University of Southern California Dissertations and Theses

Reducing statistics anxiety among learners in online graduate research methods courses

(USC Thesis Other)

Reducing statistics anxiety among learners in online graduate research methods courses

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content REDUCING STATISTICS ANXIETY AMONG LEARNERS IN ONLINE GRADUATE
RESEARCH METHODS COURSES
by
Neil Patrick Teixeira
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
August 2024
Copyright 2024 Neil Patrick Teixeira

ii
Dedication
To my family, especially my wife Candace and son Theodore. This accomplishment is all
the more meaningful to me because I achieved it through your love and support.

iii
Acknowledgments
To my dissertation chair, Dr. Corinne Hyde, and my committee members, Dr. Daniela
Baroffio, Dr. Akilah Lyons-Moore, and Dr. Binh Tran, I will be forever grateful for your
guidance, support and—most of all—patience throughout my years-long academic journey. Dr.
Hyde, your ceaseless encouragement and unwavering faith in my ability to complete this
dissertation despite many obstacles and delays carried me through to the finish line.
To Dr. Mathew Curtis, Dr. Yomna Elsayed, Dr. Cynthia Martinez, and Dr. Nithya
Muthuswamy, who contributed to this dissertation through their coordination on intervention
delivery in their communication research classes, I thank you for your involvement and support.
Similarly, to Dr. Rebecca Weintraub and Dr. Ben Lee, former master’s program directors for
communication management at my study site, I couldn’t have completed this research into
statistics anxiety without your blessing and encouragement.
To Dr. Courtney Pade, my sincerest thanks for consulting on my procedures and
quantitative statistical analyses over several sessions. Your knowledge of SPSS and helpful
tutorials illuminated the path as I proceeded to describe my findings.
To Dr. Jim Lee, Dr. Jessica Gibson, the late Dr. Terri Thomas, Jessica Ybarra, Jordan
Silva, Danielle West, Dr. Christopher Mattson and all members of the Rossier Ed.D. program
office and Doctoral Support Center, I thank you for your steadfast commitment to me and all
EDL students.
Although this dissertation was completed in 2024, data collection for this study occurred
over three academic terms spanning 2018 and 2019. It is important to acknowledge that the
setting for this study—an online graduate program at a large Tier 1 research university in
California—was already using online, synchronous webconferencing technologies like Zoom at

iv
that time. The emergence of COVID-19 in late 2019 and the subsequent global pandemic
restrictions in 2020 transformed the prevalence of online, distance learning in higher education
(among many other things) seemingly overnight. The current state of online graduate study in the
United States is substantially different than it was at the time of data collection, especially as it
relates to students’ prior experience with online learning coming into graduate degree programs,
and well as their comfort with synchronous, online web camera-enabled meeting technology.
The literature review contained in this study primarily reflects the state of online learning in the
United States around the time of data collection and only makes mention of more recent
statistical data about overall enrollment trends. The literature review purposefully omits the
impact of COVID-19 on the rapid expansion of online distance education in the U.S., as well as
the ubiquitousness of synchronous web camera-enabled interaction students engage with today
because it is not reflective of the conditions at the time of this study’s development and data
collection.
Author’s Note
Neil Teixeira is the sole author of this dissertation.
I have no known conflict of interest to disclose.
Correspondence concerning this dissertation should be addressed to Neil Patrick Teixeira,
6449 E Marita Street, Long Beach, CA 90815. Email: teixeira@usc.edu

v
Table of Contents
Dedication....................................................................................................................................... ii
Acknowledgments......................................................................................................................... iii
List of Tables............................................................................................................................... viii
List of Figures.................................................................................................................................ix
Abstract............................................................................................................................................x
Chapter One: Overview of the Study ..............................................................................................1
Background of the Problem.................................................................................................1
Statement of the Problem ....................................................................................................3
Purpose of the Study............................................................................................................3
Research Questions .............................................................................................................4
Significance of the Study.....................................................................................................4
Methodology........................................................................................................................5
Limitations...........................................................................................................................5
Delimitations .......................................................................................................................6
Definition of Terms.............................................................................................................6
Organization of the Study....................................................................................................9
Chapter Two: Literature Review...................................................................................................11
Statistics Anxiety...............................................................................................................11
Research Methods Education ............................................................................................31
Online Learning.................................................................................................................37
Conclusion.........................................................................................................................49
Chapter Three: Methodology ........................................................................................................50
Research Questions ...........................................................................................................50
Research Design ................................................................................................................51

vi
Population and Sample ......................................................................................................52
Demographic Characteristics of Study Sample.................................................................53
Instrumentation..................................................................................................................54
Measuring Statistics Anxiety.............................................................................................54
Procedure and Data Collection..........................................................................................55
Data Analysis.....................................................................................................................59
Credibility and Trustworthiness........................................................................................60
Ethics.................................................................................................................................61
Chapter Four: Results and Findings..............................................................................................62
Participants........................................................................................................................64
Demographic Characteristics of Study Sample.................................................................65
Organization of the Findings.............................................................................................68
Results for Research Question 1........................................................................................69
Discussion for Research Question 1..................................................................................72
Results for Research Question 2........................................................................................72
Discussion for Research Question 2..................................................................................74
Results for Research Question 3........................................................................................75
Discussion for Research Question 3..................................................................................78
Summary of Findings........................................................................................................78
Chapter Five: Discussion...............................................................................................................81
Purpose of the Study..........................................................................................................81
Research Questions ...........................................................................................................81
Description of the Methodology........................................................................................82
Discussion of Findings for Research Question 1 ..............................................................83
Discussion of Findings for Research Question 2 ..............................................................84

vii
Discussion of Findings for Research Question 3 ..............................................................85
Summary of Findings........................................................................................................85
Limitations of the Study ....................................................................................................86
Implications for Practice....................................................................................................87
Suggestions for Future Research .......................................................................................89
Conclusions .......................................................................................................................90
References .....................................................................................................................................93
Appendicies.................................................................................................................................104
Appendix A: Demographic and Open-Ended Questions.................................................104
Appendix B: Statistical Anxiety Rating Scale-Revised (STARS-R) ..............................108
Appendix C: Study Participation Information Sheet.......................................................111

viii
List of Tables
Table 1: Chew and Dillon’s (2014) Measures and Subscales of Statistics Anxiety ......................24
Table 2: Taylor’s (2001) Models of Distance Education—A Conceptual Framework..................40
Table 3: Quantitative Data Analysis Summary..............................................................................60
Table 4: Participant Demographics................................................................................................67
Table 5: Independent Samples T-Test Results for Instructional Delivery Method........................70
Table 6: Tests of Between-Subjects Effects for Analysis of Variance ...........................................72
Table 7: Independent Samples T-Test Results for Instructional Learning Strategy.......................75

ix
List of Figures
Figure 1: Onwuegbuzie’s (2003) Anxiety-Expectation Mediation (AEM) Model ........................22
of Statistics Achievement
Figure 2: Bandura’s (1986) Model of Triadic Reciprocality .........................................................34

x
Abstract
This dissertation examines the impact of synchronous, web camera-enabled sessions and
cooperative learning strategies on reducing statistics anxiety among graduate students in an
online research methods course. Employing a quasi-experimental, mixed-methods design, the
study involved 61 students from an online master's program in communication management.
Quantitative analysis using the Statistics Anxiety Rating Scale Revised (STARS-R) indicated a
significant reduction in statistics anxiety for students participating in synchronous sessions, while
cooperative learning strategies showed no significant effect. Qualitative data revealed that
instructor availability and approachability positively influenced students' willingness to seek
help. The findings suggest that real-time interaction and immediate feedback in synchronous
sessions effectively mitigate statistics anxiety. The study highlights the importance of instructor
engagement in online learning environments and suggests further research to explore the specific
conditions under which cooperative learning might reduce anxiety. Implications for practice
include integrating synchronous sessions and enhancing instructor presence in online courses.
Keywords: statistics anxiety, math anxiety, distance education, online learning, research
methods learning, statistics learning, graduate students, mixed methods, STARS, STARS-R.

1
Chapter One: Overview of the Study
Research methods learning in higher education settings, including the application of
statistical concepts, is a pillar of the process of academic and scientific inquiry and has long been
essential to earning an advanced degree in many fields. However, most students struggle with
debilitating amounts of anxiety when approaching their statistics or research methods
coursework that results in some students dropping out of their degree programs. As more
graduate students in the United States turn to online learning to earn their degrees, college
faculty must be prepared with strategies for alleviating research-related statistics anxiety so that
they can successfully train future researchers and produce knowledgeable consumers of
statistical information.
Background of the Problem
The growing need for statistics literacy as an outcome of postsecondary education in the
United States presents a challenge to both students and educators in online classrooms. Research
on statistics literacy acknowledges its centrality to effective decision making in an information
society (Gal, 2002), and organizations like the American Statistical Association (ASA) have long
called for strengthening statistical reasoning skills across the American population (Wallman,
1993; Moore, 1998). However, recent studies reveal that most adult citizens lack the ability to
critically evaluate statistical reporting in everyday contexts like news articles, medical studies,
and government policy (Chew & Dillon, 2014; Utts, 2003). Consequently, universities have
increasingly made statistics courses a required component of undergraduate and graduate degree
programs (Onwuegbuzie & Wilson, 2003; Stoloff et al., 2009) in an effort to improve statistical
literacy among graduates (Gould, 2010). Yet, despite being essential to earning an advanced
degree and producing a well-informed citizenry, statistics courses are often purposefully avoided

2
by students who find them to be anxiety-inducing (Zeidner, 1991). Statistics anxiety, which
occurs when encountering statistics in any form at any level, can be so severe that some graduate
students delay registration in required statistics or research methods courses, thus postponing
and, at times, interrupting degree completion entirely (Onwuegbuzie, 1997, 2004).
Data from the National Center for Education Statistics reveals that, as early as 2016, one
quarter of all university students and one third of all graduate students in the United States
engaged in online courses at some point in their academic careers, with over 7 million students
enrolled in exclusively online coursework (Allen & Seaman, 2016). Further, approximately 25%
of all graduate enrollments nationwide were taken entirely at a distance from the traditional
brick-and-mortar campus (National Center for Education Statistics, 2016). As online
undergraduate and graduate programs expand into pedagogically complex curricular areas,
faculty will be challenged to develop instructional strategies effective in synchronous, videoenabled online sessions. Statistics learning, including its role as a component of research
methods education, is one example of a curricular area that can be pedagogically complex for
instructors and anxiety-inducing for learners, even in face-to-face contexts. Tellingly, research
has evidenced a significant negative relationship that is causally linked between statistics anxiety
and academic performance, and empirical studies have concluded that the best indicator of
performance in a research methods course is a student’s level of statistics anxiety (Onwuegbuzie
& Wilson, 2003).
Reducing this anxiety must be a primary goal of our universities, particularly in nonmathematics graduate programs such as in the social sciences, where students are most likely to
struggle with anxiety towards required statistics and research methods courses (McGrath, Ferns,
Greiner, Wanamaker, & Brown, 2015). A secondary goal should be to identify instructional

3
strategies that are effective in reducing learners’ statistics anxiety, not just in traditional face-toface contexts, but in fully online learning environments as well. Unfortunately, a thorough
review of the literature on statistics anxiety revealed no research studies that address this need in
online learning communities, where an increasing number of university students are receiving
their training on statistics and quantitative and qualitative research methods. This study seeks to
address this gap by examining the relationship between asynchronous online learning instruction
and statistics anxiety. Further, it will also test whether cooperative learning activities delivered in
a virtual face-to-face environment are effective in reducing statistics anxiety.
Statement of the Problem
Statistics anxiety and negative attitudes towards statistics are detrimental to the
attainment of statistics literacy and achievement, a critical competency for many undergraduate
and graduate degree programs. Statistics anxiety can also be a factor in students’ decisions to put
off taking required statistics and research methods courses, thus delaying or interrupting
postsecondary degree attainment (Onwuegbuzie & Wilson, 2003). While there are studies that
examine the impact of students’ attitudes towards statistics and their statistics-related anxiety on
academic outcomes, there is scant research that proposes potential strategies for moderating this
effect. Furthermore, the few experimental studies that address interventions aimed at reducing
statistics anxiety have all been conducted in traditional face-to-face settings (McGrath et al.,
2015; Pan & Tang, 2004). Consequently, there is an absence of research aimed at exploring
effective strategies for reducing statistics anxiety among students in online courses.
Purpose of the Study
The purpose of this study is to evaluate the effectiveness of an activity-based
intervention, delivered via a synchronous, web camera-enabled online instructional session, on

4
reducing statistics anxiety in students completing a required research methods course in an
online communication master’s program. Specifically, this study will test whether active and
cooperative learning strategies used in prior studies and deployed in traditional, face-to-face
contexts are effective in reducing statistics anxiety in an exclusively online learning
environment.
Research Questions
This study seeks to answer the following research questions:
1. Are synchronous, web camera-enabled online sessions effective in reducing distance
learners’ statistics anxiety in online graduate research methods courses?
2. Are cooperative learning activities effective in reducing distance learners’ statistics
anxiety in online graduate research methods courses?
3. How do distance learners describe their likelihood to engage a statistics teacher for
help after participating in a synchronous, web camera-enabled online session?
Significance of the Study
The answers to these research questions are significant to the fields of statistics anxiety
and online learning because prior studies have not yet explored the effects of active learning
strategies on reducing statistics anxiety in online learners. Also, this study seeks to add to the
limited number studies that utilize a mixed methods approach to examining the effectiveness of
such instructional strategies. These data and subsequent analyses may be useful to educators
teaching statistics and quantitative research methods to online students who will likely be
required to demonstrate proficiency in those content areas to earn their degrees. Furthermore, as
fully online master’s degree programs continue to proliferate and more research methods courses

5
are taught online, it is essential to investigate and identify instructional techniques that reduce
statistics anxiety in non-traditional, distance learning environments.
Methodology
This study will employ a mixed methods approach to explore the relationship between
specific instructional techniques and learners’ statistics anxiety. The quasi-experimental research
design includes both pre- and post-tests of statistics anxiety using the STARS-R measure (Cruise
et al., 1985; McGrath et al., 2015) and subsequent quantitative analyses of control and
experimental groups to determine whether statistical differences or predictive relationships exist.
Additionally, follow-on surveys will be conducted after the post-test data is collected to further
probe the effect of the instructional techniques on the two subject groups. Demographic
information, as well as the aforementioned data, will be collected via online surveys distributed
via Qualtrics and will be analyzed in SPSS using t-tests and analysis of variance (ANOVA).
Limitations
A main limitation of this study will be the population of study, which is limited to only
online students enrolled in a particular Communication Research course at a large, private
research university. The researcher estimates the total population size to be approximately 40
students and, while every effort will be made to maximize participation in the study, there will be
concerns about the generalizability of any findings to the larger population of online graduate
students enrolled in research methods classes. Furthermore, self-selection bias could be a
limitation if students who are already experiencing elevated levels of anxiety towards enrolling
in a research methods course choose not to participate in the study, or if the students who do
participate fail to reflect the overall population of students who normally enroll in the course.

6
Delimitations
This study will use a quasi-experimental research design. The researcher will randomly
select a nonequivalent control group drawn from preexisting students enrolled in a graduate
research methods class. As such, a completely randomized sample will not be possible to obtain.
However, Campbell and Stanley (1963) note that interpretation of results is improved from this
type of quasi-experimental design compared to that of a single-group pretest-posttest design.
Additionally, this study is centered on the construct of statistics anxiety. Other related factors
such as mathematics anxiety, research anxiety, technology anxiety, as well as individual attitudes
towards these constructs, will not be measured, evaluated, or treated. Furthermore, this study is
specifically focused on reducing statistics anxiety levels among online learners in graduate
research methods courses. Although research has shown that statistics anxiety has a detrimental
effect on learning, course outcomes, and student retention (Onwuegbuzie et al., 2000;
Onwuegbuzie & Wilson, 2003), this study will not evaluate whether student performance or
course outcomes are impacted by potential changes in statistics anxiety related to the
intervention.
Definition of Terms
Varying definitions of key terms related to distance education and online learning abound
in the literature (Allen & Seaman, 2016; Bernard et al., 2009; Chen, Lambert, & Guidry, 2010;
Keegan, 1996; Rekkedal et al., 2003; Taylor, 2001; Wicks, 2010). Researchers such as Clark and
Feldon (2005) have argued the importance of clearly defining terms in studies within the
relatively nascent field of distance education. The following key terms are defined for this study
and to help the reader distinguish between the types of instructional delivery referenced in the
literature review.

7
Distance education (DE) is a form of education that is conducted in a manner that is
separate in time, place, or both, between instructor and student. In modern-day contexts preplanned instructional media and two-way computer-mediated interaction is commonly deployed.
Interaction may be synchronous or asynchronous (Bernard et al., 2004; Keegan, 1996; Rekkedal
et al., 2003).
Traditional face-to-face learning is a form of learning takes place at a set time and in a
physical location, usually on a brick-and-mortar campus (Chen et al., 2010). Lectures are
typically delivered through in-person oral and written presentations, and no online technology is
used to facilitate teaching. Student engagement with the instructor and the classroom content is
synchronous (Allen & Seaman, 2016).
Web-facilitated learning is an expansion on traditional face-to-face learning where 1-29%
of instructional content is delivered via online means (Allen & Seaman, 2016). Instructors may
use multimedia aids such as graphical and audiovisual presentations to explain a classroom
concept (Relan & Gillani, 1997). Student engagement with the instructor and the course material
is mostly synchronous but may involve some asynchronous online components. Instructors may
use a course website or learning management system (LMS) to post content (e.g. syllabi,
assignments) and make announcements (Allen & Seaman, 2016).
Blended/hybrid learning is a form of learning that combines elements of traditional faceto-face learning with substantial use of distance learning. According to Allen and Seaman
(2016), a blended or hybrid course has 30–79% of its content delivered online, most commonly
through discussion forums, resulting in a limited number of face-to-face sessions. Students
benefit from both modalities of instruction, building in-person relationships while at the same
time leveraging the flexibility of online learning (Picciano et al. 2010).

8
Online learning is a form of distance learning offered almost exclusively through the
Internet (Chen, Lambert, & Guidry, 2010), which does not rely on traditional face-to-face
instruction (Allen & Seaman, 2016). More than 80% of content must be delivered through the
Internet for it to be considered an online course (Allen & Seaman, 2016).
Statistics anxiety is a multidimensional construct that manifests as a negative state of
emotional arousal resulting from an encounter with statistics in any form and at any level, which
is typically preceded by negative attitudes towards statistics and characterized by extensive
worry, intrusive thoughts, mental disorganization, and tension. It is related to but distinct from
mathematics anxiety. Adapted from Chew and Dillon (2014) and Zeidner (1991).
Mathematics anxiety is a multidimensional construct characterized by feelings of tension
that interfere with solving mathematical problems in a wide array of ordinary and academic
situations (Richardson & Suinn, 1972).
Social-cognitive theory (SCT) is a theory that aims to understand human behavior, and
the relationship between these behaviors, personal factors, and the environment. Personal factors
refer to cognitive, affective, and biological events (Bandura, 1986, 2002).
Active learning is a form of education that proactively seeks to engage students in the
process of learning, usually through some form of activity rather than passive listening (Bonwell
& Eisen, 1991).
Cooperative learning is a form of active learning that encourages students to learn
through tasks that involve teamwork, sharing, or other forms of collaboration (Johnson, 1991).
Asynchronous learning is a learning environment where students engaging in class
modules can participate at different times (Campbell et al., 2008). According to Wicks (2010)
asynchronous learning can be achieved through a number of communication tools, including, but

9
not limited to email, online discussion forums, podcasts, as well as those that tools have come to
popularity after Wicks’ research, such as web logs (blogs), video logs (vlogs), and voicethreads.
Although student participation may not be simultaneous, there is typically a timeframe within
which an individual must respond to an assignment or prompt (Lou, Bernard, & Abrami, 2006).
Synchronous learning is a learning environment that requires simultaneous, two-way
interaction between student and instructor (Campbell et al., 2008; Lou et al., 2006). Some
common forms of synchronous online learning include video-enabled conferencing, audio
conferencing, and live chatting (Wicks, 2010).
The author acknowledges that there has been some experimentation with video games
and virtual reality simulations in both asynchronous and synchronous learning contexts (Clark,
2001; O’Neil et al., 2005). However, because research on the use of this type of technology for
distance learning is limited, the author has chosen not to incorporate these elements expressly
into the definition of synchronous learning above.
Organization of the Study
The first chapter of this study discusses online learning as a context for teaching statistics
and the effect of statistics anxiety on learners in postsecondary degree programs. It will cover the
proposed study and related research questions and establish the significance of the study. This
chapter will also describe the research methodology, outline potential limitations and
delimitations, and define key terms.
Chapter Two begins with a comprehensive review of the literature on statistics anxiety,
including definitions, antecedents, effects, and measures. Relevant experimental and nonexperimental studies on statistics anxiety will be surveyed and critiqued. Additionally, the
chapter will include a comprehensive synthesis of research methods education and related

10
teaching strategies followed by a detailed overview of the field of online learning, tracing its
evolution from correspondence courses to its modern-day Internet-based incarnation. It will
discuss the demographics of online learners and the benefits and challenges of online learning.
Chapter Three describes the methodology employed in this study. This chapter also
includes a discussion of the sample, instrumentation, research design, and data collection
process. Also, this chapter outlines the data analysis procedures and the strengths and
weaknesses of this study.
Chapter Four presents the results generated by the data analysis.
Chapter Five offers a detailed discussion of these results and an explanation of the
study’s limitations and suggestions for future research.

