Int. J. Management in Education, Vol. 12, No. 2, 2018
Does statistics anxiety affect students’
performance in higher education? The
role of students’ commitment, self-concept
and adaptability
Arsalan Najmi*, Syed Ali Raza and
Wasim Qazi
Department of Management Sciences,
IQRA University,
Karachi 75300, Pakistan
Email: arsalan-najmi@hotmail.com
Email: syed_aliraza@hotmail.com
Email: whqazi@gmail.com
*Corresponding author
Abstract: This study investigates the role of students’ commitment, selfconcept and adaptability on statistics anxiety and performance in higher
education. Data was collected from 320 students enrolled in a business school
in a Pakistan-based university by a survey questionnaire. After exploratory
and confirmatory factor analysis, results of structural equation modelling
revealed that though students’ commitment, self-concept and adaptability
have a negative relationship with statistics anxiety, but the presence of the
said attitudes mitigates the significance of statistics anxiety on students’
performance. The study concluded that the presence of affirmative attitudes
of students can minimise the significance of statistics anxiety on students’
performance. Moreover, practical implications of the findings are also
discussed.
Keywords: higher education; commitment; self-concept; adaptability; statistics
anxiety; students’ performance.
Reference to this paper should be made as follows: Najmi, A., Raza, S.A. and
Qazi, W. (2018) ‘Does statistics anxiety affect students’ performance in higher
education? The role of students’ commitment, self-concept and adaptability’,
Int. J. Management in Education, Vol. 12, No. 2, pp.95–113.
Biographical notes: Arsalan Najmi is associated with Iqra University as a
Lecturer in Department of Management Sciences. He holds Masters in
Business Administration from Iqra University. His current research is focused
on issues related to higher education and contemporary supply chain settings
and management.
Syed Ali Raza has been associated with Iqra University as Director Academics.
He holds PhD in Finance from Northern University of Malaysia, Malaysia and
also has a Certificate of Management Accounting from Chartered Institute
of Management Accountant, UK. His areas of interest include quantitative
methods in research, financial economics, energy economics, tourism
economics and corporate finance. He has many international research
publications in well reputed impact factor journals like Tourism Management,
Energy Policy, Economic Modelling, Social Indicators Research, etc.
Copyright © 2018 Inderscience Enterprises Ltd.
95
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A. Najmi, S.A. Raza and W. Qazi
Wasim Qazi is a Vice Chancellor of Iqra University. He received his post
doctorates from the Australian Catholic University (Australia) and Eastern
Kentucky University (EKU) and was an International Visiting Scholar at the
Western Michigan University (USA). He earned a PhD in Educational
Administration from Hamdard University Karachi. As an expert in education
policy, management and research, he has numerous international publications
to his credit. He has presented in a range of global forums, transnational
conferences, and peer reviewed academic papers in prestigious journals. His
scholastic interests comprise gender-specific educational interventions, school
management committees, instructional, learning and cognitive sciences.
1
Introduction
Anxiety in education had always shown a clear and discrete results on learning and
education. For instance, anxiety was considered to be a significant cognitive factor that
affects learners’ learning behaviour, performance, attention and information retrieval
(Schunk et al., 2008; Bembenutty, 2008; Bas, 2010). Among the other type of education
anxieties; hurdles, problems and situations in statistics are considered as intense stress
producing phenomena among students (Hsu et al., 2009; Sesé et al., 2015).
Quantitative courses are a mandatory part of the academic curriculum of the
graduates by the business schools worldwide (Yousef, 2013), whereas business statistics
are an important studied subject in the Business Degree Programs worldwide (Nguyen
et al., 2014). In addition to this, in the corporate world, employers valued business
graduates if they possessed problem solving numerical skills (Quek, 2005), particularly if
they had statistical analysis skills (Wellman, 2010). While preparing graduates to get
employed in the professional world, many business institutions have included proper
modules of statistical analysis for their nurturing. On the contrary, Business statistics are
a subject that was perceived by the business graduates as difficult, resulting in a high
level of anxiety that subsequently showed higher failure rates in statistical models of the
business universities (Onwuegbuzie, 2000; Williams et al., 2008).
Most of the students consider quantitative subjects as a hurdle and comparatively
difficult to pass (Blaylock and Hollandsworth, 2008). Since students under business
studies come from the different educational backgrounds, some of them have studied
statistics earlier, therefore, students have shown negative experiences while studying
quantitative courses particularly statistics (Onwuegbuzie, 2003; Onwuegbuzie and
Wilson, 2003). In addition to this, graduates considered studying statistics just like
studying any foreign language to which they are not familiar. That’s why graduates have
shown a high level of concerns and worries for the enrolment in any type of statistics
course (Lazar, 1990; Lalonde and Gardner, 1993).
The performance of students in their respective academic programs is influenced by
many factors. Researchers have explored several diverse factors in academic disciplines
among various education levels of the students having different demographic profiles
(Yousef, 2013; Law and Breznik, 2017). Yousef (2012) empirically identified that
English language & means of communication, teaching style and ways of assessments
are the important factors that affect the academic performance of the students in
quantitative courses in UAE business graduate programs. In addition to this, Zimmer and
Fuller (1996) identified that the attitude, computer experience and anxiety have
Does statistics anxiety affect students’ performance in higher education?
97
significant influence over the students’ performance. Montcalm (1999) also argued that
the factors which are affecting students’ performance should be closely monitored as
such factors, not only affect their grades, but they also significantly affect the learning
process of the students. Since this kind of learning is not only useful in the close
environment of the classrooms, but it also compliments their future analytical thinking.
As per Onwuegbuzie and Wilson (2003), more than 80% of the students of sociology,
psychology and education have shown an anxiety towards statistics. They have shown
their least interest towards statistics (Ruggeri et al., 2008). Such least interest in statistics,
resulting in willingly delay of the assignments, difficulties in learning, deferring taking
statistics courses, lower scores in exams which consequently increase the statistics
anxiety (Macher et al., 2012).
Statistical anxiety can be defined as the difficulties, tenseness and nervousness that an
individual encounter while handling, solving, analysing and interpreting any type of
statistical data (Macher et al., 2012). As such, individuals always go through the similar
kind of anxiety while handling such statistical problems, therefore, such anxiety is
described as a habitual kind of anxiety (Onwuegbuzie, 2004). Cruise et al. (1985)
explored six dimensions to measure the statistical anxiety. These are: understanding the
application of the statistics, fear of asking help, nervousness towards test and statistics
exams, afraid of teachers, fear while computing and solving statistical problems and
difficulties in interpretation of the statistical data.
