sampleAs technology is constantly innovating, it has change on.docx

sample
As technology is constantly innovating, it has change on how
people access information, new methodologies of work, and
inspire new approaches to management. For instance,
technology has improve agile methods in regards to customer
orientation and employee engagement.
When a company wants to implement a new agile method to its
operations, here are some important criteria that it needs to
account for. First of all, decide how much power to give up.
This reflects a shift of power from those at the top to the ones
performing the job, which can be complex for well-established
companies since executives do not accept giving up some power
or status. Another one is to build a structure around customers,
like building a managed team focused on a particular customer
group to make sure it maps the real needs of customers. In
addition, a company needs to give the right balance of oversight
and autonomy. Employees have the freedom to tackle any solve
any problem with the methodology the find it most suitable. Of
course, we still need to report our progress, expectations, and
future goals since we need managers to keep assessing our
progress. Lastly, we need to provide employees with growth
opportunities because as one becomes too task-focused and
results oriented, he neglects to think about his careers over the
long-term.
The implementation of an agile method is very difficult at
established companies, but it has been established in mostly
technology and IT departments, so we believe it will soon
become popular among other industries.
Birkinshaw, J. (2018). What to Expect From Agile. MIT Sloan
Management Review, 59(2), 39-42.
sample

Financial Planning as Concrete Guidance
COLLAPSE
Top of Form
As Sull et al., (2018) discussed in the article Turning Strategy
into Results, "A company’s strategic objectives should be
tangible enough that leaders and employees throughout the
organization can use them to prioritize their activities and
investments (and also to help them decide what to stop doing)"
(p. 9). Financial targets such as revenue and profitability goals
can be defined as one of the specific guidance. However, the
most important guidance is not just financial targets, but the
directions on how to get there.
When we create financial management plan for the new
brewpub, there are many questions may need to be considered
before we set final goals.
· Determine the vulnerable period of restaurant cash flow
· Ensure the new business or projects are feasible
· Estimate when the new business will make profit or break
even
· Determine budget for the new business and labor cost
· Identify the current value and risk of the restaurant to attract
investment
Keep these individual tasks in mind, we can adjust and improve
the efficiency of financial planning based on real-time
information. For example, we can gather information such as
market trends, investment cost and consumption level. With
accurate data, the financial management will be more applicable
and provide more support for the whole business plan.
Reference
Sull, Donald, Turconi, Stefano, Sull, Charles, & Yoder, James.
(2018). Turning Strategy Into Results. MIT Sloan Management
Review, 59(3), 1-12.
sample

Bottom of Form
W9 discussion
COLLAPSE
Top of Form
Every corporation has their management strategy. As Sull(2018)
discussed in “turning strategy into result”, strategy is complex
but executing it requires simplicity. Corporation need long-term
strategy, but it always lack of flexible. Managers need to think
about how to make strategy forward-looking and action-
oriented.
If we want to turn strategy into result, first we should make
strategy simple. First of all, for every project, we can develop a
small set of priorities. It can help employees to finish every
small set easily and quickly. And then, strategy is about choice.
Before every determination about business project, manager
should search about every information about project, such as
target customers, location and so on. The more corporation
know about the project, the better choice they do.
In order to consider about whether the strategy is effective, the
article tells us seven factors:
1. Limit the number of priorities to a handful.
2. Focus on mid-term objectives.
3. pull toward the future
4. Make the hard calls.
5. Address critical vulnerabilities
6. Provide concrete guidance
7. Align the top team.
Constraint management is very important in corporations. An
effective strategy need not only based on the corporation values
but also can make different department work together. Strategic
priorities should lay out what matters for the company as a
whole to win and should reflect the interdependencies among
the choices. If there is the disagreement occur in strategy. For
example, the finance highlighted the profit and cost while

human resource pay attention on employees. Management
should consider about how to achieve balance.
reference:
Sull, Donald, Turconi, Stefano, Sull, Charles, & Yoder, James.
(2018). Turning Strategy Into Results. MIT Sloan Management
Review, 59(3), 1-12
Bottom of Form
sample
Turning Strategy Into Results
COLLAPSE
Top of Form
Few companies have succeeded through a big bet. Most winning
strategies are based on a range of choices, including customer
service, scope of business, product offerings, and the ability to
interact to help companies make money. According to the article
“Turning Strategy into Results”, the article claims that leaders
should make forward-looking and action-oriented strategies, and
translate the complexity of strategy into guidelines that simple
and flexible enough to execute.
Many executives claim they use strategic priorities, but the
report says the practice is not as good as they hoped. To
develop a strategic agenda and effectively drive
implementation, strategic priorities require balanced guidance
and flexibility, balance business inertia, and unify different
parts of the business. Setting strategic priorities for all these
things, and doing them well, is a daunting task. The article
describes seven characteristics of effective strategic priorities,
explains why they are important, and suggests that actual
diagnostic managers can be used to evaluate a company's
strategic priorities.1. Limit the number of priorities to a
handful. 2. Focus on mid-term objectives. 3. Pull toward the
future. 4. Make the hard calls.5. Address critical

vulnerabilities. 6. Provide concrete guidance. 7. Align the top
team.
The ability and discipline of the company is very important. The
company's focus on strategic priorities helps to implement
strategies and drive the company toward success.
Reference:MIT Sloan Management Review, Turning Strategy
Into Results, Sull, D at al, Spring 2018
Bottom of Form
48
WORRY, INTOLERANCE OF UNCERTAINTY, AND
STATISTICS ANXIETY5
AMANDA S. WILLIAMS
Texas Tech University
[email protected]
ABSTRACT
Statistics anxiety is a problem for most graduate students. This
study investigates the relationship
between intolerance of uncertainty, worry, and statistics
anxiety. Intolerance of uncertainty was
significantly related to worry, and worry was significantly
related to three types of statistics
anxiety. Six types of statistics anxiety were significantly lower
by the end of the semester.

