Predictor Variables for Statistical Anxiety

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Assessing whether Autism, Neuroticism and Procrastination
can be employed as Predictor Variables to Statistically Forecast
Statistical Anxiety
Student Name: Vishal Sharma
Student ID: B0040668

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B0040668 Vishal Sharma
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Abstract
The current study examined the feasibility of utilising Neuroticism, Procrastination and
Autistic traits as predictor variables to assess Statistical Anxiety. All four constructs were
measured via questionnaire using a sample of 75 participants, and multiple regression
analysis was employed. The results implied low to moderate correlations between the
predictor variables and the criterion variable, indicating that Neuroticism and Autism were
significantly related, and could be utilised to calculate an individual’s Statistical Anxiety.
However the predictive capability of Procrastination was deemed to be non-significant.
The paper concludes with a discussion of the findings implications and potential areas for
future research.
Introduction
Knowledge and understanding of statistics is a skill that permeates through numerous
aspects of life (Cellan-Jones, 2008; Devlin & Lorden, 2007) and is required for many
higher education subject areas (Chapman, 2010; Dancey & Reidy, 2002; Langdridge,
2004). Yet research suggests that many individuals experience feelings of anxiety and fear
when faced with statistical problems, termed statophobia (Pretorius & Norman, 1992);
which has been documented in students on social science courses such as psychology
(Lacasse & Chiocchio, 2005; Tremblay, Gardner & Heipel, 2000), this statistical anxiety
said to be experienced by as many as 80% of graduate students (Onwuegbuzie, 2004).
This is despite statistics being employed on courses as a means to better understand one’s
data, as opposed to an end in itself (Pretorius & Norman, 1992).
Statistical Anxiety has been defined as anxiety that occurs as a result of encountering
statistics in any form and at any level (Onwuegbuzie, DaRos, & Ryan, 1997; Walsh &
Ugumba-Agwunobi, 2002), which has the propensity to have debilitating effects on one’s

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academic performance (Lalonde & Gardner, 1993; Onwuegbuzie & Daley, 1999;
Onwuegbuzie & Wilson, 2003). Statistical Anxiety is situation-specific, i.e. the symptoms
only present themselves when the learning or application of statistics is experienced in a
formal setting (Onwuegbuzie et al., 1997; Zeidner, 1991). In fact Lazar (1990) suggested
that learning statistics is akin to learning a foreign language, as the anxiety appears to
induce a complex array of emotions, from mild discomfort to severe apprehension, fear
and worry (Onweugbuzie, et al., 1997). As a result of this debilitating effect on learning
and the increasing need for the application of statistical techniques, researchers have
focused on what factors may influence Statistical Anxiety, and whether an understanding
of these factors may lead to ways of reducing anxiety (Onwuegbuzie, Leech, Murtonen &
Tähtinen, 2010), providing students with the tools to confront their anxiety and not delay in
enrolling on statistics courses, or completing statistic-related tasks (Ellis & Knaus, 1977;
Onwuegbuzie, 2000). Over the years numerous traits have been linked to Statistical
Anxiety.
For instance Solomon & Rothblum (1984) noted that nearly one-quarter of college students
report problems with Procrastination on academic tasks such as writing papers, or
preparing for an exam, and concluded that Procrastination involves a complex interaction
of behavioural, cognitive, and effective components. Procrastination is defined as the
absence of “self-regulated performance and the behavioural tendency to postpone
behaviours which are necessary to reach a goal” (Morales, 2011). Onwuegbuzie (2004)
assessed academic procrastination and statistics anxiety amongst 135 graduate students in
south-east USA. The findings revealed that a high percentage of students, ranging from 62
to 86%, reported problems with procrastination on writing term papers, studying for
examinations, and keeping up-to-date with weekly reading assignments, with similar
findings observed in undergraduates in relation to mathematics courses (Akinsola, Tella &

