development in the Course of
Formal Schooling—A Review
Chiara Eden1, Angela Heine1, Arthur M. Jacobs1,2
1Department of Psychology, Freie Universität Berlin, Berlin, Germany
2Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany
Received April 13th, 2013; revised May 16th, 2013; accepted June 14th, 2013
Copyright © 2013 Chiara Eden et al. This is an open access article distributed under the Creative Commons At-
tribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
The purpose of the this article is to provide an overview of the current state of research concerning the
development, determining factors and effects of mathematics anxiety, particularly with regard to young
elementary school age level populations. Assessment instruments, potential risk-factors, consequences of
mathematics anxiety, as well as approaches to intervention are summarized. Owing to the small number
of studies focusing on mathematics anxiety in young children, findings from adult studies are briefly
recapitulated. The available data emphasizes the need for systematic research that focuses on the one hand
on the onset of mathematics anxiety at a young age, and on the other follows the development of longer
periods of time. Furthermore, multi-method research designs may be the means to gain deeper insight into
the dynamics of causes and effects when the interaction of mathematics anxiety and mathematical
abilities are under scrutiny. Only by implementing longitudinal studies that involve different types of data
and the formulation of complex models of the dynamics of mathematics anxiety over time, can its
determining factors and its effects be generated.
Keywords: Mathematics Anxiety; Assessment; Mathematical Performance; Working Memory; Gender;
Stereotype; Self-Efficacy; Development; Interventions
According to statistical estimations, roughly 20% of the popu-
lation suffer from more or less severe psychological or physio-
logical symptoms related to feelings of anxiety when con-
fronted with tasks that require the manipulation of numerical
information (i.e. one out of five persons belongs to the group of
high-math-anxious individuals; Ashcraft & Kirk, 2001). This,
along with the typically ensuing withdrawal from mathemat-
ics-associated situations, implicates far-ranging difficulties for
educational contexts as well as everyday life for the affected
It is common knowledge that some students experience mathe-
matics as especially challenging and even aversive, independ-
ently from their general level of competencies (Ashcraft, Krause,
& Hopko, 2007; Hembree, 1990). Unfortunately, a student’s
aversive reactions to mathematics are often reinforced by their
implicitly or even explicitly approving social contexts (e.g.
Beilock, Gunderson, Ramirez, & Levine, 2010). Consequently,
the systematic avoidance of mathematics-related situations and,
in later stages of development, of mathematics-associated ca-
reers (e.g. Ashcraft & Faust, 1994; Hembree, 1990), are rather
the rule than the exception. Considering the potentially far-
reaching effects not only for the individual, but also for the
society, and additionally, taking into account the high preva-
lence rates, it is surprising that adequately complex models of
the development of mathematics anxiety are still widely lacking
even after decades of research into mathematics anxiety.
Initially described as mathemaphobia by a teacher who re-
ferred to her students’ striking emotional reactions in the face
of mathematical tasks and challenges (Gough, 1954), this phe-
nomenon has gained increasing attention not only from the
educational practice, but also from the scientific community. In
the 1970s, a widely accepted definition of the phenomenon was
provided, describing mathematics anxiety as “[...] a feeling of
tension and anxiety that interferes with the manipulation of
numbers and the solving of the mathematical problems in a
wide variety of ordinary life and academic situations” (Richard-
son & Suinn, 1972: p. 551). The detrimental consequences for
the individual and his or her development were not yet consid-
ered in this description. Recent scientific approaches investi-
gated these aspects in more detail. However, the better part of
the available empirical studies focus on individuals at the end
of their formal education. This is unfortunate, as most of the
detrimental impact of mathematics anxiety has taken its toll by
that time, which interferes with the aim to gain a deeper under-
standing of biasing factors.
However, despite the lack of studies focusing on the early
years of formal schooling (i.e., early elementary school age),
considerable insight has been gained, specifically in the context
of studies involving older students. With the present review
article, an overview of the current research on mathematics
anxiety, its assessment, the empirical findings on mathematics
anxiety at an early age, remedial intervention, as well as rele-
vant open issues will be provided.
C. EDEN ET AL.
Differentiation from Related Constructs
As early as the first mention of mathematics anxiety, its dis-
crimination from similar constructs has been considered. Dre-
ger and Aiken (1957) made three assumptions about mathe-
matics anxiety and its associations with related constructs. First,
they suggested mathematics anxiety to be a unique construct,
despite a definite relationship with general anxiety. Second,
they assumed mathematics anxiety as not entirely explained by
general ability, i.e. intelligence. Third, they suggested a nega-
tive relationship between academic performance and mathe-
matics anxiety. Since then, all three predictions were confirmed
by research on mathematics anxiety (see Hembree, 1990; Ma,
1999). Correlations between mathematics anxiety and general
anxiety are reported to be .35, between mathematics anxiety
and IQ −.17, and, finally, between mathematics anxiety and
mathematical achievement −.27 (pre-college level: Ma, 1999)
to −.31 (college level: Hembree, 1990). These findings do not
generalize to younger populations (i.e. elementary school level)
as studies on children are still wanting.
The most extensive overlap between mathematics anxiety
and potentially related constructs, however, was not mentioned
by Dreger and Aiken’s early work (1957), that is, the interrela-
tion between mathematics anxiety and test anxiety (.52; Hem-
bree, 1990). But since this relationship is considerable, mathe-
matics anxiety’s status as a unitary construct was investigated
in more detail. In this vein, Dew and colleagues (1984), for
instance, found that different assessment scores of mathematics
anxiety are intercorrelated more strongly than they are corre-
lated with test anxiety scores. Due to such empirical results,
Hembree (1990) assumes the two constructs to be distinct, just
as Ashcraft and Ridley (2005) conclude that in adults, about
two thirds of mathematics anxiety’s variance cannot be ex-
plained by test anxiety. Nevertheless, this refers to adult data
Origins of Mathematics Anxiety
So far, research has focused mainly on the consequences of
mathematics anxiety. Its antecedents, however, remain largely
unexplored. That is, only a small number of studies have fo-
cused on mathematics anxiety as a dependent variable (Jain &
Dowson, 2009). However, while little is known about how
mathematics anxiety actually develops, it is generally assumed
to be multifactorial in its origins.