Researchers are reporting a negative relationship between the statistical anxiety and
the students’ performances (Onwuegbuzie and Wilson, 2003; Onwuegbuzie, 2004;
Macher et al., 2012), and negative attitudes towards statistics (Maschi et al., 2007; Lazar,
1990). In addition to this, researchers are not only in agreement while considering
statistical anxiety as a significant predictor of the performance of the students (Lalonde
and Gardner, 1993; Onwuegbuzie and Seaman, 1995) but researchers also reported
a causal relationship between the students’ performance and the statistical anxiety
(Onwuegbuzie, 2000).
Researchers have also reported that students having a high understanding of the
concepts of the mathematics in their prior academics and higher level of interest have
lower statistical anxiety and higher achievement in their current academics (Macher
et al., 2012). In addition to this, gender is the most investigated demographic factor with
respect to statistical anxiety and it was reported that females have a high level of
statistical anxiety as compared to males (Rodarte-Luna and Sherry, 2008).
On one hand, students willingly postponed the selection of the statistics course during
their academics, and when they have admitted in any of the statistics course, they started
delaying their assignments, meeting the deadlines, preparing for the examinations which
ultimately deteriorate their learning and the academic performance (Onwuegbuzie, 2004).
On the other hand, such deterioration and statistics anxiety affects the students’ skills for
reading and understanding research articles, interpreting and applying the findings from
such articles (Onwuegbuzie, 1997).
According to Higher Education Commission of Pakistan, quantitative courses which
includes Business Mathematics, Management Sciences, Basic and Advanced Statistics
and others are included in academic curricula in order to develop and increase the
analytical skills, which compliments the decision making skills of students1. Since such
courses are a part of the curriculum, that’s why students don’t just have to study them,
but they have to get through them as well.
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A. Najmi, S.A. Raza and W. Qazi
As the learning of the quantitative course, particularly statistics is of significant
importance for the business graduates (Yousef, 2013), and the available literature
established the negative relationship between the statistical anxiety and the academic
performance of the students in different disciplines (Onwuegbuzie and Wilson, 2003;
Onwuegbuzie, 2004), this study aims to identify that whether the Pakistani business
graduates are encountered by the statistical anxiety. The literature is silent with respect to
any study that have reported any sort of relationships among the Pakistani business
graduates while handling and countering the statistics course in the curricula. Therefore,
this study addresses the following research questions:
Q1 To identify that whether Pakistani business graduates encounters statistical anxiety?
Q2 To what extent statistical anxiety significantly affects the academic performance of
the business graduates in Pakistan?
The remaining part of this paper is organised in a way that Section 2 indicates review of
the related literature, followed by Section 3 which is research design and methodology,
Section 4 estimates and results, finally Section 5 which includes conclusion and
managerial implication respectively.
2
Review of related literature
Students of business, behavioural sciences, psychology and social sciences have been
reporting a specific kind of anxiety while preparing for tests and performing, which is
known as statistics anxiety (Mji and Onwuegbuzie, 2004). Since learning of statistics
plays an important role not only in academic but also in the professional life of the
graduates, therefore, statistics anxiety creates a significant problem that needs to be
addressed (Papousek et al., 2012). The statistics anxiety is mostly measured by the
Statistical Anxiety Rating Scale (STARS) developed by Cruise and Wilkins (1980) and
then it was widely used in literature in the context of both developed and developing
economies (Baloğlu, 2002, 2003; Cruise et al., 1985; Hanna et al., 2008; Keeley et al.,
2008; Mji and Onwuegbuzie, 2004; Watson et al., 2003).
Studies reported that students who were having high statistics anxiety have shown
below average performances, delaying in selecting statistics related courses, dropping out
during the courses, and meeting hardships while doing and completing the final thesis or
any sort of research work (Macher et al., 2012; Onwuegbuzie, 2004; Rodarte-Luna and
Sherry, 2008).
Lavasani et al. (2011) empirically investigated the impact of achieving goals,
motivation towards academics and strategies for learning statistics on statistics anxiety on
the sample comprised of 345 under-graduates from the Educational Sciences and
Psychology departments of Tehran City of Iran. Results of path analysis concluded that
motivation towards academics have a direct influence on statistical anxiety whereas the
other two variables have indirect but a significant impact on the statistics anxiety.
Hanna and Dempster (2009) examined the impact of the statistics anxiety and the
attitudes towards statistics on the predicated and actual marks obtained in a statistics
course test. Data was collected with the help of STARS from the 1st year Psychological
students of a UK based University. By the help of Regression modelling, the results
revealed that self-concept and fear of asking help explain 37% of the total variance in the
predicted scores. On the other hand, when actual scores were taken as dependent
Does statistics anxiety affect students’ performance in higher education?
99
variable, then worth of statistics and anxiety during interpretation explained 20% of the
variation. The results confirmed the previous similar studies which showed a negative
relationship between the statistics anxiety and the academic performance. It was further
recommended that though students encountered the statistics anxiety, but since it has a
relatively less impact on actual performance, therefore it means that students were not
aware with their competency towards statistics. Therefore, the proper action is required,
so that students can easily tackle such kind of anxiety.
Onwuegbuzie (2000) investigated the relationship between statistics anxiety
and students’ self-perceptions. Data was collected from 146 students of a US-based
university enrolled in a research methodology course in four different education
disciplines. All of them were taught by the same instructor and Canonical Correlation
was applied to the data. The study revealed that students having lessor levels of perceived
competency and ability have greater levels of statistics anxiety. Furthermore, it was also
reported that students having high levels of negative perceptions are less likely to learn
statistics concepts and perceived other students more proficient compares to them.
Therefore, such negative perceptions need to be mitigated.
Lalayants (2012) investigated the attitude of the students towards statistics,
characteristics of the students who have shown more negative attitudes towards statistics,
identifying the most preferred method for teaching statistics, and the strategies to
overcome such kind of negative attitudes and anxiety. Data was collected from 195
graduates of social work department and descriptive and inferential statistics was applied
to the data. The results revealed that the factors which contribute to the statistical anxiety
are the fear of maths, non-relevancy of statistics to the social work students, instructor
and the classroom atmosphere. Though no significant difference was found in the
attitudes of the respondents on the basis of gender, age and the ethnic background, but
the study suggested the instructor should be more friendly, helpful, humorous and must
set the pace of the course slow. It was further suggested that the teaching environment
needs to be improved by incorporating more practical relevance of the statistics, friendly
atmosphere, and should make typical concepts of the statistics easier for the students.