11
Chapter Two: Literature Review
This chapter synthesizes the current research on statistics anxiety, research methods
education, and the growth and impact of online learning. By examining these interconnected
areas, this review aims to provide a comprehensive understanding of how statistics anxiety
affects learning and performance, the challenges in teaching research methods, and the potential
of online learning to alleviate or exacerbate these issues. This literature review is structured into
several key sections to systematically explore the multifaceted nature of statistics anxiety and its
relationship with research methods teaching and online learning. By synthesizing existing
studies, it identifies gaps in the literature and proposes areas for further investigation. The
ultimate goal of this review is to inform the development of effective interventions that can
mitigate statistics anxiety, thereby enhancing students' academic performance and their overall
experience in statistics and research methods courses.
Statistics Anxiety
The ability to understand statistical information, particularly the representation of
quantitative data in a variety of media, is critical to being a well-informed citizen and consumer
of research. Statistical data is being generated in nearly every facet of human activity, and
representations of statistical data surround us on a daily basis: television ratings, social media
management panels, web traffic analytics, reports on the effectiveness of new drugs, government
policy forecasts, specialized sports information (e.g. baseball sabermetrics), school rating
systems, credit scores, and so on and so forth. Informed debates about some of the most
important issues facing American citizens (health care, housing, transportation, job growth,
immigration, climate change) are nearly impossible to have without grasping the statistical data
that frames our understanding of them. Furthermore, as people have become more interconnected

12
through technology and our economies have grown increasingly global, so too have the planet’s
most complex problems. Statistics is the only language that allows us to universally discuss these
concerns in an evidence-based manner that is capable of being culturally and politically neutral.
Simply stated, it is hard to imagine a time when statistics literacy has been more urgently needed
as an outcome of secondary education than right now. Yet, most people receive their initial
exposure to statistics learning as adults in college where they are likely to be assessed for the
first time on their ability to apply statistical knowledge to a multitude of contexts (Onwuegbuzie
& Wilson, 2003). Although it has now become increasingly common for college students to be
required to complete an introductory statistics or quantitative research course as part of their
degree program, students often express high levels of apprehension related to enrollment in these
courses. This feeling of extensive worry when encountering statistics in any context is now
commonly referred to by researchers as statistics anxiety (Zeidner, 1991; Onwuegbuzie, Daros,
& Ryan,1997). It is estimated that as many as 80% of graduate students experience detrimental
levels of statistics anxiety during their studies (Onwuegbuzie & Wilson, 2003). Further, due to
anxiety, students often put off enrolling in statistics and research methods courses that are
required to graduate, sometimes even saving the dreaded class for their last term in school,
thereby putting degree attainment in jeopardy.
In order to more fully understand this phenomenon, let us begin with some definitions of
the construct of statistics anxiety. One of the earlier, but still most comprehensive, definitions of
statistics anxiety was put forth by Zeidner (1991), who described it as,
a performance characterized by extensive worry, intrusive thoughts, mental
disorganization, tension, and psychological arousal…when exposed to statistics content,
problems, instructional situations, or evaluative contexts, and is commonly claimed to

13
debilitate performance in a wide variety or academic situations by interfering with the
manipulation of statistics data and solution of statistics problems. (p. 319)
When Onwuegbuzie et al. (1997) published their phenomenological study of statistics
anxiety they set a much broader definition of the construct, characterizing it as an anxiety that
manifests when an individual, particularly a student, encounters statistics in any form or at any
level. Statistics anxiety is most likely to occur when encountering statistics in college; however,
as statistical concepts are now being introduced to students in secondary school, it is possible
that statistics anxiety is occurring earlier and during a more formative period (Onwuegbuzie &
Wilson, 2003). Chew and Dillon, in their 2014 paper entitled, “Statistics Anxiety Update:
Refining the Construct and Recommendations for a New Research Agenda” proposed a more
synthesized definition of statistics anxiety that retained many of Zeidner’s details about the
construct, while keeping the scope of Onwuegbuzie et al. (1997). Chew and Dillon’s (2014)
redefinition of statistics anxiety frames it as,
a negative state of emotional arousal experienced by individuals as a result of
encountering statistics in any form and at any level; this emotional state is preceded by
negative attitudes toward statistics and is related to but distinct from mathematics
anxiety. (p. 199)
In redefining statistics anxiety, the authors have distinguished it from similar constructs,
such as attitudes towards statistics or mathematics anxiety, so that future researchers are better
guided in choosing instruments for their studies.
Distinguishing from Mathematics Anxiety
Statistics anxiety is recognized as a multidimensional construct that is distinct from other
types of anxiety, including mathematics anxiety (Chew & Dillon, 2014; Onwuegbuzie et al.,

14
1997). Richardson and Suinn’s (1972) influential article on mathematics anxiety conceptualized
the construct as unidimensional and marked by “feelings of tension and anxiety that interfere
with the manipulation of numbers and the solving of mathematical problems in a wide variety of
ordinary life and academic situations” (p. 551). Richardson and Suinn’s (1972) research also
resulted in the publication of the Mathematics Anxiety Rating Scale (MARS), which helped give
rise to the broader study of mathematics anxiety (Chew & Dillon, 2014). As substantive research
in the area of mathematics anxiety emerged, many researchers still sought to frame it as a
domain-specific form of test anxiety (Bandalos, Yates, & Thorndike-Christ, 1995; Brush, 1981;
Dew, Galassi, & Galassi, 1983; Hembree, 1990). Studies were conducted to assess the
differences and similarities between test anxiety and mathematics anxiety and confirmed that,
like test anxiety, math anxiety was a multidimensional construct that featured both affective and
cognitive aspects (Meece, Wigfield, & Eccles, 1990; Wigfield & Meece, 1988; Williams, 1994).
Williams (1994) compared the validity of the affective and cognitive dimensions of both test
anxiety and mathematics anxiety using Spielberger’s (1977) Test Anxiety Inventory and
Wigfield and Meece’s (1988) Math Anxiety Questionnaire and found a lack of convergence in
the two measures’ subscales, which shared only 24% of their variance on the affective dimension
and just 13% on the cognitive dimension. Williams (1994) concluded that the affective
(emotional) and cognitive (worry) dimensions of test anxiety and mathematics anxiety were
likely to be substantively different.
Researchers have noted that statistics learning is more akin to learning a new language
than it is to learning higher mathematics (Lalonde & Gardner, 1993; Onwuegbuzie, 2003) and
research has shown a positive relationship between linguistic intelligence and reduced statistics
anxiety (Onwuegbuzie & Daley, 1997). Through their work validating the Statistics Anxiety

15
Rating Scale (STARS) Cruise, Cash and Bolton (1985) were the first to propose that statistics
anxiety be considered separate from mathematics anxiety. Cruise et al. (1985) argued that
existing measures of mathematics anxiety such as MARS did not sufficiently explain all of the
elements of statistics anxiety, such as interpretation anxiety and student views on the worth of
statistics, and created the STARS measure to address this need. Work by Onwuegbuzie et al.
(1997) and Baloğlu (1999, 2004) further delineated the distinctions between mathematics anxiety
and statistics anxiety by comparing similarities and differences in the nature, definition,
antecedents, and treatments of the two constructs. As a result, the researchers determined that
statistics anxiety and mathematics anxiety had a significant, but moderate, positive relationship
with math anxiety that only accounted for less than half of the variance in statistics anxiety
(Baloğlu, 2004). Despite the publication and availability of the STARS, some researchers
continued to use MARS for the purpose of examining statistics-related anxiety. One such
example is Schacht and Stewart’s (1990) work on reducing statistics anxiety through the use of
humor, which utilized the MARS instrument to collect students’ anxiety ratings. However, this
temporary overlap in the literature has since subsided and the STARS has become widely
adopted as the most common measure of statistics anxiety used today (Onwuegbuzie & Wilson,
2003).
Antecedents of Statistics Anxiety
It is of critical importance to understand the antecedents of statistics anxiety in order to
properly assess it and devise valuable interventions to reduce its negative impact on academic
performance. According to Onwuegbuzie & Wilson (2003) the antecedents of statistics anxiety
are categorized as situational, dispositional, and environmental. Situational antecedents of
statistics anxiety refer to those aspects that surround an individual’s engagement with statistics,

16
while dispositional antecedents are tied to an individual’s personal characteristics.
Environmental antecedents are drawn from events that have occurred in the past (Chew &
Dillon, 2014).
Situational Antecedents of Statistics Anxiety
An array of statistically significant variables have been found to relate to statistics
anxiety, including: prior knowledge of statistics, statistics grades, whether a statistics course is
required or elective, whether a statistics course is accelerated, whether a student’s major is
statistics, the student’s comfort with calculators, the student’s evaluation of the statistics course
or instructor, and the student’s satisfaction with the statistics course (Bell, 2005; Onwuegbuzie &
Wilson, 2003). Also, as statistics is a field of mathematics it is not surprising that a number of
math-related variables also correlate with statistics anxiety. Some examples of math-based
predictors of statistics anxiety include number of math courses completed, poor achievement in
math courses, and overall math skill level (Wilson, 1997; Zeidner, 1991). Additionally, Baloğlu
(2004) identified the following situational variables as positively related to statistics anxiety:
mathematics anxiety, number anxiety, mathematics course anxiety, and mathematics test anxiety.
Researchers have also determined that there is an inverse relationship between mathematics
anxiety and statistics achievement (Hunsley, 1987; Morris et al., 1978) and a predictive
relationship between mathematics anxiety and statistics anxiety (Onwuegbuzie et al., 1997). Yet,
the advent of statistical software like SPSS has lessened or even eliminated the burden of doing
mathematical calculations, leading some researchers to contend that prior mathematics
knowledge as a situational antecedent is less significant in modern statistics courses
(Onwuegbuzie & Wilson, 2003). Indeed, as early as 1981 Farbey and Roberts (as cited in
Onwuegbuzie & Wilson, 2003) recommended the use of calculators to solve complex

17
mathematical processes and thereby reduce statistics anxiety. DeVaney’s (2010) examination of
the effect of situational enrollment status (on-campus versus online) on the anxiety experienced
by students in a statistics course is particularly relevant to this dissertation. The findings revealed
that online students experienced significantly higher levels of statistics anxiety than their oncampus cohorts. However, as Chew & Dillon (2014) noted, the study had some significant
limitations, including the lack of randomly assigned participants and markedly different group
characteristics. Still, the DeVaney (2010) believed that differences due to self-selection of course
type were minimal, mostly owing to geographic location, and maintained that “the online
delivery of a statistics course is likely to lead to higher levels of statistics anxiety and less
positive attitudes toward statistics at the beginning of the course” (p. 12).
Dispositional Antecedents of Statistical Anxiety
One of the most commonly cited dispositional antecedents of statistics anxiety is
procrastination. Specifically, statistics anxiety is markedly higher in students who exhibit task
avoidance stemming from a fear of failure (Onwuegbuzie, 2004). Research shows that the
relationship between academic procrastination and statistics anxiety may be bidirectional:
students who have high levels of statistics anxiety may put off statistics work due to task
aversion; or, conversely, deadlines and assignments pile up as students put off their coursework,
thus increasing levels of anxiety (Onwuegbuzie, 2004). A separate, but similar dispositional
factor of statistics anxiety is perfectionism. Graduate students who place unreasonably high
standards on the work of their close peers (other-oriented perfectionism), or who maintain
unrealistically high standards for themselves to satisfy the expectations of others (sociallyprescribed perfectionism) are likely to possess higher levels of statistics anxiety (Onwuegbuzie
& Daley, 1999). Work by multiple researchers has also identified perceived self-concept as a

18
dispositional antecedent that appears on a number of levels, including level of self-esteem,
perceived intellectual ability, perceived scholastic competence, perceived creativity, and
mathematics self-concept (Benson, 1989; Onwuegbuzie, 2000; Zeidner, 1991). Perhaps
unsurprisingly, statistical preknowledge has been shown to have a significant relationship to
statistics anxiety. A chi square association analysis of 176 college students enrolled in statistics
courses revealed that statistical preknowledge and grade level (a situational antecedent) both
were significantly related to students’ anxiety in learning statistics (Sutarso, 1992). As discussed
earlier, there has been some research differentiating statistics anxiety from mathematics anxiety
based upon statistics education’s similarity to language learning. Onwuegbuzie and Daley (1997)
examined the levels of statistics anxiety among public school educators taking a research
methodology course through the lens of Gardner’s (1983) theory of multiple intelligences. Their
(1997) findings illustrated that students enrolled in research methods courses who were less
disposed towards linguistic intelligence also possessed greater levels of statistics anxiety.
Scholastic achievement in quantitative research methods classes has also been connected to
reading ability (Collins & Onwuegbuzie, 2002), further bolstering Onwuegbuzie and Wilson’s
(2003) view that “students who are not oriented towards linguistic intelligence tend to be the
most statistics-anxious” (p. 198).
Environmental Antecedents of Statistics Anxiety
The literature on age, gender, racial, and cross-cultural differences with relation to
statistics anxiety is rapidly evolving as more studies are produced in this area. In their work on
refining and updating the statistics anxiety construct, Chew and Dillon (2014) reassessed the
existing research on environmental antecedents of statistics anxiety and found that prior findings
were mixed and effect sizes were mostly small to moderate when present. Earlier work had

19
described the tendency of female students to experience higher levels of anxiety when
encountering statistics than their male counterparts (Benson, 1989; Benson & Bandalos, 1989;
Bradley & Wygant, 1998). More recent work on gender differences and statistics anxiety reveals
that the relationship is unclear, with some studies reinforcing earlier findings of higher statistics
anxiety in females (Baloğlu, Deniz, & Kesici, 2011), while others show no gender-related
differences (Baloğlu, 2003, Hsiao & Chang, 2011). Chew and Dillon (2014) speculate that the
underlying reason for these mixed findings is a combination of variations in the study population
(country of origin), methodological choices, and the grouping of other variables into the analysis.
Similarly, prior research examining age differences among statistics-anxious populations has
resulted in mixed outcomes. Baloğlu (2003) and Bell (2003) found that students aged 25 years or
older reported higher levels of statistics anxiety than their younger peers, echoing earlier research
by Demaria-Mitton (1987) which indicated that older college students had the highest levels of
statistics anxiety. However, a 2011 study by Bui and Alfaro looking at age, gender, and ethnicity
factors among 104 undergraduate students found that there were no significant relationships
between age and gender on statistics anxiety. Although Onwuegbuzie (1999) found higher levels
of statistics anxiety among African American graduate students compared to their Caucasian
classmates, comparisons between Latino/Hispanics and Caucasian undergraduate students
resulted in no significant differences in statistics anxiety (Bui and Alfaro, 2011).
Effects of Statistics Anxiety
Since the publication of Cruise et al.’s (1985) work establishing a valid measure for
statistics anxiety and solidifying its place as a field of study, the literature has consistently shown
a negative relationship between statistics anxiety and statistics achievement (Bell, 2001; Hanna
& Dempster, 2009; Onwuegbuzie, 1995, 2003; Onwuegbuzie & Seaman, 1995, Zeidner, 1991).

20
More specifically, citing earlier work by Onwuegbuzie et al. (2000) and Fitzgerald et al. (1996),
Onwuegbuzie and Wilson stated that, “statistics anxiety has been found to be the best predictor
of achievement in research methodology courses” (2003, p. 199). This finding is critical to the
research aims of the current study, which is directed at reducing statistics anxiety among online
learners in graduate research methods courses.
Additional research on the effects of statistics anxiety has also established a causal link
with achievement in a statistics course (Onwuegbuzie & Wilson, 2003). One such example is a
study of graduate students conducted by Onwuegbuzie (1995) that looked at test type
interactions (timed versus untimed) among randomly assigned, high-anxiety students in a
statistics course. A specific finding of this study was that female students with higher levels of
statistics anxiety were more likely to show decreased academic performance in an untimed test
scenario than their lower-anxiety counterparts under the same conditions. Galli, Ciancaleoni,
Chiesi, and Primi (2008) followed 442 undergraduate psychology students over 20 months to
determine whether their statistics anxiety scores at the start of their academic program were
predictive of their academic outcomes in future statistics courses. In total, 162 (37%) students
failed their statistics course at least once, with 42 (9.5%) failing twice and 21 (5.1%) failing three
times. Remarkably, a significant and sizable difference in statistics anxiety was observed
between students who failed and those who passed on the first attempt, indicating a strong and
stable relationship between statistics anxiety and future statistics achievement (Galli et al., 2008).
Though the debilitative effects of statistics anxiety have been established and documented with
consistency, a smaller number of studies have also pointed to the potential benefits of moderate
levels of anxiety on achievement. Onwuegbuzie and Wilson (2003) theorized that statistics
anxiety may have a facilitative role in academic outcomes since “a certain amount of statistics

21
anxiety may prevent students from being too complacent in preparing for an upcoming
examination” (p. 200). This would suggest a more curvilinear relationship between statistics
anxiety and academic performance. To empirically test this theory, Keely, Zayac, and Correia
(2008) used the STARS instrument to measure statistics anxiety in 83 college students in an
undergraduate statistics course across seven points in the term, six of which coincided with
scheduled examinations. The researchers determined that statistics anxiety scores collected by
the STARS instrument, including those of the six STARS subscales, possessed internal and testretest reliability over the investigative period (4 months). Furthermore, they established that
while statistics anxiety decreased as the term went on, its relationship to course performance
became paradoxically stronger (Keeley et al, 2008). Put in simpler terms, there was an optimal
level of statistics anxiety that resulted in better test performance. This suggestion that a
curvilinear relationship exists between statistics anxiety and statistics performance is a major
departure from prior findings, which have described only linear relationships, and should be
investigated as part of future research in the field.
Up to this point the review of the literature has covered statistics anxiety’s antecedents
and some of its effects. Now, it will turn to models that illustrate how these and other factors
interact with each other to affect statistics achievement. Onwuegbuzie’s (2003) path analysisbased Anxiety-Expectation Medication (AEM) Model of Statistics Achievement places student
expectation and statistics anxiety at the center, relating them bi-directionally with statistics
achievement, and illustrating how those two pivotal components moderate the relationship
between statistics achievement and other factors such as course load, study habits, research
anxiety, and number of statistics courses taken (see Figure 1). Onwuegbuzie (2003), guided by
Wine’s (1980) cognitive-attentional theory of test anxiety, posited that,

22
anxiety interferes with performance by impeding students’ ability to receive, to
concentrate on, and to encode statistical terminology, language, formulae, and concepts.
Moreover, … anxiety reduces the efficiency with which memory processes are utilized
while attempting to understand and to learn new statistical material. (Onwuegbuzie &
Wilson, 2003, p. 199)
Figure 1
Onwuegbuzie’s (2003) Anxiety-Expectation Mediation (AEM) Model of Statistics Achievement
Note. Adapted from “Modeling statistics achievement among graduate students,” by A. J.
Onwuegbuzie, 2003, Educational and Psychological Measurement, 63(6), p. 1026
(https://doi.org/10.1177/0013164402250989). Copyright 2003 by A.J. Onwuegbuzie.
-.14 .08
Research
Anxiety
Study Habits
Expectation
Statistics
Achievement
Statistics
Anxiety
Course Load
Number of Statistics
Courses Taken
-.18
-.16
.19
-.53
.56
-.14
-.22
.05
.29
-.43

23
While there has been some research presented that may indicate a facilitative component
to statistics anxiety (Keeley et al., 2008), the overwhelming body of evidence makes clear that
statistics anxiety has a negative effect on statistics achievement, successful course completion,
and, at its most severe, degree completion. Additionally, students who are female, older, nonCaucasian, enrolled online, or enrolled in a non-mathematics degree program are more likely to
experience higher levels of statistics anxiety and the subsequent impacts to performance.
Furthermore, as stated earlier, statistics anxiety is also the best predictor of achievement in
courses on research methodology (Onwuegbuzie & Wilson, 2003), which is the focus of this
study. Therefore, it is imperative that the appropriate measures of statistics anxiety be used, and
treatments to reduce it be examined, so that academic institutions may better support their
students (our future researchers) both on-campus and online.
Measures of Statistics Anxiety
A review of the literature turned up six instruments that have been used to measure
statistics anxiety. They are, in order of publication, the Statistical Anxiety Rating Scale (Cruise
et al., 1985), the Statistics Anxiety Inventory (Zeidner, 1991), the Statistics Anxiety Scale
(Pretorius & Norman, 1992), an instrument that is not named (Zanakis & Valenzi, 1997), the
Statistics Anxiety Measure (Earp, 2007), and the Statistical Anxiety Scale (Vigil-Colet, LorenzoSeva, & Condon, 2008). Chew and Dillon’s (2014) paper on refining the statistics anxiety
construct provides an excellent summary of these measures and their subscales and is reproduced
here in Table 1.