Jain and colleagues (2009) described mathematics anxiety as
a consequence of “an inability to handle frustration, excessive
school absences, poor self-concept, internalized negative pa-
rental and teacher attitudes toward mathematics, and an empha-
sis on learning mathematics through drill without “real” under-
standing” (p. 240). A more concise description of the causal
factors is provided by Devine and colleagues (2012) who clas-
sified variables systematically related to the development of
mathematics anxiety into three groups, namely, environmental
variables, intellectual variables and personality variables. En-
vironmental variables include negative experiences in class or
in family contexts, teacher and parent characteristics, as well as
extrinsic expectations. Intellectual variables include the child’s
level of more general cognitive abilities, while personality vari-
ables comprise concepts such as self-esteem, self-concept, atti-
tude, confidence and learning behavior.
In the following, relevant variables will be outlined in more
detail following the categorization provided by Devine and
colleagues (2012), even though classifying a certain variable as
belonging to one category or the other is not always unequivo-
Critical for the development of mathematics anxiety are the
attitudes, stereotypes and the teaching style of a child’s teachers,
since they affect a student’s attitudes, motivations, and learning
activities in a very direct manner (Ashcraft & Ridley, 2005).
Research has shown that distant and unsupportive attitudes on
the part of the teacher lead to avoidance on the part of the stu-
dents (Turner et al., 2002). Ashcraft and Ridley (2005) as-
sume that a teacher’s negative attitude and classroom style in
combination with being unsupportive in general, create avoid-
ance reactions and feelings of anxiety related to mathematics
and to mathematics testing in students.
Moreover, it was found that female elementary school teach-
ers, who are themselves anxious about mathematics, pass their
negative attitude down to their students. Interestingly, girls seem
to be more affected by female teachers’ attitudes as they are
found to endorse inappropriate stereotypes more readily than
boys (i.e. “boys are good at math, girls are good at reading”;
Beilock et al., 2010). Similar associations between levels of
mathematics anxiety and students’ previous experiences in the
context of formal mathematics education were reported by Harper
and Daane, (1998), and Jackson and Leffingwell (1999).
However, not only teachers’ but also parents’ attitudes in-
fluence a child’s attitude towards mathematics. It has been
shown, for instance, that a parent’s belief about their child’s
mathematical abilities are systematically related to a student’s
mathematical self-efficacy beliefs and performance scores (Ec-
cles, Jacobs, & Harold, 1990). Furthermore, Eccles and col-
leagues (1990) state, that “if parents hold gender-differentiated
perceptions of, and expectations for, their children’s competen-
cies in various areas, then, through self-fulfilling prophecies,
parents could play a critical role in socializing gender differ-
ences in children’s self-perceptions, interests, and skill acquisi-
tion” (p. 189). Since gender and gender stereotypes are relevant
issues in the context of research on mathematics anxiety, these
factors will be discussed separately.
Cognitive Var i abl es
It is tempting to assume mathematics anxiety to be directly
related to poor mathematical competencies which, in turn, are
determining experiences of threat in classroom situations. Chal-
lenging such a seemingly straightforward explanation, Suinn
and Edwards (1982) point out that about half of the variance in
mathematics performance measures can be explained by other
than intellectual factors. Nevertheless, intellectual aspects (e.g.
good abstract thinking abilities) can be assumed to diminish the
risk for developing mathematics anxiety.
Another domain-general factor contributing to the develop-
ment of mathematics anxiety may be poor visuo-spatial proc-
essing abilities. Maloney and colleagues (Maloney, Waechter,
Risko, & Fugelsang, 2012), for instance, suggest that higher
mathematics anxiety in females may in part be mediated by sex
differences in visuo-spatial processing abilities. That is, poor
visuo-spatial processing abilities may affect the development of
mathematics anxiety, mediated by poor mathematical abilities.
Copyright © 2013 SciRes.
C. EDEN ET AL.
However, the available empirical data concerning a casual link
between poor visuo-spatial skills and mathematical abilities are
inconclusive (but cf. Rotzer et al., 2009). Ben-Zeev and col-
leagues (2005), for example, suggest that differences in visuo-
spatial abilities may actually be largely attributable to environ-
mental influences and hence, suggest gender-related differences
rather than sex-related differences to play a major role in this
Personality Variable s
According to Stuart (2000), the development of mathematics
anxiety often takes its origin from a lack of confidence in situa-
tions involving the necessity to handle numerical information.
A recent study with adolescents demonstrates that mathematics
anxiety can be modeled as a function of both a person’s self-
regulation skills and self-efficacy beliefs (Jain & Dowson, 2009).
This suggests self-efficacy beliefs with respect to numerical and
arithmetic tasks and related self-regulation skills are key factors
in the development of anxious reactions to mathematics. Indi-
rectly, such a relationship was confirmed by studies that report
positive associations between self-efficacy and performance,
independently from the specific task domain (e.g. Bandura &
Locke, 2003; Manstead & Van-Eekelen, 1998; Newby-Fraser
& Schlebusch, 1998). These findings can be related to Ban-
dura’s (1977) theory of self-efficacy that assumes a change of
behavior (e.g., math avoidance) in response to (in this case,
weak) self-efficacy beliefs. More direct associations have been
shown by studies that related test-anxiety to self-efficacy be-
liefs (e.g., Dykeman, 1994; Hodapp & Benson, 1997) and even
more relevant for the present context, self-efficacy beliefs to
mathematics anxiety (Dennis, Daly, & Provost, 2003).
In summary, personal variables seem to play a critical role in
the origin of mathematics anxiety. The description of mathe-
matics anxiety as the outcome of a complex interplay between
test anxiety, a generalized fear to fail, negative attitudes to-
wards learning, and low self-efficacy beliefs further supports
this point (Bandalos, Yates, & Thordike-Christ, 1995).
Gender as a Critical Variable
Research on the relationship between gender, sex and the
development of mathematics anxiety is motivated mainly by the
finding that females show overall higher levels of mathematics
anxiety than males throughout their entire schooling (Devine et
al., 2012; Hembree, 1990). Yet, it is difficult to separate social
and cultural factors from those of sex per se (Ashcraft & Ridley,
2005). As a result, the underlying reasons for females’ higher
mathematics anxiety levels are still unclear. Existing research
concerning these differences mainly focuses on the specific
impact of mathematics anxiety in females, as compared to their
For instance, although Hembree’s meta-analysis (1990) re-
vealed overall higher levels of mathematics anxiety in females,
the detrimental effects of anxiety appeared to be stronger on
males. That is, mathematical anxious males show more anxi-
ety-related deficits in their mathematics performance, and more
A more recent study supports Hembree’s (1990) findings of
higher anxiety levels in females. However, this study also dem-
onstrates that when test anxiety is controlled, mathematics
anxiety is a predictor of mathematical performance only in
females (Devine et al., 2012). Without controlling for test anxi-
ety both males and females show negative correlations between
mathematics anxiety and mathematical performance. This sug-
gests test anxiety as a potential confound, and emphasizes the
relevance of controlling for all kinds of possible confounds.