Nguyen et al. (2014) empirically examined the academic achievement and attitude &
perceptions of the students with respect to the statistics and learning environment based
on IT. Data was collected through the survey based questionnaire from the 453 business
graduates of UK based university. Factor analysis was employed to validate the
questionnaire and Structural Equation Modelling was applied to the data. The study
reported a direct relationship of students’ attitude with respect to statistics while anxiety
has a negative, and an indirect relationship of learning environment based on IT with the
academic achievement. The study further suggests that interactive and friendly teaching
environment can be useful in order to mitigate the statistics anxiety. In addition to this,
the practical relevance of the statistics needs to be properly communicated to the students
so that they not only enjoy the time during learning but they can easily relate the practical
aspects of the statistics.
Onwuegbuzie (2004) argued that students encountering statistics anxiety willingly
procrastinate their activities pertaining to the statistics course. Data was collected
from 135 students from a US-based university on which the Canonical Correlation
was applied. The study revealed that fear of failure and the task procrastination was
significantly related interpretation anxiety, test & class anxiety and fear of asking for
help anxiety. It was recommended that in order to mitigate the statistics anxiety, course
instructors must break their projects into smaller parts whereas regular formal and
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A. Najmi, S.A. Raza and W. Qazi
informal evaluation was recommended. Furthermore, psychological counselling was also
advised when students willingly procrastinate after meeting statistics anxiety.
Rodarte-Luna and Sherry (2008) explored the gender differences in the relationship
between statistics anxiety and learning strategies. Survey participants included 323
students of Southwestern University US enrolled in a statistics course. Discriminant
Analysis and Canonical Correlation were applied, which revealed the difference in
gender as statistically significant. It was found that men have shown a significant positive
relationship between fear of asking, interpretation and test and class anxiety and
procrastination whereas females were found to have a negative relationship between the
aforementioned variables.
Liau et al. (2015) empirically investigated that to what extent the teaching
methodologies can significantly improve the students’ attitude towards the statistics. Data
was collected from 103 Psychology students from a Malaysian university and factor
analysis and regression analysis was applied to the responses. Teaching methodologies
were identified with the help of factor analysis. These were cooperative learning,
availability of course facilitator, etc. In regression analysis, teaching methodologies were
used as the criterion, whereas attitudes towards statistics were taken as predictors. The
analysis revealed that all the identified teaching methodologies will be helpful in
improving the positive attitudes towards statistics significantly and the same strategies
were recommended to the academic experts.
Slootmaeckers et al. (2014) empirically investigated the attitudes of political sciences
students towards statistics, particularly statistics anxiety while learning and remembering
the analysis and techniques. Data was collected from 157 students from a bachelors and
masters’ program of a university based in Belgium and regression analysis was applied to
the data. The study revealed students having high interest in statistics, have less anxiety
and difficulty while handling statistics and such students can also easily memorise and
retain the statistical techniques and analysis. It was also found that students can also
retain the statistical knowledge and skills by including the quantitative analysis and
techniques in descriptive and theoretical based courses. The same was recommended that
by normalising the statistical techniques across the course curricula, students can learn
and retain the knowledge and skills of the statistical analysis and techniques.
Macher et al. (2012) empirically explored the relationship between learning
strategies, antecedents of statistic anxiety and academic performance. Data was collected
from 147 students enrolled in a statistics course in Psychology in Austria on which the
Structural Equation model (SEM) was applied. The study revealed that, students having
high mathematics self-concept and interest in statistics pertain to have less statistics
anxiety. In addition to this, students having high statistics anxiety resulted in an increase
in procrastination, i.e. willingly delaying in course selection and preparing for exams;
decrease in learning strategies that ultimately weakens the academic performance.
Furthermore, it was recommended to mitigate the procrastination by counselling and
providing regular feedback to the students before exams whereas emphasising the
importance of statistics by relating it to the practical world can also make a significant
difference.
Adegboye and Jawid (2016) examined the students’ attitudes and their perceptions
towards statistics in the undergraduate students of Afghanistan. Data was collected from
209 students from different departments and disciplines in which Factor Analysis and
Regression Analysis were applied. The results revealed that male were having high
statistics anxiety, but shows more positive attitudes towards Statistics than Female.
Does statistics anxiety affect students’ performance in higher education?
101
Furthermore, females were found to have more anxiety in Fear for Asking for help
whereas less anxiety in computational anxiety. In addition to this, students who have
already studied statistics have reported less anxiety in comparison with the students
studying for the very first time.
Onwuegbuzie et al. (2010), concluded that since the previous literature revealed a
negative relationship between Statistics anxiety and the academic performance and the
attitude of the students towards the statistics, statistics courses should be eliminated from
the academic curricula and the research courses related to mixed methodologies which
includes qualitative and quantitative approaches that can complement the students in their
research work should be introduced. By doing that, the academic experts not only
mitigate the Statistics anxiety, but they can also increase and nurture the interest of
statistics within the students.
In the light of above literature, it was evident that statistics anxiety has a negative
relationship with students’ attitudes towards statistics (Adegboye and Jawid, 2016;
Slootmaeckers et al., 2014) and negative relationship with students’ academic performance
(Onwuegbuzie et al., 2010; Macher et al., 2012). That’s further motivated the researchers
to explain these phenomena in the scenario of a developing country.
3
Methodology
3.1 Design
The study was a quantitative study in which survey was conducted in order to explain
the causal relationships between the statistics anxiety, students’ attitudes and their
performance as stated in the earlier literature (Onwuegbuzie, 2000). Since it was a
survey, so an instrument was made so that the responses can be collected from the
respondents.
3.2 Survey instrument
The survey questionnaire was divided into two parts. The first part is comprised of the
items which were adopted to measure the statistics anxiety and students’ attitude. The
second part of the instrument was designed to measure the demographic profile of the
respondents. The details of the measures are mentioned below.
3.2.1 Statistics anxiety
Statistics anxiety was measured by three variables of the STARS instrument which was
originally developed by Cruise et al. (1985). The said three variables are fear of asking
anxiety, interpretation anxiety and test and class anxiety. For the current study, the
revised items were used which was revised by Hanna et al. (2008). Anxieties of, Fear of
asking was measured by four items, interpretation was measured by eleven items and
Test & Class was measured by eight items. The items were measured on a Five point
Likert Scale having 1 for No Anxiety and 5 for Strong Anxiety.
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3.2.2 Students’ attitudes
Students’ attitudes were measured by three variables, namely Commitment (Meyer et al.,
1993), Academic Self-Concept (Reynolds, 1988) and Personal Adaptability (Hartline and
Ferrell, 1996; Yuksel, 2006). These three attitudes were measured by five items each on
Five Point Likert Scale having 1 for Strongly Agree to 5 for Strongly Disagree.
3.2.3 Students’ performance
Students’ performance was measured by the course grades of the enrolled students. The
grades or the marks scored by the students have been used as the measure for students’
performance in various researches (Hanna and Dempster, 2009; Nguyen et al., 2014).