Keywords: Statistics education research; Academic anxiety;
Graduate students
1. INTRODUCTION
It is quite common for graduate students to avoid taking
statistics classes for as long as possible,
frequently waiting until their last semester to enroll in a
statistics course. This is not new: Roberts and
Bilderback (1980) noted as much when discussing student
attitudes toward statistics. The authors
pointed out that the fear students feel when finally forced to
enroll is often detrimental to a successful
experience in statistics classes. Other authors also acknowledge
the problem of graduate students’
procrastination when it comes to enrolling in statistics classes
(e.g., Onwuegbuzie, 1997), attributing
their avoidance to high levels of statistics anxiety. Statistics
anxiety has been related to several
variables, such as academic outcomes, attitudes, self-concepts,
and personal characteristics such as the
tendency to procrastinate. Statistics anxiety has also been
shown to be detrimental to students in
several ways, but cognitive processes related to this form of
anxiety have apparently not been
explored. Specifically, intolerance of uncertainty, and worry
have each been related to generalized
anxiety, and worry has been related to other forms of anxiety,
but neither construct has yet been
related to statistics anxiety.

2. REVIEW OF THE LITERATURE
2.1. STATISTICS ANXIETY
Statistics anxiety has been defined as “a feeling of anxiety when
taking a statistics course or doing
statistical analysis; that is gathering, processing, and
interpreting data” (Cruise, Cash, & Bolton, 1985,
p. 92). Statistics anxiety is believed to be a multi-dimensional
construct, comprised of six types of
anxiety: worth of statistics, interpretation anxiety, test and class
anxiety, computation self-concept,
fear of asking for help, and fear of statistics teachers (Cruise et
al.). Worth of statistics refers to
students’ perception of the usefulness of statistics in everyday
life. Cruise et al. state that students
scoring high on this factor see no purpose in taking statistics
courses and a higher score may indicate
a negative attitude toward the subject. Interpretation anxiety
refers to the anxiety felt when students
are required to interpret statistical results or decide which type
of analysis to use. Those who score
high on this factor find statistical interpretation difficult and
anxiety provoking. Test and class anxiety
pertains to the general anxiety experienced by taking a statistics
class. Students who score high on this
factor feel anxiety when enrolling in a statistics course,
attending statistics classes, or taking exams.
Computation self-concept refers to anxiety experienced when
computing statistical problems. Students
who score high on this dimension doubt their ability, regardless
of true ability, to solve statistical
problems which may reflect their attitude toward the subject.

Fear of asking for help reflects the
Statistics Education Research Journal, 12(1), 48-59, http://iase-
web.org/Publications.php?p=SERJ
ation for Statistical Education
(IASE/ISI), May, 2013
49
anxiety students feel when asking for help. Students who score
high on this factor experience higher
levels of anxiety when approaching their instructor, or a
classmate, for help understanding statistical
problems or statistical descriptions in journal articles. The last
factor, fear of statistics teachers, refers
to the students’ perceptions of the statistics instructor. Those
who score high on this dimension are
more likely to perceive the instructor as being unable or
unwilling to relate to the students as a human
being, and to regard the instructor as someone to fear.
Zeidner (1991) furthers the definition by adding that statistics
anxiety is accompanied by worry,
tension, and physiological symptoms of stress when students are
faced with taking a statistics class.
Similarly, Onwuegbuzie, Da Ros, and Ryan (1997), through a
qualitative study, found that students
affected by statistics anxiety experience symptoms ranging from
mild discomfort to depression, panic,
stress, headaches, sweating, emotionality, and other
psychological and physical manifestations of
anxiety.

Adverse effects of statistics anxiety have also been reported.
For example, students’ performance
is often affected in both statistics and research classes (Chiesi
& Primi, 2010; DeVaney, 2010;
Keeley, Zayac, & Correia, 2008; Lalonde & Gardner, 1993;
Murtonen & Lehtinen, 2003;
Onwuegbuzie, 1997; Onwuegbuzie, 2000; Onwuegbuzie &
Seaman, 1995; Perepiczka, Chandler, &
Becerra, 2011; Zanakis & Valenza, 1997). In a sample of
undergraduate psychology students, Lalonde
and Gardner (1993) found that statistics anxiety had affected
students’ attitude and motivation toward
the subject, thereby affecting their learning. Similarly, Zanakis
and Valenza (1997) explored the
relationship between student grades and statistics anxiety in 166
students enrolled in business
statistics courses. The authors found that students’ anxiety,
though still high at course end, was
reduced simply as a result of exposure and that this contributed
to a reduction in interpretation
anxiety. Further, the increase in perceived worth of statistics
had the greatest influence on student
achievement, with the increase contributing to higher grades.
More recently, Chiesi and Primi used
structural equation modeling to explore in a sample of 487
students both cognitive and non-cognitive
factors related to statistics performance. They found that
achievement was indirectly affected by
statistics anxiety through poor attitude toward statistics, with
the overall model explaining 67% of the
variance in achievement. Specifically, pre-course attitude was
significantly related to anxiety (r = -
0.37, p < 0.05), which was related to negative post-course
attitude (r = -0.38, p < 0.05), which was in
turn related to achievement (r = 0.21, p < 0.05). This suggests

that students’ preconceived ideas about
statistics classes affect their anxiety, and that anxiety in turn
affects their attitudes toward statistics
even at course end. Comparatively, Kesici, Baloglu, and Deniz
(2011), in a sample of 320 college
students, used canonical correlation analysis to explore the
relationship between the six dimensions of
statistics anxiety (Cruise et al., 1985) and a set of nine types of
self-regulation. These authors found
that the three significant canonical variants combined accounted
for 54% of the variability in self-
regulated learning strategies and 65% of the variability in
statistics anxiety. These authors contend
that students who use higher-level learning strategies (e.g.,
elaboration and organization) also have
lower levels of statistics anxiety.
Onwuegbuzie and Seaman (1995) found that students who were
given statistics tests under timed
conditions showed significantly lower levels of performance
than students who were tested under
untimed conditions. In a later study, Onwuegbuzie (1997)
explored anxiety in 81 graduate students in
a research class and showed that anxiety over writing research
proposals was comprised of library
anxiety, statistics anxiety, composition anxiety, and research
anxiety. Two forms of statistics anxiety
(interpretation anxiety and fear of asking for help) and two
types of library anxiety (affective barriers
and knowledge of the library) significantly lowered scores on
students’ research proposal
assignments. Additionally, these factors explained 35.9% of the
variance in proposal writing
propensity.
Students’ self-perceptions are also affected by statistics anxiety.