Another of these potential confounds constitutes a person’s
willingness to confess to feelings of anxiety, which is generally
assumed to be higher in females (Ashcraft & Ridley, 2005).
This is critical for investigations into mathematics anxiety where
assessment relies on self-report mostly. Additionally, females
tend to be more critical of their own mathematical performance
(Flessati & Jamieson, 1991), and to have lower expectations
regarding their mathematical skills, compared to males (e.g.
Eccles et al., 1990). However, females’ higher anxiety levels
may also be related to underlying sex differences in mathemat-
ics-associated competencies which, in turn, can be assumed to
be related to the development of mathematics anxiety (e.g.
spatial processing ability; Maloney et al., 2012). Overall, ire-
spective of both individual mathematical achievement levels
and the specific reasons for higher anxiety scores, the available
data confirms that females, as compared to males, are generally
less confident about their own mathematical abilities, and ex-
hibit higher mathematics anxiety levels (e.g. Hembree, 1990),
as well as more stable mathematics anxiety ratings across the
years (Ma & Xu, 2004).
As the case for biologically-determined differences is weak
(Ben-Zeev et al., 2005), current research focuses more on the
effects of environmental influences, such as gender stereotypes,
on mathematics performance and anxiety in females. Regard-
less of their plausibility, or the lack theory, gender stereotypes
seem to critically affect the behavior, and specifically mathe-
matical performance, of female students (Aronson et al., 1999;
Beilock et al., 2010). Following Fennema’s (1989) autonomous
learning behavior model external influences such as stereotypes
affect a person’s beliefs and attitudes, which in turn affect
self-directed learning behavior (e.g. amount of time spent on
practice), which ultimately affects individual performance
(Ashcraft & Ridley, 2005). And in this vein, several studies
have confirmed differential performance scores after manipu-
lating the “threat of stereotypes” (e.g. Aronson et al., 1999;
Spencer, Steele, & Quinn, 1999). For instance, Spencer and
colleagues (1999) found that female students performed worse,
when told beforehand that the upcoming test was supposedly
harder for females than for males (Ben-Zeev et al., 2005).
Mathematics Anxiety and Performance
It has been shown repeatedly that there is a significant rela-
tionship between levels of mathematics anxiety and perform-
ance as assessed by standardized tests. Ashcraft and Ridley
(2005) describe this relationship as “not at all surprising” (p.
318). Nevertheless, the question whether mathematics anxiety
is the cause or rather consequence of poor performance is not
In general, achievement scores in standardized mathematical
performance tests tend to be lower the higher the mathematics
anxiety level of an individual is. A negative correlation of −.27
is reported in a meta-analysis on pre-college level studies (Ma,
1999), and, similarly, in a meta-analysis on college-age studies,
Hembree (1990) reported a correlation of −.31. Unfortunately,
no condensed information is available for elementary school
levels. Studies focusing on mathematics anxiety in earlier years
Copyright © 2013 SciRes. 29
C. EDEN ET AL.
of schooling do not always find such an effect at all (e.g.
Krinzinger et al., 2009; Ramirez et al., in press). That is, anxi-
ety levels and achievement scores do not necessarily show
negative correlations at that age. Trying to reconcile such con-
tradictory findings for younger as compared to older students
some authors have challenged their own designs (Krinzinger et
al., 2009). However, the missing correlation between achieve-
ment and mathematics anxiety in younger populations may
indicate that mathematics anxiety cannot be fully explained by
a failure to perform, and may, thus, not be a consequence of
poor mathematical ability. Thomas and Dowker (2000) (as
cited in Krinzinger, Kaufmann, & Willmes, 2009) suggest that
the often demonstrated relationship between mathematics anxi-
ety and achievement may actually be age dependent, i.e. getting
more pronounced with increasing age and longer schooling.
This is in line with the global avoidance theory proposed by
Ashcraft and Faust (1994) that describes mathematics anxiety
to play a major role in the origin of poor mathematical perform-
ance. According to this theory, mathematics anxious individuals
tend to avoid mathematics-associated situations and as a result,
develop poorer mathematical abilities than their non-anxious peers.
In contrast to these feed forward models, Wu and colleagues
(2012) describe the relationship between mathematical achieve-
ment and anxiety as a feedback loop, where anxiety provokes
avoidance and negative beliefs about their own competencies,
which, in turn, lead to less practice along with resultant diffi-
culties due to lack of practice. The latter leads to even lower
self-confidence and, again, to higher anxiety scores.
All in all, the relationship of mathematics anxiety and achieve-
ment is more complex than typically acknowledged even by
researchers in the field. This is due to mathematics anxiety’s
twofold influence, i.e. on the one hand, it affects learning mo-
tivations and attitudes in general, on the other hand, it specifi-
cally interferes with the acquisition of mathematics-related
competencies (Ashcraft & Ridley, 2005).
The Issue of Interpretability of Achievement Scores
An early study by Ashcraft and Faust (1994) that examined
processing speed and accuracy of high versus low mathemati-
cally anxious individuals found that anxiety had hardly any
effect on a subject’s efficiency in simple addition and multipli-
cation tasks. Effects of mathematics anxiety were pronounced
only when more complex problems were presented (i.e., two-
column addition problems). The authors found that while
highly anxious individuals tended to respond to test items as
quickly as their non-anxious peers, they differed considerably
in terms of accuracy. The authors assumed that highly anxious
individuals may sacrifice precision in order to complete the task
as fast as possible and, thus, escape the aversive situation more
quickliy. This speed-accuracy tradeoff (Ashcraft & Faust, 1994)
corroborates the assumption that poor performance of highly
anxious individuals may, at least to some extent, be due to
avoidance of stressful situations regardless of the individual’s
true abilities. More recent studies demonstrate that reappraising
instructions (e.g., “people who feel anxious during a test might
actually do better. [...] arousal could be helping you do well”),
may help to change subjects’ performance levels to the better
(Jamieson, Mendes, Blackstock, & Schmader, 2010). Finally,
when discussing the relationship between anxiety and perform-
ance, it is important to consider the possibility that achievement
may deteriorate due to more general effects of anxiety related to
stressful test situations. Ashcraft and Moore (2009) describe
this effect as an affective drop in performance.