3.3 Participants
The participants of this study were the students of a ranked 1st business school, by the
Higher Education Commission of Pakistan2, of a private sector university of the Karachi,
who were enrolled in the Business Administration program. Since the study was about
the statistics anxiety therefore, students enrolled in the statistics course in the business
administration program was targeted. The participants comprised of both females and
males, enrolled in both bachelors and masters’ program of the business administration,
employed and unemployed and having any age.
3.4 Procedure
As mentioned earlier, the target participants were the students who were enrolled courses
based on statistics. Therefore, the data were collected from the students enrolled in
Courses namely: Statistical Inference, Statistical Inference for Managers, Quantitative
Techniques in Analysis and the Research Methods. The duration of the semester was
16 classes, so the responses from the students were collected through a survey
questionnaire in between of the semester. As it was intended that the students’
performance will be measured by the final grades, therefore, for they said, the
registration ID of the students was noted. By doing that, the final grades of the students at
the end of the semester were retrieved.
3.5 Hypotheses
Following is the list of hypotheses followed by the Conceptual Framework of the study.
H1: Personal adaptability (PA) has a negative impact on Test & Class Anxiety (TC).
H2: Personal adaptability (PA) has a negative impact on Fear of Asking for Help (FA).
H3: Personal adaptability (PA) has a negative impact on Interpretation Anxiety (IA).
H4: Commitment (COM) has a negative impact on Test & Class Anxiety (TC).
H5: Commitment (COM) has a negative impact on Fear of Asking for Help (FA).
Does statistics anxiety affect students’ performance in higher education?
103
H6: Commitment (COM) has a negative impact on Interpretation Anxiety (IA).
H7: Academic Self-Concept (ASC) has a negative impact on Test & Class Anxiety (TC).
H8: Academic Self-Concept (ASC) has a negative impact on Fear of Asking for Help (FA).
H9: Academic Self-Concept (ASC) has a negative impact on Interpretation Anxiety (IA).
H10: Test & Class Anxiety (TC) has a negative impact on Student Performance (SP).
H11: Fear of Asking for Help (FA) has a negative impact on Student Performance (SP).
H12: Interpretation Anxiety (IA) has a negative impact on Student Performance (SP).
Figure 1
Conceptual model framework
Personal
Adaptability
(PA)
H1
H2
Test & Class
Anxiety (TC)
H10
H3
Commitment
(COM)
H4
H5
Fear of Asking
for Help (FA)
H11
H6
H7
Academic
Self-Concept
(ASC)
H8
H9
Student
Performance
(SP)
H12
Interpretation
Anxiety (IA)
Source: Author’s construction
This section includes the estimates and the results of the statistical techniques applied to
the final sample size of 320 respondents.
3.6 Demographic profile of respondents
Table 1 summarises the demographic profile of the data. The data consist of 193 (60.3%)
males and 127 (39.7%) females, of which 87 (27.2%) and 233 (72.8%) enrolled in BBA
and MBA program respectively. Out of 320, 156 (48.8%) were employed and 164
(51.2%) were unemployed, whereas 36 (11.3%), 203 (63.4%), 78 (24.4%), 2 (0.6%) and
1 (0.3%) belongs to 20 years or less, 21 to 25 years, 26 to 35 years, 36 to 40 years and
more than 45 years respectively. On the other hand, Table 2 depicts the Mean, Standard
Deviation and Pearson’s Correlation. The values of the correlations encounter the
existence of multi-collinearity as all values are less than 0.5 (Tabachnick and Fidell,
2007).
104
Table 1
A. Najmi, S.A. Raza and W. Qazi
Demographic profile
Descriptive profile (n=320)
Male
Gender
Female
BBA
Program
MBA
Employed
Job Status
Unemployed
20 Years or Less
21 to 25 Years
Age
26 to 35 Years
36 to 40 Years
More than 45 Years
Table 2
Notes:
Percent
60.3
39.7
27.2
72.8
48.8
51.2
11.3
63.4
24.4
0.6
0.3
Mean, standard deviation and Pearson’s correlations
Mean
TC
SP
PA
FA
COM
IA
ASC
Frequency
193
127
87
233
156
164
36
203
78
2
1
2.775
3.324
2.018
2.233
2.098
2.752
3.850
Standard
TC
deviation
0.966
1
1.404
–0.154**
0.608
0.202**
0.705
0.301**
0.693
0.113*
0.809
0.341**
0.635
–0.293**
SP
PA
FA
1
–0.146**
–0.113*
–0.126*
–0.058
0.111*
1
0.233**
0.301**
0.162**
–0.401**
1
0.088
0.304**
–0.160**
COM
IA
1
0.055
1
–0.321** –0.132*
ASC
1
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).
3.7 Common method biasness
Bagozzi and Yi (1991) explained common method variance as the variance showing
attribution to the method which is measured rather to the concerned construct.
Researchers have argued that the common method variance must be accounted before
testing the respective hypotheses (Podsakoff et al., 2003; Woszczynski and Whitman,
2004; Ashkanasy, 2008; Richardson et al., 2009; Craighead et al., 2011). Craighead et al.
(2011) identified that common method variance might lead to the conclusion erroneously
by providing distorted associations therefore it must be checked in order to mitigate the
possible doubts on the outcome of the study.
The said issue has been dealt with Harman’s single factor test by using a principal
axis factoring framework, taking Promax rotation and fixing the factor of 1, which
revealed the variance explanation less than 50% i.e. 19.28% (Podsakoff et al., 2003). As
per that, the data were found to be free from the said issue and hence poses no ambiguity
on the findings.
Does statistics anxiety affect students’ performance in higher education?
4
105
Estimation and results
4.1 Exploratory factor analysis (EFA)
For sampling adequacy, researchers of this study used KMO and Bartlett’s Test. The
value in this study of KMO’s Test is 0.776 which meets the minimum criteria as reported
by the Kaiser (1974) which states the value between 0.7 and 0.79 as well. This states that
sample is sufficient enough so that Factor Analysis can be applied to it (Leech et al.,
2005).
In addition to this, the Barletts’s Test of Sphericity also needs to be significant
(less than 0.05) as this study has (Approx. Chi-square = 3107.975, df = 325, p < .000).
This shows that the correlation matrix is significantly different from an identity matrix
which means that correlations exists between factors (Leech et al., 2005) and factors
scored are unbiased as they only correlates within their own factor loadings (Tabachnick
and Fidell, 2007) (see Table 3).
Table 3
KMO & Bartlett’s test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.776
Approx. Chi-Square
3107.975
df
325
Sig.