24
Table 1
Chew and Dillon’s (2014) Measures and Subscales of Statistics Anxiety
Measure Subscale
51-item STARS (Cruise, Cash, & Bolton, 1985) Interpretation Anxiety
Test and Class Anxiety
Fear of Asking for Help
Worth of Statistics
Computation Self-Concept
Fear of Statistics Teachers
40-item Statistics Anxiety Inventory (Zeidner, 1991) Statistics Test Anxiety
Statistics Content Anxiety
10-item Statistics Anxiety Scale (Pretorius & Norman, 1992) Unidimensional
36-item unnamed instrument (Zanakis & Valenzi, 1997) Student Interest in and
Perceived Worth of Statistics
Anxiety When Seeking Help
for Interpretation
Computer Usefulness and
Experience
Math Anxiety
Understanding
Test Anxiety
44-item Statistics Anxiety Measure (Earp, 2007) Anxiety
Attitude Towards Class
Fearful Behaviour
Attitude Towards Math
Performance
24-item Statistical Anxiety Scale (Vigil-Colet, LorenzoSeva, & Condon, 2008)
Examination Anxiety
Asking for Help Anxiety
Interpretation Anxiety
Note. Adapted from “Statistics Anxiety Update: Refining the Construct and Recommendations
for a New Research Agenda,” by P. K. H. Chew and D. B. Dillon, 2014, Perspectives on
Psychological Science, 9(2), 196-208. Copyright 2014 by P. K. H. Chew and D. B. Dillon.

25
Of the aforementioned six measures of statistics anxiety, two of them were developed in
a manner that relied heavily on existing mathematics anxiety assessments and do not distinguish
statistics anxiety and attitudes towards statistics sufficiently. Zeidner’s (1991) Statistics Anxiety
Inventory and Pretorius and Norman’s (1992) Statistics Anxiety Scale were both fashioned by
swapping out mathematics-related words with statistics-related words in the Mathematics
Anxiety Rating Scale, or MARS, (Richardson & Woolfolk, 1980) and Mathematics Anxiety
Scale (Betz, 1978), respectively (Chew & Dillon, 2014). Another two measures, the unnamed
instrument by Zanakis and Valenzi (1997) and Earp’s (2007) Statistics Anxiety Measure, gauge
attitudes towards statistics as well as statistics anxiety and are therefore less discriminatory in
measuring anxiety. Researchers who use any of these four assessments to collect statistics
anxiety data are likely to find high correlations between statistics anxiety, mathematics anxiety,
and attitudes towards statistics (Chew & Dillon, 2014).
This leaves two remaining measures of statistics anxiety that are neither built off of a
preexisting mathematics anxiety instrument nor designed to measure another construct besides
statistics anxiety. Both the STARS (Cruise et al., 1985) and Statistical Anxiety Scale (VigilColet et al., 2008) instruments are suitable tools for researchers seeking to measure statistics
anxiety, but the STARS has been used far more commonly and more widely (Baloğlu, 2002;
Hanna, Shelvin, & Dempster, 2008; Liu, Onwuegbuzie, & Meng, 2011; McGrath et al., 2015;
Mji & Onwuegbuzie, 2004; Papousek et al., 2012). As Chew and Dillon (2014) point out,
“STARS has been extensively utilized by researchers because of the superiority of its reliability
and validity data compared with that of other measures” (p. 199). Therefore, researchers such as
this one are inclined and encouraged to use the STARS instrument for further investigations
relying on statistics anxiety construct. However, having the proper measure is simply the first

26
step in assessing and treating statistics anxiety. The subsequent section will look at the body of
literature that investigates interventions designed to reduce statistics anxiety.
Treatments and Interventions for Statistics Anxiety
Although statistics anxiety has been investigated by academic researchers for over 30
years, there are surprisingly few studies that have examined interventions aimed at reducing it.
The widespread prevalence of statistics anxiety, however, and the negative consequences
associated with it, makes it a sizable target for dedicated research on reduction strategies. While
most work in the area of statistics anxiety-reduction has involved non-experimental studies
(Anyikwa & Rapp-McCall, 2016; D’Andrea & Waters, 2002; Davis, 2003; Dillon, 1982; Pan &
Tang, 2004; Wilson, 1999), there was a pair of experimental studies conducted recently
examining differences in statistics anxiety levels between treatment groups (McGrath et al.,
2015; Williams, 2010). D’Andrea and Waters’s (2002) research showed reduction in statistics
anxiety among students who used statistical concepts to solve detective-style word problems, and
Dillon (1982) sought to reduce statistics anxiety by allowing students to openly discuss their
fears and negative emotions toward statistics, which was then followed by a lecture on effective
coping strategies for dealing with those feelings. Wilson (1999) used a comprehensive set of
teaching strategies, including cooperative learning and application-based activities, as well as the
use of humor, to affect anxiety reduction across a spectrum of affective dimensions. Wilson’s
results concluded that students’ perceptions of their instructor’s interpersonal delivery were more
relevant to anxiety reduction than the instructional techniques themselves (1999). Furthermore,
the effect of working in cooperative groups showed some variance as a means of statistics
anxiety reduction, particularly when students perceived their group partners to be less efficacious
with statistics (Wilson, 1999). An experimental study by Williams (2010) looked at instructor

27
immediacy—actions associated with instructor behavior that are designed to reduce physical or
psychological distance—as it related to statistics anxiety levels among 76 graduate students
enrolled across a wide spectrum of academic fields. Williams (2010) hypothesized that behaviors
linked to higher levels of instructor immediacy, such as calling on students by name, seeking
their opinions, making eye contact, and being humorous, would reduce overall statistics anxiety
levels, as measured by the STARS instrument. The findings revealed that students who perceived
higher levels of instructor immediacy experienced significant reductions in their posttest
statistics anxiety measurement with instructor immediacy accounting for up to 20% of the
variance between groups (Williams, 2010).
Although experimental research designs are especially relevant to this study, RappMcCall and Anyikwa’s (2016) survey of teaching strategies in an online Master of Social Work
program supports Williams’ (2010) findings on the impact of instructor immediacy in decreasing
statistics anxiety and enhancing the perception of research methods knowledge. Rapp-McCall
and Anyikwa’s (2016) asked over one hundred online graduate students across eight sections and
two academic terms to rate the efficacy of several learning strategies on their perceived
understanding of course material and level of statistics anxiety. Their findings revealed that
contact with the professor, synchronous class sessions, and synchronous class activities, such as
games and discussions, had the highest mean ratings by students for both reducing levels of
statistics anxiety and increasing perception of research methods knowledge (Rapp-McCall &
Anyikwa, 2016). Although instructor contact occurred both synchronously (class discussions)
and asynchronously (email correspondence), the researchers noted that the most effective
strategies involved active learning and/or instructor immediacy, whereas less effective strategies,
such as online instructor announcements, discussion forums, course textbook, PowerPoint

28
lectures, and homework assignments, were all asynchronous (Rapp-McCall & Anyikwa, 2016).
While Rapp-McCall and Anyikwa’s (2016) survey bolsters prior research on the impact of
instructor immediacy and active learning on reducing statistics anxiety, the study’s findings are
limited by their chosen research methodology – a single survey that collected both pre- and postdata at the end of the term, which relied on respondents assessing their own perceived levels of
gained knowledge and reduced anxiety over the duration of the course. A more effective
methodology would have assessed participants’ pre-term anxiety level and utilized more
objective measures of newly gained research methods knowledge, such as quiz scores,
assignment grades, and overall course outcomes. The researchers acknowledged these limitations
and recommended that subsequent studies utilize a standardized measure of statistics anxiety
with pre- and post-test collection framework (Rapp-McCall & Anyikwa, 2016).
Pan and Tang’s (2004) paper on innovative instructional techniques for statistics anxiety
reduction is a noteworthy example of a successful non-experimental study that employs such a
framework. Their one-group pretest-posttest study was conducted with graduate education
students in an introductory statistics course and utilized the Statistics Anxiety Scale (Pretorius &
Norman, 1992) to measure the effects of a multi-pronged instructional strategy on statistics
anxiety (Pan & Tang, 2004). Pan and Tang hypothesized that, since statistics anxiety is a
multidimensional construct, a multifaceted instructional delivery mechanism would be best
suited to address the six different dimensions of statistics anxiety (2004). Their intervention
consisted of the following: an orientation letter that addressed course logistics and how to cope
with statistics anxiety, expanded and flexible office hours, extra feedback opportunities, cheat
sheets for exams, and a pass/fail grading option. Additionally, instructors were encouraged to use
humor when appropriate and administer assignments that related statistics to real-world

29
application, such as asking students to write informative letters to non-statistics learners about
using statistics in everyday scenarios (Pan & Tang, 2004). Their results, which were controlled
for individual differences, showed significant reductions in statistics anxiety among social
sciences students brought about by a comprehensive instructional approach focused on
increasing instructor awareness of statistics anxiety and application-oriented assignments (Pan &
Tang, 2004). This application-orientation finding echoes work by Dilevko (2000) who argued
that statistics anxiety can be reduced greatly by increasing student perceptions about the value of
statistics. The authors noted that the small sample size and lack of control group were areas
where future studies could improve upon their methods (Pan & Tang, 2004).
Building off Pan and Tang’s (2004) work, McGrath et al. (2015) conducted a similar
investigation into the relationship between instructional activities and statistics anxiety, but with
an experimental research design instead of a single group pretest-posttest. McGrath et al. (2015)
deployed a multifaceted instructional framework (orientation letter, speaking to the challenges of
the course, showing attentiveness, being humorous) aimed at reducing statistics anxiety among
graduate psychology students in an advanced statistics course. McGrath et al.’s instructional
model consisted of the following elements: thirteen class lectures, two cooperative learning
exercises on factor analysis and mediation/moderation, no exams, and five total statistics
assignments, graded excellent/acceptable/unacceptable (2015). Pretest and posttest data was
collected on statistics anxiety using the STARS measure and on statistics self-efficacy. Outcome
measures of students’ understanding of factor analysis and mediation/moderation – key elements
of their learning for that term – were also deployed posttest. Finally, students participated in
focus groups to discuss the interventions and surveys were distributed to collect teaching
evaluation data (McGrath et al., 2015). As noted earlier, a major difference between this study

30
and those that came before is the inclusion of a control group. While the experimental group
received the active learning interventions, the control group read from their books in a separate
environment. Both the experimental and control groups received the benefits of the active
learning exercises; however, the control group received them both times after completing their
posttest surveys of statistics anxiety and self-efficacy (McGrath et al., 2015). The researchers
hypothesized that, like Pan and Tang (2014), their results would show significant decreases in
students’ levels of statistics anxiety for their experimental group (McGrath et al., 2015).
Interestingly, while the focus group data confirmed the students’ perceived value of a
multifaceted instructional framework that utilizes active and cooperative learning techniques, the
two groups did not experience a statistically significant difference in statistics anxiety reduction
and self-efficacy improvement between them (McGrath et al., 2015). The authors suggest that
the minimal amount of time invested in the activities (15 minutes), as well as the use of short
multiple-choice assessments to gauge knowledge acquisition, may have contributed to a lack of a
sizable effect (McGrath et al., 2015). McGrath et al. propose that future researchers modify their
research design to identify which elements of the framework are most effective (2015).
Being able to define and isolate statistics anxiety as a construct and knowing its negative
relationship with statistics and research methods performance, helps researchers better
understand how statistics anxiety plays a role in interfering with research methods education.
Additionally, knowledge of the various measures of statistics anxiety and how they came to be
allowed a researcher to select appropriate measures for their research. While it is often difficult
or even impossible to create a truly random group when investigating in an educational context,
researchers are encouraged to use nonequivalent control groups to perform quasi-experimental
studies whose output hews closer to that of truly experimental research designs (Chew & Dillon,

31
2014). Work by Pan & Tang (2004) and McGrath et al. (2015) provides an excellent framework
for investigating statistics anxiety in graduate research methods courses, and gives this
researcher confidence that a well-designed synchronous, online intervention is likely to succeed
in reducing statistics anxiety within that context. To better understand how to deploy such an
intervention, the next section of this literature review will briefly describe the nature of research
methods education and identify some potential learning strategies for reducing statistics anxiety.
Research Methods Education
Research methods coursework, leading to the comprehension and application of
statistical concepts, has long been crucial to the attainment of advanced academic degrees. As
noted earlier, researchers have observed an increasing number of institutions of higher learning
requiring research methods and statistics learning at both the undergraduate and graduate level
(Mundfrom et al., 2003; Stoloff et al., 2009). In many cases these mandatory courses are in place
to ensure that students are adequately prepared to conduct their own research in their respective
fields for a thesis or dissertation (Ball & Pelco, 2006). There is also consensus that courses in
research methods are essential to developing the skills required for students to become savvy
consumers of research, even if they are not seeking to create new studies of their own (Aldrich,
2005; Jiao & Onwuegbuzie, 2012). Equally important though is the broad application of research
inquiry, particularly quantitative and qualitative analysis, in professions beyond the halls of
academia. Zablotsky (2001) makes this argument by describing research skills as a foundation
for informed decision making in students’ post-academic careers. Atkin (2010) views this unique
skill set—the ability to read, comprehend, translate, and critique research produced by the
academic and popular press—as vital to success at the graduate level and in the competitive job
market.

32
Challenges of Research Methods Teaching
Despite the critical importance of research methods education, there is a tremendous
amount of disagreement in the literature about what research methods education is and how it is
defined. Early’s (2014) synthesis of research methods education describes the field as a
complicated domain that is not definitely established in the literature as other fields are, like
nursing, mathematics, or science education. Additionally, research methods themselves are a
complex array of interwoven activities and procedures which do not lend themselves to a unified
definition (Lehti & Lehtinen, 2005, as cited in Early, 2004). Onwuegbuzie and Leech (2005)
provide further evidence by detailing the often contentious, mostly counterproductive divide
between qualitative and quantitative researchers. They label academics who stubbornly limit
themselves to only one type of methodological approach—so called “uni-researchers”—as “a
threat to the advancement of the social and behavioral sciences” (Onwuegbuzie & Leech, 2005,
p. 267-268). Tashakkori and Teddlie (2003) note that the process of teaching research methods is
also commonly conducted along methodological dividing lines. Unsurprisingly, the complicated
nature of research methods teaching and learning leaves many educators suffering from a lack of
useful academic research to guide their instructional practices (Early, 2004). Compounding the
challenge of effectively teaching the subject is the fact that the literature on research methods
education frequently notes that it is perceived by students to be unpopular and is often described
as the most challenging, disinteresting, and anxiety-inducing component of their degree
programs (Ball & Pelco, 2006; Fife, 2008; Lundahl, 2008; Schulze, 2009). Indeed, Onwuegbuzie
and Wilson (2003) have noted in their synthesis of statistics anxiety research that the
overwhelming majority of graduate students (as many as 4 out of 5) experience high levels of
this anxiety as they take, or even prepare to take, required research methods courses.

33
Unfortunately for students and teachers alike, statistics anxiety has been negatively linked to
course performance and achievement (Onwuegbuzie, 1997; Onwuegbuzie & Seaman, 1995;
Zeidner, 1991). In fact, a student’s level of statistics anxiety has been found to be the best
predictor of course achievement in research methods courses (Onwuegbuzie et al, 2002). Further,
the link between statistics anxiety and course achievement has been empirically proven to be
causal in its effect (Onwuegbuzie & Seaman, 1995), creating an obvious need within the
academic community to find strategies for reducing its often-debilitating impact.
Active and Cooperative Learning for Research Methods Education
While there is no clear consensus on how to teach research methods in higher education
contexts, there is a growing body of literature that points to the benefits of active and cooperative
learning strategies for reducing statistics anxiety and increasing student performance. Generally,
researchers in the area of statistics and research methods learning have suggested that
constructivist, student-centered approaches are far better suited for engaging students and
increasing their motivation, self-efficacy, and overall desire to learn the material than the more
traditional, instructor-centered pedagogical approach (Aldrich, 2015; Ball & Pelco, 2006;
DeWitt, 2010, Jiao & Onwuegbuzie, 2012). This constructivist approach to teaching, which
forces students to take an active role in their learning, can be deployed within a social-cognitive
framework that acknowledges the interrelation between an individual’s beliefs (cognitions),
behaviors, and their environment (Bandura, 1986). Bandura’s (2002) social-cognitive theory
proposes a triadic reciprocity between these three factors, in which changes in any one domain
affect the other two (see Figure 2). In addition, Bandura (2002) theorizes that an individual’s
experiences and his or her cognitive interpretations of those events will lead to changes in future
behavior. Within the context of statistics anxiety and research methods learning, this means that

34
a student’s fear of mathematics, negative perceptions about a teacher, modality of course
enrollment (online versus on-campus), or even the attitudes of fellow cohorts towards a difficult
class, may affect the student’s future engagement with that class or even his or her willingness to
persist through the degree program. Environmental factors are also at play and given what has
already been discussed about the uninteresting and anxiety-inducing nature of research methods
education, it is important to develop an environment that is inviting, collaborative, and
nonthreatening for students in research methods and statistics courses.
Figure 2
Bandura’s (1986) Model of Triadic Reciprocality
Note. Adapted from Social Foundations of Thought and Action: A Social Cognitive Theory (p.
23), by A. Bandura, 1986, Prentice-Hall. Copyright 1986 by Prentice-Hall.
Behavior
Environment Personal Factors

35
Active learning strategies, including cooperative and problem-based learning, are
frequently mentioned in the literature as among the most effective methods to reduce anxiety,
increase self-efficacy and engagement, and improve the perceived value of research methods or
statistics (Aldrich, 2015; Ball & Pelco, 2006; Jiao & Onwuegbuzie, 2012; Kilburn et al., 2014).
Active learning is generally defined as a form of instruction that proactively seeks to engage
students in the process of learning, usually through some form of activity rather than passive
listening (Bonwell & Eisen, 1991). Cooperative learning, a form of active learning, has been
widely used across a number of learning environments and academic fields from elementary
school up through graduate programs (Jiao & Onwuegbuzie, 2012). Cooperative learning
encourages students to learn through tasks that involve teamwork, sharing, or other forms of
collaboration (Johnson, 1991). Johnson’s (1991) work on cooperative learning draws upon
cognitive- and behavioral-learning theories, as well as a social interdependence framework to
formulate the following key elements of cooperative learning activities:
● positive interdependence among participants
● promotion of face-to-face (visual) interaction
● individual accountability for the activity
● use of social communication skills
● collaborative thinking or group processing
● establishment and maintenance of trust
● resolving conflicts in a constructive manner
Jiao and Onwuegbuzie (2012) note that over 1,200 studies comparing the effectiveness of
cooperative learning activities versus more individualistic or competitive activities have been
evaluated in the literature and their findings point to higher levels of achievement among

36
adopters of cooperative learning techniques. These findings have encouraged more research into
specific applications of cooperative learning activities in a diverse assortment of academic fields,
including research methods. The following section presents a specific cooperative learning study
that will serve as the basis for a cooperative learning exercise in the methodologic procedures of
this study.
Cooperative Learning Activity for Research Methods
Aldrich (2015) designed a cooperative learning activity aimed at improving the
understanding of independent variables (IV) and dependent variables (DV) among
communication students enrolled in an introductory research methods course. Students then used
this understanding to create sample hypotheses out of the previously identified IVs and DVs in
pairs and groups. According to Aldrich (2015), the theoretical grounding was a constructivist
approach that employed cooperative learning by having students work on a task
interdependently. The author believed that this learning strategy would yield reductions in
anxiety, higher levels of metacognitive thinking, and the creation of meaning for students
learning about abstract concepts (Aldrich, 2015). The activity began with students preparing for
the session by reading about IVs, DVs, and hypotheses in their textbook. The instructor then
explained some of the key points from the learning on these constructs and gave examples in a
manner that modeled the expected future behavior of the students, which is common to a socialcognitive learning framework (Bandura, 1986). The instructor had a number of different
independent and dependent sample variables on slips of paper that would be handed out one at a
time to each student. For round one, the instructor handed out the variables and asked students to
pair up with each other for a few minutes; the goal of the team-up being to create a hypothesis
with their given variables and correctly identify the IV and DV. Students would then come back

37
together as a group and share out their work, helping others who may have had more difficulty
working through their variables. Once that was completed, the instructor would have each
partner switch roles, with his or her variable flipping from dependent to independent, or vice
versa. Round two would repeat this process but with new student pairings and thus different
hypothesis combinations. According to Aldrich (2015), the activity can run through several
rounds, anywhere from 10 minutes to an hour depending on class size and number of pairings.
Although this activity’s effect on engagement and anxiety was not empirically assessed in the
study, Aldrich (2015) reported that the student-student and student-instructor interaction
provided by the activity reduced student anxiety towards research methods, improved student
self-efficacy in sharing ideas, asking questions, and speaking critically, and fostered active
engagement in future elements of the course.
By combining the literature on research methods education and cooperative learning with
the previously discussed academic knowledge on statistics anxiety, it is clear that one potential
avenue for reducing statistics anxiety is through the deployment of an instructional approach that
is noncompetitive, nonthreatening, humorous, practically applied, and leverages cooperative
learning strategies. Aldrich’s (2015) template, while not empirically validated, would fit neatly
into the multidimensional frameworks employed by Pan & Tang (2014) and McGrath et al.
(2015) by taking the place of their face-to-face cooperative interventions. However, before such
an activity can be adapted for a synchronous, online class setting, it is necessary to explore the
research on that population of students and how learning is delivered to them online.
Online Learning
The emergence of online learning in the late twentieth century has ushered in a new era
of innovation and experimentation that is transforming the field of American higher education.

