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MATH ANXIETY, INTRUSIVE THOUGHTS AND
PERFORMANCE
Exploring the relationship between mathematics anxiety and performance: The role of
intrusive thoughts
Thomas E. Hunt
1
, David Clark-Carter
2
, David
Sheffield
1
1
University of Derby, U.K.
2
Staffordshire University
U.K.
Thomas E. Hunt
Department of Psychology,
Faculty of Education, Health & Sciences,
University of Derby, Derby,U.K.
t.hunt@derby.ac.uk
+(44) 1332 592015
Abstract -The current study examined the relationship between math anxiety and arithmetic performance by focusing on intrusive
thoughts experienced during problem solving. Participants (N = 122) performed two-digit addition problems on a verification task.
Math anxiety significantly predicted response time and error rate. Further, the extent to which intrusive thoughts impeded calculation
mediated the relationship between math anxiety and per cent of errors on problems involving a carry operation. Moreover, results
indicated that participants experienced a range of intrusive thoughts and these were related to significantly higher levels of math
anxiety. The findings lend support to a deficient inhibition account of the math anxiety-to-performance relationship and highlight the
importance of considering intrusive thoughts in future work.
Key Words: arithmetic performance; cognitive intrusions; intrusive thoughts; math anxiety
I. INTRODUCTION
Mathematics anxiety can be described as “a feeling of
tension and anxiety that interferes with the manipulation of
numbers and the solving of mathematical problems in a wide
variety of ordinary life and academic situations” (Richardson &
Suinn, 1972, p.551). It is thought to affect a large number of
people (Ashcraft & Moore, 2009), is experienced in children
(Wu, Barth, Amin, Malcarne & Menon, 2012) and adults
(Ashcraft, 2002), and extends to a range of contexts, such as
nursing (McMullan, Jones & Lea, 2012) and consumer
behaviour (Jones, Childers & Jiang, 2012).
There is now substantial empirical evidence to suggest that
math anxiety is negatively correlated with overall math
performance (Ashcraft & Moore, 2009; Hembree, 1990; Ma,
1999). Specifically, research has demonstrated that math
anxiety is more consistently and negatively related to
performance on complex, compared to simple arithmetic (e.g.,
Ashcraft & Faust, 1994), and particularly when a problem
involves a carry operation (Faust, Ashcraft & Fleck, 1996).
However, the mechanisms underlying these relationships are
poorly understood. One possible mechanism linking math
anxiety to performance is intrusive or worrisome thoughts.
Tendency to experience intrusive thoughts has been found to
correlate with performance on a range of cognitive tasks
(Munoz, Sliwinski, Smyth, Almeida & King, 2013) and,
according to processing efficiency theory (Eysenck & Calvo,
1992), on which the more recent attentional control theory
(Eysenck, Derakshan, Santos & Calvo, 2007) is based,
worrisome thoughts interfere with the limited resources
available within the working memory system. Ashcraft and
Krause (2007) propose that preoccupation with one’s fears and
anxieties pertaining to math may act as a ‘secondary task’,
resulting in the depletion of resources necessary for arithmetic
task completion. Related to this, Hopko, Ashcraft, Gute,
Ruggiero & Lewis (1998) argued that high math anxious
individuals may have difficulty in inhibiting attention towards
intrusive or worrisome thoughts. Inhibition theory (Hasher &
Zacks, 1988; Connelly, Hasher & Zacks, 1991) proposes that
there is a mechanism for suppressing, or inhibiting, task-
irrelevant distracters. If this mechanism is not working
adequately then task-irrelevant information may interfere with
working memory processes and consequently result in poor
performance. Indeed, Hopko et al (1998) demonstrated that
high and medium math anxious groups took significantly
longer than a low math anxious group to read through text that
included irrelevant information, suggesting poor inhibitory
control. Similarly, Hopko, McNeil, Gleason and Rabalais
(2002) found that response times of high math anxious
individuals were longer on a card counting task with numeric
stimuli, compared to a card-counting task involving letters,
again implicating inhibitory mechanisms related to working
memory.