In a sample of 146 graduate
students, Onwuegbuzie (2000a) found that perceived creativity,
intellectual ability, and academic
competence were all significantly related to six dimensions of
statistics anxiety, indicating that
students who had higher levels of statistics anxiety also
perceived themselves as less creative, as well
as having less intellectual ability and competence for learning.
In a different look at self-perception,
Perepiczka et al. (2011) examined the effects of statistics
anxiety, attitude toward statistics, and social
support on students’ self-efficacy for learning statistics. In their
sample of 166 graduate students,
52.8% of the variance in self-efficacy to learn statistics was
accounted for by the combination of
50
statistics anxiety, attitude toward statistics, and social support.
Individually, statistics anxiety and
attitude toward statistics were significant predictors, accounting
for 3% and 7% of the variance in
self-efficacy to learn statistics, whereas social support was not
significant. From these data, it appears
that students’ anxiety and attitudes affect their perception of
whether they are capable of learning
statistics.
Statistics anxiety has been found to contribute even to students’
academic procrastination. In a
sample of 135 graduate students, Onwuegbuzie (2004) measured
students’ levels of procrastination
and the extent to which their procrastination was due to fear of

failure or to task aversion. The author
also measured students’ levels of statistics anxiety in the six
areas delineated by Cruise et al. (1985),
and found that 40%-60% of the students reported
procrastination on reading assignments and studying
for exams, and that both the task aversion and fear of failure
components were significantly related to
the six types of statistics anxiety. Additionally, the author
reports that as many as 41.5% of the
students surveyed felt that procrastination was a concern.
Less attention in the research has been focused on alleviating
statistics anxiety in students. Dillon
(1982) described how students’ anxiety may be reduced by
encouraging them to discuss their
concerns, and then suggesting ways that they can cope with
their anxiety. Schacht and Stewart (1990)
explored the use of humorous cartoons in statistics classes to
reduce anxiety. By introducing cartoons
and applying statistical applications to the content (e.g.,
calculating probability of runaway pets using
fictitious data based on a cartoon depicting a man looking for
his runaway cat), the authors found that
this type of humor not only lowered the students’ anxiety levels,
but also improved their learning. In
1991, the same authors reported on their use of attention-getting
teaching techniques (termed
“gimmicks”) in statistics classes, asserting that such techniques
should be utilized more often in
statistics classes. By gathering opinion-related data from the
students themselves, and then having
them perform simple calculations such as obtaining the mean,
they found that students’ anxiety was
reduced and their motivation to become involved in the class
was increased. Wilson (1996) found that
although humor was somewhat effective in reducing students’

anxiety in statistics class, instructor
personality and reassurances were even more effective. In
subsequent studies, Wilson (1999, 2000)
found that the instructor’s interpersonal style was more
effective than specific strategies used to
address students’ anxiety. After gathering answers to the open-
ended question “What, if anything, did
your instructor do to reduce anxiety in the statistics class?” for
three years, the author concluded that
instructor behaviors such as conveying a positive attitude,
encouragement, reassurances of the
students’ ability, acknowledgement of students’ anxiety, and
use of humor reduced their anxiety at
higher rates than did allowing students to work together or
“making it easy to get an A.” Additionally,
Pan and Tang (2005) used a focus group format to find that
when the instructor was sensitive to
students’ concerns, students’ anxiety was reduced and learning
was enhanced.
2.2. ANXIETY AND WORRY
Statistics anxiety is a problem not only because of the adverse
affects on student outcomes,
attitudes, self-concepts, and tendency to procrastinate, but also
because it can affect students’
decisions to enroll in statistics courses early in their programs
of study (Onwuegbuzie, 1997; Roberts
& Bilderback, 1980). This delay may contribute to further
avoidance, thereby increasing student
anxiety. As Rachman (2004) states, avoidance is successful in
the short term for relieving anxiety, but
in the long run it contributes to further avoidance and helps

strengthen the original anxiety.
Anxiety is described as “a tense, unsettling anticipation of a
threatening but vague event; a feeling
of uneasy suspense” (Rachman, 2004, p. 3). A person feeling
anxiety has a difficult time identifying a
specific cause, yet the anxiety is persistent and encompassing.
There are two main types of anxiety:
trait anxiety and state anxiety. Trait anxiety refers to a
relatively enduring characteristic of a person,
whereas state anxiety is a response to a specific threatening
situation that is only present when the
threat is present. Barlow (2002) points out that anxiety also
involves a perception of lack of control
over future events and that it may become associated with any
number of different situations. A
common response to anxiety is the attempt to escape the threat
and to avoid situations where the threat
may be encountered (Rachman, 2004). An example of this
response might be students who avoid
statistics courses in order to reduce the discomfort of anxiety.
51
Anxiety is characterized by worry, which was originally
described by Borkovec, Robinson,
Pruzinsky, and Dupree (1983):
Worry is a chain of thoughts and images, negatively affect-
laden and relatively uncontrollable; it
represents an attempt to engage in mental problem-solving on
an issue whose outcome is
uncertain but contains the possibility of one or more negative