38
The availability of fully online degree programs from a wide range of regional, national, and
international colleges is challenging old perceptions of educational quality, access, and
pedagogy, setting the stage for broader enhancements to learning (Picciano et al., 2010). Before
the advent of multimedia technologies in the mid-to-late twentieth century, distance education
(DE)—primarily via mailed correspondence—had widely been considered an inferior way of
delivering instruction and being educated (Thompson, 1990, as cited in Bernard at al., 2009).
Advances in Internet-based delivery and computer-mediated communication have helped dispel
that notion and now over three-quarters of academic leaders rate online learning outcomes
comparable to face-to-face learning (Allen & Seaman, 2014). Furthermore, a growing body of
research on online and blended learning has found equivalent levels of engagement, motivation,
and academic attainment when compared to face-to-face learning (Campbell et al., 2008;
Picciano et al., 2010; Rabe-Hemp et al., 2009; Sitzmann et al., 2006). Although the
transformative potential of online learning has been heralded by many, including scholars and
college administrators (Christensen et al., 2008), there have been a number of challenges in
determining whether learning outcomes are actually improved by new technologies (Clark et al.,
2010).
The following section traces the emergence and evolution of distance education from its
early roots in correspondence-based courses to its modern-day, Internet-facilitated incarnation
more commonly referred to as online learning. It will also provide a review of online learning’s
enrollment trends, demographics, and will discuss the perceptions of academic leaders towards
its growing influence. Additionally, because there is inconsistency in the literature regarding
terminology, the following section will seek to establish a common lexicon of terms used to
describe elements of online and distance learning that are relevant to this discussion. This section

39
will conclude with a synthesis of some of the literature describing the benefits and challenges of
online learning.
Emergence of Online Learning
In order to best understand the recent growth of online learning in the United States, it is
important to examine its evolution, beginning with the historical roots of distance education. The
earliest progenitors of what we would now term distance education (DE) were the
correspondence courses of the early eighteenth century (Kaplan & Haenlein, 2016). These
courses, which were facilitated by the postal service and usually self-paced, were necessary
during a time when many Americans were geographically removed from large cities with
schools and colleges. Rural citizens relied on paper workbooks and mailed correspondence to
learn new skills or attain certifications, and this need helped sustain correspondence education
through the late nineteenth and early twentieth century (Walker & Fraser, 2005).
Taylor’s (2001) conceptual framework of distance education models (see Table 2)
organizes print-based correspondence as the first of five distinct generations of DE. The second
generation is characterized as the multimedia model, combining print, audio, and video
resources, followed by the third generation, which Taylor calls the telelearning model due to the
added element of synchronous telecommunication (2001). The flexible learning model, the
fourth generation, features web-facilitated learning, computer-mediated communication and
interactive media, which then serves as the foundation for the fifth generation, the intelligent
flexible learning model, which is characterized by automated computer response systems and a
campus-wide portal for online learning resources (Taylor, 2001). This study concerns itself
primarily with the fourth and fifth generations of DE that Taylor (2001) describes.

40
Table 2
Taylor’s (2001) Models of Distance Education—A Conceptual Framework
Note. Adapted from “The Future of Learning—Learning for the Future: Shaping the Transition,”
by J. C. Taylor, 2001, Open Learning, 16(2), 113-128. Copyright 2001 by J. C. Taylor.
ModelsofDistanceEducation and
Associated Delivery Technologies
Characteristics of Delivery Technologies
Flexibility Highly
Refined
Materials
Advanced
Interactive
Delivery
Institutional
Variable
Costs
Approaching
Zero
Time Place Pace
FIRST GENERATIONThe Correspondence Model
• Print Yes Yes Yes Yes No No
SECOND GENERATIONThe Multi-media Model
• Print
• Audiotape
• Videotape
• Computer-based learning (e.g.
CML/CAL/IMM)
• Interactive video (disk and tape)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
No
No
No
No
No
THIRD GENERATIONThe Telelearning Model
• Audioteleconferencing
• Videoconferencing
• Audiographic Communication
• Broadcast TV/Radio and
Audioteleconferencing
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
FOURTH GENERATIONTheFlexibleLearningModel
• Interactive multimedia (IMM) online
• Internet-based access to WWW
resources
• Computer mediated communication
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
FIFTHGENERATION -
The IntelligentFlexibleLearningModel
• Interactive multimedia (IMM) online
• Internet-based access to WWW
resources
• Computer mediated communication,
using automated response systems
• Campus portal access to institutional
process and resources
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

41
Growth of Online Learning in the 21st Century
Allen and Seaman (2016) tracked the explosive growth of distance education in the
United States beginning with their first annual report, Sizing the Opportunity: The Quality and
Extent of Online Education in the United States in 2003. Their initial findings (2003) indicated
that about 11% of all higher education enrollees, or 1.6 million students, had taken at least one
online course in 2002. Additionally, a little over half of academic leaders surveyed (57%) agreed
that learning outcomes for online education are on par with face-to-face learning. The 2014 issue
of Allen and Seaman’s report revealed that 77% of academic leaders believed that learning
outcomes for online instruction are equivalent or superior to traditional classroom instruction,
with most of the disagreement coming from institutions with low or non-existent levels of online
instruction. Allen and Seaman’s final publication in the series reported that over 6 million
students enrolled in one or more online classes in 2015, with nearly half taking all of their
courses online (2017). Further, the rate of growth for online enrollments over the period of 2012-
2014 was 9% for public institutions and an astounding 26% in private not-for-profit colleges
(Allen & Seaman, 2016). The same report notes that 38% of graduate-level students enrolled via
distance at public colleges, which echoes NCES (2016) data that over 25% of all graduate
students enrolled in fully online coursework. All this extraordinary data led Allen and Seaman to
the clear conclusion that distance education had become mainstream (2016).
More recently, enrollment trends in online master’s programs in the United States show
continued growth, particularly as institutions have increasingly embraced online education as a
strategic priority. As of 2024, online master’s programs have outpaced traditional classroomonly programs, with nearly 500,000 master’s degrees being conferred in programs available
online, compared to around 397,000 in classroom-only settings. Institutions are increasingly

42
recognizing the importance of these programs and are expanding their online offerings to meet
the diverse needs of their student populations (Council of Graduate Schools, 2024). The National
Student Clearinghouse reports that graduate enrollment grew by 3.0% in spring 2024, adding
approximately 88,000 students, with 44 states witnessing growth. This resurgence is attributed to
the heightened demand for flexible and accessible education options, such as online learning,
which caters to diverse student needs and schedules (National Student Clearinghouse, 2024). In
the realm of communication master’s programs, enrollment in online courses has also seen
notable growth, driven by the evolving demands of the digital age. These programs are typically
designed to equip students with the skills needed to excel in areas such as digital marketing,
media relations, and strategic communication, which are becoming increasingly vital in the
contemporary job market. The Council of Graduate Schools (2024) highlights that there is a
rising interest in programs that offer practical and relevant skills applicable to today’s fast-paced,
data-driven communication landscape. Furthermore, the flexibility of online master’s programs
allows students to tailor their education to fit their career goals, contributing to their growing
popularity and importance in the higher education ecosystem (National Student Clearinghouse
Research Center, 2024).
Demographics of Online Learning
The rise of online learning in postsecondary contexts has made higher education
increasingly more accessible to a diverse array of students. A decade ago, the United States
Department of Education found that 97% of associate’s degree-granting community colleges
were making online classes available to their students (Parsad & Lewis, 2008). Information
collected through the NCES’ Integrated Postsecondary Education Data System (IPEDS) in 2013
revealed that more than 95% of all colleges with over 5,000 students, and 70% of all degree-

43
granting institutions, offered distance learning courses (Allen & Seaman, 2016). As mentioned
earlier, and of particular note to this study, the percentage of graduate students taking at least one
online class in 2014 was 32.7%, and those taking their courses exclusively online represent
24.9% of the total enrolled population (National Center for Education Statistics, 2016). These
percentages have only risen as a larger proportion of postbaccalaureate degree seekers gravitate
towards the flexibility of online programs. Initially, online education primarily attracted nontraditional students, such as working adults, military personnel, and individuals seeking career
advancement. Now, however, the demographic profile of online learners has diversified greatly.
Recent data indicates a growing number of traditional-aged students (18-24 years) opting for
online courses, driven by the need for flexibility and the rise of hybrid learning models (Snyder,
de Brey, & Dillow, 2019). Moreover, there has been a noticeable increase in the enrollment of
minority groups in online programs, suggesting that online learning is contributing to greater
inclusivity in higher education (Ginder, Kelly-Reid, & Mann, 2020).
The ever-growing percentage of online enrollments in higher education has led
researchers to explore whether there are significant differences between online students and their
on-campus counterparts. In their article on the transformative potential of online learning,
Picciano et al. (2010) observed that the flexibility of online courses allowed students to balance
their work and family life with greater ease while pursuing higher education. Students were no
longer tied to the geographic constraints of living near the college they were applying to or
attending and, more often than not, they could participate in their classes asynchronously which
provided additional flexibility (Lorenzetti, 2005). A pair of studies that compared on-campus and
online students enrolled in U.S.-based graduate social work programs found that those who
chose to enroll in online courses are statistically more likely to be older, possess more years of

44
professional experience, and reside geographically farther from campus than students enrolled in
traditional face-to-face classes (Freddolino & Sutherland, 2000; Forster & Washington, 2000).
Haga and Heitkamp (2000) also looked at demographic differences between social work students
enrolled at an American university and found that the majority of distance students (68%) were
married compared to only 40% of on-campus students. Additionally, the distance students were
more likely to report being financially secure versus their face-to-face colleagues (Haga and
Heitkamp, 2000). Interestingly, two of these studies reported no statistically significant
differences with regards to ethnicity (Forster & Washington, 2000; Haga & Heitkamp, 2000),
while one reported the on-campus population being more ethnically diverse (Forster &
Washington, 2000). Still, other researchers believe that online learning has opened the door for
more diversity in student bodies, particularly in community and technical colleges, citing that
online courses have enabled nontraditional students and those who have historically lacked
access to quality higher education to obtain advanced degrees (Xu & Jaggers, 2011). Indeed, the
2008 National Survey of Student Engagement (NSSE), which collected responses from over
23,000 undergraduate students in U.S. institutions, revealed that racial minorities and part-time
enrollees were statistically more likely to register for online classes (Chen et al., 2010). The data
also indicated that undergraduate students enrolled in a professional degree program were more
likely to enroll in an online course as they approach graduation (Chen et al., 2010).
Variations in online enrollment by gender have also been observed in the literature. A
report by the NCES entitled The Condition of Education 2015 found that women represented the
majority of undergraduate students enrolled in higher education contexts, at both the
undergraduate (56%) and graduate (59%) levels (Kena et al., 2015). However, among online
students, women make up an even greater percentage. Clinefelter and Aslanian (2014) surveyed

45
a nationally representative field of 1,500 adults who were enrolling in fully online undergraduate
or graduate degree programs and found that female students made up 71% of online
undergraduate and 66% of online graduate enrollees. Their report, Online College Students 2014:
Comprehensive Data on Demands and Preferences puts forth the following profile of a typical
online student according to their data: native English speaking, non-military, Caucasian female,
aged 25–29, who resides in a suburban community, is employed full time, and is not the first in
her family to go to college (Clinefelter & Aslanian, 2014). This finding that a typical online
student is an older female is of particular interest to this study because it highlights two factors
that have been associated with increased levels of statistics anxiety: female gender (Baloğlu et
al., 2011; Benson, 1989; Bradley & Wygant, 1998) and age over 25 years (Baloğlu, 2003; Bell,
2003; Demaria-Mitton, 1987). Keeping these typical profile characteristics in mind, this next
section will look at some of the benefits and challenges associated with online learning and how
technology may play a role in a student’s ability to succeed at a distance.
The Benefits and Challenges of Online Learning
Online learning has provided some clear benefits to higher education, most notably the
ability to provide increased capacity at the institutional level and increased flexibility at the
learner level. The development of online learning has allowed an array of institutions—large,
small, public, private, national, and international—to expand their degree offerings, recruit more
broadly, encourage a more diverse student body, expand enrollment, and increase revenues while
keeping costs in check (Brown, 2012; Castle & McQuire, 2010; del Valle & Duffy, 2009).
Colleges and universities have been looking to online learning as a means to overcome the
limitations of class size or other physical campus resources (del Valle & Duffy, 2009).
Overcrowding of popular and required classes has been common at many institutions, leading

46
administrators to invest in online learning capabilities as a solution (Brown, 2012). While these
technological investments may be costly, they are favored by higher education leaders because
they allow for greater reach and more flexibility than the alternative, which would be to expand
physically and build satellite campuses (del Valle & Duffy, 2009). This is especially true because
online learning programs are often seen as a key source of lower-cost, scalable revenue
generation for many schools (Castle & McGuire, 2010).
Higher education institutions have seen great increases in their online enrollment
numbers over the past decade as more and more students are drawn to the flexibility of distance
learning options (Allen & Seaman, 2016). In addition to engendering a more diverse student
population (Forster & Washington, 2000; Xu & Jaggers, 2011), colleges and universities are
pulling in working professionals in greater numbers by offering flexible online degree programs
(Campbell et al., 2008). Online distance programs are popular among fully employed adult
learners who are often limited by their work schedules and cannot afford to spend time
commuting to campus (del Valle & Duffy, 2009). Further, as del Valle and Duffy (2009) make
clear, there are also a number of online enrollees who benefit from the opportunity of distance
courses because they cannot be away from home, such as students who have young children, or
physical disabilities that prevent mobility. Yet, there have been consistent problems with student
retention in online programs. Within the last fifteen years several studies have been conducted to
evaluate student persistence and retention within the online learning context, with most studies
concluding that attrition rates are higher for online courses (Diaz & Cartnal, 2006; Willging &
Johnson, 2004). During 2002-2004, a study of 640 graduate degree-seeking students in business
administration and communication sciences at national research university was conducted by
Patterson and McFadden (2009) to evaluate where on-campus versus online delivery impacted

47
dropout rates. Their findings were compelling and uncovered that the online cohorts were 6-7
times more likely to drop their program of study than their traditional face-to-face counterparts.
Additionally, the researchers noted that older business administration students were statistically
more likely to engage in online learning and, ultimately, more likely to drop out (Patterson &
McFadden, 2009).
Improved accessibility is undoubtedly a key strength of online learning; however, there
are broader learning benefits to be gained by students enrolled in online classes. Learning
technologies used in online classrooms allow for more engagement between student and
instructor, and the broader range of mediums used to facilitate discussions or solicit questions
may have benefits for students who are less inclined to engage in face-to-face conversation
(Chen et al., 2010). Researchers have also noted that the use of computer-mediated
communication technologies can help foster stronger relationships between student and
instructor, benefitting his or her motivation and ability to persist through the rigors of the course
(Clark & Feldon, 2005: Lou et al., 2006). The fact that technology plays a mediating role in the
student-instructor relationship has led academic researchers to investigate the benefits and
challenges that technology brings into the learning environment. Chen et al. (2010) wrote
extensively about the use of Internet-based learning technologies and positive learning outcomes.
They further noted that learners who utilized web-based technologies to communicate with the
instructors or engage in their course content were more likely to show higher levels of
engagement, including on measures of active and collaborative learning and student-instructor
interaction (2010). Overall, the authors believe that technology plays a positive, constructive role
in encouraging students to integrate metacognitive thinking in their work and reflect more deeply
on their learning (Chen et al., 2010). However, there are other researchers who believe that

48
online learning technology and other new forms of media are wholly unrelated to any noticeable
improvements in motivation or engagement (Clark et al., 2010). Their view is that any positive
relationship between electronic educational media and student outcomes is most likely the result
of the learning strategies that undergird the instruction and not the delivery media itself (Clark et
al., 2010). And while few would question the need for web-based online learning to bridge
geographically disparate communities and provide education access to historically
disenfranchised groups, it is possible that technology itself may be causing problems for
students. In the first study of its kind, Celik and Vehbi (2013) surveyed nearly 500 pre-service
teachers on their computer anxiety, perceived computer self-efficacy, and other general attitudes
towards technology to determine how it would impact their ability to use technology for
learning. Their results concluded that students’ computer anxiety, attitudes toward technology,
and computer self-efficacy were, when combined, all significantly and positively related to
adopting computer-based education (Celik and Vehbi, 2013). In other words, a student’s comfort
with technology will play a determinant factor in whether the individual chooses to participate in
a technology-heavy program. However, Celik and Vehbi (2013) also point to the importance of
the instructor’s role modeling comfort with technology and encouraging the importance of its use
to improve student’s attitudes. This is relevant to the study at hand because statistical software is
used in many graduate research and statistics courses to perform mathematical operations, and
any intervention designed to reduce anxiety about statistics should be presented by the instructor
in a manner that highlights the relative ease, convenience, and comfort of using software for
running calculations.

49
Conclusion
Students’ statistics anxiety is as inevitable as it is detrimental to their performance.
Knowing that it is the most predictive measure of research methods course performance, though,
provides teachers and researchers with a tool by which they can assess students’ apprehensions
and address them (Onwuegbuzie & Wilson, 2003). Statistics anxiety levels also serve as a
barometer by which instructors can gauge their effectiveness in creating the proper environment
for learning the complex concepts involved in statistics and research methods learning. Further,
awareness of students’ individual characteristics can help teachers form ideas about who may
experience the highest levels of disruptive statistics anxiety when learning research methods.
The review of the literature compiled above has revealed that statistics anxiety is a debilitating
feeling of tension that interferes with learning statistics in any form, at any level, including
within required research methods courses (Onwuegbuzie et al., 1997). This feeling can be so
overwhelming that some students fail to finish their degree programs because of research and
statistics course avoidance (Onwuegbuzie & Wilson, 2003). Currently, the percentage of
graduate learners in the United States earning their degrees fully online is at its highest mark
(National Center for Education Statistics, 2016) and, yet not a single academic research article
can be found by this researcher that addresses interventions for reducing statistics anxiety within
a fully online environment. This study aims to change that by adapting prior frameworks from
earlier research on statistics anxiety reduction in traditional face-to-face contexts (Aldrich, 2015;
McGrath et al., 2015; Pan & Tang, 2004) and apply them to a synchronous, webcam-enabled
online class session to determine whether delivery method and instructional strategy impacts
statistics anxiety in online research methods learners.

50
Chapter Three: Methodology
The purpose of this study is to assess the statistics anxiety of online graduate students
studying and determine whether it can be reduced through active and cooperative learning
strategies. Statistics education is increasingly common in undergraduate education and remains
an essential component of graduate-level research methods training. The ability to analyze and
interpret data in statistical terms is central to quantitative research, whether in the natural
sciences or the social sciences. Producing social science researchers who are comfortable with
quantitative research methods and adept at using statistical tools for data analysis is a critical
outcome of most master’s programs. As more master’s programs in the social sciences are
offered fully online, educators and students alike must be prepared to overcome the negative
effects of statistics anxiety on statistics learning. Yet very little research exists that would shine a
light on how statistics anxiety can be reduced through instructional practices. Further, no
published studies on strategies for reducing statistics anxiety in online learners could be found by
this researcher after an exhaustive review of the literature. The following sections present this
study’s research questions, an overview of the research methodology, the sampling procedure
and population, instrumentation, and procedures for data collection and analysis.
Research Questions
This study seeks to answer the following research questions:
1. Are synchronous, web camera-enabled online sessions effective in reducing distance
learners’ statistics anxiety in online graduate research methods courses?
2. Are cooperative learning activities effective in reducing distance learners’ statistics
anxiety in online graduate research methods courses?