Whilst previous math anxiety research has made reference
to worrisome or irrelevant thoughts, surprisingly little research
has attempted to examine the influence of worrisome thoughts
on arithmetic performance. In an early study, Hunsley (1987)
measured math anxiety, test anxiety, performance appraisals,
performance attributions and internal dialogue amongst 96
university students before and after midterm statistics
examinations. Participants’ negative internal dialogue was
measured using the Cognitive Interference Questionnaire (CIQ,
Sarason, 1978), which required participants to rate (on a five-
point scale) the frequency with which they experienced
negative thoughts during the exam. Math and test anxiety
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accounted for 15% and 10% of the variance in CIQ scores,
respectively. Further, Beilock, Kulp, Holt and Carr (2004)
found that students performing arithmetic in a high pressure
condition (involving carry operations) had significantly
increased perceptions of performance pressure and reported
thoughts and worries about the high pressure situation and its
consequences, compared to students in a low pressure
condition. More recently, DeCaro, Rotar, Kendra and Beilock
(2010) reported significant negative correlations between task-
related thoughts and arithmetic problem solving accuracy. Such
findings give support to the idea that poor performance may, in
part, be due to insufficient inhibition of intrusive or worrisome
thoughts. However, math anxiety was not measured, so its
relationship with intrusive thoughts and performance remains
unclear.
II. CURRENT STUDY
The current study aimed to test the deficient inhibition
explanation of the math anxiety-to-performance relationship,
by examining the negative internal dialogue that participants
may experience during math performance. Also, we examined
whether the perceived severity of intrusive thoughts
experienced during a math task, was related to math anxiety. It
was hypothesised that there would be a negative relationship
between math anxiety and performance (increased errors and
longer response times) on complex addition problems involving
a carry operation, but no relationship when a carry operation
is not required. Further, it was hypothesised that there would
be a negative relationship between self-reported impact of
intrusive thoughts (and effort to reduce such thoughts) and
performance. In addition, we explored the types of thought that
participants indicated they had during the math task, and their
relations with math anxiety.
III. METHOD
PARTICIPANTS
Participants were 122 (31 men, 91 women) undergraduate
psychology students from two Midlands universities in the
U.K. The sample included participants from all three years of
undergraduate study. Ages ranged from 18 to 51 years (mean =
24.95; SD = 8.76). The mean age is slightly higher than would
be expected from a student sample at traditional universities,
but is consistent with the slightly higher ages of students in
post-1992 universities in the U.K, which make up
approximately half of the number of universities in the U.K
(Universities Colleges and Admissions Service, 2009).
Participants came from an opportunity sample of the general
university population gained via advertising at the universities.
QUESTIONNAIRE MEASURES
The Mathematics Anxiety Scale-U.K. (MAS-U.K., Hunt,
Clark-Carter & Sheffield, 2011) was used to measure math
anxiety. This is a 23-item scale that uses a five-point Likert-
type scale and asks participants to respond how anxious they
would feel in a variety of situations involving math. The scale
has excellent internal consistency (Cronbach’s alpha = .96) and
very high test-retest reliability between four and ten weeks (r =
.89).
In order to measure intrusive thoughts that may occur
during arithmetic items from the Cognitive Intrusions
Questionnaire (Freeston et al., 1991, English translation by
Freeston, 1994) were selected and modified. The first part of
the original questionnaire involves a list of thoughts that
participants are required to endorse if they experienced them
during a preceding task. This was modified so that the list only
contained thoughts related specifically to the math task
undertaken: “making mistakes”, “time pressure”, “method of
problem solving”, “what people would think”, “panicking”,
“previous math experiences”, and “physical changes”. Next
there was a series of five-point Likert-type scale items, relating
to different aspects of the “most worrisome or troubling”
thoughts. Wording of the original items was modified so that
each one related to the math task and pertained to the
following: frequency of the thoughts, difficulty in removing the
thoughts, extent to which the thoughts impeded calculation,
and the amount of effort used to stop/reduce the thoughts. A
final question asked participants to indicate whether they had
experienced intrusive thoughts that were non-math-task related,
for example relationship problems or health problems.
The study also included a measure of trait anxiety, taken
from the State-Trait Anxiety Inventory (Spielberger, Gorsuch,
Lushene, Vagg & Jacobs, 1984) but, the zero-order correlations
showed no relationship between trait anxiety and error rates
and response time, so analyses including trait anxiety are not
presented here (these are available from the first author).
EXPERIMENTAL STIMULI AND PROCEDURE
Using the experiment-building software E-prime, 80 two-
digit addition problems were presented using a verification
task, for example ‘37 + 18 = 52’. Sixty of these problems had a
solution that was true; 20 had an incorrect solution. Of the 60
true problems, 30 involved a carry operation, for example “17
+ 18 = 35”, and 30 involved no carry, for example “17 + 12 =
29”. Addends were randomly taken from a range of 10-89.