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Tella, 2007). Further analysis suggested that academic procrastination resulted from a fear
of failure, and that task aversiveness was significantly related to the six components of
statistical anxiety as identified by Cruise et al. (1985, cited in Vigil-Colet, Lorenzo-Seva,
& Condon, 2008). However the study focused on American students therefore it is unclear
whether the results can be generalised to non-American populations. Additionally, the use
of the Procrastination Assessment Scale-Students (PASS; Solomon & Rothblum, 1984)
focuses solely on academic procrastination, and does not consider non-academic
procrastination. Thus looking at whether one’s general level of procrastination is related to
Statistical Anxiety may highlight an overarching personality trait that requires research.
In addition to the identification of Procrastination as a potential predictor variable, research
lately has been interested in the role that personality variables play in academic
performance. Past research has suggested that statistical anxiety is related to specific
measures of anxiety, including Neuroticism (Vigil-Colet, et al., 2008; Chamorro-Premuzic
& Furnham, 2003a). For instance, Chamorro-Premuzic & Furnham (2003b) looked to see
whether academic performance was related to personality using 247 British university
students. The results suggested that Neuroticism had significant negative correlations with
academic performance, i.e. greater levels of Neuroticism resulted in a decrease in academic
performance. A similar impairment in academic performance due to Neuroticism has been
observed in other studies (Chamorro-Premuzic & Furnham, 2003a; Duff, Boyle, Dunleavy
& Ferguson, 2004; Poropat, 2011). In contrast other researchers (Conrad, 2006; Hair &
Hampson, 2006) have failed to find a significant relationship between Neuroticism and
academic performance. However, these past studies have assessed an average measure of
academic performance, such as Grade-Point Average (GPA; Conrad, 2006; Duff et al.,
2004) and relied on self-reported information regarding the student’s SATs (Standard
Assessment Tests) and GPA scores. Additionally there is some variation in the personality

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measures employed, for instance Vigil-Colet, et al. (2008) adopted the Eysenck Personality
Questionnaire Revised whereas Conrad (2006) employed the NEO Five –Factor Inventory.
Thus it is difficult to make accurate comparisons between the studies, and therefore
additional research into the effect of Neuroticism on academic performance for a specific
subject is required; considering none of the prior studies have looked at the effects of
Neuroticism on a specific subject area, such as statistics.
Numerous past studies have looked at statistical anxiety in a linear fashion, i.e. identifying
what factors increase the likelihood of Statistical Anxiety in an individual; however there
is also the opposing view which seeks to identify whether there are specific traits in
individuals who do not have Statistical Anxiety. Baron-Cohen has spent numerous years
researching into Autism, and identifying potential relationships between Autistic
individuals and specific occupations and academic decisions, one such study looked at
whether mathematical talent is linked to Autism (Baron-Cohen, Wheelwright, Burtenshaw
& Hobson, 2007). The study looked at mathematics undergraduates, deemed strong at
systematizing which is the drive to analyse and/or build a system based on identifying
input-operation-output-rules (Baron-Cohen, 2002; Baron-Cohen et al., 2007), in
comparison to a control group and found that after controlling for sex and general
population sampling there was a three to seven-fold increase for autism spectrum
conditions amongst the mathematicians than the control group. Furthermore scientists, as
opposed to non-scientists, score higher on the Autism-Spectrum Quotient Scale, a self
report questionnaire devised to assess Autistic traits in individuals (Baron-Cohen,
Wheelwright, Skinner, Martin & Clubley, 2001), with mathematicians scoring highest
within the scientist group (Baron-Cohen, et al, 2001). The researchers concluded that there
was a link between Autism and maths-based subjects (Frith, 1991; James, 2010).
Therefore it was likely that individuals who score highly on a measure of Autistic traits are

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less likely to suffer from Statistics Anxiety, a sub-branch of the wider mathematics arena
(Jones, 2011; Olshausen, 2010), due to a preference for systematizing based subjects.
In sum, the aim of the study was to assess whether Neuroticism, Procrastination and
Autism can be employed as predictor variables to forecast the degree of Statistical Anxiety
an individual may experience. Based on this aim the following hypotheses are proposed:
Experimental Hypothesis: Neuroticism, Procrastination and Autism can be employed as
Predictor Variables to forecast an individual’s score on Statistical Anxiety.
Null Hypothesis: Neuroticism, Procrastination and Autism cannot be employed as
Predictor Variables for Statistical Anxiety.
Method:
Participants: Seventy-five participants who had studied statistics beyond G.C.S.E.
mathematics, therefore they had chosen to study statistics at a higher level, were recruited
using opportunity (convenience) sampling methods (Langdridge, 2004). The sample
consisted of 36 male (48%; mean (x¯
) age of 29.14, standard deviation (σ) of 11.90) and 39
female (52%; x¯
age of 28.82, σ of 12.00) participants, with overall ages ranging from 18 to
81, and an overall x¯
Design and Measures: For the study a within participant multiple regression design was
employed with Neuroticism, Procrastination and Autism conceptualised as predictor
variables, and Statistical Anxiety as the criterion variable. To assess these four variables
previously validated measures were utilised.
age of 28.97, and σ of 11.87 years (appendix 3a).
Neuroticism Measure: Neuroticism was assessed via the Big Five Inventory (BFI; John,
Naumann, & Soto, 2008). The complete questionnaire consists of 44 items assessing five
aspects of one’s personality, Extraversion, Agreeableness, Conscientiousness, Openness