outcomes; consequently, worry
relates closely to the fear process. (p. 10)
A later description of worry is provided by MacLeod, Williams,
and Bekerian (1991), who proposed
that worry is “a cognitive phenomenon, … concerned with
future events where there is uncertainty
about the outcome, the future being thought about is a negative
one, and this is accompanied by
feelings of anxiety” (p. 478). Barlow (2002) adds to this
description the idea that worry is “an anxious
apprehension for future, negative events.” He goes on to say
that worry represents an attempt to cope
with anxiety, so in effect as anxiety increases, worry increases
as well.
In clinical studies, worry is the main feature of generalized
anxiety disorder (GAD) and is present
in many other anxiety disorders as well (American Psychiatric
Association, 2000; Barlow, 2002).
Research studies have shown that individuals with GAD report
significantly higher levels of worry
than in non-pathological samples (Fresco, Mennin, Heimberg, &
Turk, 2003; Molina & Borkovec,
1994), while others (Ruscio, 2002; Ruscio & Borkovec, 2004)
find that high levels of worry are not
necessarily associated with GAD. For example, Ruscio found
that only 20% of high-worriers were
also diagnosable as experiencing GAD. This finding helped
illustrate that the tendency to worry out of
proportion can also be found outside clinical samples.
There have been fewer studies utilizing non-clinical samples
investigating worry, with or without
anxiety. In one of the first studies investigating worry in non-
clinical participants, Tallis, Davey, and

Capuzzo (1994) asked 128 college students and working adults
about their experiences with worry.
Thirty-eight percent reported worrying at least once a day and
that the typical time spent worrying
was 10 minutes or less, typically about upcoming events or
interpersonal interactions. Smaller
percentages were reported for worrying more than once a day
and for longer than 10 minutes.
Similarly, Szabo and Lovibond (2002) assessed a sample of 57
psychology students concerning
the content of naturally-occurring worry episodes. Fifteen were
classified as analogue GAD
participants (scoring high enough on GAD measures to be
diagnosable, yet not clinically diagnosed),
21 were classified as moderate worriers, and 21 as low worriers.
The authors found that 20% of the
worry content reported by participants focused on negative
potential outcomes while 50% of the
content was focused on problem-solving thoughts. Additionally,
those who scored higher on worry-
proneness did not differ significantly from moderate or low
worriers on this outcome. The only
difference suggested by the data indicates that high worriers
judge their own problem-solving
thoughts to be significantly more ineffective than those who
worry moderately or less, pointing to a
negative relationship between worry-proneness and problem-
solving effectiveness.
In a comparable study, Ladouceur, Blais, Freeston, and Dugas
(1998) investigated problem
orientation and problem-solving skills in relation to anxiety in a
sample of college students and GAD
patients. Fifteen of the students were classified as moderate
worriers and 14 as analogue GAD

worriers, with the remaining sample made up of 14 participants
clinically diagnosed with GAD.
Across all groups, problem orientation was related to anxiety
levels, but problem-solving skill was not
related. The authors also found that those worriers with higher
levels of anxiety also expressed a
stronger belief that worry is beneficial while reporting a lower
tolerance for uncertainty.
2.3. ANXIETY, WORRY, AND INTOLERANCE OF
UNCERTAINTY
Another characteristic of anxiety appears to be intolerance of
uncertainty, which is believed to
lead directly to the tendency to worry (Koerner & Dugas, 2006).
These authors describe a model of
GAD that incorporates not only worry as a function of anxiety,
but also describes the intolerance of
uncertainty as the instigator of worry, which in turn is due to
negative problem orientation, the belief
that worry is valuable, and cognitive avoidance. Intolerance of
uncertainty is defined as a
dispositional characteristic that affects how a person perceives
and responds to uncertain situations on
a cognitive, emotional, and behavioral level. Those who show
an intolerance of uncertainty
experience uncertainty as stressful, believe that situations where
uncertainty exists are best avoided,
and experience an undermining of their ability to function (Buhr
& Dugas, 2002).

52
Researchers have demonstrated a significant relationship
between worry and intolerance of
uncertainty (Dugas, Freeston, & Ladouceur, 1997; Freeston,
Rheaume, Letarte, Dugas, & Ladouceur,
1994; Ladouceur, Gosselin, & Dugas, 2000; Tallis & Eysenck,
1994). For example, Dugas, Gosselin,
and Ladouceur (2001) examined the specificity of the
relationship between the two by adding other
variables known to be related to worry. In the sample of 347
undergraduate students, the authors
found that intolerance of uncertainty was highly related to
worry (r = 0.70), moderately related to
obsessions/compulsions (r = 0.48), and weakly related to panic
sensation (r = 0.33). Further, through
regression analysis, the authors found that intolerance of
uncertainty explained 42% of the variance in
worry after accounting for other variables. In a second
regression, worry explained 34% of the
variance in intolerance of uncertainty beyond that of the other
variables. Similarly, Buhr and Dugas
(2006) surveyed 197 college students concerning intolerance of
uncertainty, worry, intolerance of
ambiguity, perfectionism, and perceived control. Though worry
was related to all of the variables
except other-oriented perfectionism (a subscale of
perfectionism) and perceived mastery (a subscale
of sense of control), the strongest relationship among the
variables occurred between worry and
intolerance of uncertainty (r = 0.63).
As the literature suggests, anxiety, worry, and intolerance of
uncertainty appear to be inseparable
components. Researchers have explored the relationship

between worry and anxiety largely in terms
of GAD, but many have expanded our understanding by
investigating other types of anxiety such as
the myriad of evaluative anxieties (e.g., test anxiety, math
anxiety, social anxiety, sports anxiety,
computer anxiety) in which worry is recognized as a major
cognitive component (see Zeidner &
Matthews, 2011). The development of the intolerance for
uncertainty construct (Koerner & Dugas,
2006) helps explain worry, and research concerning the
relationship between intolerance of
uncertainty, worry, and anxiety has been growing. However, one
type of anxiety has thus far not been
studied in relation to worry and intolerance of uncertainty, that
of statistics anxiety. Considering the
ample research relating generalized anxiety and other forms of
anxiety to the worry construct, and the
evidence of intolerance of uncertainty also relating to worry and
anxiety, it is logical to expect worry
and intolerance of uncertainty to have some relationship with
statistics anxiety. Therefore, the current
study seeks to investigate the relationship between intolerance
of uncertainty, worry, and statistics
anxiety.
3. PURPOSE OF THE STUDY
The focus of the current study is on the relationships among
intolerance of uncertainty, worry, and
statistics anxiety. If intolerance of uncertainty leads to worry,
and worry is the main characteristic of
anxiety (Koerner & Dugas, 2006), then it is reasonable to
expect this relationship to exist in terms of