Problem-size was counterbalanced across addends and
carry/no-carry conditions so that performance could be
attributed to factors other than the size of the problems.
Problems where both addends ending in zeros, for example ‘20
+ 30’, or fives, for example ‘25 + 35’, were not included.
Incorrect problems were divided approximately equally with
splits of +/- 1, +/- 3, and +/- 5, counterbalancing the number of
positive and negative splits.
Participants gave informed consent and completed the
mathematics anxiety scale. Stimuli were presented in the centre
of a VDU, in Times New Roman, size 40, bold font. Following
the on-screen instructions and five practice trials, participants
were asked to respond ‘true’ or ‘false’ to the proposed answers.
This was achieved by pressing the ‘z’ and ‘m’ keys on a
keyboard, for ‘true’ and ‘false’, respectively. There was no
time limit for participants to respond. After responding, a pause
screen, consisting of ‘+++++’ appeared, and this remained until
participants pressed one of the keys to proceed to the next trial.
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Immediately after completion of the arithmetic task,
participants completed the cognitive intrusions questionnaire.
Finally, they were debriefed and thanked.
GENERAL DATA SCREENING AND DIAGNOSTIC CHECKS
Initially participants were asked to select from a list which
non-task related intrusive thoughts they had experienced.
However, the results demonstrated a bi-modal distribution.
Therefore the decision was made to dichotomise the variable
into the levels “yes” (at least one non-task related thought) and
“no” (no non-task related thoughts), to represent experience of
non-task related thoughts.
Visual inspection of histograms of the data showed the data
to be sufficiently univariately normally distributed. For each
regression, normality of standardised residuals was tested by
visual inspection of histograms; these were found to be normal.
Standardised residuals and standardised predicted values were
also plotted against each and no obvious curvilinear
relationships were apparent, with the display also indicating the
presence of homoscedasticity. Checks for bivariate outliers
were also made using scattergraphs and no outliers were
identified. In order to test for multivariate outliers Cook’s
distance and leverage values were plotted against each other;
no cases appeared to obviously deviate from the main cluster of
cases. In addition, checks of tolerance values and variance
proportions indicated that there were no problems with
multicollinearity among the data.
Reliability analyses for the MAS-U.K demonstrated a
Cronbach’s alpha of .94, indicating excellent internal
consistency.
IV. RESULTS
PROBLEM TYPE ANALYSIS
A within-subjects t-test was used to compare the difference
in per cent of errors between problems with a carry term (mean
= 5.13%; SD = 5.72) and problems without (mean = 2.39%;
SD = 3.32). Significantly more errors were made in response to
problems that included a carry term, t(121) = 5.24, p < .001,
two-tailed test, 95% CIs [1.70, 3.77], d = 0.58, indicating a
medium effect (Cohen, 1988). A within-subjects t-test revealed
that participants took significantly longer to respond to
problems including a carry term (mean = 5730.97ms; SD =
1696.32) than to problems not including a carry term (mean =
3835.15ms; SD = 1052.82), t(121) = 20.60, p < .001, two-tailed
test, 95% CIs [1713.60, 2078.04], d = 1.34.
ZERO-ORDER CORRELATIONS
The zero-order correlations between all variables can be
seen in Table I. Math anxiety was significantly positively
correlated with percentage of errors on problems involving a
carry operation but was not correlated with percentage of errors
to no-carry problems. Math anxiety was also significantly
positively correlated with response time to problems with and
without a carry operation.
Math anxiety was very strongly and highly significantly
positively correlated with perceived frequency of the most
troublesome/worrisome thoughts, effort to reduce thoughts and
perceived impedance of thoughts on calculation. Frequency of
the most troublesome/worrisome thoughts, difficulty removing
the thoughts, effort to reduce the thoughts, and perceived
TABLE I. ZERO-ORDER CORRELATIONS BETWEEN MATH ANXIETY, INTRUSIVE THOUGHTS MEASURES AND MATH PERFORMANCE.