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and Neuroticism; with participants indicating their level of agreement with each item using
a 7-point Likert scale ranging from “strongly disagree” to “strongly agree”. As the study
was looking solely at Neuroticism, the 8 items related to this were identified (John, 2009)
and employed for the questionnaire. To calculate one’s Neuroticism average score across
the 8 items is calculated resulting in a Neuroticism score ranging from 1 to 5. The BFI was
constructed as a short measure of personality in comparison to other longer measures such
as the NEO-PI-R (Costa & McCrae, 1992; Rammstedt & John, 2007). Over the years the
BFI has been administered on numerous occasions with results indicating moderate
reliability and structural validity (Srivastava, 2011; Worrell & Cross, 2004), with mean
alpha values ranging from 0.77 to 0.81, and test-retest correlations greater than 0.75
(Borroni, Marchione & Maffei, 2011).
Procrastination Measure: To assess one’s Procrastination the Tuckman Procrastination
Scale (TPS; Tuckman, 1991) was employed. This was originally a 35-item scale
consisting of a 4-point Likert scale, however a shortened (16-item) scale was also
developed by Tuckman using factor analysis with a reliability of 0.86 (Van Wyk, 2004) in
comparison to a reliability rating of 0.90 for the original scale (Tuckman, 1991), for the
study the 16-item scale was employed. Potential scores range from 16 to 64, with higher
scores indicating higher levels of Procrastination. The 16-item TPS has previously been
employed to assess level of Procrastination with results suggesting a high degree of
reliability (Akinsola, et al., 2007; Tuckman, 2005).
Autism Measure: The final predictor variable was Autism, and this was assessed using the
Autism Quotient Scale (AQ Scale; Baron-Cohen, et al., 2001). The original scale
consisted of 50-items; however it was felt that a 50-item scale would be too long for
participants to complete, in addition to the other measures. As such a basic analysis was
conducted using the results from Baron-Cohen, et al. (2001) to reduce the number of items.

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The original 50-item scale comprises of 10 questions assessing 5 different areas (social
skill, attention switching, attention to detail, communication, and imagination). As such
the analysis identified 4 items for each area to be employed in the questionnaire. This was
based on the highest average score per item across the three groups employed by Baron-
Cohen et al. (2001). This resulted in 20-item scale which was deemed more appropriate
for the purposes of the current study, with scores ranging from 0 to 20, with higher scores
indicating the individual possess a higher number of Autistic traits. Due to the creation of
a revised Autism scale the results section includes an analysis of the reduced AQ scale to
assess its reliability. The original AQ scale has been validated with clinical diagnosis as
being a reliable tool to assess how many Autistic traits individuals possess (Bishop, et al.,
2004; Woodbury-Smith, Robinson & Baron-Cohen, 2005).
Statistical Anxiety Measure: To assess the criterion variable the Statistics Anxiety Scale
(SAS; Pretorius & Norman, 1992) was employed. This measure consists of 10-items with
a 5-point Likert Scale with anxiety defined as the total score across the items, resulting in
anxiety levels ranging from 10 to 50. The SAS has been assessed for internal-consistency
reliability and test-retest reliability over a 3 month interval, with the scores being .90 and
.75, respectively (Pretorius & Norman, 1992; Vigil-Colet, et al., 2008).
Procedure: Prospective participants were asked if they had studied mathematics beyond
G.C.S.E., and if this was the case they were briefed and invited to complete a consent form
(appendix 1) which detailed their rights as participants, such as the right to withdraw
within 7 days of completing the questionnaire. Once consent was obtained participants
were asked to complete the questionnaire booklet (appendix 2) containing the four
measures detailed above, as well as providing responses to two demographic questions
(gender and age). All questionnaires were anonymous, and this anonymity was maintained
through the use of unique participant codes which were written on the questionnaire and

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the tear off slip returned to the participant. This allowed participants the right to withdraw,
whilst ensuring their anonymity was not compromised. The participants were also fully
briefed, debriefed, and provided with the contact information of the researchers should
they have any questions at a later date (appendix 1). The completed questionnaires were
collated and prepared for analysis.
Results:
Rescoring Responses and Descriptive Statistics: The first part of the analysis was to load
the data into a Microsoft Excel spreadsheet which had been configured to automatically
reverse questionnaire responses; employing Excel ensured consistency in data rescoring
amongst all researchers. Once all the data had been entered into Excel and the reversed
scores had been calculated, the relevant data was exported for further analysis to SPSS
version 19.0.0 (IBM, 2011). The initial analysis within SPSS was to calculate the
standardised Z-scores to identify any outliers in the data. The analysis highlighted an
outlier for the Neuroticism measure which was subsequently marked as an outlier (9999);
no other extreme scores were identified. The outlier may have been indicative of a highly
neurotic individual in comparison to the other participants; therefore retaining the score
would have resulted in potentially skewed results. The next step was to calculate the
descriptive statistics for the predictor and criterion variables (table 1; appendix 3b):
Additionally histograms, with the normal distribution curve (appendix 3c) were created to
assess the data for normal distribution. Initial assessment of the histograms and the
skewness and kurtosis values (table 1) suggested all the scores were reasonably normally
distributed. However an additional assessment of the skewness and kurtosis using the
Shapiro-Wilk Test of Normality (appendix 3d) suggested that the data for Procrastination
was not normally distributed (W(74)=0.958, p=0.015), and a review of the Normal Q-Q Plot

