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 ([email protected]) Amy Campbell ([email protected])

Kayleigh Mike ([email protected]) Anuradha Sharma ([email protected])

Vishal Sharma ([email protected])

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.


mailto:[email protected]�





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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.



4.4: I delay making tough decisions.



4.5: I keep putting off improving my work habits.



4.6: I manage to find an excuse for not doing something.



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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.



4.9: I am a time waster now but I can't seem to do anything about it.



4.10: When something’s too tough to tackle, I believe in postponing it.



4.11: I promise myself I’ll do something and then drag my feet



4.12: Whenever I make a plan of action, I follow it.



4.13: Even though I hate myself if I don’t get started, it doesn’t get me moving



4.14: I always finish important jobs with time to spare.



4.15: I get stuck in neutral even though I know how important it is to get started.



4.16: Putting something off until tomorrow is not the way I do it.



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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 ([email protected])

Amy Campbell ([email protected])

Kayleigh Mike ([email protected])

Anuradha Sharma ([email protected])

Vishal Sharma ([email protected])






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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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The above histograms display the dispersion of the scores for each of the measures.

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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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Normal Q-Q Plots for Autism_Odds and Autism_Evens:

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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)

= 1.0041.502

= 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)

= 1.0921.546

= 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.

Documents

Predictor Variables for Statistical Anxiety