*p ≤.05 **p ≤.01 **p ≤.001
Variable
Carry errors %
No-carry errors %
Carry RT
No-carry RT
Math anxiety total
Freq. Most
troublesome/
worrisome thought
Difficulty removing
thought
Impeding calculation
Effort to reduce
thought
Carry errors %
1
No-carry errors %
.28**
1
Carry RT
.06
.02
1
No-carry RT
.25**
.17
.83***
1
Math anxiety total
.25**
-.00
.30***
.30***
1
Freq. of most troublesome / worrisome
thought
.19*
.03
.23*
.26**
.59***
1
Difficulty removing
.20*
.10
.11
.16
.56***
.62***
1
Impeding calculation
.35***
.04
.25**
.28**
.52***
.57***
.55***
1
Effort to reduce thought
.21*
.05
.22*
.24**
.61***
.69***
.74***
.65***
1
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impediment to calculation were significantly positively related
to percentage of errors to carry-problems. Perceived frequency
of the most troublesome/worrisome thoughts, effort to reduce
thoughts and perceived impedance of thoughts on calculation
were significantly positively correlated with response time to
both carry-problems and no-carry problems. Perceived
frequency of the most troublesome/worrisome thoughts was
strongly and significantly correlated with perceived difficulty
in removing thoughts, effort to reduce the impact of thoughts
and perceived impedance on calculation.
REGRESSION ANALYSES
A series of hierarchical multiple regressions were then
conducted. In all models tested, math anxiety was included in
the first step, followed by variables related to the self-reporting
impact of the most troublesome/worrisome thoughts, including
frequency of the thoughts, difficulty in removing the thoughts,
and impact of the thoughts on the calculation process. Step
three included self-reported effort in reducing the impact of the
thoughts.
PERCENTAGE OF ERRORS ON CARRY PROBLEMS
The final regression model was significant, F(5, 116) =
3.57, p = .005, accounting for 13.3% (Adj R
2
= .096) of the
variance. As shown in Table II, whilst math anxiety was a
significant predictor at step 1, it became non-significant at step
2, remaining non-significant in the final stage. All other
predictor variables were non-significant, with the exception of
the variable impeding calculation, representing the level of
which the most troublesome/worrisome thoughts impeded the
participant’s calculation of the math problems. Impeding
calculation was significantly positively related to percentage of
errors to carry problems and remained so in the final step,
suggesting a mediation effect. The criteria for mediation,
suggested by Baron and Kenny (1986), were met. As such, the
indirect path between math anxiety, impeding calculation and
percentage of errors was tested using a Sobel test, which
demonstrated a significant indirect path, p = .006.
PERCENTAGE OF ERRORS ON NO-CARRY PROBLEMS
The final regression model was not significant, F(5, 116) =
0.34, p = .89, accounting for <1% (Adj R
2
<.01) of the variance
in percentage of errors to no-carry problems. As shown in
Table 2, no individual variable was a significant predictor.
RESPONSE TIME TO CARRY PROBLEMS
The final regression model was significant, F(5, 116) =
3.35, p = .007, accounting for 12.6% (Adj R
2
= .088) of the
variance in response time to problems involving a carry
operation. As shown in Table III, math anxiety had a high level
of predictive power, being significantly positively related to
response time to carry problems and explaining 9.0% of the
variance. Inclusion of intrusive thoughts measures did not add
significantly to the model. Math anxiety remained significant
through steps two and three.
RESPONSE TIME TO NO-CARRY PROBLEMS
The final regression model was significant, F(5, 116) =
3.31, p = .008, accounting for 12.5% (Adj R
2
= .087) of the
variance in response time to problems that did not involve a
carry operation. Math anxiety was a significant predictor at
step one but no variables were significant in the remaining
steps.
TYPES OF INTRUSIVE THOUGHTS
As shown in Table IV, the most frequent intrusive thought
experienced by participants related to making mistakes.
Approximately half of all participants experienced thoughts
about time pressure or method of problem solving. 41.8% of
participants reported having thoughts that were non-task
related, with similar numbers experiencing thoughts about
previous math experiences or what people might think. Almost
a third of all participants reported thoughts relating to physical
changes and just over one fifth of participants reported having
thoughts about panic. A comparison of math anxiety levels
between those with and those without specific intrusive
thoughts revealed several significant differences. With the
exception of thoughts about method of problem solving,
endorsement of each of the other intrusive thoughts was
associated with significantly higher math anxiety.