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for Procrastination indicated a small amount of snaking (skewness; appendix 3d).
According to Dancey & Reidy (2002) one of the assumptions of multiple regression
analysis is that the data is drawn from a normally distributed population; although they do
advise that slight skewness is acceptable. Therefore it was decided to proceed with the
multiple regression analysis with all three predictor variables.
Neuroticism SAS Procrastination Autism
Mean 2.63 28.80 36.29 7.41
Median 2.62 28.00 37.00 8.00
Mode 2.63 24.00 37.00 9.00
S.D. 0.59 9.07 9.12 3.52
Variance 0.35 82.22 83.43 12.35
Skewness -0.58 0.21 -0.08 -0.1
Std. Error of Skewness 0.28 0.28 0.28 0.28
Skewness/Std. Error of Skewness -2.08 0.75 -0.31 -0.34
Kurtosis 0.70 0.08 -0.85 0.04
Std. Error of Kurtosis 0.55 0.55 0.55 0.55
Kurtosis / Standard Error 1.27 0.15 -1.55 0.08
Table 1: Descriptive Statistics for the predictor and criterion variables
Multiple Regression Analysis: The first step was to test the data for multi-collinearity, the
output suggested that none of the predictor variables were highly (threshold of 0.8)
correlated with each other (table 2; appendix 3e), and furthermore assessment of the
scatter-plots for the predictor variables against the criterion variable indicate a linear
relationship (appendix 3e). Thus all the assumptions to conduct the multiple regression
analysis, using the enter method, had been met (Dancey & Reidy, 2002).
SAS Neuroticism Procrastination Autism
SAS 1.000 0.272 0.132 -0.281
Neuroticism 0.272 1.000 0.416 0.255
Procrastination 0.132 0.416 1.000 0.156
Autism -0.281 0.255 0.156 1.000
Table 2: Multi-Collinearity Statistics for the Predictor and Criterion Variables
The correlation between the criterion and predictor variables was R=0.456, with an
adjusted R2
of 0.174, indicating that 17.4% of the variance in Statistical Anxiety was

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forecasted by the predictor variables (appendix 3f), moreover the post-hoc Power
calculation suggested the study had an observed power of between 0.91 (Borenstein, 2010)
and 0.92 (Soper, 2011; appendix 3f). The Regression ANOVA table (appendix 3e) showed
that the amount of variation in Statistical Anxiety that could be forecasted by the predictor
variables was significant (F(3, 70)=6.109, p=0.001), indicating the variables were better than
chance at predicting Statistical Anxiety, therefore the null hypothesis could be rejected. As
the F-value was significant additional tests were conducted to assess the individual affect
of the predictor variables. The t-tests (appendix 3f) indicated that Neuroticism (t(73) =
2.921, p = 0.005, 95% C.I. between 1.691 and 8.974) and Autism (t(73) = 3.426, p = 0.001,
95% C.I. between -1.541 and -0.407) significantly predicted Statistical Anxiety. However,
the result for Procrastination was not significant (t(73)
The results indicate that for each one unit/σ increase in Neuroticism, Statistical Anxiety
would increase by 5.333units or 0.350σ, where all other values are held constant,
demonstrating a positive relationship. In contrast, for Autism a one unit/σ increase on the
AQ results in Statistical Anxiety falling by 0.974 units or 0.378σ, again where all other
values are held constant. Finally, for a one unit/σ increase in Procrastination, Statistical
Anxiety increases by 0.046 units or 0.045σ, where all other values are held constant. The
standardised regression coefficients suggest that Autism (0.378) was a better predictor of
Anxiety than the other predictor variables (Neuroticism: 0.350σ; Procrastination: 0.045σ).
Based on the analysis the following regression equation was defined:
=0.387, p=0.700, 95% C.I. between -
0.193 and 0.285). The fact that confidence interval values crossed the zero threshold
suggest a lack of confidence in Procrastination as a predictor variable.
Statistical Anxiety = 20.156 + (5.333*Neuroticism) + (0.046*Procrastination) –
(0.974*Autism)