The Relationship between Mathematics Anxiety,
Performance and Working Memory
The relationship between working memory functioning, ma-
thematics anxiety and mathematical performance is probably
one of the most intensely investigated issues in the field. For
example, the deficient inhibition mechanism-theory (Hopko,
Ashcraft, Gute, Ruggiero, & Lewis, 1998) describes mathemat-
ics anxious individuals as exhibiting deficient abilities to inhibit
attention from shifting towards distracting information. Conse-
quently, working memory resources are withdrawn by task-
irrelevant distracters and hence, lacking for processing the task
at hand. In mathematics anxious individuals this mechanism seems
to be effective also when stimuli are non-mathematical in nature.
Additionally, it has been shown that a mathematics anxious
individual’s performance does not deteriorate when he or she is
confronted with simple arithmetic problems, whereas perform-
ance does suffer considerably when the person is confronted
with more difficult problems that draw on working memory
resources more heavily (Ashcraft & Kirk, 2001). That is, the
performance of mathematics anxious individuals is more se-
verely affected the more complex a given task is. This dynam-
ics is what Ashcraft and Kirk (2001) refer to as a transitory
disruption of working memory.
The processing efficiency theory (Eysenck & Calvo, 1992), a
domain-general model of the relationship between anxiety and
cognitive performance, describes anxiety-related performance
deficits to be more severe when more working memory re-
sources are needed for task completion. The authors assume the
intrusive worrisome thoughts to compete with ongoing cogni-
tive operations for the limited processing resources. Although
this theory refers to anxiety in general, it is assumed to apply
also for mathematics anxiety (Ashcraft & Kirk, 2001). The
authors suggest that “a major contributor to the performance
deficits found for high-math-anxiety participants involves work-
ing memory” (2001: p. 225).
Assessment of Mathematics Anxiety
Only a few years after the concept of mathemaphobia was
originally brought to the attention of a wider audience (Gough,
1954), a first diagnostic scale to assess numerical anxiety was
published (Dreger & Aiken, 1957). The authors added three
mathematical items to the already established Taylor Manifest
Anxiety Scale and thereby constructing a new numerical anxi-
ety scale. Unofficially, this constituted the starting point for
research on mathematics anxiety and on its assessment.
Only much later, Richardson and Suinn’s mathematical an-
xiety rating scale (MARS, 1972) was published, providing an
assessment instrument that was developed exclusively to inves-
tigate mathematics anxiety. This original 98-item-questionnaire
is based on situational anxiety ratings on a 1 to 5 Likert-type
scale, focusing on both everyday situations and more formal
settings. Due to its high re-test reliability (.85; Tryon, 1980)
and its availability, the MARS became the test of choice for
researchers examining mathematics anxiety in college students,
regardless of its rather laborious administration (Ashcraft &
Moore, 2009). A more recent reliability generalization analysis
conformed its internal consistency and test-retest reliability
Copyright © 2013 SciRes.
C. EDEN ET AL.
(Capraro, Capraro, & Henson, 2001).
An alternative instrument to the MARS was offered by Fen-
nema and Sherman (1976) with their mathematics anxiety scale
(MAS), one out of several domain-specific Likert-type scales
that assesses attitudes assumed to be related to and relevant for
mathematics learning and development (other scales surveyed
parents’ or teachers’ attitudes towards math). The MAS is a
12-item scale, intending to assess “feelings of anxiety, dread,
nervousness, and associated bodily symptoms related to doing
mathematics” (Fennema & Sherman, 1976: p. 4). For the MAS,
a split-half reliability of .89 was reported. Originally designed
for high school age, the MAS has been adopted for middle
school levels (e.g. Dew, Galassi, & Galassi, 1984). Despite
being published more than 30 years ago, the MAS is still in use
(Sherman & Wither, 2003; Zakaria & Nordin, 2008).
Only a few years later, Betz (1978) discarded two of the 12
original MAS items, rephrased several others, and thus adapted
the scale for assessing mathematics anxiety in college students.
A split-half reliability of .92 was reported. However, factor
loadings suggest that the main measurement construct of the
MAS is worry, a component of anxiety, that has been shown to
be provoked mainly by perceptions of insufficient self-efficacy
(Bandalos, Yates, & Thorndike-Christ, 1995).
Later work on alternative assessment tools has relied mainly
on the MARS. For instance, in order to investigate mathematics
anxiety in children and adolescents, the MARS was adapted by
Suinn and Edwards for middle and high school pupils (MARS-
A; 1982), and for upper elementary school children (MARS-E;
Suinn, Taylor, & Edwards, 1988). For the latter, factor analytic-
cal evaluation revealed two main situational components of
mathematics anxiety, namely mathematics test anxiety and ma-
thematics performance adequacy anxiety. But since the au-
thors did not analyze their data separately by grade or age
group, no further insights can be gained about the developmen-
tal pathways of these two aspects (Gierl & Bisanz, 1995). More-
over, these tests are considered to be somewhat dated (Ashcraft
& Moore, 2009).
Considering the complexity of the original MARS, Plake and
Parker (1982) provided a 24-item version of the MARS (MARS-
revised) to increase assessment efficiency. The revised scale
has an estimated alpha coefficient of .98 and correlates with .97
with the full scale, while loading on the same constructs as the
original MARS (i.e. state, trait and test anxiety). A principal
axes factor analysis of the revised MARS identified two factors,
learning mathematics anxiety and mathematics evaluation an-
xiety (Plake & Parker, 1982). A recent confirmatory factor ana-
lysis however, documented a remarkably poor model-to-data fit
for the two-factor structure (Hopko, 2003).
Similarly to Plake and Parker (1982), Alexander and Martray
(1989) published another abbreviated version of MARS (A-
MARS) that amounts to only 25 items, trying to provide a
convenient means of assessment available at no cost. For the
A-Mars, a factor analysis revealed a three factor structure, indi-
vidually labeled as math test anxiety, numerical test anxiety and
math course anxiety. A two week test-retest analysis revealed a
reliability of .86. The correlation with the original MARS is .97.