.000
Bartlett's Test of Sphericity
Table 4 summarises the total variation explanation of the data. Based on the Eigen Value
as the Kaiser Criterion, Test & Class Anxiety (TC) explained 21.813%, Student
Performance (SP) explained 10.356%, and Personal Adaptability (PA) explains 8.154%
of the variation. In addition to this, Fear of Asking for Help Anxiety (FA), Level of
Commitment for being a student (COM), Interpretation Anxiety (IA) and Academic Self
Concept (ASC) explained 7.214%, 5.152%, 4.990% and 4.115% of the total variation
respectively. In addition to this, the data were then rotated by the Varimax orthogonal
rotation, which maximise the variety of the loadings by making high loadings higher and
lower the low loadings (Tabachnick and Fidell, 2007). Table 5 shows the factor loading
in which loadings below 0.4 were suppressed which shows a very strong convergent
validity (Tharenou et al., 2007) and are above 0.55 which is used to be considered as
good (Tabachnick and Fidell, 2007, Hair et al., 2010).
Table 6 contains the values of Cronbach’s alpha which was used to measure the
reliability of the data. As evident from the table, all of the values are more than 0.6 which
is the minimum criteria as reported by Hair et al. (1998).
Table 4
Results of variance explained
Items
TC
SP
PA
FA
COM
IA
ASC
Variance explained by
each factor in percentage
21.813
10.356
8.154
7.217
5.152
4.990
4.115
Cumulative variance
explained in percentage
21.813
32.169
40.323
47.540
52.692
57.682
61.797
Eigen values
5.671
2.693
2.120
1.876
1.340
1.297
1.070
Note:
Extraction method: principal components analysis.
Source: Authors’ estimation
106
Table 5
A. Najmi, S.A. Raza and W. Qazi
Rotated component matrix
Test_Class_Anxiety3
Test_Class_Anxiety1
Test_Class_Anxiety2
Test_Class_Anxiety4
Test_Class_Anxiety6
Test_Class_Anxiety8
Test_Class_Anxiety7
Grades
Final_Grades
Mid_Grades
Adaptability2
Adaptability1
Adaptability3
Fear_of_Ask3
Fear_of_Ask2
Fear_of_Ask4
Fear_of_Ask1
Commitment2
Commitment3
Commitment1
Interp_Anxiety10
Interp_Anxiety9
Interp_Anxiety11
SelfConcept1
SelfConcept5
SelfConcept2
Notes:
Table 6
TC
.843
.808
.808
.750
.715
.701
.696
SP
PA
Component
FA
COM
IA
.958
.796
.790
.803
.727
.718
.765
.654
.624
.567
.856
.715
.642
.767
.722
.653
.755
.668
.631
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalisation (rotation converged in
six iterations)
Reliability analysis
Variables
TC
SP
PA
FA
COM
IA
ASC
Overall
Source: Authors’ estimation
ASC
Items
7
3
3
4
3
3
3
26
Cronbach’s alpha
0.895
0.814
0.711
0.621
0.651
0.635
0.605
0.700
Does statistics anxiety affect students’ performance in higher education?
107
4.2 Confirmatory factor analysis (measurement model)
In order to evaluate the construct validity, confirmatory factor analysis (CFA) was also
applied to the data after EFA. CFA depicts the links between the observed and
unobserved variables (Byrne, 2010). In this study, CFA was applied to the loaded
26 items from EFA having 7 items of Test and Class Anxiety (TC), 3 items of
Interpretation Anxiety (IA), 4 items of Fear of Asking for Help Anxiety (FA), 3 items of
Personal Adaptability (PA) and 3 each item of Commitment (COM), Academic Self
Concept (ASC) and Students Performance (SP) respectively. A CFA model is said to
efficient if the model fitness has been assessed. The existing literature is in agreement of
not replying to any single index, rather prefer to report combination of index as all have
different characteristics with respect to the model fitness (Crowley and Fan, 1997).
Researchers have identified the ratio of Chi Square statistics to the degree of freedom
(CMIN/DF), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI),
Normed Fit Index (NFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI) and
Root Mean Square Error of Approximation (RMSEA) with PCLOSE as the widely used
indexes while evaluating the model fitness (Bagozzi and Yi, 1988; Bentler, 1990; Byrne,
2010; Kline, 2011; Loehlin, 2004; Marcoulides and Schumacker, 2001).
According to Tabachnick and Fidell (2007) the threshold value for CMIN/df should
be less than 2 and in our case it is 1.199. Apart from that, the value of GFI is 0.930 and
AGFI is 0.907 which are as per the threshold value i.e. ≥ 0.85 for GFI (Byrne, 2010) and
≥ 0.80 for AGFI (Bagozzi and Yi, 1988) respectively. In addition to this, the value of
NFI and TLI in our case is 0.901 and 0.978 which needs to be Close to 1 as per Bentler
(1990) as these are. On the other hand, the CFI is 0.982 which shows an excellent model
fitness (≥. 95) as per Hu and Bentler (1999) and the RMSEA is 0.025 (PCLOSE = 1)
against the threshold value of ≤ 0.05 (PCLOSE > 0.05) as per Browne and Cudeck
(1993). So the analysis of these fitness shows that our data is fit for the desired model
(see Table 7).
Table 7
Summary of model fitness and the threshold values
Goodness-of-fit
measures
CMIN/D
F
GFI
AGFI
Recommended
value
<2
≥ 0.85
≥ 0.80
CFA Measurement
Model f
1.199
0.930
0.907
0.901
0.978
0.982
0.025
(1.000)
SEM Structural
Model f
1.318
0.921
0.898
0.888
0.964
0.970
0.032
(1.000)
NFI
TLI
CFI
Close to 1 Close to 1 ≥ 0.95
RMSEA
(PCLOSE)
≤ 0.05
(> 0.05)
4.3 Structured equation modelling
Table 8 summarises the results of the structured model. It consists of the Hypothesised
Paths, their respective coefficients weights, standard errors, critical ratios, probability
values and remarks of the hypothesis. The results suggested that Personal Adaptability
has significant negative impact on Test & Class Anxiety (TC) (B –1.454< 0.05),
Interpretation Anxiety (IA) (B –0.559< 0.10) and Fear of Asking for Help Anxiety (FA)
(B –1.103< 0.05). In addition to this, Commitment was also found to have a significant
negative impact on Test & Class Anxiety (TC) (B –0.689< 0.05), Interpretation Anxiety
108
A. Najmi, S.A. Raza and W. Qazi
(IA) (B –0.549< 0.05) and Fear of Asking for Help Anxiety (FA) (B –0.390< 0.05).