51
3. How do distance learners describe their likelihood to engage a statistics teacher for
help after participating in a synchronous, web camera-enabled online session?
Research Design
This study employed a quasi-experimental, mixed methods research design with a
nonequivalent control group. This design was selected because it allowed for examination of
potential differences in students’ statistics anxiety related to both instructional delivery methods
(synchronous versus asynchronous) and cooperative learning strategies. This methodology is
understood to be superior for data interpretation in place of, or in addition to, a single group
pretest-posttest design, which is common in educational settings (Chew & Dillon, 2014). Data
was collected pretest and posttest on students’ level of statistics anxiety and open-ended
questions were posed about how students describe their attitudes and experiences concerning
statistics anxiety factors. Additionally, the use of a control group of online students who did not
receive the intervention via a synchronous, web camera-enabled session aided in establishing
whether synchronous interaction had any impact on statistics anxiety. Quantitative analyses, such
as independent samples t-test and analysis of variance (ANOVA) in SPSS determine whether
statistical differences or predictive relationships exist. In research question one, the independent
variable was instructional delivery method, either synchronous, webcam-enabled online session
or asynchronous, online recording with subsequent asynchronous discussion board. The
dependent variable was statistics anxiety (STARS-R), represented as a calculated variable
STARSDiff (the difference between posttest and pretest STARS-R scores). In the second
research question, the independent variable was instructional learning strategy, either cooperative
or non-cooperative learning activity. The dependent variable was again statistics anxiety
(STARS-R), represented as STARSDiff.

52
Population and Sample
The population for this study was graduate students enrolled in an introductory research
methods course as part of a fully online master’s degree program in communication management
at Coast Western University (pseudonym). Situated in the urban core of an expansive
metropolitan area, Coast Western University is a Tier 1 research institution with large
undergraduate and graduate populations drawn from around the world. The School of
Communication at Coast Western University is a global leader in cutting-edge communication
research and teaching, offering advanced degrees in communication, journalism, public relations,
social media, and public diplomacy. Communication master’s students at Coast Western
University are required to complete a core research requirement by enrolling in a class entitled
Communication Research in their first term. Each term approximately 40–60 students enroll in
the online offering of Communication Research and are randomly distributed into 2–3 sections
of 20 students or fewer. Data collection for this study spanned 2018–2019 and students enrolled
across all online sections of Communication Research during the period of three academic terms,
Spring 2018, Fall 2018 and Summer 2019 were invited to volunteer for the study at the start of
each term. Students who elected to participate in the research received extra credit toward their
final course grade. There was no penalty for declining to participate and the course instructor
provided multiple opportunities for the same amount of extra credit to be achieved separately
from this study so that no group or individual was disadvantaged.
Ultimately, a total of 61 students volunteered to participated in the experiment and
completed all elements of the pretest and posttest surveys on statistics anxiety, including the
demographic questionnaire. A range of demographics were collected, including age, gender,
ethnicity, enrollment status, remote enrollment location, employment status, relationship status,

53
and an assortment of items related to academic histories, particularly with mathematics, statistics
and online courses.
Demographic Characteristics of Study Sample
Among the graduate students who participated in the study, a significant majority were
female (n=50, 81.97%), while male students represented a smaller fraction (n=11, 18.03%). The
average age of respondents was 33.69 years, with a standard deviation of 10.29 years (n=59). On
average, students completed their previous education 8.02 years ago, with a standard deviation of
6.69 years (n=58). Most students were enrolled full-time (n=34, 55.74%) with the remaining
portion enrolled part-time (n=26, 42.62%). One participant did not report their enrolment status.
A large majority of participants were employed full-time (n=48, 78.69%), which was expected
given that the online master’s program primarily recruited working professionals. Smaller groups
of participants were either not employed (n=6, 9.84%), part-time employed (n=5, 8.20%), or
disabled (n=2, 3.28%). The ethnic and racial distribution of the sample was diverse with the
largest groups being White (n=25, 40.98%) and Hispanic/Latino (n=18, 29.51%), followed by
Asian (n=6, 9.84%), Black or African American (n=6, 9.84%) and smaller representations from
Middle Eastern, Native Hawaiian or other Pacific Islander, Indian, and two or more races (each
less than 3.28%). Most respondents had never married (n=36, 59.02%), with a sizable group of
married (n=16, 26.23%) and divorced students (n=7, 11.48%) also present. Participants
predominantly resided in suburban (n=34, 55.74%) or urban areas (n=25, 40.98%), with only
two living in rural settings (3.28%).

54
Instrumentation
A survey instrument was developed with the items from the Statistics Anxiety Rating
Scales Revised (STARS-R) and distributed via Qualtrics before (pretest) and after (posttest) the
introductory module within the Communication Research course. Both pretest and posttest
surveys included open-ended questions that sought to address research question three. To combat
stereotype threat, only the posttest survey concluded with demographic items. The collected
demographic data included gender, age, racial/ethnic identity, employment status, highest level
of educational attainment, self-reported GPA, undergraduate major, previous graduate degree,
previous statistics course, time since last mathematics course, and time since last college course
(see Appendix A). The following section describes the instrument used to measure the statistics
anxiety construct.
Measuring Statistics Anxiety
Statistics anxiety was measured using the Statistics Anxiety Rating Scale (STARS), a 51-
item survey on a 7-point Likert scale developed by Cruise and Wilkins (1980) and validated by
Cruise, Cash, and Bolton (1985). The STARS scale contains 6 subscales that measure the
following content domains: interpretation anxiety (IA), test and class anxiety (TCA), fear of
asking for help (FoAH), fear of statistics teachers (FoST), computation self-concept (CSC), and
the perceived worth of statistics (WoS) (Cruise et al. 1985). Due to the time constraints of the
classroom, a shortened version of the STARS instrument named the STARS-revised (STARS-R)
was deployed (see Appendix B). The STARS-R is an 18-item survey on a 5-point Likert scale
adapted by McGrath et al. (2015). The 18 items were chosen by the researchers from the original
51 STARS items to reflect items that are most pertinent to graduate students learning statistics
within each of the 6 subscale domains. The STARS-R was pilot tested by McGrath et al. with

55
students in a face-to-face graduate psychology course in advanced statistics and the Cronbach’s
Alpha for the pilot test was .88 (pre-course) and .87 (post-course), demonstrating high internal
consistency reliability of the measure (2015). However, no internal consistency and reliability
data was available for the six STARS subscales, which were truncated from 7-8 items per factor,
down to 2-3 items per factor in the STARS-R instrument. As such, this study did not evaluate
subscale factors when performing quantitative analysis of the experimental conditions on
statistics anxiety.
Procedure and Data Collection
Prior to data collection, this study received approval from the university’s Institutional
Review Board (IRB) and the online master’s program in communication management that served
as the study site. After gaining approval from IRB and the academic program, participants were
recruited via email from graduate students enrolled in an introductory research methods course
during the first week of classes. An information sheet was distributed to all students enrolled in
sections of the course explaining the nature of the research to be conducted and the voluntary
nature of their potential participation (see Appendix C). Potential participants were informed that
their responses would not be personally identifiable or shared with their course instructors, and
they would not be penalized for declining to participate. All surveys and research instruments
were distributed electronically through Qualtrics via a personal invitation to the participant’s
university email address. Participants’ pretest and posttest responses were linked through
Qualtrics via an authentication panel associated with their university student ID number.
Once participants were recruited and confirmed, a survey instrument was distributed that
collected pretest data on statistics anxiety prior to participation in an intervention protocol.

56
Participants were then randomly split into two nonequivalent groups after completing
their pretest on statistics anxiety. Participants were further sorted into synchronous or
asynchronous conditions, which determined whether they received the intervention during a live,
web camera-enabled online session or a pre-recorded video, as well as cooperative and noncooperative conditions. The cooperative experimental group received a cooperative learning
intervention on independent/dependent variables and levels of measurement based off the work
of Aldrich (2015) that was delivered by the course instructor during a 60-minute synchronous,
web camera-enabled live session, or via a pre-recording for those in the asynchronous condition.
Participants in the cooperative condition were organized into pairs via virtual breakout rooms
during each round of the synchronous cooperative activity, patterned after Aldrich (2015), or as
paired teams in an asynchronous discussion board in the asynchronous condition. The noncooperative group received the same information and were asked the same questions but
completed non-cooperative activities and provided individual responses whether synchronously
or asynchronously. All participants were then provided a 24-hour window to complete a posttest
survey on statistics anxiety as well as a demographic questionnaire.
Synchronous, Cooperative Experiment Protocol
In this experimental session, students engaged in various learning activities designed to
address anxiety about math and statistics, understand the application of statistics in professional
settings, and grasp the concept of variables and levels of measurement. The session started with
an introduction by the principal investigator (PI) who explained the session's purpose, thanked
participants, and introduced the course instructor, who served as the presenter and moderator
(PM) for the session. Students were informed about the focus of the discussion and the protocol
for questions and participation.

57
The first topic discussed anxiety about math and statistics. The PM normalized this
anxiety, highlighting its potential benefits and sharing the PM’s own experiences with statistics
anxiety to create a supportive environment. Students participated in a breakout room activity to
discuss strategies for overcoming anxiety in their daily lives and how these strategies can be
applied to managing statistics anxiety. This peer-to-peer interaction aimed to foster a sense of
community and mutual support among students as they shared personal experiences and coping
mechanisms.
The second topic explored the prevalence of statistics in industry and everyday life. The
PM provided examples of how statistical information is used in various contexts, emphasizing
that students are more familiar with statistics than they may realize. In another breakout room
activity, students discussed how they use statistics in their jobs and their motivations for learning
more about the subject. This discussion was intended to help students connect theoretical
knowledge to practical applications. The final topic introduced variables and levels of
measurement, where the PM explained these concepts and provides examples. Students then
worked in pairs to identify and categorize new variables, reinforcing their understanding through
hands-on practice and collaborative learning. The session concluded with reminders about
available support and the importance of completing the follow-up survey.
Synchronous, Non-Cooperative Experiment Protocol
In this experimental session, students engaged in non-cooperative learning activities
designed to address math and statistics anxiety, understand the application of statistics in
professional settings, and learn about variables and levels of measurement. The session began
with an introduction by principal investigator (PI) who explained the purpose of the session,
introduced the course instructor as the presenter and moderator (PM), and outlined the protocol

58
for participation and questions. Students were also informed about the follow-up survey to be
completed after the session.
During the first topic of discussion, which addressed anxiety about math and statistics,
the PM highlights the normalcy and potential benefits of anxiety, sharing the PM’s own
experience with statistics anxiety to create a supportive environment. Students were then invited
to participate individually by sharing their strategies for overcoming anxiety in a raise hand/call
on student fashion. This activity intended to promote individual reflection and sharing within a
supportive group setting, allowing students to learn from each other's experiences and coping
mechanisms.
The second topic focused on the prevalence of statistics in industry and daily life. The
PM provided examples of statistical applications and emphasized that students are more familiar
with statistics than they might think. Similar to the first topic, students engaged individually by
responding to a question about their use of statistics on the job and their motivations for learning
more about it. The final topic involved a more structured individual learning activity where
students silently work on identifying and categorizing variables into different levels of
measurement. After a period of individual work, students shared their responses in a raise
hand/call on student fashion, allowing for a combination of silent reflection and group
discussion. The session concluded with reminders about available support and the importance of
completing the follow-up survey.
Asynchronous, Non-Cooperative Experiment Protocol
Participants in the control group received the same instructional material on
independent/dependent variables and levels of measurement, however they received it
asynchronously via a recording of their instructor (PM), which was also released simultaneously

59
to the experimental group receiving the synchronous intervention. Students in the control group
were then asked by their PM to perform an individual posting about identifying
independent/dependent variables and levels of measurement on an asynchronous, online
discussion forum within 48 hours of receipt of the recording. After both interventions were
delivered and the asynchronous discussion posts completed, a posttest survey instrument was
distributed by the researcher to all participants, once again collecting STARS-R scores along
with demographic data.
Data Analysis
Data from Qualtrics was downloaded and anonymized using participant numbers that
were not personally identifiable. Descriptive statistics were produced via SPSS for all variables
and series of independent t-tests were performed on the independent variables related to
instructional delivery method and instructional learning strategy to determine their impact, if
any, on the dependent variable of statistics anxiety (STARS-R). The difference between posttest
and pretest STARS-R scores was calculated as STARSDiff, which served as the primary
representation of the interventions’ impact on statistics anxiety. Further, a two-way analysis of
variance (ANOVA) was used to analyze between-subjects and main variable effects. Table 3 lists
the variables, their levels of measurement, and corresponding statistical tests for research
question. The researcher will discuss data analysis and results in Chapters Four and Five. Lastly,
open-ended descriptive data about students’ perceptions of the intervention on various factors
related to statistics anxiety, such as fear of statistics teachers (FoST) and fear of asking for help
(FoAfH) were coded for qualitative analysis. This constituted the data set for analyzing research
question three.

60
Table 3
Quantitative Data Analysis Summary
Research Question Statistical Tests IV Level of
Measurement DV Level of
Measurement
Are synchronous, web
camera-enabled online
sessions effective in
reducing distance
learners’ statistics
anxiety in online
graduate research
methods courses?
Independent
samples t-test
Instructional
delivery method
(synchronous/
asynchronous)
Nominal Statistics
anxiety
(STARS)
Interval
Two-way
analysis of
variance
(ANOVA)
Instructional
delivery method
(synchronous/
asynchronous)
Instructional
learning strategy
(cooperative/
non-cooperative)
Nominal Statistics
anxiety
(STARS)
Interval
Are synchronous, web
camera-enabled online
sessions effective in
reducing distance
learners’ statistics
anxiety in online
graduate research
methods courses?
Independent
samples t-test
Instructional
delivery method
(synchronous/
asynchronous)
Nominal Statistics
anxiety
(STARS)
Interval
Two-way
analysis of
variance
(ANOVA)
Instructional
delivery method
(synchronous/
asynchronous)
Instructional
learning strategy
(cooperative/
non-cooperative)
Nominal Statistics
anxiety
(STARS)
Interval
Credibility and Trustworthiness
In order to mitigate the effect of researcher bias in the collection and evaluation of
qualitative responses, this study evaluated typed, open-ended responses that were submitted by
participants as part of their pre- and post-test surveys.

61
Ethics
Although I was not an instructor or director of any course or academic program where
participants were drawn for this study, I did hold an administrative appointment within the
academic unit where the research was conducted. To mitigate the influence of my position on the
behavior of potential participants, this study issued clear, written and verbal communication to
indicate that participation was completely voluntary and that no one would be penalized for not
participating. Further, I made clear that I was communicating to participants solely in my
capacity as a doctoral degree candidate conducting a line of independent inquiry and not as an
administrator seeking to examine student performance (see Appendix C).

62
Chapter Four: Results and Findings
Online learning has become increasingly prominent in the rapidly evolving landscape of
higher education, particularly in the realm of graduate studies in the United States. This
transition has spotlighted the critical role of research methods and statistical literacy as
foundational elements of academic and scientific inquiry, deemed essential for advanced degrees
across various fields (Gal, 2002; Moore, 1998; Wallman, 1993). Yet, the integration of these
elements into curricula has revealed a significant challenge: statistics anxiety, a prevalent issue
that impedes students' academic progress and potentially their career trajectories (Onwuegbuzie,
1997, 2004; Zeidner, 1991).
Statistics anxiety, defined as a multidimensional construct characterized by emotional
distress experienced when engaging with statistical content (Chew & Dillon, 2014; Zeidner,
1991), is not merely an academic inconvenience but a barrier that affects a substantial portion of
the student population. This anxiety can lead to avoidance behaviors, such as postponing or
entirely withdrawing from required courses, thus affecting degree completion timelines and
academic performance (Onwuegbuzie & Wilson, 2003). This issue is particularly acute in online
learning environments, where traditional face-to-face reassurances are absent, and where more
than a third of all graduate students now enroll at some point during their academic journey
(Allen & Seaman, 2016; National Center for Education Statistics, 2016).
The increasing dependence on online platforms for delivering education necessitates a
reevaluation of pedagogical strategies to mitigate the effects of statistics anxiety. The purpose of
this study is to explore the efficacy of active and cooperative learning strategies—proven in
traditional classrooms—to alleviate statistics anxiety in online settings. Specifically, the study
aims to determine whether synchronous, web camera-enabled sessions and cooperative learning

63
activities can effectively reduce statistics anxiety among students enrolled in an online
communication master’s program. This inquiry is guided by three research questions:
1. Are synchronous, web camera-enabled online sessions effective in reducing distance
learners’ statistics anxiety in online graduate research methods courses?
2. Are cooperative learning activities effective in reducing distance learners’ statistics
anxiety in these contexts?
3. How do distance learners describe their likelihood to engage a statistics teacher for
help after participating in a synchronous, web camera-enabled online session?
The significance of addressing these questions lies in the potential to enhance the
educational experience and outcomes for online learners, an ever-growing demographic. By
identifying and implementing effective strategies to reduce statistics anxiety, educators can
improve academic performance and, by extension, statistical literacy among graduates—an
outcome that aligns with the goals of influential organizations like the American Statistical
Association (Wallman, 1993; Moore, 1998). Furthermore, this study contributes to the scholarly
dialogue by addressing a notable gap in the literature concerning statistics anxiety in online
learning environments, offering insights that may be generalized to similar educational contexts.
The theoretical underpinnings of this study are rooted in Social-Cognitive Theory (SCT),
which posits that learning occurs within a triadic relationship among personal factors, behavioral
patterns, and environmental influences (Bandura, 1986, 2002). This framework is particularly apt
for examining the interplay between student anxiety, behavior (e.g., avoidance of statistical
coursework), and online educational settings. By leveraging SCT, this study seeks to not only
explore the direct impacts of instructional interventions on anxiety reduction but also understand
how these interventions modify learners' perceptions and engagement with statistical content.

64
As online education continues to expand, understanding and mitigating the challenges
associated with statistics anxiety becomes crucial. This study aims to fill a critical research void
by testing targeted interventions within this context, thereby aiming to support the development
of more effective educational practices that can lead to improved outcomes for students facing
statistics anxiety. Through this research, educators and administrators can better prepare to meet
the evolving needs of their students, fostering an environment conducive to both academic
success and the attainment of essential statistical competencies.
Participants
The participants for this study were drawn from graduate students enrolled in a core
research methods course as part of a fully online communication management master’s degree
program at Coast Western University (pseudonym). Coast Western University is a Tier 1
research institution in California with large undergraduate and graduate populations. Coast
Western’s School of Communication is a global leader in cutting-edge communication research
and teaching, offering advanced degrees in communication, journalism, public relations, social
media, and public diplomacy. All communication management master’s students are required to
complete a core research requirement by enrolling in Communication Research during their first
term of attendance. Each term approximately 40–60 students enroll in the online offering of
Communication Research and are randomly distributed into 2–3 sections of 20 students or fewer.
Over the period of three academic terms in 2018 (spring and fall) and 2019 (summer), nearly 150
students enrolled across all online sections of Communication Research, and all were invited to
participate in this study. Ultimately, a total of 61 students participated in the experiment and
completed all elements of the pretest and posttest surveys on statistics anxiety, including the
demographic questionnaire. A range of demographics were collected, including age, gender,

65
ethnicity, enrollment status, remote enrollment location, employment status, relationship status,
and an assortment of items related to academic histories, particularly with mathematics, statistics
and online courses.
Demographic Characteristics of Study Sample
Among the graduate students who participated in the study, a significant majority were
female (n=50, 81.97%), while male students represented a smaller fraction (n=11, 18.03%). The
average age of respondents was 33.69 years, with a standard deviation of 10.29 years (n=59). On
average, students completed their previous education 8.02 years ago, with a standard deviation of
6.69 years (n=58). Most students were enrolled full-time (n=34, 55.74%) with the remaining
portion enrolled part-time (n=26, 42.62%). One participant did not report their enrolment status.
A large majority of participants were employed full-time (n=48, 78.69%), which was expected
given that the online master’s program primarily recruited working professionals. Smaller groups
of participants were either not employed (n=6, 9.84%), part-time employed (n=5, 8.20%), or
disabled (n=2, 3.28%). The ethnic and racial distribution of the sample was diverse with the
largest groups being White (n=25, 40.98%) and Hispanic/Latino (n=18, 29.51%), followed by
Asian (n=6, 9.84%), Black or African American (n=6, 9.84%) and smaller representations from
Middle Eastern, Native Hawaiian or other Pacific Islander, Indian, and two or more races (each
less than 3.28%). Most respondents had never married (n=36, 59.02%), with a sizable group of
married (n=16, 26.23%) and divorced students (n=7, 11.48%) also present. Participants
predominantly resided in suburban (n=34, 55.74%) or urban areas (n=25, 40.98%), with only
two living in rural settings (3.28%).
Questions about the academic histories of the participants and their families revealed that
many students have parents with a bachelor's degree (n=30, 49.18%) and some have parents with

66
a master's degree (n=13, 21.31%). Other levels of parental educational attainment included high
school diploma or GED (n=6, 9.84%) and doctoral degree (n=4, 6.56%), with less than high
school, some college, and associate's degree each less than 5%. Turning to the educational
experiences of the participants themselves, a nearly half had never taken online classes prior to
their master’s program (n=29, 47.54%). Others had varied experiences with some reporting
having taken more than three (n=18, 29.51%) while those reporting three, two or one prior online
class representing less than 5% each. The distribution of participants who had taken a prior
college statistics course varied considerably with none (n=26, 42.62%) being the most common
response. A sizable share had taken either one course (n=17, 27.87%) or two courses (n=13,
21.31%) with fewer students who had taken three or more than three courses (less than 5% each).
Participants were more likely to have taken a number of prior college math courses, with two
courses (n=15, 24.59%) and three courses (n=14, 22.95%) being common, and 9 respondents
(14.75%) having taken more than three. Only 11 participants (18.03%) had never taken a college
math course and 12 (19.67%) had taken only one. Participants last took a college math course on
average 10.83 years prior to the study, with a standard deviation of 6.42 years (n=60). A very
small number of students held a prior master’s degree (n=4, 6.56%).