Although items refer to rather advanced mathematical concepts,
A-MARS can also be used at a high school-level.
Since most of the available instruments have been developed
for adults or adolescents, and were not appropriate for children
in elementary or lower middle school, Chiu and Henry (1990)
published a mathematics anxiety scale for children (MASC).
The MASC is yet another adaptation of MARS as it is based on
the items of MARS-R (Plake & Parker, 1982). All items have
been revised and two were excluded. Consequently, the MASC
consists of 22 revised items and is applicable from fourth to
eighth grade. Internal consistency reliability coefficients ranged
from .90 to .93. A principal component analysis revealed a four
factor structure with factors labeled mathematics evaluation
anxiety, mathematics learning anxiety, mathematics problem
solving anxiety, and a fourth and somewhat vague factor mathe-
matics teacher anxiety .
The first instrument that can be used as early as third grade is
the mathematics anxiety survey (MAXS) developed by Gierl
and Bisanz (1995). The authors adapted questions from MARS-
E for younger populations, aiming to assess the two aspects of
mathematics anxiety that were identified for MARS-E (Suinn et
al., 1988). This focus was a consequence of the observation that
mathematics anxiety was typically assessed as if it were a uni-
tary construct, even though different aspects of mathematics
anxiety were previously identified (e.g. Suinn et al., 1988).
The first scale suitable for studying math anxiety as early as
second grade, is the math anxiety questionnaire (MAQ) de-
veloped by Thomas & Dowker (2000) (as cited in Krinzinger,
Kaufmann & Willmes, 2009). Children have to rate experiences
of unhappiness and worry caused by problems in arithmetic.
The responses are given on a 5 point scale consisting of differ-
ent pictures that vary for different types of questions. Interest-
ingly, the authors of the German translation of the MAQ re-
ported that this diagnostic tool may actually not be an appropri-
ate means of assessment when the mutual influences of mathe-
matics anxiety and mathematics ability in early years of
schooling are under scrutiny (Krinzinger, Kaufmann, & Will-
mes, 2009). This emphasizes the need for standardized instru-
ments to assess the development of mathematics anxiety during
early primary school years.
More recently, a number of tools suitable for mathematics
anxiety assessment of adults and of younger populations were
published. However, Hopko and colleagues (2003) reported
that the psychometric properties of most currently available
abbreviated measures are generally inadequate. The authors
point out methodological limitations such as small sample sizes,
the lack of test-retest analyses (e.g. Plake & Parker, 1982), as
well as validity data (e.g. Alexander & Martray, 1989). This
being said, Hopko and colleagues developed the abbreviated
math anxiety scale (AMAS; 2003) using a large representative
sample. The authors assessed internal consistency, test-retest
reliability (.85), convergent/divergent validity, as well as the
assessment of a model, and generalizability. Additional as-
sessment yielded a 2-factor structure, with the factors learning
math anxiety and math evaluation/math test anxiety accounting
for 70% of the overall variance in scores. Due to this high level
of psychometric soundness and its convenient 9-item scale, “the
AMAS appears to be the test of choice for future work on math
anxiety” (Ashcraft & Ridley, 2005: p. 316) in high school and
Considering the fact that assessment convenience increases
with decreasing item quantity, Ashcraft and Moore (2009) tried
to correlate only one informal question (i.e., “on a scale from 1 -
10, how math anxious are you?”) with the full A-MARS scores
to assess its informative value. Surprisingly, the correlations
ranged from .48 to .85, endorsing this simple assessment
tool for preliminary screenings of math anxiety. They empha-
sized however, that such a one-item assessment tool has to be
Copyright © 2013 SciRes. 31
C. EDEN ET AL.
Copyright © 2013 SciRes.
ment geometry, and mathematical reasoning. However, of
SEMA’s 20 items only 10 refer to these issues whereas the
other 10 focus on feelings of anxiety related to social and test-
ing situations. This structure is mirroring the results of a previ-
ous principal component factor analytical study that yielded a
two-factor structure, with factors labeled numerical processing
anxiety and situational and performance anxiety. For the SEMA,
reliability is reported with a Cronbach’s alpha coefficient of .87
and a split-half reliability of .77.
phrased differently for younger students, as they may not be
familiar with expressions such as “anxious”. Unfortunately,
alternative questions that are more suitable for children were
More recent research into the development of diagnostic
measures focuses on the early development of mathematics
anxiety which is, as already mentioned, one of the major unre-
solved issues in the field (Vukovic, Kieffer, Bailey, & Harari,
2013). For example, Aarnos and Perkkilä (2012) developed a
pictorial test for early signs of math anxiety (37 pictures) suit-
able for six to eight-year-old children. Children are asked to
concentrate on pictures (i.e., photographs, drawings or graphi-
cal illustrations of mathematical tasks) and have to give spon-
taneous feedback regarding their affective states and their mathe-
matical intuitions. In order to avoid an entanglement with test
anxiety there are no clear-cut correct or incorrect responses for
the individual items.
Finally, the most recently published means for mathematics
anxiety assessment is another 12 item scale based on MARS-E
and MAQ (Vukovic et al., 2013). Children are asked to indicate the
degree to which they consent to different statements mainly related
to classroom situations (e.g. I like being called on in math ), while
choosing their responses from four different options (i.e. yes, kind
of, not really, no). Reliability is reported with a Cronbach’s alpha
of .80. A correlation with MARS-E (in fourth grade) of .48, pro-
vides moderate convergent validity (for an overview of all assess-
ment instruments and their applicability see Table 1).
A recently published 8-item scale, the child math anxiety
questionaire (CMAQ; Ramirez, Gunderson, Levine, & Beilock,
in press), has also been developed for the use in younger popu-
lations. The CMAQ is another adaptation of the MARS-E
(Suinn et al., 1988). However, it is reported to be only mar-
ginally reliable with a Cronbach’s alpha of .55, which may be
related to the fact that alpha values under .70 are not uncom-
mon when investigating attitudes among primary school chil-
This being said, it has to be pointed out that the general as-
sessment practice which is mainly based on self-reports may be
biased. For instance, Ben-Zeev, Duncan and Forbes (2005)
state critically that “...verbal reports of anxiety may not be reli-
able between individuals. Identical responses on a Likert scale
may index different levels of subjective anxiety for different
people.” Furthermore, the authors point out that “correlations
between self-reports of anxiety and physiological markers of
anxiety tend to be low [...]” (Ben-Zeev et al., 2005: p. 243; see
also Ehlers & Breuer, 1996; Wilhelm & Roth, 1998). It would,
thus, be useful for future research to combine psychophysi-
ological and neuroimaging methods with traditional question-
naire-based assessment approaches to understand mathematics
anxiety on a more fine-grained level.