Similarly, Academic Self Concept (ASC) also has significant negative impact on Test &
Class Anxiety (TC) (B –4.407< 0.05), Interpretation Anxiety (IA) (B –3.366< 0.01) and
Fear of Asking for Help Anxiety (FA) (B –2.206< 0.05). On the contrary, Student
Performance (SP) was found to have negative, but insignificant impact by Test & Class
Anxiety (TC) (B –0.087> 0.10), Interpretation Anxiety (IA) (B –0.046> 0.10) and Fear of
Asking for Help Anxiety (FA) (B –0.098> 0.10) (see Table 8).
Figure 2
The structural model results
Personal
Adaptability
(PA)
–1.454
-0.559
Test & Class
Anxiety (TC)
–0.087
–1.103
Commitment
(COM)
–0.689
–0.390
Fear of Asking
for Help (FA)
Student
Performance
(SP)
–0.098
–0.549
–4.407
Academic
Self-Concept
(ASC)
–2.206
–0.046
Interpretation
Anxiety (IA)
–3.366
Source: Author’s construction
Table 8
Summary of the SEM hypothesis testing
Hypothesis
Hypothesised Path
Coefficient
S.E.
C.R.
P-Value
Remarks
H1
TC ← PA
–1.454
0.598
–2.432
.015**
Supported
H2
FA ← PA
–0.559
0.309
–1.813
.070*
Supported
H3
IA ← PA
–1.103
0.451
–2.445
.014**
Supported
H4
TC ← COM
–0.689
0.286
–2.409
.016**
Supported
H5
FA ← COM
–0.390
0.162
–2.401
.016**
Supported
H6
IA ← COM
–0.549
0.228
–2.403
.016**
Supported
H7
TC ← ASC
–4.407
1.364
–3.230
.001***
Supported
H8
FA ← ASC
–2.206
0.702
–3.140
.002***
Supported
H9
IA ← ASC
–3.366
0.997
–3.377
.000***
Supported
H10
SP ← TC
–0.087
0.106
–0.821
.412
Not Supported
H11
SP ← FA
–0.098
0.156
–0.626
.531
Not Supported
H12
SP ← IA
–0.046
0.119
–0.390
.696
Not Supported
Note:
***p < 0.01, **p < 0.05, *p < 0.10.
Does statistics anxiety affect students’ performance in higher education?
5
109
Discussion, conclusion and practical implications
The present study investigated the relationship between students’ attitudes towards
Statistics Anxiety and statistics anxiety affecting student performance. For the said
purpose, the data were collected through a survey questionnaire from the students of a
private sector university of Pakistan.
This study was focused on the role of three students’ attitudes namely Personal
Adaptability (PA), Commitment (COM) and Academic Self Concept (ASC) in Statistics
Anxiety and Student Performance. As evident from the results, all three attitudes tend to
have a negative impact on three subscales of Statistics Anxiety namely: Test & Class
Anxiety (TC), Interpretation Anxiety (IA) and Fear of Asking for Help Anxiety (FA). It
results of the relationships between PA with TC, FA and IA means that a student who is
adaptable enough to adjust himself or herself along with learning approaches, can easily
mitigate the statistics anxiety while in the classroom, giving the test, doing interpretation
or while seeking help from others. Furthermore, relationships between COM with TC,
FA and IA means that a student who is attached and committed with studies can also
encounter the statistics anxiety which he or she may face while engage with statistics.
Moreover, relationships between ASC with TC, FA and IA means that a student who
perceived and/or justified himself or herself academically sound can easily cope-up with
the identified three dimensions of anxiety pertaining to statistics.
Among these relationships, ASC was founded to have a relatively more negative
impact towards TC, IA and FA. It means that an increase in ASC will significantly
decrease the TC, IA and FA as compared to COM and ASC, whereas PA was found to
decrease the statistics anxiety more significantly in comparison with COM. It shows that
if students show more confidence over themselves represented by ASC, more committed
towards the education represented by COM and is flexible enough with their approaches
towards learning represented by PA than they can easily counter the statistics anxiety
(Maschi et al., 2007; Lazar, 1990; Slootmaeckers et al., 2014). Through such positive
attitudes, students can minimise the risks of failure in the statistics courses and they
can mitigate the statistics anxiety which shows a negative insignificant impact on their
performance because of such positive attitudes (Onwuegbuzie and Wilson, 2003;
Onwuegbuzie, 2004; Macher et al., 2012).
5.1 Practical implications
The findings of this study are of equal importance for both local and international. As the
statistics courses are being taught in business and management studies along with the
other disciplines across the world, therefore international academicians can also extract
potential inference from this study. The present study suggests the implications to the
academic experts, curriculum planner, instructors, course facilitators, students and others
that the lectures and sessions during coursework should be more interactive so that
students can decrease their level of statistics by their active participations. Moreover,
assignments and projects should be given on a group basis so that students can mutually
solve the questions that will ultimately result in reducing the stress levels. Proper
awareness and trainings should be given to the students about the anxieties and their
levels so that they can manage it easily by themselves specifically when they encounter
statistics anxiety. In addition to these, the entire course work of the academic program
should be designed in a way that students must have a statistics course in every semester
110
A. Najmi, S.A. Raza and W. Qazi
so that they can easily climb to the research as they will be familiar with it. Instructors
should create a humorous environment of the classroom and must relate the statistical
problems with the practical life so that students can easily relate it, whereas advanced
statistical techniques should be explained and solved by using the different Statistical
Software because business students are much interested in business and management
studies. Manually solving of such techniques will increase their level of anxiety. On the
other hand, proper counselling and evaluation of the students must be done regularly so
that positive attitudes can be fostered and students can be updated about their standings.
Based on the findings of this study, we anticipate that future researchers can explore
this phenomenon in other disciplines of social sciences in order to make a
valuable contribution to the literature. Moreover, comparative analysis based studies, like
gender based, students from private and public sector institutions and others are also
recommended.
References
Adegboye, O.A. and Jawid, A. (2016) ‘Multivariate multilevel models for attitudes toward
statistics: multi-disciplinary settings in Afghanistan’, Journal of Applied Statistics, Vol. 43,
No. 1, pp.244–261.
Ashkanasy, N.M. (2008) ‘Submitting your manuscript’, Journal of Organizational Behavior,
Vol. 29, No. 3, pp.263–264.
Bagozzi, R.P. and Yi, Y. (1988) ‘On the evaluation of structural equation models’, Journal of the
Academy of Marketing Science, Vol. 16, No. 1, pp.74–94.
Bagozzi, R.P. and Yi, Y. (1991) ‘Multitrait-multimethod matrices in consumer research’, Journal
of Consumer Research, pp.426–439.
Baloğlu, M. (2002) ‘Psychometric properties of the statistics anxiety rating scale’, Psychological
Reports, Vol. 90, No. 1, pp.315–325.
Baloğlu, M. (2003) ‘Individual differences in statistics anxiety among college students’,
Personality and Individual Differences, Vol. 34, No. 5, pp.855–865.