TABLE II. RESULTS OF HIERARCHICAL REGRESSION WITH PERCENTAGE OF ERRORS AS THE OUTCOME VARIABLE
*P ≤.05 **P ≤.01
Carry problems
No-carry problems
Step
Variables Entered
Beta
R
2
change
Model R
2
Beta
R
2
change
Model R
2
1
Math anxiety
.251**
.063**
-.004
<.001
2
Math anxiety
.131
.069*
.132**
-.078
.014
.014
Frequency
-.069
-.028
Difficulty in removing
-.018
.147
Impeding calculation
.331**
.016
3
Math anxiety
.141
.002
1.33**
-.078
<.001
.014
Frequency
-.052
-.028
Difficulty in removing
.012
.147
Impeding calculation
.347**
.016
Effort to reduce thoughts
-.074
.002
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V. DISCUSSION
Consistent with previous findings (e.g., Ashcraft & Faust,
1994) overall response time was significantly longer to
problems involving a carry operation, and significantly more
errors were made on problems involving a carry operation.
Math anxiety was related to poor performance, but once
intrusive thoughts data, namely frequency, difficulty in
removing and impeding calculation, were accounted for, math
anxiety did not predict percentage of errors to either carry or
no-carry problems. However, math anxiety was a significant
predictor of response time for carry problems. In contrast,
intrusive thoughts measures were unrelated to responses times
for carry and no-carry problems. Contrary to expectations, self-
reported frequency of intrusive thoughts did not predict error
rates to either carry problems or no-carry problems. Similarly,
self-reported difficulty in removing intrusive thoughts did not
predict error rates. However, there was a significant positive
relationship between the self-reported extent to which intrusive
thoughts impeded calculation and percentage of errors on
problems involving a carry operation, partially supporting the
hypothesis that the self-reported impact of intrusive thoughts
would be related to performance. Therefore, perceived impact
of intrusive thoughts was a predictor of performance whereas
self-reported frequency of thoughts was not. This is consistent
with other recent findings that showed frequency of thoughts to
be unrelated to working memory performance (Nixon et al.,
2008).
Moreover, the extent to which intrusive thoughts impeded
calculation mediated the relationship between math anxiety and
error rates on problems involving a carry operation; there were
no relationships between math anxiety or intrusive thoughts
measures, on the one hand, and error rates on problems
involving no carry operation, on the other. These findings lend
support to inhibition theory (Hasher & Zacks, 1988; Connelly,
TABLE III. RESULTS OF HIERARCHICAL REGRESSION WITH RESPONSE TIME AS THE OUTCOME VARIABLE
*P ≤.05 **P ≤.01 **P ≤.001
Carry problems
No-carry problems
Step
Variables Entered
Beta
R
2
change
Model R
2
Beta
R
2
change
Model R
2
1
Math anxiety
.299***
.090***
.302***
.091***
2
Math anxiety
.262*
.034
.124**
.213
.033
.124**
Frequency
.105
.114
Difficulty in removing
-.198
-.127
Impeding calculation
.166
.179
3
Math anxiety
.249*
.002
1.26**
.207
.001
.125**
Frequency
.085
.104
Difficulty in removing
-.234
-.144
Impeding calculation
.146
.170
Effort to reduce thoughts
.089
.042
TABLE IV. FREQUENCY OF INTRUSIVE THOUGHTS AND MATH ANXIETY LEVEL AS A FUNCTION OF SPECIFIC THOUGHTS
*BASED ON A POOLED STANDARD DEVIATION
Thought occurrence
Yes
No
Frequency
Math anxiety
Frequency
Math anxiety
T (& p) value
Effect size
(d)*
Nature of
thought
Making mistakes
109
(89.3%)
52.72
(16.51)
13
(10.7%)
43.00
(11.11)
2.06
(.04)
0.61
Time pressure
60
(49.2%)
56.03
(16.67)
62
(50.8%)
47.47
(14.82)
3.00
(.003)
0.54
Method of problem
solving
58
(47.5%)
53.29
(17.08)
64
(52.5%)
50.22
(15.48)
1.04
(.30)
0.19
What people might
think
48
(39.3%)
59.58
(16.53)
74
(60.7%)
46.55
(13.96)
4.68
(<.001)
0.87
Panicking
26
(21.3%)
65.08
(19.01)
96
(78.7%)
48.05
(13.40)
5.22
(<.001)
1.15
Previous math
experiences
49
(40.2%)
55.67
(16.97)
73
(59.8%)
49.00
(15.32)
2.26
(.03)
0.42
Physical changes
36
(29.5%)
60.97
(17.84)
86
(70.5%)
47.79
(13.92)
4.38
(<.001)
0.87
Non task-related
51
(41.8%)
56.43
(15.25)
71
(58.2%)
48.27
(16.22)
2.81
(.006)
0.52
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Hasher & Zacks, 1991) and provide the first data to suggest
that failure to inhibit intrusive thoughts is responsible for the
math anxiety-to-performance relationship, particularly on
problems involving the transitory maintenance of a carry term
(e.g. Faust et al., 1996).