Another MARS-based diagnostic instrument is the scale for
early mathematics anxiety (SEMA; Wu, Barth, Amin, Mal-
carne, & Menon, 2012), which was also developed with the aim
to create a mathematics anxiety test appropriate and valid for
the use in second and third graders. Age-appropriateness was
ensured by the items being based on data from content analy-
ses of curricula for grades 2 and 3. The test focuses on the con-
structs number sense , basic mathematical functions, measure-
An overview of mathematics anxiety assessment instruments and their applicability for different age groups.
Mathemat i cs anxiety rating scale (MARS; Richardson & Suinn, 1972)
Mathema t i cs anxiety scale (MAS; Fennema & Sherman, 1976)
MARS-Adolescents (MARS -A; Suinn & Edwards, 1982)
MARS-Revised (MARS-R; Plake & Parker, 1982)
MARS-Elementary (MARS-E; Suinn, Taylor & Edwards, 1988)
Shortend MARS (s-Mars; Alexander & Martray, 1989)
Mathematics anxiety scale for children (MASC; Chiu & Henry, 1990)
Mathema t i cs anxiety survey (MAXS; Gierl & Bisanz, 1995)
Math anxiety questionnaire (MAQ; Thomas & Dowker, 2000) (as cited in
Krinzinger, Kaufmann & Willmes, 2009)
Abbreviated math anxiety scale
(AMAS, Hopko, Mahadevan, Bare, & Hunt, 2003)
Scale for e arly mathematics anxiety
(SEMA, based on MARS; Wu, Barth, Amin, Malcarne, & Menon, 2012)
Pictorial test for early signs of math anxiety (Aarnos & Perkkilä, 2012)
Child math a n xiety questionnaire
(CMAQ, based on MARS-E; Ramirez, Gunderson, Levine, & Beilock, in press)
12-item mathematics anxiety scal e (Vukovic, Kieffer, Bailey, & Harari, 2013)
C. EDEN ET AL.
Even though poor mathematical competency may be an an-
tecedent to negative experiences in mathematics-related con-
texts and may increase the risk of mathematics anxiety to de-
velop, it does not suggest itself as a target for intervention. It is
rather assumed to be more effective to focus on affective as-
pects of mathematics-related experiences.
Hembree’s (1990) meta-analysis on mathematics anxiety in
college and pre-college age revealed that “treatments that re-
sulted in significant mathematics anxiety reduction were ac-
companied by significant increases in mathematics test scores.
The largest increases referred to the treatments providing the
largest mathematics anxiety reduction [...]” (p. 43). Poor achieve-
ment scores of anxious individuals tend to increase almost to
average performance levels after completion of treatments tar-
geting mathematics anxiety as such. And as none of the in-
cluded intervention studies was based on mathematical instruct-
tion, this data corroborates with the assumption that deficient
mathematical performance may be reduced as a consequence of
reduced mathematics anxiety.
Furthermore, Hembree (1990) compared classroom interven-
tions and out-of-class psychological treatments in terms of their
effects on mathematics anxiety and performance respectively. It
was shown that whole-class psychological treatments (i.e. be-
havioral or cognitive-behavioral approaches trying to relieve
“emotionality” toward mathematics and worry about mathe-
matics), as well as whole-class interventions that focused on
numerical abilities were neither effective in alleviating mathe-
matics anxiety, nor concerning improvements of mathematical
performance. Individual trainings, however, specifically sys-
tematic desensitization, anxiety management and conditioned
inhibition training, were successful for both, while cognitive
modifications (i.e. restructuring of false beliefs and threatening
ideas) showed moderate effects.
As a consequence, recent publications (e.g. Maloney & Beilock,
2012), refer to treatment of affective aspects rather than skills
trainings when considering remedial approaches to mathematics
anxiety. Appraoches such as writing down worries prior to
mathematics tests (Park, Ramirez, & Beilock, 2011) (as cited in
Maloney & Beilock, 2012), reappraising certain physiological
responses related to threat (Jamieson et al., 2010), or stressing
the need for regulation strategies for more efficient control of
negative emotions (Lyons & Beilock, 2011) are the first choice
when dealing with mathematics anxiety. And finally, since
mathematics anxiety causes avoidance of related contexts
which, again, does entail detrimental effects on learning success
(e.g. Ashcraft & Faust, 1994; Wu et al., 2012), it is beyond
question that interventions have to start as early as possible.
Summarizing the current state of research on mathematics
anxiety, the main message is that more systematic research also
integrating neurocognitive methods is needed in order to gain a
better understanding of the critical factors that determine the
development of mathematics anxiety, of its dynamics over time,
and its effect for the individual and its cognitive and emotional
On the one hand, there is not enough data on the prevalence
and manifestations of mathematics anxiety in younger popula-
tions, on how the different variables that were previously iden-
tified to be related to mathematics anxiety interact in the course
of development, and on the longitudinal effects of mathematics
anxiety. On the other hand, intervention studies should be im-
plemented in order to evaluate different remedial approaches
and their effectiveness for different age groups.
The only way to gain deeper insights into the underlying
mechanisms of the development of mathematics anxiety will be
through longitudinal investigations that compare anxious and
non-anxious individuals over an adequate period of time, i.e.
starting as early as possible, ideally at pre-school age, and end-
ing only after completion of formal schooling. In order to work
out possible causal links between the relevant factors identified
by previous studies, the collected data should range from in-
formation regarding an individual’s social and cultural back-
ground to the data on his or her domain-general and do-
main-specific cognitive abilities. Additionally, future research
should guard against the collection of potentially biased data
(e.g. self-reports). Thus, assessment in the context of research on
mathematics anxiety should be made more objective by includ-
ing (psycho-)physiological (e.g. ECG or EDA) or even neuroi-
maging methods (e.g. fMRT; Young, Wu, & Menon, 2012) into
the study designs.