Bas, G. (2010) ‘Effects of multiple intelligences instruction strategy on students achievement levels
and attitudes towards English lesson’, Cypriot Journal of Educational Sciences, Vol. 5, No. 3,
pp.167–180.
Bembenutty, H. (2008) ‘Self-regulation of learning and test anxiety’, Psychology Journal, Vol. 5,
No. 3, pp.122–139.
Bentler, P.M. (1990) ‘Comparative fit indexes in structural models’, Psychological Bulletin,
Vol. 107, No. 2, p.238.
Blaylock, B. and Hollandsworth, R. (2008) ‘Improving the impact of classroom student
engagement on out-of-class mental focus in quantitative courses’, The Journal of Learning in
Higher Education, Vol. 4, pp.37–44.
Browne, M.W. and Cudeck, R. (1993) ‘Alternative ways of assessing model fit’, Sage Focus
Editions, Vol. 154, pp.136–136.
Byrne, B.M. (2010) Structural Equation Modeling Using AMOS: Basic Concepts, Applications,
and Programming, Taylor and Francis Group.
Craighead, C.W., Ketchen, D.J., Dunn, K.S. and Hult, G.T.M. (2011) ‘Addressing common method
variance: guidelines for survey research on information technology, operations, and supply
chain management’, Engineering Management, IEEE Transactions on, Vol. 58, No. 3,
pp.578–588.
Crowley, S.L. and Fan, X. (1997) ‘Structural equation modeling: basic concepts and applications
in personality assessment research’, Journal of Personality Assessment, Vol. 68, No. 3,
pp.508–531.
Does statistics anxiety affect students’ performance in higher education?
111
Cruise, R.J. and Wilkins, E.M. (1980) STARS: Statistical anxiety rating scale, Unpublished
manuscript, Andrews University, Berrien Springs, MI.
Cruise, R.J., Cash, R.W. and Bolton, D.L. (1985) ‘Development and validation of an instrument to
measure statistical anxiety’, Paper presented at the proceedings of the American Statistical
Association.
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998) Multivariate Data Analysis,
Prentice Hall.
Hair, J.F., Black, W.C., Babin B.J. and Anderson, E.R. (2010) Multivariate Data Analysis, Prentice
Hall.
Hanna, D. and Dempster, M. (2009) ‘The effect of statistics anxiety on students’ predicted and
actual test scores’, The Irish Journal of Psychology, Vol. 30, Nos. 3/4, pp.201–209.
Hanna, D., Shevlin, M. and Dempster, M. (2008) ‘The structure of the statistics anxiety rating
scale: a confirmatory factor analysis using UK psychology students’, Personality and
Individual Differences, Vol. 45, pp.68–74.
Hartline, M. and Ferrell, O. (1996) ‘The management of customer-contact service employees:
an empirical investigation’, Journal of Marketing, Vol. 60, pp.52–70.
Hsu, M.K., Wang, S.W. and Chiu, K.K. (2009) ‘Computer attitude, statistics anxiety and
selfefficacy on statistical software adoption behavior: an empirical study of online MBA
learners’, Computers in Human Behaviour, Vol. 25, No. 2, pp.412–420.
Hu, L.T. and Bentler, P.M. (1999) ‘Cutoff criteria for fit indexes in covariance structure
analysis: conventional criteria versus new alternatives’, Structural Equation Modeling: A
Multidisciplinary Journal, Vol. 6, No. 1, pp.1–55.
Kaiser, H.F. (1974) ‘An index of factorial simplicity’, Psychometrika, Vol. 39, No. 1, pp.31–36.
Keeley, J., Zayac, R. and Correia, C. (2008) ‘Curvilinear relationships between statistics anxiety
and performance among undergraduate students: evidence for optimal anxiety’, Statistics
Education Research Journal, Vol. 7, pp.4–15.
Kline, R.B. (2011) Principles and Practice of Structural Equation Modeling, Guilford Press.
Lalayants, M. (2012) ‘Overcoming graduate students’ negative perceptions of statistics’, Journal of
Teaching in Social Work, Vol. 32, No. 4, pp.356–375.
Lalonde, R.N. and Gardner, R.C. (1993) ‘Statistics as a second language? A model for predicting
performance in psychology students’, Canadian Journal of Behavioral Science, Vol. 25,
pp.108–125.
Lavasani, M.G., Weisani, M. and Ejei, J. (2011) ‘The role of achievement goals, academic
motivation, and learning strategies in statistics anxiety: testing a causal model’, ProcediaSocial and Behavioral Sciences, Vol. 15, pp.1881–1886.
Law, K.M. and Breznik, K. (2017) ‘Impacts of innovativeness and attitude on entrepreneurial
intention: among engineering and non-engineering students’, International Journal of
Technology and Design Education, Vol. 27, No. 4, pp.683–700.
Lazar, A. (1990) ‘Statistics courses in social work education’, Journal of Teaching in Social Work,
Vol. 4, pp.17–30.
Leech, N.L., Barrett, K.C. and Morgan, G.A. (2005) SPSS for Intermediate Statistics: Use and
Interpretation, Psychology Press.
Liau, A.K., Kiat, J.E. and Nie, Y. (2015) ‘Investigating the pedagogical approaches related to
changes in attitudes toward statistics in a quantitative methods course for psychology
undergraduate students’, The Asia-Pacific Education Researcher, Vol. 24, No. 2, pp.319–327.
Loehlin, J.C. (2004) Latent Variable Models: An Introduction to Factor, Path, and Structural
Equation Analysis, Psychology Press.
Macher, D., Paechter, M., Papousek, I. and Ruggeri, K. (2012) ‘Statistics anxiety, trait anxiety,
learning behavior, and academic performance’, European Journal of Psychology of
Education, Vol. 27, No. 4, pp.483–498.
112
A. Najmi, S.A. Raza and W. Qazi
Marcoulides, G.A. and Schumacker, R.E. (2001) New Developments and Techniques in Structural
Equation Modeling, Psychology Press.
Maschi, T., Bradley, C., Youdin, R., Killian, M., Cleaveland, C. and Barbera, R. (2007) ‘Social
work students and the research process: the thinking, feeling, and doing of research’, Journal
of Baccalaureate Social Work, Vol. 13, No. 1, pp.1–12.
Meyer, J.P., Allen, N.J. and Smith, C. (1993) ‘Commitment to organizations and occupations:
extension and test of a three-component conceptualization’, Journal of Applied Psychology,
Vol. 78, No. 4, pp.538–551.
Mji, A. and Onwuegbuzie, A.J. (2004) ‘Evidence of score reliability and validity of the Statistical
Anxiety Rating Scale among Technikon students in South Africa’. Measurement and
Evaluation in Counseling and Development, Vol. 36, pp.238–251.