In addition to assessing participants’ perceived impact of
intrusive thoughts, the CIQ (Freeston et al., 1991) permitted
identification of specific intrusive thoughts. Participants
reported a range of intrusive thoughts, with almost 90% having
thoughts about making mistakes and nearly half reporting
thoughts about time pressure and method of problem solving.
Further, participants with higher math anxiety scores were
more likely to endorse intrusive thoughts, particularly relating
to what people might think, panicking and physical changes.
The current study represents the first occasion in which
intrusive thoughts related to completing a math task have been
have examined using the CIQ (Freeston et al., 1991). However,
the precise nature of the relationships observed remains
unclear, in part due to the self-report approach taken. For
example, it is unclear whether thoughts about time pressure
were an antecedent to response time or whether they occurred
following response time. The present study is limited by the
extent to which math anxiety is manipulated. However,
experimental designs could be used to examine the importance
of particular thoughts in math anxious individuals by
manipulating them; for example regular reminders of a time
limit (c.f. Kellogg, Hopko & Ashcraft, 1999) or the use of a
flashing camera light to increase awareness that performance is
being observed. It would also be interesting to investigate the
relationship between math anxiety and arithmetic performance,
and the importance of intrusive thoughts about previous math
experiences, as previous research has emphasised the
importance of negative math experiences as antecedents to
math anxiety (Trujillo & Hadfield, 1999).
In conclusion, math anxiety was shown to be a significant
predictor of response time to both carry and no-carry problems.
In addition, math anxiety was related to a higher error rate on
problems involving a carry operation. Importantly, the extent to
which intrusive thoughts impeded calculation mediated this
relationship. These findings provide support for an inhibition
theory account of math anxiety effects on performance.
Intrusive thoughts should be the focus of future research that
investigates the relationship between math anxiety and
performance.
REFERENCES
[1] M. H. Ashcraft, (2002). Math anxiety: Personal, educational, and
cognitive consequences. Current Directions in Psychological Science,
11, 181–185. doi:10.1111/1467-8721.00196
[2] M. H. Ashcraft, M.W. Faust, (1994). Mathematics anxiety and mental
arithmetic performance: An exploratory investigation. Cognition and
Emotion, 8, 97–125. doi: 10.1080/02699939408408931
[3] M. H. Ashcraft, a. M. Moore, (2009). Mathematics anxiety and the
affective drop in performance. Journal of Psychoeducational
Assessment, 27, 197–205. doi:10.1177/0734282908330580
[4] S. L. Beilock, C. A. Kulp, L. E. Holt, T. H. Carr, (2004). More on the
fragility of performance: Choking under pressure in mathematical
problem solving. Journal of Experimental Psychology: General, 133,
584-600. doi: 10.1037/0096-3445.133.4.584
[5] J. Cohen, (1988). Statistical power analysis for the behavioral sciences
(2nd Edn.). Hillsdale, New Jersey: Lawrence Erlbaum Associates.
[6] S. L. Connelly, L. Hasher, R. T. Zacks, (1991). Age and reading: The
impact of distraction. Psychology and Aging, 6, 533–541.
[7] M. S. DeCaro, K. E. Rotar, M. S. Kendra, S. L. Beilock, (2010).
Diagnosing and alleviating the impact of performance pressure on
mathematical problem solving. The Quarterly Journal of Experimental
Psychology: Human Experimental Psychology, 63, 1619-1630. doi:
10.1080/17470210903474286
[8] M. W. Faust, M. H. Ashcraft, D. E. Fleck, (1996). Mathematics anxiety
effects in simple and complex addition. Mathematical Cognition, 2, 25–
62. doi: 10.1080/135467996387534
[9] M. H. Freeston, R. Ladouceur, H. Letarte, N. Thibodeau, F. Gagnon,
(1991). Cognitive intrusions in a non-clinical population. I. Response
style, subjective experience, and appraisal. Behaviour Research and
Therapy, 29, 585-597.