statistics anxiety. In short, intolerance of uncertainty should be
related to worry, and worry should be
related to statistics anxiety. Specifically, the research
hypotheses are:
1. Intolerance of uncertainty is significantly correlated with
worry in statistics students at pretest.
2. Worry is significantly correlated with six types of statistics
anxiety at pretest.
3. Student levels of intolerance of uncertainty, worry, and
statistics anxiety will be significantly
reduced from pretest to posttest.
4. METHOD
4.1. PARTICIPANTS
The participants for the study were recruited from the college of
education in a large public
southwestern university. Students in three sections of a graduate
level introductory statistics course
over the fall and spring semesters of 2010 were asked to
volunteer, and all students agreed to
participate. Of the ninety-seven (n = 97) participants, most were
female (66.0%), and white (64.9%),
and just over half were master’s students (53.6%). The great
majority of the students (90.7%) were
more than halfway through their degree programs, with 61.9%
having less than 33 hours remaining.
Though students were not specifically asked to indicate their
majors, enrollment records indicate that
the fields of educational psychology, sports psychology, higher

education, counseling, hospitality
53
administration, nutritional science, mass communications, and
family/consumer science were
represented. Table 1 presents the demographic characteristics of
the participants.
Table 1. Sample demographics (n = 97)
n % mean SD
Male 33 34.0
Female 64 66.0
Age 31.47 8.78
White 63 64.9
Asian 16 16.5
Hispanic 9 9.3
African-American 2 2.1
Other ethnicity 7 7.2
Master’s level 52 53.6
Doctorate level 45 46.4
4.2. INSTRUMENTS
Intolerance of uncertainty was assessed with the Intolerance of
Uncertainty Scale-12 (IUS-12;
Carleton, Norton, & Asmundson, 2007), consisting of 12 items

designed to measure one’s tolerance
for uncertainty. The twelve statements are measured on a 5-
point Likert scale ranging from strongly
disagree to strongly agree. Items are summed for a total
intolerance score, and higher scores indicate
higher levels of intolerance. Sample items include “It frustrates
me not having all the information I
need” and “When it’s time to act, uncertainty paralyzes me.”
Factor analysis was used to support
validity of the 12-item scale, shortened from the original 27-
item French version (Freeston et al.,
1994), and showed an internal consistency reliability coefficient
of 0.91 (Carleton et al., 2007). In the
current study, Cronbach’s alpha reliability coefficient for the
IUS-12 scale was 0.85 at pretest and
0.86 at posttest.
The Penn State Worry Questionnaire (PSWQ; Meyer, Miller,
Metzger, & Borkovec, 1990) was
employed as a measure of students’ tendency to worry.
Tendency to worry is assessed via 16
statements, with 5 being reverse-scored. All items are measured
on a 5-point Likert scale and then
summed for a total worry score. Higher scores indicate a higher
tendency toward worry. Sample items
include “I know I should not worry about things, but I just
cannot help it” and “Once I start worrying,
I cannot stop.” An example of a reverse-scored item is “I find it
easy to dismiss worrisome thoughts.”
The authors conducted factor analysis to support construct
validity, and found that the items loaded on
one general factor with loadings ranging from 0.38 to 0.73.
Reliability was established through
internal consistency with a coefficient of 0.93 for the 16 items.
For the current study, Cronbach’s
alpha reliability coefficient for the PSWQ was 0.91 at pretest

and 0.88 at posttest.
Statistics anxiety was measured using the Statistics Anxiety
Rating Scale (STARS; Cruise et al.,
1985), which consists of 51 items measured on a 5-point Likert
scale. The instrument consists of six
factors designed to assess anxiety in the areas of worth of
statistics, interpretation anxiety, test and
class anxiety, computation self-concept, fear of asking for help,
and fear of statistics teachers. Worth
of statistics refers to students’ perceptions of the usefulness of
statistics either in their personal,
academic, or future professional lives. Sample items include “I
feel statistics is a waste” and “I’m
never going to use statistics, so why should I have to take it?”
and are scored along the continuum of 1
(strongly disagree) to 5 (strongly agree). Interpretation anxiety,
referring to how much anxiety
students feel when faced with having to interpret statistical data
or make a decision about an analysis
outcome, is scored from 1 (no anxiety) to 5 (high anxiety).
Sample items reflecting this type of
anxiety include “Making an objective decision based on
empirical data” and “Figuring out whether to
reject or retain the null hypothesis.” Test and class anxiety are
measured on the same scale, with items
such as “Doing the homework for a statistics course” and
“Finding that another student in class got a
different answer than you did to a statistical problem.”
Computation self-concept is intended to
represent students’ anxiety concerning working on math
problems as well as their self-perceptions of
mathematical ability (rather than actual mathematical ability).
Sample items from this subscale,

54
measured on a 5-point Likert scale from strongly disagree to
strongly agree, include “I haven’t had
math for a long time. I know I’ll have problems getting through
statistics” and “I could enjoy statistics
if it weren’t so mathematical.” The final two subscales, fear of
asking for help and fear of statistics
teachers, are also measured on a 5-point Likert scale with 1
indicating no anxiety and 5 indicating
high anxiety. These subscales are designed to assess students’
anxiety over asking for help in
understanding statistics material, and students’ perceptions of
statistics teachers. Sample items include
“Asking one of your professors for help in understanding a
printout” and “Most statistics teachers are
not human.” Higher scores on each of the subscales indicate
higher anxiety levels for that area. Cruise
et al. (1985) reported that factor analysis was used to establish
construct validity for the instrument,
resulting in loadings for the 51 retained items of 0.50 or greater
for the six factors. The authors also
reported test-retest reliability for the six factors ranging from
0.67 to 0.80, and internal consistency
reliabilities ranging from 0.68 to 0.94. For the current study,
Cronbach’s reliability coefficients for the
six subscales were, at pretest and posttest respectively, 0.93 and
0.91 (worth of statistics), 0.88 and
0.88 (interpretation anxiety), 0.93 and 0.92 (test and class
anxiety), 0.88 and 0.86 (computation self-
concept), 0.89 and 0.86 (fear of asking for help), and 0.77 and
0.82 (fear of statistics teachers).