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(SPSS output for the multiple regression analysis can be found in appendix 3).
Revised AQ Reliability Analysis: As mentioned previously, the original AQ scale was
revised to reduce the number of items from 50 to 20. Consequently additional analysis was
conducted on the split-half reliability of the revised AQ measure employed in the study.
The 20 items were divided into two groups (odds and evens), and the two groups were
tested for outliers and normality of data (table 3; appendix 4).
AQ Odds AQ Evens
Mean 3.60 3.81
Median 4.00 4.00
Mode 4.00 4.00
S.D. 2.24 1.75
Variance 5.00 3.07
Skewness 0.326 -0.217
Std. Error of Skewness 0.277 0.277
Skewness/Std. Error of Skewness 1.177 -0.783
Kurtosis 0.160 0.059
Std. Error of Kurtosis 0.548 0.548
Kurtosis / Standard Error 0.292 0.108
Table 3: Descriptive Statistics for the AQ Scale
Based on analysis of the descriptive and histograms (appendix 4) no outliers were
identified, however the Shapiro-Wilk analysis suggested a degree of skewing in both data
sets (W(75 =0.953, p=0.007 and W(75)=0.958, p=0.014), indicating the data was not
normally distributed. As such a split-half correlational analysis was conducted using
Spearman’s Rho. The results indicated a correlation of 0.502, and the Reliability
Coefficient (Ra) of 0.68, in contrast the Pearson’s r value resulted in a Ra of 0.71
(appendix 4). This suggests that the revised AQ scale has a high degree of internal
consistency. Next the Cronbach alpha value was calculated as 0.683 (~0.7) which
indicates that the revised AQ test is 68.3% reliable at measuring Autistic traits.
Unfortunately as specific details were not available from the original AQ scale study it was

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not possible to assess concurrent validity. Nevertheless the results suggest the revised AQ
scale was a reliable measure to assess for Autistic traits.
(SPSS output for the reliability analysis can be in appendix 4).
Discussion:
Past research has indicated that Procrastination, Neuroticism and Autism are related to
statistics or mathematics (Baron-Cohen et al., 2007; Onwuegbuzie, 2004; Vigil-Colet, et
al., 2008), as such the current study looked at assessing whether these three predictor
variables could be utilised to assess Statistical Anxiety amongst participants who have
studied statistics beyond G.C.S.E. level. The results indicated that both Neuroticism and
Autism are related to Statistical Anxiety to a significant extent, although in contrast to
prior research (Akinsola, et al., 2007; Onwuegbuzie, 2004) Procrastination was not found
to be significantly related to Statistical Anxiety.
With regards to Neuroticism the analysis indicated a moderate positive correlation with
Statistical Anxiety (0.272); which suggests that an increase in Neuroticism is met with a
slight increase in Statistical Anxiety. Prior research has found differing results when
assessing the role of Neuroticism on academia and focused primarily on average GPA as
opposed to researching specific subject areas (Conrad, 2006; Duff et al., 2004). The results
obtained suggest that Neuroticism does impact on Statistical Anxiety, and indicates that
potentially an individual’s feelings of neuroticism may vary between subjects.
It appears that no prior attempts have been made to assess the relationship between Autism
and Statistical Anxiety; however Baron-Cohen et al (2001 & 2007) did observe that
mathematicians presented with a greater number of Autistic traits, therefore it was
reasonable to suggest that individuals that scored highly on the AQ would present with
reduced Statistical Anxiety. The results obtained from the current research support this

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view, based on the negative association with Statistical Anxiety (-0.281), suggesting
individuals with higher AQ scores are less likely to suffer from Statistical Anxiety,
potentially due to a preference for systematising (Baron-Cohen et al., 2001 & 2007).
The final predictor variable employed in the study was Procrastination, which was not
significantly related to Statistical Anxiety, despite prior research indicating otherwise
(Akinsola, et al., 2007; Onwuegbuzie, 2004). Potential explanations for this could be
related to differences in the measure employed, for instance Solomon & Rothblum (1984)
employed the PASS to assess Procrastination which is specific to academic
procrastination; conversely the TPS, employed in the study, assesses generic
procrastination. Therefore, it could be that the level of procrastination displayed by an
individual varies depending on the specific task, for example an individual may be more
likely to procrastinate over a Statistics assignment than over an English assignment. This
view is corroborated as the SAS looks at statistical anxiety related to a course, rather than
generic Statistical Anxiety experienced as part of everyday life. An alternative explanation
for not finding significant results could be the data itself, as according to the Shapiro-Wilk
analysis, the Procrastination scores presented with a small amount of skewing. Given that
the 16-item TPS had previously been verified, the lack of finding significance may indicate
a limitation in the current study in terms of the sampling methods utilised.
One of the limitations of employing opportunity sampling is that it is not possible to assess
whether the sample is representative of the population (Langdridge, 2004). Furthermore,
focusing primarily on known associates there is a possibility the non-normal distribution
observed was the result of potential social desirability effects or demand characteristics, for
example some participants may have wanted to portray themselves in a specific way, either
to assist/hinder the experiment, or to avoid judgement from the researcher; the latter being
more likely due to a personal relationship with the individual. These weaknesses may be