67
Table 4
Participant Demographics
Variable Sub-Level N % Mean SD
Age 59 33.69 10.29
Years Since College 58 8.02 6.69
Years Since Last Math Course 60 10.83 6.42
Gender Female 50 81.97
Male 11 18.03
Enrollment Status Full-time 34 55.74
Part-time 26 42.62
Missing 1 1.64
Employment Status Full-time 48 78.69
Not employed 6 9.84
Part-time 5 8.2
Disabled 2 3.28
Ethnicity White 25 40.98
Hispanic/Latino 18 29.51
Asian 6 9.84
Black or African American 6 9.84
Middle Eastern 2 3.28
Native Hawaiian or Pacific Islander 1 1.64
Indian 1 1.64
Two or more races 1 1.64
Relationship Status Never married 36 59.02
Married 16 26.23
Divorced 7 11.48
Parents' Education Level Bachelor's degree 30 49.18
Master's degree 13 21.31
High school diploma/GED 6 9.84
Doctoral degree 4 6.56
Some college 3 4.92
Less than high school 3 4.92
Associate's degree 2 3.28

68
Variable Sub-Level N % Mean SD
Location Suburban 34 55.74
Urban 25 40.98
Rural 2 3.28
Prior Master's No 55 90.16
Yes 4 6.56
Prior College Statistics Courses None 26 42.62
One 17 27.87
Two 13 21.31
Three 3 4.92
More than three 2 3.28
Prior College Math Courses Two 15 24.59
Three 14 22.95
One 12 19.67
None 11 18.03
More than three 9 14.75
Prior Online Classes None 29 47.54
More than three 18 29.51
One 5 8.2
Two 5 8.2
Three 4 6.56
Comfort with Online Learning
Technology Somewhat comfortable 18 81.97
Comfortable 16 18.03
Very comfortable 15 55.74
Very uncomfortable 7 42.62
Uncomfortable 5 1.64
Organization of the Findings
This study’s findings are organized by research question, with each section discussing the
purpose, significant quantitative and qualitative results, and their relevance to theory and existing
literature. The analysis varied based on whether the research question employed a quantitative or
qualitative approach. This study utilized the Statistical Anxiety Rating Scale Revised (STARS-

69
R), an 18-item survey on a 5-point Likert scale adapted by McGrath et al. (2015), to collect
participants’ statistics anxiety levels pre- and post-intervention. Several independent samples ttests were performed to determine the effects of instructional delivery method
(synchronous/asynchronous) and instructional learning strategy (cooperative/non-cooperative) on
participant’s statistics anxiety (STARS-R). Additionally, a two-way analysis of variance
(ANOVA) was performed to ascertain between-subjects effects of instructional delivery method
and instructional learning strategy on statistics anxiety. Finally, participants’ open-ended
responses to posttest survey questions about instructor engagement and likelihood to ask them
for help were coded, analyzed and grouped into themes using a qualitative framework.
Results for Research Question 1
Research question one asked, “Are synchronous, web camera-enabled online sessions
effective in reducing distance learners’ statistics anxiety in online graduate research methods
courses?” Understanding and addressing statistics anxiety among graduate-level students crucial,
especially as higher education increasingly adopts fully online modalities. Statistics anxiety,
characterized by extensive worry and intrusive thoughts when engaging with statistical content,
significantly impacts students’ academic performance (Onwuegbuzie & Wilson, 2003). The
phenomenon affects approximately 80% of graduate students, often leading to procrastination
and avoidance of required statistics courses, which can jeopardize degree attainment
(Onwuegbuzie & Wilson, 2003; Zeidner, 1991). This anxiety is distinct from mathematics
anxiety, with unique antecedents and effects, necessitating specialized interventions (Chew &
Dillon, 2014). Given the prevalence of online learning and its potential to exacerbate statistics
anxiety due to reduced face-to-face interactions and increased self-directed learning demands, it
is imperative to investigate effective interventions in this context. Prior research highlights the

70
potential benefits of instructor immediacy, active learning strategies, and the use of humorous,
non-threatening instructional approaches to reduce statistics anxiety in traditional settings
(McGrath et al., 2015; Pan & Tang, 2004). This study aims to adapt and evaluate these
interventions in an online graduate research methods course to determine their efficacy in
mitigating statistics anxiety towards the goal of improving student outcomes.
Quantitative Analysis and Findings for Research Question 1
Quantitative analysis on STARS-R scores found that synchronous online instructional
delivery had a statistically significant impact on reducing statistics anxiety among participants.
An independent samples t-test was performed comparing the STARSDiff (STARS-R posttest
score minus STARS-R pretest score) among the two instructional delivery groups: synchronous
and asynchronous. STARSDiff scores among the two groups were approximately normally
distributed and not significantly different (p > .05). Group statistics were synchronous group (N
= 22) M = -6.64, SD = 7.69, and asynchronous group (N = 39) M = 0.05, SD = 7.75. The
independent t-test revealed that students saw a significant decrease in overall statistics anxiety
after receiving the intervention protocol through synchronous web camera-enabled online
delivery compared with asynchronous online delivery t(59) = -3.25, p = .002.
Table 5
Independent Samples T-Test Results for Instructional Delivery Method
F Sig. t df
Significance
Mean SD
OneSided
TwoSided
STARSDiff Equal
variances
assumed
.110 .741 -3.245 59 <.001 .002 -6.68765 2.06098
Equal
variances
not assumed
-3.253 44.003 .001 .002 -6.68765 2.05607

71
Since the experimental conditions involved two simultaneously delivered independent
variables, a two-way ANOVA test was conducted analyzing the effect of the instructional
delivery method (SYNC) and instructional learning strategy (COOP) on statistics anxiety
(STARSDiff). The results indicate that the corrected model is statistically significant, F(3, 57) =
4.154, p = .010, suggesting that the model explains a significant portion of the variance in
STARSDiff. The model accounts for approximately 17.9% of the variance in STARSDiff, as
indicated by the R-squared value, with an adjusted R-squared value of 13.6%. The main effect of
SYNC is significant, F(1, 57) = 7.376, p = .009, indicating that synchronization has a significant
impact on reducing statistics anxiety. The main effect of COOP, however, is not significant, F(1,
57) = 1.765, p = .189, suggesting that cooperation alone does not significantly affect statistics
anxiety. The interaction effect between SYNC and COOP is also not significant, F(1, 57) = .718,
p = .400, indicating that the combined effect of synchronization and cooperation does not
significantly influence statistics anxiety (Table 6).
Descriptive statistics from the ANOVA show that participants in the synchronous
condition (sync) with cooperation (coop) did experience a greater reduction in statistics anxiety,
with a mean STARSDiff of -8.13 (SD = 7.75, N = 15) than those in the synchronous condition
without cooperation (non-coop) M = -3.43, SD = 6.99 (N = 7). Overall, the synchronous
condition participants had a mean STARSDiff of -6.64 (SD = 7.69, N = 22), indicating a
substantial decrease in anxiety. Conversely, participants in the asynchronous condition (async)
showed minimal change in statistics anxiety. Those with cooperation had a mean STARSDiff of
-0.43 (SD = 7.65, N = 21), while those without cooperation had a mean STARSDiff of 0.61 (SD
= 8.06, N = 18). The overall mean STARSDiff for the asynchronous condition was 0.05 (SD =
7.75, N = 39).

72
Table 6
Tests of Between-Subjects Effects for Analysis of Variance
Dependent Variable: STARSDiff
Source
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 745.197a 3 248.399 4.154 .010
Intercept 414.105 1 414.105 6.924 .011
SYNC 441.102 1 441.102 7.376 .009
COOP 105.529 1 105.529 1.765 .189
SYNC * COOP 42.958 1 42.958 .718 .400
Error 3408.868 57 59.805
Total 4494.000 61
Corrected Total 4154.066 60
a. R Squared = .179 (Adjusted R Squared = .136)
Discussion for Research Question 1
Previous research found that instructor immediacy and active learning strategies can
significantly reduce statistics anxiety. For instance, Williams (2010) found that behaviors
associated with higher levels of instructor immediacy, such as personalized interactions and
humor, led to substantial reductions in students’ anxiety levels. Similarly, Rapp-McCall and
Anyikwa (2016) demonstrated that synchronous activities that involved direct contact with
instructors were highly effective in alleviating statistics anxiety among online graduate students.
The findings in this study further reinforce the role that synchronous learning—in this case
online, web camera-enabled synchronous sessions—can play in reducing statistics anxiety
among graduate students studying research methods.
Results for Research Question 2
Research question two asked, “Are cooperative learning activities effective in reducing
distance learners’ statistics anxiety in online graduate research methods courses?” Prior studies

73
on active learning, cooperative learning, and social cognitive theory have shown promise in
reducing statistics anxiety and enhancing learning outcomes. Active learning, which involves
engaging students through activities rather than passive listening, has been shown to significantly
reduce anxiety and increase engagement (Bonwell & Eison, 1991). Cooperative learning, a form
of active learning that emphasizes teamwork and collaboration, has also been effective in
reducing anxiety and improving academic performance. Key elements of cooperative learning
include positive interdependence, face-to-face interaction, individual accountability, and the use
of social communication skills (Johnson, 1991; Jiao & Onwuegbuzie, 2012). Studies have shown
that cooperative learning can lead to higher levels of achievement compared to individualistic or
competitive activities (Jiao & Onwuegbuzie, 2012). Furthermore, Bandura’s social cognitive
theory, which highlights the interplay between personal factors, behaviors, and environmental
influences, provides a framework for understanding how these learning strategies can impact
students’ experiences and outcomes (Bandura, 1986). For instance, creating a supportive and
non-threatening learning environment can help alleviate anxiety and improve self-efficacy,
leading to better academic performance (Bandura, 2002). These findings suggest that integrating
cooperative learning strategies within a social cognitive framework can be highly effective in
reducing statistics anxiety and promoting better learning outcomes (Aldrich, 2015; McGrath et
al., 2015; Pan & Tang, 2004).
Quantitative Analysis and Findings for Research Question 2
Quantitative analysis on STARS-R scores found that cooperative learning had no
significant impact on reducing statistics anxiety among participants. An independent samples ttest was performed comparing the STARSDiff among the two activity groups: cooperative and
non-cooperative. STARSDiff scores among the two groups were approximately normally

74
distributed and not significantly different (p > .05). The independent t-test revealed that the mean
decreases in statistics anxiety for the coop group (N = 36) M = -3.63, SD = 8.50, and non-coop
group (N = 25) M = -0.52, SD = 7.85, were not statistically significant, t(59) = -1.45, p > .05
(Table 7). Results from a two-way ANOVA found that the main effect of COOP was not
significant, F(1, 57) = 1.765, p = .189, further reinforcing that cooperative learning did not
significantly affect statistics anxiety in this study.
Discussion for Research Question 2
While previous theoretical and empirical studies support the moderating effect of
cooperative learning on statistics anxiety, its application in this specific study did not yield the
expected significant results (Aldrich, 2015; McGrath et al., 2015; Pan & Tang, 2004). For
example, Bonwell and Eison (1991) highlighted that active learning, which involves engaging
students through activities, significantly reduces anxiety and increases engagement. Similarly,
cooperative learning, which emphasizes teamwork and collaboration, had been found to reduce
anxiety and improve academic performance, as evidenced by the work of Johnson (1991) and
Jiao and Onwuegbuzie (2012). This approach aligns with Bandura’s (1986) social cognitive
theory, which posits that a supportive learning environment can enhance self-efficacy and reduce
anxiety (Bandura, 2002). Despite these promising theoretical frameworks and prior studies, the
quantitative results of this study indicate that cooperative learning did not have a statistically
significant impact on reducing statistics anxiety, as measured by the STARS-R scores. An
independent samples t-test and a two-way ANOVA both showed no significant differences in
anxiety reduction between cooperative and non-cooperative groups. Thus, while cooperative
learning holds potential according to previous research, its effectiveness in this context remains
inconclusive.

75
Table 7
Independent Samples T-Test Results for Instructional Learning Strategy
F Sig. t df
Significance
Mean SD
OneSided
TwoSided
STARSDiff Equal
variances
assumed
.010 .919 -1.453 59 .076 .152 -3.11889 2.14644
Equal
variances
not assumed
-1.474 54.302 .073 .146 -3.11889 2.11548
Results for Research Question 3
Research question three asked, “How do distance learners describe their likelihood to
engage a statistics teacher for help after participating in a synchronous, web camera-enabled
online session?” By asking this question within the context of an online research methods class,
this study aimed to expand prior research on how instructor interactions might mitigate the
anxieties captured by the STARS measure, particularly in terms of students' willingness to seek
help and their perceptions of their instructors (Cruise et al., 1985; Onwuegbuzie, 2004; Zeidner,
1991). Statistics anxiety, a well-documented barrier to academic success in statistics courses, is
characterized by a range of emotional responses, including "fear of asking for help" and "fear of
statistics teachers" (Onwuegbuzie, 2004; Zeidner, 1991). These specific anxieties can
significantly hinder a student's willingness to seek assistance, thereby impacting their overall
performance and confidence in the learning statistics and completing require research methods
courses. These fears are particularly relevant in online education, where the lack of face-to-face
interaction can exacerbate anxiety. The Statistics Anxiety Rating Scale (STARS) measures these

76
dimensions among others, providing a comprehensive tool to assess students' anxiety levels
(Baloglu, 2002; Cruise et al., 1985).
Qualitative Analysis and Findings for Research Question 3
A qualitative analysis of synchronous participants’ open-ended responses to the posttest
survey questions, "How would you describe your instructor’s level of engagement with the
class?" and "How would you describe your feelings about asking statistics questions of your
instructor?" revealed salient themes on instructor availability and openness to asking for help.
Theme 1: Instructor Availability and Approachability
In examining the responses to the question about the instructor's level of engagement
with the class, a predominant theme is the availability and accessibility of the instructor. Several
students noted the high level of engagement and accessibility of their instructors.
For instance, one respondent mentioned:
"My instructor's engagement goes above and beyond my expectations. She brings a level
of excitement to the content, makes herself available for questions and explains
instructions and expectations thoroughly – I never walk away from an email or live
session confused."
Another student emphasized the consistent availability and involvement of their instructor:
"She is very involved and in communication with us always."
The theme of availability is further supported by responses highlighting the instructors' readiness
to assist and engage with students:
"The professor thus far has been helpful and readily available to offer more assistance."
and

77
"My instructor is very accessible and very engaged with our class. She is vested in our
enrichment and success in this course."
However, not all responses were uniformly positive. Some students experienced a lack of
engagement from their instructors:
"I have had zero engagement with my actual instructor in 3 weeks. As for the TA
teaching the class, she seems very nice the few times there have been [live class]
meetings."
These contrasting responses indicate a varied experience among students regarding their
instructors' engagement levels, with the majority expressing positive interactions and a minority
reporting low or no engagement.
Theme 2: Openness to Asking for Help
The responses to the question about feelings toward asking statistics questions of the
instructor reveal another critical theme: openness to seeking help. Many students feel
comfortable and confident asking questions, which is indicative of a supportive and
approachable instructor.
For example, one respondent shared:
"I am 100% confident in asking my instructor statistics questions. Her teaching style and
approach to the material has definitely established herself as a resource that welcomes
and even encourages us to seek her out with any questions."
Similarly, another student expressed:
"I feel comfortable asking questions about statistics."
and
"If I don't know something, I don't have a problem asking."

78
These responses suggest that when instructors establish a welcoming and supportive
environment, students feel more at ease seeking help. However, not all students share this
sentiment. Some feel hesitant or anxious about asking questions:
"I generally don't ask instructors questions because I haven't had a good experience
asking in the past – I guess I fear asking."
and
"If I am confused, I am afraid of how to word my question or assess which part I don’t
understand."
These sentiments highlight the importance of instructors creating a safe and non-judgmental
space for students to ask questions, which can significantly impact their willingness to seek help.
Discussion for Research Question 3
The thematic analysis of the responses reveals two critical themes: instructor availability
and openness to asking for help. Students who perceive their instructors as highly engaged and
accessible are more likely to feel comfortable asking questions, while those who experience low
engagement may hesitate to seek assistance. Ensuring consistent engagement and fostering an
open, supportive environment can significantly enhance students' learning experiences and
reduce anxiety associated with seeking help.
Summary of Findings
The study found that the method of instructional delivery, specifically synchronous
versus asynchronous, significantly impacted the reduction of statistics anxiety among online
graduate students. Using the Statistical Anxiety Rating Scale Revised (STARS-R), the study
measured participants' anxiety levels before and after the intervention. An independent samples
t-test indicated a statistically significant reduction in anxiety for students in the synchronous

79
web-camera-enabled sessions compared to those in asynchronous sessions (t(59) = -3.25, p
= .002). Specifically, the synchronous group exhibited a mean reduction in anxiety (M = -6.64,
SD = 7.69), whereas the asynchronous group showed almost no change (M = 0.05, SD = 7.75).
These findings align with prior research that emphasizes the benefits of synchronous learning
environments, which often incorporate elements of instructor immediacy and active engagement
known to mitigate anxiety (Williams, 2010; Rapp-McCall & Anyikwa, 2016).
Further, the two-way ANOVA results confirmed that the instructional delivery method
had a significant main effect on statistics anxiety F(1, 57) = 7.376, p = .009). The synchronous
delivery method's effectiveness in reducing anxiety is corroborated by prior studies highlighting
the importance of real-time interaction and immediate feedback in alleviating students' concerns
about complex subjects like statistics (McGrath et al., 2015). Thus, the synchronous instructional
method appears to be a critical factor in reducing statistics anxiety in online graduate courses.
However, contrary to some prior research findings, this study found that cooperative
learning strategies did not have a significant mediating effect on statistics anxiety. The
independent samples t-test revealed no significant difference in anxiety reduction between
cooperative and non-cooperative learning groups t(59) = -1.45, p > .05. Additionally, the twoway ANOVA showed that the main effect of cooperative learning was not significant F(1, 57) =
1.765, p = .189. These findings challenge the prevailing notion that cooperative learning, which
emphasizes teamwork and mutual support, effectively reduces anxiety and improves academic
performance (Johnson, 1991; Jiao & Onwuegbuzie, 2012).
While theoretical frameworks and empirical studies suggest that cooperative learning can
foster a supportive learning environment and enhance self-efficacy (Bandura, 2002), the present
study's results indicate that these benefits did not translate into a significant reduction in statistics

80
anxiety. This discrepancy suggests that while cooperative learning may have other academic
benefits, its direct impact on statistics anxiety may be less substantial than previously thought,
particularly in an online learning context where face-to-face interaction is limited.
Qualitative analysis of participants' open-ended responses revealed that instructor
engagement and approachability significantly influence students' willingness to seek help. Two
main themes emerged: instructor availability and openness to asking for help. Students who
perceived their instructors as highly engaged and accessible reported feeling more comfortable
and confident in seeking assistance. For example, one student noted, "My instructor's
engagement goes above and beyond my expectations. She makes herself available for questions
and explains instructions thoroughly" (Participant A). This positive perception aligns with the
findings of Cruise et al. (1985) and Onwuegbuzie (2004), who identified fear of asking for help
as a significant component of statistics anxiety. Conversely, students who experienced low
engagement from their instructors were less likely to seek help, underscoring the importance of
instructor presence in online settings. The qualitative data suggest that fostering a supportive and
accessible learning environment can mitigate students' anxiety and encourage proactive helpseeking behaviors. This finding highlights the need for instructors to actively engage with
students and create an atmosphere where seeking help is normalized and encouraged.