4.3. PROCEDURE
At the beginning of the fall and spring semesters, graduate
students in three sections (one in the
fall and two in the spring) of introductory statistics classes were
invited to participate in the study.
The students were told that the researcher was interested in how
they felt about statistics and how they
perceived themselves in terms of worry and uncertainty, and
were assured of confidentiality and
anonymity. Before any statistics instruction began, students who
agreed to participate were given an
envelope containing a pretest and a posttest. In addition to
demographics questions, the pretest and
posttest instruments consisted of the IUS-12, PSWQ, and
STARS instruments. Students were then
instructed to complete the pretest questionnaires only. Upon
completion, the students were asked to
return the pretest instruments to their envelopes, seal the
envelopes, and write the last four digits of
their phone numbers on the outside for temporary identification
purposes. Near the last day of the
semester, the sealed envelopes were returned to the students and
they were asked to complete the
posttest instruments and destroy the outer envelopes in order to
preserve anonymity.
5. RESULTS
Descriptive statistics were calculated for the research variables.
Means and standard deviations for

intolerance of uncertainty, worry, and the six dimensions of
statistics anxiety are given in Table 2.
Table 2. Means and standard deviations for IUS-12, PSWQ, and
STARS
Pretest Posttest
Variable mean SD mean SD
Intolerance of Uncertainty 34.55 7.89 33.44 7.35
Worry 54.56 11.97 52.05 12.55
Worth of statistics 33.32 10.42 30.94 8.55
Interpretation anxiety 27.97 8.61 22.71 7.40
Test and class anxiety 25.87 8.65 20.87 8.45
Computational self-concept 16.60 6.30 14.06 4.90
Fear of asking for help 8.88 4.31 7.39 3.61
Fear of statistics teacher 11.22 3.81 8.95 3.12
In order to test hypotheses one and two, Pearson’s r correlation
coefficients were calculated among
intolerance of uncertainty, worry, and the six types of statistics
anxiety. As a control for the family-
wise error rate, the Bonferroni adjustment was applied using a
0.05 alpha level (0.05/28 = 0.002).
Therefore, only those correlations that were significant at the
adjusted level of 0.002 or lower were
deemed significant at the desired 0.05 level when overall error
was controlled. The results are
presented in Table 3. As expected, intolerance of uncertainty
and worry were significantly and

55
positively correlated, with a large effect size (i.e., d > 0.50;
Cohen, 1988). Intolerance of uncertainty
was significantly positively related to four of the six
dimensions of statistics anxiety with the
exceptions being worth of statistics and fear of statistics
teachers. Worry was significantly positively
related to three dimensions of statistics anxiety with the
exceptions being worth of statistics, fear of
asking for help, and fear of statistics teachers. Effect sizes were
moderate (i.e., 0.30 ≤ d ≤ 0.50) for the
relationships between intolerance of uncertainty and the STARS
factors of interpretation anxiety,
test/class anxiety, computation self-concept, and fear of asking
for help. Effect sizes were also
moderate for the relationships between worry and the STARS
factors of interpretation anxiety,
test/class anxiety, and computation self-concept. Therefore,
hypothesis one is supported and
hypothesis two is partially supported.
Table 3. Pearson’s r correlation coefficients among research
variables at pretest
Variable IUS PSWQ WS IA TCA CSC FAH FST
IUS - .55* .14 .34* .41* .32* .47* .21
PSWQ - .16 .32* .38* .32* .27 .25
WS - .22 .33* .58* .21 .35*
IA - .64* .48* .47* .38*
TCA - .61* .61* .40*
CSC - .39* .49*
FAH - .40*
FST -

Note. IUS: Intolerance of Uncertainty Scale; PSWQ: Penn State
Worry Questionnaire; WS: Worth of Statistics;
IA: Interpretation Anxiety; TCA: Test and Class Anxiety; CSC:
Computation Self-Concept; FAH: Fear of
Asking for Help; FST: Fear of Statistics Teacher
*overall error rate controlled at α = 0.05
Hypothesis three predicted that students’ intolerance of
uncertainty, worry, and statistics anxiety
would be significantly reduced from pretest to posttest. A
repeated-measures within-subjects
multivariate analysis of variance (MANOVA) was utilized to
test this hypothesis. Due to the strength
of the relationship between uncertainty, worry, and the six
dimensions of statistics anxiety, all were
included in the analysis as the dependent variables with stage of
test (pretest or posttest) as the
independent variable. Dependent variable difference scores
were investigated for normality using the
Kolmogorov-Smirnov test prior to analysis and found to be
slightly skewed. Transformation of the
data as a solution to the lack of normality was dismissed due to
the robustness of analysis of variance
procedures (Wilcox, 2005), adequacy of sample size ensuring
normality of the sampling distribution
(i.e., n > 30), and potential changes to the constructs being
tested (Games, 1984). The assumption of
sphericity is necessarily met since there are only two levels of
the independent variable (O’Brien &
Kaiser, 1985). Results of the multivariate test indicate an
overall statistically significant change from
pretest to posttest for the combination of dependent variables, λ
= 0.61, F (8, 89) = 7.16, p < 0.001, η2
= 0.39.