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rectified in future research where a greater sample size is tested, and where alternative
sampling techniques are employed. Despite these weaknesses, the study does possess a
number of strengths. Firstly, the revised AQ scale, based on analysis of internal reliability,
does appear to measure Autistic traits in individuals. Furthermore, the current study added
to existing literature regarding the impact of Neuroticism, and opened a potential new area
of research involving the role of Autistic traits in one’s Statistical Anxiety.
The study does have some implications for future research, firstly, additional research is
required to assess whether a revised AQ scale can be created, and thus conceiving an even
shorter self-report questionnaire, which was the initial aim of Baron-Cohen et al., (2001),
and whether the results obtained regarding the relationship between Autism and Statistical
Anxiety can be replicated. Moreover additional research is warranted to assess whether
one’s general level of Procrastination and Neuroticism is perhaps situation specific, i.e. it
only appears in certain situations, as opposed to a general level present all the time.
Nowadays large numbers of students are required to take statistics modules as part of their
undergraduate degree, yet many students experience Statistical Anxiety, which in turn may
affect their academic performance (Zeidner, 1991). The current study assessed whether
Neuroticism, Procrastination and Autism can predict Statistical Anxiety, the results
indicated a significant relationship between Neuroticism and Autism with Statistical
Anxiety. However, in contrast to prior studies the relationship between Procrastination and
Statistical Anxiety was not deemed to be significant. Given the impact Statistical Anxiety
has on an individual, and knowledge obtained from past research, interventions designed to
attenuate the effects could be devised and these are likely to prove worthwhile and
desirable at enabling individuals to overcome their statistical anxiety.

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Appendix 1: Consent Form for Participants
Below is the consent form that was employed as part of the study. The unique participant
ID displayed on the slip is also written on the questionnaire, thus enabling identification of
a participant, without compromising their confidentiality.

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Appendix 2: Questionnaire provided to participants
Below is the complete questionnaire, including briefing and debriefing sections that were
utilised in the study:

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Thank you for agreeing to take part in the research study. The study is part of a Master’s
assessment for a module as such the results will only be made available to the researchers
and the module lecturers. The study involves completing the attached questionnaire about
individual traits. The questionnaire consists of five sections and should take approximately
20 minutes to complete. As well as questions about specific character traits there will also
be some general questions about you, such as your gender and age.
The questionnaire requires you to highlight the answer which most accurately represents
your own views and opinions. All completed questionnaires will be analysed to identify
general trends with regards to individual character traits, for example can one trait be used
to identify the likelihood that also you possess another trait.
Participant Rights:
By taking part in the questionnaire you have the following rights, please review these and
contact the researchers should you have any queries:
Confidentiality - No personal information will be requested during the questionnaire, as
such your responses will remain completely anonymous; meaning that it will not be
possible to link your questionnaire responses directly to you. In addition, none of the
questions are mandatory. If you feel uncomfortable with any question please leave it blank
and continue with the questionnaire.
Consent – Please ensure you have signed the consent form prior to completing the
questionnaire. If you have any questions regarding the study please speak to one of the
researchers. By signing the consent form and completing the questionnaire you are
agreeing for your responses and any findings from analysis to be utilised in the final report.
Withdraw – No personally identifiable information is captured as part of the study, despite
this if you feel you wish to withdraw please contact one of the researchers within seven
days of completing the questionnaire. If you decide to withdraw, your questionnaire will
be safely destroyed and any results obtained will be excluded from the final report for the
module assessment.
Please note: if you have any questions or concerns regarding any aspect of this research
study and/or your participation please contact one of the Researchers (see below).