[10] Hasher, R. T. Zacks, (1988). Working memory, comprehension, and
aging: A review and a new view. The Psychology of Learning and
Motivation, 22, 193–225.
[11] R. Hembree, (1990). The nature, effects, and relief of mathematics
anxiety. Journal of Research for Mathematics Education, 21, 33-46.
[12] D. R. Hopko, M. H. Ashcraft, R. Gute, K. J. Ruggiero, C. Lewis,
(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
[13] D. R. Hopko, D. W. McNeil, P. J. Gleason, A. E. Rabalais, (2002). The
emotional Stroop paradigm: Performance as a function of stimulus
properties and self-reported mathematics anxiety. Cognitive Therapy
and Research, 26, 157-166. doi: 0147-5916/02/0400-0157/0
[14] J. Hunsley, (1987). Cognitive processes in mathematics anxiety and test
anxiety: The role of appraisals, internal dialogue, and attributions.
Journal of Educational Psychology, 79, 388-392.
[15] T. E. Hunt, D. Clark-Carter, D. Sheffield, (2011). The development and
part-validation of a U.K. scale for mathematics anxiety. Journal of
Psychoeducational Assessment, 29, 455-466. doi:
10.1177/0734282910392892
[16] W. J. Jones, Childers, T. L., & Jiang, Y. (2012). The shopping brain:
math anxiety modulates brain responses to buying decisions. Biological
Psychology, 89, 201–13. doi:10.1016/j.biopsycho.2011.10.011
[17] J. S. Kellogg, D. R. Hopko, M. H. Ashcraft, (1999). The effects of time
pressure on arithmetic performance. Journal of Anxiety Disorders, 13,
591–600. doi: 10.1016/S0887-6185(99)00025-0
[18] X. Ma, (1999). A meta-analysis of the relationship between anxiety
toward mathematics and achievement in mathematics. Journal for
Research in Mathematics Education, 30, 520-540.
[19] M. McMullan, Jones, R., & Lea, S. (2012). Math anxiety, self-efficacy,
and ability in British undergraduate nursing students. Research in
Nursing & Health, 35, 178–86. doi:10.1002/nur.21460
[20] E. Munoz, M. J. Sliwinski, J. M. Smyth, D. M. Almeida, H. a. King,
(2013). Intrusive thoughts mediate the association between neuroticism
and cognitive function. Personality and Individual Differences, 55, 898–
903. doi:10.1016/j.paid.2013.07.019
[21] R. Nixon, A. Menne, L. King, A. Steele, J. Barnes, H. Dognt, S. A. Ball,
H. Tyler, (2008). Metacognition, working memory, and thought
suppression in acute stress disorder. Australian Journal of Psychology,
60, 168-174. doi: 10.1080/00049530701867813
[22] F. C.Richardson, R. M. Suinn, (1972). The Mathematics Anxiety Rating
Scale. Journal of Counseling Psychology, 19, 551-554.
[23] I. G. Sarason, (1978). The test anxiety scale: Concept and research. In C.
D. Spielberger & I. G. Sarason (Eds.), Stress and anxiety (vol. 5, pp.
193-218). Washington, DC: Hemisphere.
[24] C. D. Spielberger, R. L. Gorsuch, R. Lushene, P. R. Vagg, G. A. Jacobs,
(1984). State-Trait Anxiety Inventory. Consulting Psychological Press,
Inc.
[25] K. M. Trujillo, O. D. Hadfield, (1999). Tracing the roots of mathematics
anxiety through in-depth interviews with preservice elementary teachers.
College Student Journal, 33, 219-232.
S C I
CI E NT I F I CS
UBLICATIONP
www.sci-pub.com
ISSN: 1339-1488, VOLUME 2, ISSUE 2, 2014
-- PSYCHOLOGY --
- 74 -
[26] Universities and Colleges Admissions Service. (2009). Retrieved from
http://www.ucas.ac.uk/about_us/stat_services/stats_online/
[27] S. S. Wu, M. Barth, H. Amin, V. Malcarne, V. Menon, (2012). Math
anxiety in second and third graders and its relation to mathematics
achievement. Frontiers in Psychology, 3, 162.
doi:10.3389/fpsyg.2012.00162
Journal of Education, Psychology and Social Sciences
S C I
CI E NT I F I CS
UBLICATIONP
www.sci-pub.com
-- PSYCHOLOGY --
- 75 -