In conclusion, future studies on mathematics anxiety that
combine multi-method research designs with longitudinal ap-
proaches may help to generate more complex models of the
dynamics of mathematics anxiety over time, its determining
factors, and its detrimental effects for the individual and devel-
Aarnos, E., & Perkkilä, P. (2012). Early signs of mathematics anxiety?
Procedia-Social and Behavioral Sciences, 46, 1495-1499.
Alexander, L., & Martray, C. R. (1989). The development of an ab-
breviated version of the Mathematics Anxiety Rating Scale. Meas-
urement and Evaluation in Counseling and Development, 22, 143-
Aronson, J., Lustina, M. J., Good, C., Keough, K., Steele, C. M., &
Brown, J. (1999). When white men can’t do math: Necessary and
sufficient factors in stereotype threat. Journal of Experimental Social
Psychology, 35, 29-46. doi:10.1006/jesp.1998.1371
Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and
mental arithmetic performance: An exploratory investigation. Cog-
nition & Emotion, 8, 97-125. doi:10.1080/02699939408408931
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among wor-
king memory, math anxiety, and performance. Journal of Expe-
rimental Psychology: General, 130, 224-237.
Ashcraft, M. H., Krause, J. A., & Hopko, D. R. (2007). Is math anxiety
a mathematical learning disability? In D. B. Berch, & M. M. M.
Mazzocco (Eds.), Why is math so hard for some children? (pp.
329-348). Baltimore, MD: Brookes Publishing.
Ashcraft, M. H., & Moore, A. M. (2009). Mathematics anxiety and the
affective drop in performance. Journal of Psychoeducational Assess-
ment, 27, 197-205. doi:10.1177/0734282908330580
Ashcraft, M. H., & Ridley, K. S. (2005). Math anxiety and its cognitive
consequences—A tutorial review. In J. I. D. Campbell (Ed.), Hand-
book of mathematical cognition (pp. 315-327). New York: Psy-
Bandalos, D. L., Yates, K., & Thorndike-Christ, T. (1995). Effects of
math self-concept, perceived self-efficacy, and attributions for failure
and success on test anxiety. Journal of Educational Psychology, 87,
Bandura, A. (1977). Social learning theory. New York: General Learn-
Copyright © 2013 SciRes. 33
C. EDEN ET AL.
Bandura, A., & Locke, E. (2003). Negative self-efficacy and goal
effects revisited. Journal of Applied Psychology, 88, 87-99.
Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010).
Female teacher’s math anxiety affects girls’ math achievement.
PNAS, 107, 1860-1863. doi:10.1073/pnas.0910967107
Ben-Zeev, T., Duncan, S., & Forbes, C. (2005). Stereotypes and math
performance. In J. I. D. Campbell (Ed.), Handbook of mathematical
cognition (pp. 235-249). New York: Psychology Press.
Betz, N. E. (1978). Prevalence, distribution, and correlates of math
anxiety in college students. Journal of Counseling Psychology, 25,
Capraro, M. M., Capraro, R. M., & Henson, R. K. (2001). Measurement
error of scores on the mathematics anxiety rating scale across studies.
Educational and Psychological Measuremen t, 61, 373-386.
Chiu, L. H., & Henry, L. L. (1990). Development and validation of the
mathematics anxiety scale for children. Measurement and evaluation
in counseling and development, 23, 121-127.
Dennis, K., Daly, C., & Provost, S. C. (2003). Prevalence, contributing
factors, and management strategies for test and maths anxiety in
first-year psychology students. Australian Journal of Psychology,
Supplement, 55, 176.
Devine, A., Fawcett, K., Szűcs, D., & Dowker, A. (2012). Gender
differences in mathematics anxiety and the relation to mathematics
performance while controlling for test anxiety. Behavioral and Brain
Functions, 8, 1-9. doi:10.1186/1744-9081-8-33
Dew, K. H., Galassi, J. P., & Galassi, M. D. (1984). Math anxiety:
Relation with situational test anxiety, performance, physiological
arousal, and math avoidance behavior. Journal of Counseling Psy-
chology, 31, 580-583. doi:10.1037/0022-0126.96.36.1990
Dreger, R. M., & Aiken, L. R. (1957). The identification of number
anxiety in a college population. Journal of Educational Psychology,
48, 344-351. doi:10.1037/h0045894
Dykeman, B. F. (1994). The effects of motivational orientation, self-
efficacy, and feedback condition on test anxiety. Journal of instruc-
tional psychology, 21, 114-120.
Eccles, J. S., Jacobs, J. E., & Harold, R. D. (1990). Gender role
stereotypes, expectancy effects, and parents’ socialization of gender
differences. Journal of Social Issues, 46, 183-201.
Ehlers, A., & Breuer, P. (1996). How good are patients with panic dis-
order at perceiving their heartbeats? Biological Psychology, 42,
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance:
The processing efficiency theory. Cognition and Emotion, 6, 409-
Fennema, E. (1989). The study of affect and mathematics: A proposed
generic model for research. In D. B. McLeod, & V. M. Adams (Eds.),
Affect and mathematical problem solving: A new perspective (pp.
205-219). New York: Springer. doi:10.1007/978-1-4612-3614-6_14
Fennema, E., & Sherman, J. A. (1976). Fennema-Sherman Mathematics
Attitudes Scales: Instruments designed to measure attitudes toward
the learning of mathematics by females and males. Journal for Re-
search in Mathematics Education, 7, 324-326.
Flessati, S. L., & Jamieson, J. (1991). Gender differences in mathe-
matics anxiety: An artefact of response bias? Anxiety, Stress &
Coping, 3, 303-312.
Gierl, M. J., & Bisanz, J. (1995). Anxieties and attitudes related to
mathematics in grades 3 and 6. The Journal of Experimental
Education, 63, 139-158. doi:10.1080/00220973.1995.9943818
Gough, M. F. (1954). Mathemaphobia: Causes and treatments. Clearing
House, 28, 290-294.
Harper, N. W., & Daane, C. J. (1998). Causes and reduction of math
anxiety in preservice elementary teachers. Action in Teacher Edu-
cation, 19, 29-38. doi:10.1080/01626620.1998.10462889
Hembree, R. (1990). The nature, effects and relief of mathematics an-
xiety. Journal for Research in Mathematics Education, 21, 33-46.