Follow-up repeated-measures within-subject ANOVAs indicated
statistically significant effects on
all dependent variables except intolerance of uncertainty and
worry. The Bonferroni adjustment was
applied to the alpha level of the follow-up ANOVAs to control
for family-wise error rate, generating
an adjusted significance level of 0.006 (0.05/8 = 0.006). At this
significance level, there is evidence
that the six types of statistics anxiety are significantly reduced
from pretest to posttest, but intolerance
of uncertainty and worry were not significantly reduced.
Therefore, hypothesis three was partially
supported. Results of the univariate ANOVAs are presented in
Table 4.
6. DISCUSSION AND CONCLUSIONS
The goal of the current study was to explore the association
between intolerance of uncertainty,
worry, and statistics anxiety. Through their work over the
preceding decade a research group led by
Koerner and Dugas (2006) found evidence that intolerance of
uncertainty led to worry in both clinical
56
Table 4. Follow-up repeated-measures within-subject ANOVAs

for repeated-measures within-subject MANOVA
Test Variable F df p η2
IUS 1.88 96 .063 .04
PSWQ 2.72 96 .008 .07
WS 2.90* 96 .005 .08
IA 5.93* 96 .000 .27
TCA 6.02* 96 .000 .27
CSC 4.77* 96 .000 .19
FAH 3.18* 96 .002 .10
FST 5.56* 96 .000 .24
Note. IUS: Intolerance of Uncertainty Scale; PSWQ: Penn State
Worry Questionnaire;
WS: Worth of Statistics; IA: Interpretation Anxiety; TCA: Test
and Class Anxiety;
CSC: Computation Self-Concept; FAH: Fear of Asking for
Help; FST: Fear of
Statistics Teacher
*overall error rate controlled at α = 0.05
and non-clinical samples in terms of generalized anxiety, with
intolerance of uncertainty accounting
for as much as 42% of the variance in worry scores (Dugas et
al., 2001). Due to the stability of their
findings, it was expected that intolerance of uncertainty would
account for a substantial amount of
variance in worry scores in regards to statistics anxiety. This
hypothesis was supported with the
finding that intolerance of uncertainty accounted for 30.2% of
the variance in worry scores for the
current sample of graduate statistics students.

With intolerance of uncertainty being significantly related to
worry, and because worry is the
main component in generalized anxiety (American Psychiatric
Association, 2000; Barlow, 2002), it
was expected that worry would then be significantly related to
statistics anxiety. Moderate effect sizes
for correlations between worry and three of the six dimensions
of statistics anxiety proposed by
Cruise et al. (1985) indicated that this relationship exists at
least somewhat. The exceptions were the
dimension of worth of statistics, fear of asking for help, and
fear of statistics teachers. Worth of
statistics is much more of an attitude construct (see Cruise et
al.,) than a direct anxiety construct,
which may explain the lack of relationship between this
construct and worry. Fear of asking for help
and fear of statistics teachers are more social in nature, as both
require interaction with another
person, and perhaps tap into more of a social apprehension
construct rather than an anxiety construct.
Even so, the data provide evidence that worry is related to some
forms of statistics anxiety.
A decrease was expected for all of the variables from pretest to
posttest. Through repeated-
measures MANOVA, this hypothesis was also partially
supported. Levels of all six types of statistics
anxiety were significantly lower in the current sample at
posttest, but worry and intolerance of
uncertainty was not significantly different. It is unclear whether
simply going through the statistics
course or other factors not measured in the current study may be
influencing worry and intolerance of
uncertainty in statistics students. Indeed, Koerner and Dugas
(2006) suggest as much with their
contention that intolerance of uncertainty, which leads to worry,

is a function of three other
constructs: positive beliefs about worry, cognitive avoidance,
and negative problem orientation. Also,
it seems likely that since intolerance of uncertainty is defined as
a dispositional construct (Koerner &
Dugas), and anxiety is an affective construct (Rachman, 2004;
Startup & Erickson, 2006) that tends to
be transitory (Rachman, p. 30), becoming familiar with
statistics would be more likely to have an
effect on the latter. Even so, the current study provides a
beginning to the potentially useful
investigation of the relationship between worry, related
constructs, and statistics anxiety in graduate
students.
The results of the current study should certainly be generalized
with caution. The eight dependent
variables were utilized simultaneously which may have resulted
in loss of power for detecting
differences from pretest to posttest on those variables found to
be unaffected. With the strongest effect
sizes indicating that change from pretest to postest explains just
27% of the variance in both
interpretation anxiety and test/class anxiety, it is clear that
other variables are also having an effect on
students’ statistics anxiety. Fewer variables included over
multiple studies, as well as the addition of a
57
control group, could enhance the information given by this
study. With the significant correlations
found between the majority of the research variables, the nature

of the relationships between
intolerance of uncertainty, worry, and statistics anxiety bears
further investigation. Future research
should continue this endeavor with the addition of other
variables known to relate to intolerance of
uncertainty, worry, and anxiety.
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AMANDA S. WILLIAMS
Texas Tech University
College of Education
3008 18th
Lubbock, TX 79409

Article Critique
6
Critical Review of Published Articles
For the assignment, the students will read and review “Worry,
Intolerance of Uncertainty, and Statistics Anxiety” by Amanda
S. Williams. The article located in Content section within
Session 9. A critical review report summarizes and evaluates
the research article. Your job is to determine if the researcher
wrote a first class, grade “A” article for publication. That is,
was the article worthy of publication? Empirical research
articles go through a blind publication procedure. The journal’s
reviewers do not know who wrote the articles considered for
publication, which would be a one- to two-year process.
The report must be between 3- to 5-pages. Below, carefully read
the requirements for the report. If you have questions, please
ask.
Do not deviant from the required information for the report.
Going beyond the requirements will negatively affect the final
score of the report. Carefully, proofread the report, use the F7
Key on the keyboard to help with proofreading and have a
friend proofread the report. In addition, you may submit the
report to UMUC Writing Center. They may spot writing errors
that you missed.
This is a nine-week assignment; therefore, you may start the
assignment. There will be no due date extension for the
assignment. Five percent deduction per day for late submission
past the due date; however, once the submitted reports have
been graded, the grading process has stopped. Therefore, please
submit the report by the due date. Please do not wait until the
last moment to read the article and write the report.
Click onto the Critical Review site with tips.
Required Information for the Report
1. Title page, which does not count for the required 3- to 5-