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Researchers:
Saiqa Akhtar (a6028257@my.shu.ac.uk) Amy Campbell (b0046278@my.shu.ac.uk)
Kayleigh Mike (ksmike@my.shu.ac.uk) Anuradha Sharma (b0049018@my.shu.ac.uk)
Vishal Sharma (b0040668@my.shu.ac.uk)
Section 1: Please answer each question by placing a tick in the box which applies to you
and/or writing in the appropriate spaces.
1.1: Are you male or female? Please tick one box:
 Male  Female
1.2: What is your age?
______________ years old
Section 2: Please read the statements below and circle the response which accurately
describes you.
2.1: I am someone who is depressed, blue
Strongly disagree 1 2 3 4 5 Strongly agree
2.2: I am someone who is relaxed and handles stress well
2.3: I am someone who can be tense.
2.4: I am someone worries a lot.
2.5: I am someone who is emotionally stable, not easily upset.

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2.6: I am someone who can be moody.
2.7: I am someone who remains calm in tense situations.
2.8: I am someone who gets nervous easily.
describes you.
3.1: It wouldn't bother me at all to take more statistics courses.
3.2: I have usually been at ease during tasks involving statistics.
3.3: I have usually been at ease during my statistics courses.
3.4: I usually don't worry about my ability to solve statistical problems.
3.5: I almost never get uptight whilst taking statistics exams.
3.6: I get really uptight during statistics exams
3.7: I get a sinking feeling when I think about tackling difficult statistical problems.

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3.8: My mind goes blank and I am unable to think clearly when conducting statistical
analyses.
3.9: Statistical analyses make me feel uncomfortable and nervous.
3.10 Statistical analyses make me feel uneasy and confused.
describes you.
4.1: I needlessly delay finishing jobs, even though they are important
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.2: I postpone starting in on things I don't like to do.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.3: When I have a deadline, I wait until the last minute.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.4: I delay making tough decisions.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.5: I keep putting off improving my work habits.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.6: I manage to find an excuse for not doing something.
That’s me for
sure
1 2 3 4
That’s not me for
sure

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4.7: I put all the necessary time into even boring tasks, like studying.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.8: I am an incurable time waster.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.9: I am a time waster now but I can't seem to do anything about it.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.10: When something’s too tough to tackle, I believe in postponing it.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.11: I promise myself I’ll do something and then drag my feet
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.12: Whenever I make a plan of action, I follow it.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.13: Even though I hate myself if I don’t get started, it doesn’t get me moving
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.14: I always finish important jobs with time to spare.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.15: I get stuck in neutral even though I know how important it is to get started.
That’s me for
sure
1 2 3 4
That’s not me for
sure
4.16: Putting something off until tomorrow is not the way I do it.
That’s me for
sure
1 2 3 4
That’s not me for
sure

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describes you.
5.1: I prefer to do things with others rather than on my own.
Definitely Agree 1 2 3 4 Definitely Disagree
5.2: I frequently get so strongly absorbed in one thing that I lose sight of other things.
5.3: I often notice small sounds when others do not.
5.4: I find social situations easy.
5.5: I tend to notice details that others do not.
5.6: I find making up stories easy.
5.7: I tend to have very strong interests, which I get upset about if I can’t pursue.
5.8: I enjoy social chit-chat.
5.9: I find it hard to make new friends.
5.10: I notice patterns in things all the time.
5.11: I frequently find that I don’t know how to keep a conversation going.

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5.12: I don’t usually notice small changes in a situation or a person’s appearance.
5.13: I am good at social chit-chat.
5.14: People often tell me that I keep going on and on about the same thing.
5.15: I like to collect information about categories of things (e.g. types of cars, birds,
trains, plants etc.)
5.16: I find it difficult to imagine what it would be like to be someone else.
5.17: I like to plan any activities I participate in carefully.
5.18: I find it difficult to work out people’s intentions.
5.19: New situations make me anxious.
5.20: I find it very easy to play games with children that involve pretending.
End of Questionnaire
Thank you for your participation

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Debrief Sheet
Thank you for taking part in the study.
The purpose of which was to investigate feelings of anxiety towards using statistics as part
of a psychological research methods module assessment. The study is important as
statistical anxiety is experienced by many people however there is little research in the
area. Our intention is to help develop the literature and research into this topic for future
explanations and solutions to minimising the effects of statistical anxiety in individuals. In
this study we asked individuals to complete a questionnaire which consisted of 5 sections
investigating level of statistical anxiety, procrastination, and specific personality traits.
Feedback:
Do you have any questions about this study?
When you were doing the study what did you think the study was about?
Was there any part of the study that was difficult?
What would you change about the study?
The researchers are available to contact should you have any further questions regarding
the study or if you would like to withdraw your data from the study within 7 days
following this debrief. Your identity will remain confidential and the data you provided
will remain anonymous, therefore there are no direct links between you and your
questionnaire responses.
Again, thank you for your participation in our research.
Researchers:
Saiqa Akthar (a6028257@my.shu.ac.uk)
Amy Campbell (b0046278@my.shu.ac.uk)
Kayleigh Mike (ksmike@my.shu.ac.uk)
Anuradha Sharma (b0049018@my.shu.ac.uk)
Vishal Sharma (b0040668@my.shu.ac.uk)

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Appendix 3a: Descriptive Statistics for the Participants:
The above table displays descriptive regarding the participants’ age split by gender.
The above table provides an overall indication of the participants’ age, regardless of
gender.