81
Chapter Five: Discussion
Statistics anxiety is a prevalent issue among graduate students, particularly those enrolled
in online research methods courses. This anxiety can significantly hinder students' performance
and delay or interrupt their degree completion (Onwuegbuzie & Wilson, 2003). The rise in online
graduate programs necessitates strategies to mitigate this anxiety, ensuring students can
successfully engage with essential research methodologies in pursuit of advanced research.
Purpose of the Study
This study aimed to evaluate the effectiveness of an activity-based intervention delivered
via synchronous, web camera-enabled online instructional sessions in reducing statistics anxiety
among students in an online communication master’s program. Specifically, it tested whether
synchronous and cooperative learning strategies, previously effective in face-to-face settings,
could reduce statistics anxiety in an online environment.
Research Questions
This study sought to answer the following research questions:
1. Are synchronous, web camera-enabled online sessions effective in reducing distance
learners’ statistics anxiety in online graduate research methods courses?
2. Are cooperative learning activities effective in reducing distance learners’ statistics
anxiety in online graduate research methods courses?
3. How do distance learners describe their likelihood to engage a statistics teacher for help
after participating in a synchronous, web camera-enabled online session?

82
Description of the Methodology
This study employed a quasi-experimental, mixed-methods research design with a
nonequivalent control group. This approach is preferred for evaluating differences in students'
statistics anxiety concerning instructional delivery methods (synchronous versus asynchronous)
and cooperative learning strategies. This design is advantageous over a single group pretestposttest design for interpreting data in educational research (Chew & Dillon, 2014).
The study targeted graduate students enrolled in an introductory research methods course
in a fully online master's program in communication management at Coast Western University.
The sample consisted of 61 students, predominantly female (81.97%), with an average age of
33.69 years, most of whom were employed full-time (78.69%). Data collection spanned three
academic terms (Spring 2018, Fall 2018, and Summer 2019), with participants randomly
distributed into experimental and control groups. Extra credit was offered for participation, with
alternatives provided to ensure no disadvantage for non-participants.
The primary instrument was the STARS-R, an 18-item survey on a 5-point Likert scale,
adapted from the original 51-item STARS. This instrument was chosen for its high internal
consistency reliability (Cronbach’s Alpha of .88 pre-course and .87 post-course) (McGrath et al.,
2015). Pretest and posttest surveys included open-ended questions addressing research questions,
and demographic data were collected posttest to avoid stereotype threat. Data collection occurred
through pretest and posttest surveys, assessing students' statistics anxiety using the Statistics
Anxiety Rating Scale Revised (STARS-R). Open-ended questions also explored students'
attitudes and experiences related to statistics anxiety. The study involved experimental groups
receiving synchronous, webcam-enabled sessions and cooperative learning activities, and control
groups participating in asynchronous, non-cooperative activities. Quantitative analysis, including

83
independent samples t-tests and ANOVA in SPSS, examined statistical differences and predictive
relationships.
The study was approved by the university’s IRB and the relevant academic program.
Surveys were administered via Qualtrics, with pretest responses collected before the intervention
and posttest responses afterward. Participants were split into experimental and control groups,
with the experimental group engaging in synchronous, cooperative learning activities and the
control group in asynchronous, non-cooperative tasks. The intervention aimed to reduce statistics
anxiety through activities designed to normalize anxiety, highlight the relevance of statistics in
daily life, and reinforce concepts through peer interaction and individual reflection.
Quantitative data were analyzed using SPSS, with descriptive statistics produced for all
variables. ANOVA were used to analyze main variables, contingent on adequate response rates.
Qualitative data from open-ended survey responses were coded and analyzed to provide insights
into students' perceptions of the interventions and their impact on statistics anxiety factors. The
study’s mixed-methods design, leveraging both quantitative and qualitative data, offers a
comprehensive examination of how instructional delivery methods and cooperative learning
strategies influence statistics anxiety among online graduate students.
Discussion of Findings for Research Question 1
Research question one examined whether synchronous, web camera-enabled online
sessions are effective in reducing distance learners’ statistics anxiety in online graduate research
methods courses. Addressing statistics anxiety is crucial due to its high prevalence among
graduate students and its significant impact on academic performance (Onwuegbuzie & Wilson,
2003; Zeidner, 1991). Quantitative analysis using the Statistical Anxiety Rating Scale Revised
(STARS-R) showed a statistically significant reduction in statistics anxiety for participants in

84
synchronous sessions compared to those in asynchronous sessions. An independent samples t-test
revealed a significant decrease in anxiety for the synchronous group (M = -6.64, SD = 7.69)
compared to the asynchronous group (M = 0.05, SD = 7.75), t(59) = -3.25, p = .002.
Additionally, a two-way ANOVA indicated that the instructional delivery method had a
significant effect on reducing statistics anxiety, F(1, 57) = 7.376, p = .009, while the instructional
learning strategy did not, F(1, 57) = 1.765, p = .189. These findings support previous research on
the benefits of synchronous learning environments, which incorporate instructor immediacy and
active engagement (Williams, 2010; Rapp-McCall & Anyikwa, 2016). The study confirms that
synchronous instructional methods effectively reduce statistics anxiety, aligning with theories
that emphasize real-time interaction and immediate feedback as key factors in alleviating student
anxiety (McGrath et al., 2015).
Discussion of Findings for Research Question 2
Research question two investigated whether cooperative learning activities are effective
in reducing distance learners’ statistics anxiety in online graduate research methods courses.
Cooperative learning, characterized by teamwork and collaboration, has been theorized to reduce
anxiety and improve academic performance (Bonwell & Eison, 1991; Johnson, 1991).
Quantitative analysis showed no significant impact of cooperative learning on reducing statistics
anxiety. The independent samples t-test and two-way ANOVA indicated no significant
differences in anxiety reduction between cooperative (M = -3.63, SD = 8.50) and noncooperative (M = -0.52, SD = 7.85) groups, t(59) = -1.45, p > .05; F(1, 57) = 1.765, p = .189.
Contrary to prior research suggesting the efficacy of cooperative learning in reducing anxiety
(Jiao & Onwuegbuzie, 2012; Bandura, 2002), this study found no significant impact. This
discrepancy highlights the need for further research to understand the specific conditions under

85
which cooperative learning might effectively reduce statistics anxiety, especially in online
settings.
Discussion of Findings for Research Question 3
Research question three explored how distance learners describe their likelihood to
engage a statistics teacher for help after participating in a synchronous, web camera-enabled
online session. This question aims to understand the role of instructor interactions in mitigating
anxieties related to asking for help and engaging with instructors (Cruise et al., 1985;
Onwuegbuzie, 2004). Qualitative analysis of open-ended responses revealed two main themes:
instructor availability and openness to asking for help.
• Instructor Availability and Approachability: Students reported high levels of engagement
and accessibility from their instructors, which increased their comfort in seeking help.
Positive comments highlighted instructors’ availability and proactive engagement.
• Openness to Asking for Help: Many students felt confident and comfortable asking
questions, indicating that supportive and approachable instructors can significantly
reduce anxiety related to seeking help.
These findings align with previous research identifying fear of asking for help as a major
component of statistics anxiety (Onwuegbuzie, 2004). The study underscores the importance of
instructor presence and engagement in online learning environments to foster a supportive
atmosphere that encourages students to seek help, thereby reducing anxiety.
Summary of Findings
The study revealed that synchronous instructional delivery significantly reduces statistics
anxiety among online graduate students. This aligns with existing literature emphasizing the
benefits of real-time interaction and instructor immediacy (Williams, 2010; Rapp-McCall &

86
Anyikwa, 2016). However, cooperative learning strategies did not show a significant impact on
anxiety reduction, challenging previous research and suggesting the need for further
investigation into the conditions that might enhance their effectiveness (Jiao & Onwuegbuzie,
2012; Bandura, 2002). Qualitative findings highlighted the crucial role of instructor engagement
and approachability in mitigating students’ anxiety about seeking help, reinforcing the
importance of a supportive and accessible online learning environment.
Limitations of the Study
This study had several limitations that may affect the interpretation and generalizability of
the findings. Firstly, the study's population was limited to graduate students enrolled in a specific
online research methods course at a large, private research university. This focus on a single
institution restricts the ability to generalize the results to other populations, such as
undergraduate students or students at different types of institutions. The unique characteristics of
the study site, including its status as a Tier 1 research university, may also influence the findings
and limit their applicability to other contexts. Another significant limitation was the sample size.
Although efforts were made to maximize participation, the total number of students involved in
the study was relatively small. Small sample sizes can lead to issues with statistical power,
making it more challenging to detect significant differences or relationships. Additionally, selfselection bias could have influenced the results. Students who chose to participate in the study
might differ systematically from those who did not, particularly regarding their levels of statistics
anxiety or their attitudes towards online learning. This self-selection could skew the results and
affect the study's validity. The study also relied heavily on self-reported data, which can
introduce several biases, including social desirability bias and recall bias. Participants may have
responded to surveys in ways they believed were expected or desirable, rather than reflecting

87
their true feelings and experiences. Furthermore, the accuracy of self-reported data can be
influenced by participants' ability to recall past events accurately, which might affect the
reliability of the data collected. In terms of methodology, the quasi-experimental design, while
robust, has inherent limitations. The lack of truly random assignment to control and experimental
groups means that there may be preexisting differences between the groups that could influence
the outcomes. Although every effort was made to match groups as closely as possible, the
absence of randomization limits the ability to infer causality from the results. Lastly, personal
factors inherent to the differing course instructors who served as PMs for the intervention may
have influenced students’ levels of statistics anxiety related to factors such as fear of statistics
teachers or fear of asking for help. Further, the differences in the temporal context of the three
different terms in which the data was collected may have influenced the relevance of the
findings.
Implications for Practice
The findings of this study have several significant implications for practice, particularly
in the context of online graduate research methods education and the reduction of statistics
anxiety. One of the most noteworthy implications is the demonstrated effectiveness of
synchronous, web camera-enabled sessions in alleviating statistics anxiety among online
learners. This suggests that incorporating real-time interaction and immediate feedback into
online courses that teach statistics or research methods can be a powerful tool in reducing
students' anxiety levels and improving their overall learning experience. Educators and
instructional designers should consider integrating synchronous sessions into their online
research methods courses to provide students with the benefits of live interaction, which can help
mitigate feelings of isolation and anxiety commonly associated with online learning

88
environments. Moreover, the study underscores the importance of instructor presence and
engagement in online courses. The qualitative findings revealed that students who perceived
their statistics instructors as highly engaged and approachable were more likely to seek help and
felt more supported in their learning journey. This highlights the need for educators to adopt
proactive engagement strategies, such as regular check-ins, personalized feedback, and open
office hours, to create a supportive and interactive online learning environment. By fostering a
sense of connection and accessibility, statistics and research methods instructors can significantly
reduce students' statistics anxiety and encourage active participation. Another practical
implication is the need for institutions to provide professional development and training for
research methods instructors on effective online teaching practices, particularly those that
address statistics anxiety. Given the rapid expansion of online education, it is crucial that
educators are equipped with the skills and knowledge to design and deliver courses that are both
engaging and supportive. Professional development programs should focus on best practices for
synchronous instruction, strategies for increasing instructor presence, and techniques for
implementing synchronous learning activities in virtual settings. Furthermore, the study's
findings highlight the importance of continuous assessment and adaptation of instructional
strategies to meet the evolving needs of online learners. Institutions should encourage a culture
of reflective practice among educators, where they regularly evaluate the effectiveness of their
teaching methods and make necessary adjustments based on student feedback and learning
outcomes. This iterative process can help ensure that instructional practices remain relevant and
effective in reducing statistics anxiety and enhancing student success.

89
Suggestions for Future Research
The findings and limitations of this study open several avenues for future research to
further understand and mitigate statistics anxiety in online learning environments. One primary
suggestion is to conduct longitudinal studies that assess the long-term impact of synchronous,
web camera-enabled sessions and cooperative learning activities on statistics anxiety.
Longitudinal research could provide deeper insights into how these interventions affect students
over multiple terms or academic years, revealing any sustained benefits or potential drawbacks
that might not be evident in shorter studies. Another critical area for future research is to explore
these interventions across a more diverse range of student populations and institutional settings.
The current study focused on a specific cohort of graduate students at a single, large, private
research university, which may limit the generalizability of the findings. Expanding research to
include undergraduate students, students from different types of institutions (e.g., community
colleges, public universities), and those from various geographic regions and cultural
backgrounds could provide a more comprehensive understanding of how different demographics
experience and respond to these anxiety-reducing strategies. This study collected an assortment
of demographic data (parental educational attainment, number of prior collegiate math and
statistics courses, prior degree type, etc.) that could be used to examine potential differences in
statistics anxiety levels. Collecting a similarly robust array of demographic data as part of a
longitudinal study of antecedents of statistics anxiety would greatly inform the literature as well
as the analysis and development of potential new interventions.
Further research should also examine the specific elements of instructor engagement that
are most effective in reducing statistics anxiety. This study highlighted the importance of

90
instructor presence, but more detailed investigations could pinpoint which behaviors and
practices—such as timely feedback, empathetic communication, and active facilitation of
discussions—are most beneficial. Understanding these nuances could help educators refine their
approaches and create even more supportive online learning environments. Lastly, given the
rapid evolution of online learning technologies and practices, it is essential to continuously
update and reassess the effectiveness of statistics anxiety-reducing strategies. Future research
should keep pace with technological advancements and changing student expectations, ensuring
that interventions remain relevant and effective in contemporary educational contexts. By doing
so, researchers can contribute to a dynamic and evolving body of knowledge that supports the
ongoing improvement of online education.
Conclusions
The findings of this study underscore the critical role of synchronous instructional
methods in reducing statistics anxiety among graduate students in online research methods
courses. The statistically significant reduction in anxiety for students participating in
synchronous, web camera-enabled sessions highlight the importance of real-time interaction and
immediate feedback (Williams, 2010; Rapp-McCall & Anyikwa, 2016). These results suggest
that incorporating synchronous elements into online courses can effectively address statistics
anxiety, aligning with existing theories that emphasize the benefits of instructor immediacy and
active engagement (McGrath et al., 2015). Conversely, the study found that cooperative learning
strategies did not significantly impact statistics anxiety reduction, contradicting some prior
research (Jiao & Onwuegbuzie, 2012; Bandura, 2002). This discrepancy suggests a need for
further investigation to identify specific conditions under which cooperative learning might be
more effective in statistics or research methods courses, particularly in online settings. It is

91
possible that the online environment requires different or additional elements to facilitate the
same level of anxiety reduction observed in face-to-face contexts. Qualitative findings further
reinforce the importance of instructor presence and approachability in online learning
environments. Students reported increased comfort in seeking help and engaging with instructors
who were perceived as accessible and supportive. This aligns with previous studies highlighting
fear of asking for help as a major component of statistics anxiety (Onwuegbuzie, 2004). The
emphasis on instructor engagement underscores the need for educators to adopt strategies that
foster a supportive atmosphere, thereby reducing anxiety and enhancing student participation.
These findings carry several implications for practice. Firstly, educators and instructional
designers should consider integrating synchronous sessions into online research methods courses
to leverage the benefits of real-time interaction. This approach can help mitigate feelings of
isolation and anxiety commonly associated with online learning. Additionally, proactive
engagement strategies, such as regular check-ins, personalized feedback, and open office hours,
should be implemented to create a supportive and interactive online learning environment.
Institutions should also provide professional development for instructors on effective online
teaching practices, focusing on synchronous instruction, instructor presence, and cooperative
learning activities. The study's limitations, including the focus on a single institution and a
relatively small sample size, suggest the need for caution in generalizing the results. Future
research should explore these interventions across diverse student populations and institutional
settings to gain a more comprehensive understanding of their effectiveness. Longitudinal studies
could also provide insights into the sustained impact of these strategies over time. Moreover,
further research should examine the specific elements of instructor engagement that are most
effective in reducing statistics anxiety, helping educators refine their approaches to better support

92
students. In conclusion, while this study highlights the effectiveness of synchronous instructional
methods in reducing statistics anxiety, it also points to areas where further research and practice
can enhance the online learning experience. By continuously assessing and adapting instructional
strategies to meet the evolving needs of online learners, educators can create more supportive
and effective learning environments that reduce statistics anxiety and promote academic success.

93
References
Aldrich, C. (2005). Learning by doing. San Francisco. CA. Pfeiffer, 5.
Allen, I. E., & Seaman, J. (2010). Class differences: Online education in the United States,
2010. Babson Survey Research Group and Quahog Research Group.
http://www.onlinelearningsurvey.com/reports/class-differences.pdf
Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in
the United States. Babson Survey Research Group and Quahog Research Group.
http://www.onlinelearningsurvey.com/reports/changingcourse.pdf
Allen, I. E., & Seaman, J. (2014). Grade change: Tracking online education in the United
States. Babson Survey Research Group and Quahog Research Group.
http://www.onlinelearningsurvey.com/reports/gradechange.pdf
Allen, I. E., & Seaman, J. (2015). Grade level: Tracking online education in the United States.
Babson Survey Research Group and Quahog Research Group.
http://www.onlinelearningsurvey.com/reports/gradelevel.pdf
Allen, I. E., & Seaman, J. (2016). Online report card: Tracking online education in the United
States. Babson Survey Research Group and Quahog Research Group.
https://www.bayviewanalytics.com/reports/onlinereportcard.pdf
Allen, I. E., & Seaman, J. (2017). Digital learning compass: Distance education enrollment
report 2017. Babson Survey Research Group, e-Literate, and WCET.
https://www.bayviewanalytics.com/reports/digtiallearningcompassenrollment2017.pdf
Atkin, C. (2010). Training the communication researcher of the future. Spectra, 46(1), 16-19.
Ball, C. T., & Pelco, L. E. (2006). Teaching research methods to undergraduate psychology
students using an active cooperative learning approach. International Journal of
Teaching and Learning in Higher Education, 17(2), 147-154.
Baloğlu, M. (1999). A comparison of mathematics anxiety and statistics anxiety in relation to
general anxiety. http://files.eric.ed.gov/fulltext/ED436703.pdf
Baloğlu, M. (2002). Psychometric properties of the Statistics Anxiety Rating Scale.
Psychological Reports, 90, 315–325.
Baloğlu, M. (2003). Individual differences in statistics anxiety among college students.
Personality and Individual Differences, 34, 855–865. https://doi.org/10.1016/S0191-
8869(02)00076-4
Baloğlu, M. (2004). Statistics anxiety and mathematics anxiety: Some interesting differences I.
Educational Research Quarterly, 27(3), 38–48.

94
Baloğlu, M., Deniz, M. E., & Kesici, S¸. (2011). A descriptive study of individual and crosscultural differences in statistics anxiety. Learning and Individual Differences, 21, 387–
391. https://doi.org/10.1016/j.lindif.2011.03.003
Bandalos, D. L., Yates, K., & Thorndike-Christ, T. (1995). Effects of math self-concept,
perceived self-efficacy, and attributions for failure and success on test anxiety. Journal of
Educational Psychology, 87(4), 611.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory.
Prentice-Hall.
Bandura, A. (2002). Social cognitive theory in cultural context. Applied psychology, 51(2),
269-290.
Bell, J. A. (2001). Length of course and levels of statistics anxiety. Education, 121, 713–716.
Bell, J. A. (2003). Statistics anxiety: The nontraditional student. Education, 124, 157–162.
Bell, J. A. (2005). Length of term and levels of statistics anxiety: A comparison of types of
offerings. Academy of Information and Management Sciences Journal, 8(1), 103–108.
Benson, J. (1989). Structural components of statistical test anxiety in adults: An exploratory
model. Journal of Experimental Education, 57, 247-261.
Benson, J. & Bandalos, D. (1989). Structural model of statistical test anxiety, in: R. Schwarzer,
H.M. Van Der Ploeg & C.D. Spielberger (Eds) Advances in Test Anxiety Research, Vol.
5, pp. 137–154 (Hillsdale, NJ, Lawrence Erlbaum Associates).
Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., &
Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance
education. Review of Educational Research, 79(3), 1243-1289.
Betz, N. E. (1978). Prevalence, distribution, and correlates of math anxiety in college students.
Journal of Counseling Psychology, 25, 441–448.
Bonwell, C. C., & Eison, J. A. (1991). Active Learning: Creating Excitement in the Classroom.
1991 ASHE-ERIC Higher Education Reports. ERIC Clearinghouse on Higher Education,
The George Washington University, One Dupont Circle, Suite 630, Washington, DC
20036-1183.
Bradley, D.R. & Wygant, C.R. (1998). Male and female differences in anxiety about statistics
are not reflected in performance, Psychological Reports, 82, pp. 245–246.
Brown, J. L. M. (2012). Online learning: A comparison of Web-based and land-based courses.
The Quarterly Review of Distance Education, 13(1), 39-42.