Hodapp, V., & Benson, J. (1997). The multidimensionality of test
anxiety: A test of different models. Anxiety, Stress & Coping, 10,
Hopko, D. R. (2003). Confirmatory factor analysis of the math anxiety
rating scale (revised). Educational and Psychological Measurement,
63, 336-351. doi:10.1177/0013164402251041
Hopko, D. R., Ashcraft, M. H., Gute, J., Ruggiero, K. J., & Lewis, C.
(1998). Mathematics anxiety and working memory: Support for the
existence of a deficient inhibition mechanism. Journal of Anxiety
Disorders, 12, 343-355. doi:10.1016/S0887-6185(98)00019-X
Hopko, D. R., Mahadevan, R., Bare, R. L., & Hunt, M. K. (2003). The
abbreviated math anxiety scale (AMAS): Construction, validity, and
reliability. Assessment, 10, 178-182.
Jackson, C. D., & Leffingwell, R. J. (1999). The role of instructors in
creating math anxiety in students from kindergarten through college.
The Mathematics Teacher, 92, 583-586.
Jain, S., & Dowson, M. (2009). Mathematics anxiety as a function of
multidimensional self-regulation and self-efficacy. Contemporary
Educational Psychology, 3 4, 240-249.
Jamieson, J. P., Mendes, W. B., Blackstock, E., & Schmader, T. (2010).
Turning the knots in your stomach into bows: Reappraising arousal
improves performance on the GRE. Journal of Experimental Social
Psychology, 46, 208-212. doi:10.1016/j.jesp.2009.08.015
Krinzinger, H., Kaufmann, L., & Willmes, K. (2009). Math anxiety and
math ability in early primary school years. Journal of Psycho-
educational Assessment, 27, 206-225.
Lyons, I. M., & Beilock, S. L. (2011). Mathematics anxiety: Separating
the math from the anxiety. Cerebral Cortex, 22, 2102-2110.
Ma, X. (1999). A meta-analysis of the relationship between anxiety
toward mathematics and achievement in mathematics. Journal for
Research in Mathematics Education, 30, 520-540.
Ma, X., & Xu, J. (2004). The causal ordering of mathematics anxiety
and mathematics achievement: A longitudinal panel analysis. Jour-
nal of Adolescence, 27, 165-179.
Maloney, E. A., & Beilock, S. L. (2012). Math anxiety: Who has it,
why it develops, and how to guard against it. Trends in Cognitive
Sciences, 16, 404-406. doi:10.1016/j.tics.2012.06.008
Maloney, E. A., Waechter, S., Risko, E. F., & Fugelsang, J. A. (2012).
Reducing the sex difference in math anxiety: The role of spatial pro-
cessing ability. Learning and Individual Differences, 22, 380-384.
Manstead, A. S. R., & Van-Eekelen, S. A. M. (1998). Distinguishing
between perceived behavioral control and self-efficacy in the domain
of academic intentions and behaviors. Journal of Applied Social
Psychology, 28, 1375-1392.
Newby-Fraser, E., & Schlebusch, L. (1998). Social support, self-ef-
ficacy and assertiveness as mediators of student stress. Journal of
human behavior, 34, 61-69.
Plake, B. S., & Parker, C. S. (1982). The development and validation of
a revised version of the mathematics anxiety rating scale. Edu-
cational and Psychological Measurement, 42, 551-557.
Ramirez, G., Gunderson, E. A., Levine, S. C., & Beilock, S. L. (in
press). Math anxiety, working memory and math achievement in
early elementary school. Journal of Cognition and Development.
Richardson, F. C., & Suinn, R. M. (1972). The mathematics anxiety
rating scale: Psychometric data. Journal of Counseling Psychology,
19, 551-554. doi:10.1037/h0033456
Rotzer, S., Loenneker, T., Kucian, K., Martin, E., Klaver, P., & von
Aster, M. (2009). Dysfunctional neural network of spatial working
memory contributes to developmental dyscalculia. Neuropsychologia,
47, 2859-2865. doi:10.1016/j.neuropsychologia.2009.06.009
Sherman, B., & Wither, D. (2003). Mathematics anxiety and mathe-
matics achievement. Mathematics Education Research Journal, 15,
Copyright © 2013 SciRes.
C. EDEN ET AL.
Copyright © 2013 SciRes. 35
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat
and women’s math performance. Journal of Experimental Social
Psychology, 35, 4-28. doi:10.1006/jesp.1998.1373
Stuart, V. B. (2000). Math curse or math anxiety? Teaching Children
Mathematics, 6, 330-338.
Suinn, R. M., & Edwards, R. (1982). The measurement of mathematics
anxiety: The mathematics anxiety rating scale for adolescents—
MARS-A. Journal of Clinical Psychology, 38, 576-580.
Suinn, R. M., Taylor, S., & Edwards, R. W. (1988). Suinn mathematics
anxiety rating scale for elementary school students (MARS-E):
Psychometric and normative data. Educational and Psychological
Measurement, 48, 979-986. doi:10.1177/0013164488484013
Tryon, G. S. (1980). The measurement and treatment of test anxiety.
Review of Educational Research, 50, 343-372.
Turner, J. C., Midgley, C., Meyer, D. K., Gheen, M., Anderman, E. M.,
Kang, Y. et al. (2002). The classroom environment and students’
reports of avoidance strategies in mathematics: A multimethod study.
Journal of Educational Psychology, 94, 88-106.
Vukovic, R. K., Kieffer, M. J., Bailey, S. P., & Harari, R. R. (2013).
Mathematics anxiety in young children: Concurrent and longitudinal
associations with mathematical performance. Contemporary Educa-
tional Psychology, 38, 1-10. doi:10.1016/j.cedpsych.2012.09.001
Wilhelm, F. H., & Roth, W. T. (1998). Taking the laboratory to the
skies: Ambulatory assessment of self-report, automatic, and res-
piratory responses in flying phobia. Psychophysiology, 35, 596-606.
Wu, S. S., Barth, M., Amin, H., Malcarne, V., & Menon, V. (2012).
Math anxiety in second and third graders and its relation to ma-
thematics achievement. Frontiers in Psychology, 3, 162.
Young, C. B., Wu, S. S., & Menon, V. (2012). The neurodevelop-
mental basis of math anxiety. Psychological Science , 23, 492-501.
Zakaria, E., & Nordin, M. N. (2008). The effects of mathematics an-
xiety on matriculation students as related to motivation and achieve-
ment. Eurasia Journal of Mathematics Science and Technology
Education, 4, 27-30.