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Appendix 3b: Descriptive Statistics for the Predictor Variables (Neuroticism,
Procrastination and Autism) and the Criterion Variable (Statistical Anxiety):
The above table provides details of the measures of average scores for each of the predictor
variables (Neuroticism, Procrastination, and Autism) and the criterion variable (Statistical
Anxiety). As an outlier was detected in the Neuroticism data the number of participants
for Neuroticism is reduced to 74 participants in comparison to the 75 participants for the
other measures.

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Appendix 3c: Histograms for the Predictor and Criterion Variables with the Normal
Distribution Curve:

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Appendix 3d: The Shapiro-Wilk Test of Normality and the Normal Q-Q Plots:
The above Shapiro-Wilk test indicates that the data for Procrastination is not likely to be
normally distributed, and indication of the skewness in the data can be observed in the
Normal Q-Q Plot below.

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Appendix 3e: Multi-Collinearity Statistics and Scatter-Plots for the Predictor and
Criterion Variables
The test for Multi-Collinearity between the data suggests that none of the data obtained is
highly correlated (threshold of 0.8), indicating the questionnaires are highly unlikely to be
measuring the same construct.

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Appendix 3f: Multiple Regression Analysis Results
The Model Summary table indicated that the relationship between the predictor and
criterion variables has a correlation of 0.456, with an adjusted R squared value of 0.174.
This suggests that 17.4% of the variance between the statistical anxiety scores can be
explained by the predictor variables employed in the study, and that over 82% of the
variance is likely to be due to other factors.
The ANOVA table indicates an F value of 6.109, with an associated probability of 0.001,
suggesting the multiple correlation value (0.456) is significantly different from zero.
Due to the significance of the ANOVA, further analysis can be conducted. The above
Coefficients table suggests that only Neuroticism (N) and Autism (A) can be employed to
significantly predict an individual’s Statistical Anxiety (SA), in contrast the results for
Procrastination (P) were not significant. A 1σ increase in N results in 0.350σ increase in
SA, whereas a 1σ increase in P leads to a 0.045σ increase in SA. In contrast, a 1σ increase

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in A results in a 0.378σ decrease in SA. The above table provides the following regression
equation to calculate one’s SA, based on the three predictor variables employed:
SA = 20.156 + 5.333N + 0.046P – 0.974A
Post-Hoc Power Calculation (Soper, 2011):
Post-Hoc Power Calculation (Borenstein, 2010):
A post-hoc assessment of the study’s power was calculated using two alternative sources
(Borenstein, 2010; Soper, 2011) indicating the study had an overall power of between 0.91
and 0.92. The difference in the Power calculations is likely to be a limitation in the Power
and Precision 4 software (Borenstein, 2010) which only allows for the R squared value to
be entered up to 2 decimal places.

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Appendix 4: SPSS results for Split-Half Reliability and Cronbach’s Alpha for the
Revised AQ Scale
Descriptive Statistics for the two halves of the AQ:
Histograms for the two halves of the AQ:

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Shapiro-Wilk Test of Normality:
The above Shapiro-Wilk test for Normality indicate that both sets of data are not normally
distributed, and the Normal Q-Q plots below suggest a small degree of skewness or
snaking in the data obtained. Based on this the Spearman’s Rho was utilised to assess the
correlation between the two halves.

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Split-Half Correlational Analysis for Autism:
Spearman’s Rho for the Split-Half Reliability Assessment:
Reliability Coefficient (Ra) =
(2 x 0.502)
(1+0.502)
=
1.004
1.502
= 0.668442 ~ 0.67

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Pearson’s r for the Split-Half Reliability Assessment:
Reliability Coefficient (Ra) =
(2 x 0.546)
(1+0.546)
=
1.092
1.546
= 0.706339 ~ 0.71
Review of the correlations from the Spearman’s Rho and Pearson’s PMCC suggest that the
two sets of data are highly correlated, and rounding the data up to one decimal place
provides a correlation of 0.7, which suggests a strong correlation indicating the reduced
AQ measure has a high degree of internal consistency.
Results of the Cronbach Alpha Calculation:
Furthermore, analysis of the data obtained as part of the reduced AQ using Cronbach
Alpha indicates a high correlation, approximately 0.7, which again suggests the reduced
AQ scale is a reliable measure for Autistic traits.

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