Montcalm, D.M. (1999) ‘Applying Bandura’s theory of self-efficacy to the teaching of research’,
Journal of Teaching in Social Work, Vol. 19, Nos. 1/2, pp.93–107.
Nguyen, T.H., Charity, I. and Robson, A. (2014) ‘Students’ perceptions of computer-based
learning environments, their attitude towards business statistics, and their academic
achievement: implications from a UK university’, Studies in Higher Education, DOI:
10.1080/03075079.2014.950562.
Onwuegbuzie, A. (2004) ‘Academic procrastination and statistics anxiety’, Assessment &
Evaluation in Higher Education, Vol. 29, No. 1, pp.3–19.
Onwuegbuzie, A.J. (1997) ‘Writing a research proposal: the role of library anxiety, statistics
anxiety, and composition anxiety’, Library & Information Science Research, Vol. 19,
pp.5–33.
Onwuegbuzie, A.J. (2000) ‘Statistics anxiety and the role of self-perceptions’, The Journal of
Educational Research, Vol. 93, No. 5, pp.323–330.
Onwuegbuzie, A.J. (2003) ‘Modeling statistics achievement among graduate students’,
Educational and Psychological measurement, Vol. 63, No. 6, pp.1020–1038.
Onwuegbuzie, A.J. and Seaman, M.A. (1995) ‘The effect of time constraints and statistics
test anxiety on test performance in a statistics course’, Journal of Experimental Education,
Vol. 62, pp.115–124.
Onwuegbuzie, A.J. and Wilson, V. (2003) ‘Statistics anxiety: nature, etiology, antecedents, effects,
and treatments – a comprehensive review of the literature’, Teaching in Higher Education,
Vol. 8, pp.95–209.
Onwuegbuzie, A.J., Leech, N.L., Murtonen, M. and Tähtinen, J. (2010) ‘Utilizing mixed methods
in teaching environments to reduce statistics anxiety’, International Journal of Multiple
Research Approaches, Vol. 4, No. 1, pp.28–39.
Papousek, I., Ruggeri, K., Macher, D., Paechter, M., Heene, M., Weiss, E.M. et al. (2012)
‘Psychometric evaluation and experimental validation of the statistics anxiety rating scale’,
Journal of Personality Assessment, Vol. 94, No. 1, pp.82–91.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003) ‘Common method biases
in behavioral research: a critical review of the literature and recommended remedies’, Journal
of Applied Psychology, Vol. 88, No. 5, p.879.
Quek, A.H. (2005) ‘Learning for the workplace: a case study in graduate employees’ generic
competences’, Journal of Workplace Learning, Vol. 17, No. 4, pp.231–242.
Reynolds, W. (1988) ‘Measurement of academic self-concept in college students’, Journal of
Personality Assessment, Vol. 52, No. 2, pp.223–240.
Richardson, H.A., Simmering, M.J. and Sturman, M.C. (2009) ‘A tale of three perspectives:
examining post hoc statistical techniques for detection and correction of common method
variance’, Organizational Research Methods, Vol. 12, No. 4, pp.762–800.
Rodarte-Luna, B. and Sherry, A. (2008) ‘Sex differences in the relation between statistics anxiety
and cognitive/learning strategies’, Contemporary Educational Psychology, Vol. 33, No. 2,
pp.327–344.
Does statistics anxiety affect students’ performance in higher education?
113
Ruggeri, K., Dempster, M., Hanna, D. and Cleary, C. (2008) ‘Experiences and expectations: the
real reason nobody likes stats’, Psychology Teaching Review, Vol. 14, pp.75–83.
Schunk, D.H., Pintrich, P.R. and Meece, J.L. (2008) Motivation in Education: Theory, Research,
and Application, 3rd ed., Merrill/Prentice-Hall, Upper Saddle River, NJ.
Sesé, A., Jiménez, R., Montaño, J.J. and Palmer, A. (2015) ‘Can attitudes toward statistics and
statistics anxiety explain students’ performance?’ Revista de Psicodidáctica/Journal of
Psychodidactics, Vol. 20, No. 2, pp.285–304.
Slootmaeckers, K., Kerremans, B. and Adriaensen, J. (2014) ‘Too afraid to learn: attitudes towards
statistics as a barrier to learning statistics and to acquiring quantitative skills’, Politics,
Vol. 34, No. 2, pp.191–200.
Tabachnick, B.G. and Fidell, L.S. (2007) Using Multivariate Statistics, 5th ed., Pearson, Boston,
MA.
Tharenou, P., Donohue, R. and Cooper, B. (2007) Management Research Methods, Cambridge
University Press, Melbourne, p.338.
Watson, F.S., Kromrey, J.D. and Hess, M.R. (2003, February) ‘Toward a conceptual model for
statistics anxiety intervention’, Paper Presented at the Annual Meeting of the Eastern
Educational Research Association, Hilton Head, SC.
Wellman, N. (2010) ‘The employability attributes required of new marketing graduates’,
Marketing Intelligence and Planning, Vol. 28, No. 7, pp.908–930.
Williams, M., Payne, G., Hodgkinson, L. and Poade, D. (2008) ‘Does British sociology count?
Sociology students’ attitudes toward quantitative methods’, Sociology, Vol. 42, No. 5,
pp.1003–1021.
Woszczynski, A.B. and Whitman, M.E. (2004) ‘The problem of common method variance in IS
research’, The Handbook of Information Systems Research, pp.66–77.
Yousef, D.A. (2012) ‘Factors affecting academic performance of non-English speaking business
students in quantitative courses: a study at a private university in the UAE’, Journal of
International Business Education, Vol. 7, pp.103–120.
Yousef, D.A. (2013) ‘Predicting the performance of undergraduate business students in
introductory quantitative methods courses: the case of a private university in the UAE’,
Quality Assurance in Education, Vol. 21, No. 4, pp.359–371.
Yuksel, S. (2006) ‘Undergraduate students’ resistance to study skills units’, College Student
Journal, Vol. 40, No. 1, pp.158–65.
Zimmer, J. and Fuller, D. (1996) ‘Factors affecting undergraduate performance in statistics: a
review of literature’, Paper presented at the Annual Meeting of the Mid-South Educational
Research Association, Tuscaloosa, AL (ERIC Document Reproduction Service
No. ED406424).
Notes
1
2
See,
http://www.hec.gov.pk/InsideHEC/Divisions/AECA/CurriculumRevision/Documents/
BusinessAdmin-2012.pdf
See, http://hec.gov.pk/InsideHEC/Divisions/QALI/Others/RankingofUniversities/Documents/
Ranking_Doc.pdf