pages:
a. title of the empirical research article and author’s name(s),
b. your name,
c. running head with continuous page numbers, and
d. date.
2. Summary of the article (150- to 250-words), which does not
count for the required 3- to 5-pages. Summarize the article with
the following information from the article (do not quote from
the article).
a. Purpose of the study (explicitly, write the word “purpose” in
the sentence),
b. Participants of the study (explicitly, write the word
“participants” in the sentence),
c. Research hypotheses of the study (explicitly, write the word
“hypotheses,”
d. Measurement instruments (explicitly, write the words
“measure instruments” in the sentence), and
e. Conclusions of the study (explicitly, write the word
“conclusions” in the sentence).
3. Analysis of Data. This is the most important section of the
report. This is an opportunity to demonstrate your critical
thinking skills. From the empirical research article, include the
page number(s) in the citation for the answers. Finally, do not
quote information from the article. The following questions
must be answered in the report and use the questions as
subheadings:
a. For Table 2 (page 54) and for Table 3 (page 55), which
statistical procedures did the researcher use to calculate the raw
data?
i. Define the terms with citations to support the definitions.
b. Did the statistical procedures for Tables 2 and 3 support the
hypotheses of the study? Please explain with specific data
examples from the research article to evaluate the procedures.
Were the statistical procedures the correct ones, why or why not
to support the hypotheses?
c. Did Tables 2 and 3 properly display the data results? To

support your answers, please explain with specific data
examples from the research article to evaluate the table’s
format. That is, were the tables easy or difficult to understand,
why or why not the tables were easy or difficult to understand?
4. Conclusion/Discussion. Answer the following questions in
the report and use subheadings. In addition, from the empirical
research report, cite the page number(s) where you located the
information for the questions. Finally, do not quote text from
the article.
a. What were the conclusions based on the data results? Did the
conclusions support or not support the hypotheses of the study?
Please explain with information from the research article to
support your analysis.
b. All empirical research studies have limitations; therefore,
what do you think were the limitations of the study. That is,
were there flaws in the research procedures? Please explain with
information from the research article to support your analysis.
5. Recommendations.
a. If you were to conduct the study, please explain with details
the changes you would make to strengthen the validity of the
study.
6. Reference(s). Does not count for the 3- to 5-pages.
a. Use APA Reference format.
Grading Policy
Grading rubric for the review report.
All students will have ten weeks to complete the review;
therefore, perfection is necessary. Please write a professional
paper as if you were submitting the report for publication. Do
not wait until the last moment to write the report.
Grading Rubric: Article Critique Assignment
Name:
Date paper was downloaded:
Weight

Category
Description
Score
20
Length and Style
Category encompasses issues of appropriate length (as outlined
in classroom) and formatting, layout, and style, including APA
formatting, originality, etc.
20
Presentation/Writing
Includes elements of spelling, grammar, punctuation, flow, and
the following of business writing conventions. Paper should
stay on focus and have logical progression.
30
Summary
The student provided explicit details of the research article for
the reader to understand the essence of study.
30
Analysis/Conclusion
The student demonstrates critical thinking skills with concrete
evidence to support the analysis.
100
TOTAL
0
Letter grade description.
“A” grade (90 to 100) = the report contained the required
information, but some changes would be required for
publication.
“B” grade (80 to 89) = the report contained the required

information, but many changes would be required for
publication.
“C” grade (70 to 79) = the report did not contain the required
information. Publication denied.
APA Writing Style
To help with the organization of the review information, the
students are required to use APA format 6th edition: (a) 3- to -
5-pages, (b) doubled-space, (c) Times Roman font, (d) 12 pt.
type size, and (e) include headings and subheadings to introduce
and divide the various concepts of the report. The organization
and communication of the text will influence the final score of
the report. Finally, do not include quotes from the article or
other sources. You must paraphrase the information.
Tips for Writing Critiques
1. Go through several drafts. NO ONE can write a perfect essay
in one try. Your first draft should be much longer than the final
draft. Then as you edit, try to make the same amount of
information more concise and clear, continually refining your
writing.
2. Write in formal English avoiding casual language. Though we
want to read your opinions, avoid using ‘I’ or ‘me.’ For
example, instead of writing: “I think this research overlooked
an important factor,” just write, “This research overlooked an
important factor.”
3. However, it is important to have a broad and strong
vocabulary, do not use-stilted language for the sake of using
fancy words. It makes the paper more difficult to read than is
necessary.
4. Write your critique as if your reader has NOT yet read the
paper/essay you are reviewing.
5. Write in active voice, which is more direct, bold, and concise
than passive voice. Examples from The Elements of Style by
William Strunk Jr. and E. B. White:
Passive: My first visit to Boston will always be remembered by
me.

Active: I shall always remember my first visit to Boston.
Passive: There were a great number of dead leaves lying on the
ground.
Active: Dead leaves covered the ground.
6. Run spelling and grammar-checking programs carefully,
double-checking the spelling of all Specialized and scientific
terms your dictionary will not recognize. Many spelling errors
will be caught by these programs and these errors SHOULD
NEVER persist in any work that is to be handed in for grading.
Types of Grammatical Errors
MINOR ERRORS
1. Misuse of commas, e.g., placing of a single comma between
subject and verb of a sentence; Using commas to set off
essential modifying phrase,
2. Misuse of colons or semi-colons,
3. Failure to close a quotation or parenthetical remark, and
4. Typos or misspelled words that WILL NOT be caught by
spell-checking programs
MAJOR ERRORS
1. Subject and verb of a sentence disagree (plural vs. singular),
2. Incomplete sentences (usually dependent clauses),
3. Run-on sentences (usually separated by only a comma), and
4. Typos or misspelled words that will be caught by spell-
checking programs.

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