95
Brush, L. R. (1981). Some Thoughts for Teachers on Mathematics Anxiety. Arithmetic
Teacher, 29(4), 37-39.
Bui, N. H., & Alfaro, M. A. (2011). Statistics anxiety and science attitudes: Age, gender, and
ethnicity factors. College Student Journal, 45, 573–585.
Campbell, D. T., & Stanley, J. C. (1963). Experimental designs for research on teaching.
Handbook of research on teaching, 171-246.
Campbell, M., Gibson, W., Hall, A., Richards, D., & Callery, P. (2008). Online vs. face-to-face
discussion in a web-based research methods course for postgraduate nursing students: A
quasi-experimental study. International Journal of Nursing Studies, 45, 750-759.
Castle, S. R., & McGuire, C. J. (2010). An analysis of student self-assessment of online,
blended, and face-to-face learning environments: Implications for sustainable education
delivery. International Education Studies, 3(3), 36-40.
Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and
computer anxiety as predictors of computer supported education. Computers &
Education, 60(1), 148-158.
Chen, P. D., Lambert, A. D., & Guidry, K. R. (2010). Engaging online learners: The impact of
web-based learning technology on college student engagement. Computer & Education,
54, 1222-1232.
Chew, P. K. H., & Dillon, D. B. (2014). Statistics anxiety update: Refining the construct and
recommendations for a new research agenda. Perspectives on Psychological Science,
9(2), 196-208. https://doi.org/10.1177/1745691613518077
Christensen, C. M., Horn, M. B., & Johnson, C. W. (2008). Disrupting class: How disruptive
innovation will change the way the world learns (Vol. 98). New York: McGraw-Hill.
Clark, R. E. (2001). Learning from Media: Arguments, Analysis and Evidence. Information Age
Publishers. ISBN 1-930608-77-2
Clark, R. E., Yates, K., Early, S. & Moulton, K. (2010). An Analysis of the Failure of Electronic
Media and Discovery-based learning: Evidence for the performance benefits of Guided
Training Methods. In Silber, K. H. & Foshay, R. (Eds.). Handbook of Training and
Improving Workplace Performance, Volume I: Instructional Design and Training
Delivery. Wiley.
Clinefelter, D. L., & Aslanian, C. B. (2014). Online college students 2014: Comprehensive data
on demands and preferences.
http://www.aslanianmarketresearch.com/sites/default/files/OCS2015-Final_Report.pdf

96
Collins, K.M.T. & Onwuegbuzie, A.J. (2002). Relationship Between Reading Ability and
Achievement in a Graduate-level Research Methodology Course. Paper presented at the
annual meeting of the American Educational Research Association, New Orleans, LA,
April.
Council of Graduate Schools. (2024). Graduate enrollment and degrees. Retrieved from
https://cgsnet.org/data-insights/admissions-enrollment-and-completion/graduateenrollment-and-degrees
Cruise, R.J. & Wilkins, E.M. (1980). STARS: Statistical Anxiety Rating Scale, Unpublished
manuscript, Andrews University, Berrien Springs, MI.
Cruise, J. R., Cash, R. W., and Bolton, L. D. (1985). Development and validation of an
instrument to measure statistical anxiety. American Statistical Association Proceedings of
the Section on Statistical Education, 92-98.
D’Andrea, L., & Waters, C. (2002). Teaching statistics using short stories: Reducing anxiety and
changing attitudes. Paper presented at the Sixth International Conference on Teaching
Statistics, Cape Town, South Africa, July.
Del Valle, R., & Duffy, T. M. (2009). Online learning: Learner characteristics and their
approaches to managing learning. Instructional Science, 37(2), 129-149.
Demaria-Mitton, P.A. (1987). Locus-of-control, gender and type of major as correlates to
statistics anxiety in college students (Doctoral dissertation, American University)
DeVaney, T. A. (2010). Anxiety and attitude of graduate students in on-campus vs. online
statistics courses. Journal of Statistics Education, 18(1).
https://ww2.amstat.org/publications/jse/v18n1/devaney.pdf
Dew, K. H., & Galassi, J. P. (1983). Mathematics anxiety: Some basic issues. Journal of
Counseling Psychology, 30(3), 443.
Diaz, D., & Cartnal, R. (2006). Term length as an indicator of attrition in online
learning. Innovate: Journal of Online Education, 2(5), 7.
Dilevko, J. (2000). A new approach to teaching research methods courses in LIS
programs. Journal of education for library and information science, 307-329.
Dillon, K. M. (1982). Statisticophobia. Teaching of Psychology, 9(2), 117-117.
Earley, M. A. (2014). A synthesis of the literature on research methods education. Teaching in
Higher Education, 19(3), 242-253.
Earp, M. S. (2007). Development and validation of the statistics anxiety measure (Doctoral
dissertation, University of Denver).

97
Farbey, L.J. & Roberts, D.M. (1981). Effects of Calculator Usage and Task Difficulty on State
Anxiety in Solving Statistical Problems. Paper presented at the annual meeting of the
American Educational Research Association, Los Angeles, CA, April.
Fife, E. (2008). Student Reaction to Research Methods Classes. Journal of the Northwest
Communication Association, 37.
Fitzgerald, S.M., Jurs, S. & Hudson, L.M. (1996). A model predicting statistics achievement
among graduate students, College Student Journal, 30, pp. 361–366.
Forster, M. and Washington, E. (2000). A model for developing and managing distance
education programs using interactive video technology. Journal of Social Work
Education, 36, 147-158.
Freddolino, P.P. & Sutherland, C.A. (2000). Assessing the comparability of classroom
environments in graduate social work education delivered via interactive instructional
television (IITV). Journal of social work education, 36(1), 115-129.
Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International
Statistical Review, 70, 1–25. https://doi.org/10.2307/1403713
Galli, S., Ciancaleoni, M., Chiesi, F., & Primi, C. (2008). Who failed the introductory statistics
examination? A study on a sample of psychology students. Paper presented at the 11th
International Congress on Mathematical Education, Monterrey, Mexico, July.
Gardner, H. (1983). Frames of Mind: the theory of multiple intelligences (New York, Basic
Books).
Ginder, S. A., Kelly-Reid, J. E., & Mann, F. B. (2020). Enrollment and employees in
postsecondary institutions, fall 2019; and financial statistics and academic libraries,
fiscal year 2019 (NCES 2020-009). National Center for Education Statistics.
Gould, R. (2010). Statistics and the modern student. International Statistical Review, 78, 297–
315. https://doi.org/10.1111/j.1751-5823.2010.00117.x
Haga, M., Heitkamp, T. (2000). Bringing Social Work Education to the Prairie. Council
on Social Work Education, 36, 309-324.
Hanna, D., & Dempster, M. (2009). The effect of statistics anxiety on students’ predicted and
actual test scores. The Irish Journal of Psychology, 30, 201–209.
https://doi.org/10.1080/03033910.2009.10446310
Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the Statistics Anxiety Rating
Scale: A confirmatory factor analysis using UK psychology students. Personality and
Individual Differences, 45, 68–74. https://doi.org/10.1016/j.paid.2008.02.021

98
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research
in Mathematics Education, 21, 33-46.
Hsiao, T., & Chiang, S. (2011). Gender differences in statistics anxiety among graduate students
learning English as a foreign language. Social Behavior and Personality, 39, 41–42.
https://doi.org/10.2224/sbp.2011.9.1.41
Hunsley, J.D. (1987). Cognitive processes in mathematics anxiety and test anxiety: the role of
appraisals, internal dialogue, and attributions, Journal of Educational Psychology, 79, pp.
388–392.
Jiao, Q. G., & Onwuegbuzie, A. J. (2012). The Role of Statistics Anxiety in Cooperative
Learning Groups in Graduate-Level Research Methodology Courses. International
Journal of University Teaching and Faculty Development, 3(4), 311.
Johnson, D. W. (1991). Cooperative Learning: Increasing College Faculty Instructional
Productivity. ASHE-ERIC Higher Education Report No. 4, 1991. ASHE-ERIC Higher
Education Reports, George Washington University, One Dupont Circle, Suite 630,
Washington, DC 20036-1183.
Kaplan, A. M., & Haenlein, M. (2016). Higher education and the digital revolution: About
MOOCs, SPOCs, social media, and the Cookie Monster. Business Horizons, 59(4), 441-
450.
Keeley, J., Zayac, R., & Correia, C. (2008). Curvilinear relationships between statistics anxiety
and performance among undergraduate students: Evidence for optimal anxiety. Statistics
Education Research Journal, 7(1), 4–15.
Kena, G., Musu-Gillette, L., Robinson, J., Wang, X., Rathbun, A., Zhang, J., ... & Velez, E. D.
V. (2015). The Condition of Education 2015. NCES 2015-144. National Center for
Education Statistics.
Kilburn, D., Nind, M., & Wiles, R. (2014). Learning as researchers and teachers: The
development of a pedagogical culture for social science research methods?. British
Journal of Educational Studies, 62(2), 191-207.
Lalonde, R. N., & Gardner, R. C. (1993). Statistics as a second language? A model for predicting
performance in psychology students. Canadian Journal of Behavioral Science, 25, 108-
125.
Liu, S., Onwuegbuzie, A. J., & Meng, L. (2011). Examination of the score reliability and validity
of the Statistics Anxiety Rating Scale in a Chinese population: Comparisons of statistics
anxiety between Chinese college students and their Western counterparts. Journal of
Educational Enquiry, 11, 29–42.
Lorenzetti, J. P. (2005). How e-Learning is changing higher education: A new look.

99
Distance education report. pp. 4–7.
Lou, Y., Bernard, R. M., & Abrami, P. C. (2006). Media and pedagogy in undergraduate
distance education: A theory-based meta-analysis of empirical literature. Educational
Technology Research and Development, 54(2), 14-176.
Lundahl, B. (2008). Teaching research methodology through active learning. Journal of
Teaching in Social Work, 28(1-2), 273-288. https://doi.org/10.1080/08841230802179373
McGrath, A. L., Ferns, A., Greiner, L., Wanamaker, K., and Brown, S. (2015). Reducing anxiety
and increasing self-efficacy within an advanced graduate psychology statistics course.
The Canadian Journal for the Scholarship of Teaching and Learning: 6(1), 5.
https://doi.org/10.5206/cjsotl-rcacea.2015.1.5
Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its influence on
young adolescents' course enrollment intentions and performance in mathematics.
Journal of educational psychology, 82(1), 60.
Mji, A., & 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, 36, 238–251.
Moore, D.S. (1998). Statistics among the liberal arts. Journal of the American Statistical
Association, 93(444), 1253.
Morris, L.W., Kellaway, D.S. & Smith, D.H. (1978). Mathematics Anxiety Rating Scale:
predicting anxiety experiences and academic performance in two groups of students,
Journal of Educational Psychology, 70, 589–594.
Mundfrom, D. J., Young, S., Shaw, D. G., Thomas, A., & Moore, A. D. (2003). A common
knowledge base in introductory graduate research courses. Research in the Schools,
10(2), 71-78.
National Center for Education Statistics (2016). Digest of education statistics, 2015.
https://nces.ed.gov/programs/digest/d15/tables/dt15_311.15.asp
National Student Clearinghouse. (2024). Current term enrollment estimates: Spring 2024.
Retrieved from https://nscresearchcenter.org/current-term-enrollment-estimates/
O’Neil, H.F., Wainess, R., & Baker, E.L. (2005). Classification of learning outcomes:
evidence from the computer games literature. The Curriculum Journal, 16(4), 455-474.
Onwuegbuzie, A.J. (1995). Statistics test anxiety and women students, Psychology of Women
Quarterly, 19, pp. 413–418.
Onwuegbuzie, A.J. (1999). Statistics anxiety among African-American graduate students: an

100
affective filter? Journal of Black Psychology, 25, pp. 189–209.
Onwuegbuzie, A. J. (2000). Statistics anxiety and the role of self-perceptions, Journal of
Educational Research, 93, pp. 323–335.
Onwuegbuzie, A. J. (2003). Modeling statistics achievement among graduate students.
Educational and Psychological Measurement, 63(6), 1020–1038.
https://doi.org/10.1177/0013164402250989
Onwuegbuzie, A. J. (2004). Academic procrastination and statistics anxiety. Assessment &
Evaluation in Higher Education, 29, 1–19.
https://doi.org/10.1080/0260293042000160384
Onwuegbuzie, A. J., & Daley, C. E. (1997). The role of multiple intelligences in statistics
anxiety. Reflection and Research, 3(2).
Onwuegbuzie, A. J., & Daley, C. E. (1999). Perfectionism and statistics anxiety. Personality and
Individual Differences, 26, 1089-1102. https://doi.org/10.1016/S0191-8869(98)00214-1
Onwuegbuzie, A. J., & Leech, N. L. (2005). On becoming a pragmatic researcher: The
importance of combining quantitative and qualitative research methodologies.
International journal of social research methodology, 8(5), 375-387.
Onwuegbuzie, A.J. & Seaman, M. (1995). The effect of time and anxiety on statistics
achievement, Journal of Experimental Psychology, 63, 115–124.
Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents,
effects, and treatments – A comprehensive review of the literature. Teaching in Higher
Education, 8, 195–209. https://doi.org/10.1080/1356251032000052447
Onwuegbuzie, A. J., DaRos, D., & Ryan, J. (1997). The components of statistics anxiety: A
phenomenological study. Focus on Learning Problems in Mathematics, 19(4), 11-35.
Onwuegbuzie, A.J., Slate, J., Paterson, F., Watson, M. & Schwartz, R. (2000). Factors associated
with underachievement in educational research courses, Research in the Schools, 7, 53–
65.
Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on
reducing statistics anxiety for graduate students in the social sciences. Journal of
Instructional Psychology, 31, 149–159.
Papousek, I., Ruggeri, K., Macher, D., Paechter, M., Heene, M.,Weiss, E. M., . . . Freudenthaler,
H. H. (2012). Psychometric evaluation and experimental validation of the Statistics
Anxiety Rating Scale. Journal of Personality Assessment, 94, 82–91.
https://doi.org/10.1080/00223891.2011.627959

101
Patterson, B., & McFadden, C. (2009). Attrition in online and campus degree programs. Online
Journal of Distance Learning Administration, 12(2), n2.
Picciano, A. G., Seaman, J., & Allen, I. E. (2010). Educational transformation through online
learning: To be or not to be. Journal of Asynchronous Learning Networks, 14(4), 17-35.
Pretorius, T.B. & Norman, A.M. (1992). Psychometric data on the statistics anxiety scale for a
sample of South African students. Educational and Psychological Measurement, 52,
933–937.
Rabe-Hemp, C., Woollen, S., & Humiston, G. S. (2009). A comparative analysis of student
engagement, learning, and satisfaction in lecture hall and online learning settings. The
Quarterly Review of Distance Education, 10(2), 207-218.
Rapp-McCall, & Anyikwa, V. (2016). Active learning strategies and instructor presence in an
online research methods course: Can we decrease anxiety and enhance knowledge?
Advances in Social Work, 17(1), 1–14. https://doi.org/10.18060/20871
Rekkedal, T., Qvist-Eriksen, S., Keegan, D., Súilleabháin, G. Ó., Coughlan, R., & Fritsch, H.
(2003). Internet based e-learning, pedagogy and support systems. Norway: NKI Distance
Education.
Relan, A., & Gillani, B. B. (1997). Web-based instruction and the traditional classroom:
Similarities and differences. Khan, 62, 41-46.
Richardson, F. C., & Suinn, R. M. (1972). The Mathematics Anxiety Rating Scale: Psychometric
data. Journal of Counseling Psychology, 19, 551–554. https://doi.org/10.1037/h0033456
Richardson, F. C., & Woolfolk, R. (1980). Mathematics anxiety. In I. G. Sarason (Ed.), Test
anxiety: Theory, research, and applications (pp. 271–288). Hillsdale, NJ: Erlbaum.
Schacht, S., & Stewart, B. J. (1990). What’s funny about statistics? A technique for reducing
student anxiety. Teaching Sociology, 18, 52-56.
Schulze, S. (2009). Teaching research methods in a distance education context: Concerns and
challenges. South African Journal of Higher Education, 23(5), 992-1008.
https://doi.org/10.4314/sajhe.v23i5.48812
Sitzman, T., Kraiger, K., Stewart, D., & Wisher, R. (2006). The comparative effectiveness of
web-based and classroom instruction: A meta-analysis. Journal of Personnel
Psychology, 59, 623-664.
Snyder, T. D., de Brey, C., & Dillow, S. A. (2019). Digest of education statistics 2018 (NCES
2020-009). National Center for Education Statistics.
Spielberger, C. D. (1977). The Test Anxiety Inventory. Consulting Psychologists Press. Palo

102
Alto, California.
Stoloff, M., McCarthy, M., Keller, L., Varfolomeeva, V., Lynch, J., Makara, K., . . . Smiley, W.
(2009). The undergraduate psychology major: An examination of structure and sequence.
Teaching of Psychology, 37(1), 4–15.
Sutarso, T. (1992). Some Variables in Relation to Students’ Anxiety in Learning Statistics,
paper presented at the annual meeting of the Mid-South Educational Research
Association, Knoxville, TN, November.
Tashakkori, A., & Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses
in social and behavioural sciences: US perspective. International Journal of Social
Research Methodology, 6(1), 61-77.
Taylor, J. C. (2001). Fifth generation distance education.
https://core.ac.uk/download/pdf/11034576.pdf
Utts, J. (2003). What educated citizens should know about statistics and probability. The
American Statistician, 57, 74–79. https://doi.org/10.2307/30037236
Vigil-Colet, A., Lorenzo-Seva, U., & Condon, L. (2008). Development and validation of the
Statistical Anxiety Scale. Psicothema, 20(1), 174–180.
Walker, S. L. & Fraser, B. J. (2005). Development and validation of an instrument for assessing
distance education learning environments in higher education: The distance education
learning environments survey (DELES). Learning Environments Research, 8, 289-308.
Wallman, K.K. (1993). Enhancing statistical literacy: Enriching our society. Journal of the
American Statistical Association, 88, 1-8.
Wicks, M. (2010). A national primer on K-12 online learning: Version 2. International
Association for K-12 Online Learning. Retrieved from
http://www.inacol.org/resources/publications/inacol-reports/
Wigfield, A., & Meece, J. L. (1988). Math anxiety in elementary and secondary school
students. Journal of educational Psychology, 80(2), 210.
Willging, P. A., & Johnson, S. D. (2004). Factors that influence students’ decision to drop out of
online courses. Journal of Asynchronous Learning Network, 8(4), 105–118.
Williams, A. S. (2010). Statistics anxiety and instructor immediacy. Journal of Statistics
Education, 18(2). http://www.amstat.org/publications/jse/v18n2/williams.pdf
Williams, J. E. (1994). Anxiety Measurement: Construct Validity and Test
Performance. Measurement and Evaluation in Counseling and Development, 27(1),
302-7.

103
Wilson, V.A. (1999). Student Response to a Systematic Program of Anxiety-reducing Strategies
in a Graduate-level Introductory Educational Research Course. Paper presented at the
annual meeting of the American Educational Research Association, Montreal, Quebec,
April.
Xu, D., & Jaggers, S. S. (2011). The effectiveness of distance education across Virginia’s
community colleges: Evidence from introductory college-level math and English
courses. Educational Evaluation and Policy Analysis, 33(3), 360-377.
Zablotsky, D. (2001). Why do I have to learn this if I'm not going to graduate school? Teaching
research methods in a social psychology of aging course. Educational Gerontology,
27(7), 609-622.
Zanakis, S.H. & Valenzi, E.R. (1997). Student anxiety and attitudes in business statistics,
Journal of Education for Business, 72(5), pp. 10–16.
Zeidner, M. (1991). Statistics and mathematics anxiety in social science students: Some
interesting parallels. British Journal of Educational Psychology, 61, 319–328.

104
Appendix A: Demographic and Open-Ended Questions
Please answer the following demographic questions:
1. What is your gender?
• male
• female
• other (please specify)
2. What is your age in years?
3. What is your current employment status?
• employed full-time (More than 35 hours/week)
• employed part-time (35 hours/week or less)
• unemployed
• retired
• disabled
4. Please indicate your ethnicity.
• Hispanic/Latino
• American Indian or Alaska Native
• Asian
• Black or African American
• Native Hawaiian or other Pacific Islander
• White
• two or more races
• other—text field
5. Please indicate your relationship status.

105
• married
• widowed
• divorced
• separated
• never married
6. What is the highest level of education either of your parents has completed?
• less than high school
• high school diploma/GED
• some college
• associate's degree
• bachelor’s degree
• master’s degree
• doctoral degree
7. How would you describe the location from which you are completing this online
degree program?
• rural
• suburban
• urban
8. What was your undergraduate major? Text answer
9. Do you have a previous graduate degree? Text answer
10. If prior graduate degree (yes): What was your area of study?
11. How many years ago did you complete your last college course?
Number dropdown

106
12. How many prior statistics courses have you taken?
• none
• one
• two
• three
• more than three
13. How many mathematics courses have you taken in college?
• none
• one
• two
• three
• more than three
14. What is your current college GPA? (4.0 scale) Text answer
15. How many years ago did you complete your last mathematics course?
Number dropdown
16. My current enrollment level is:
• part-time (4 or fewer units)
• full-time (5 or more units)
17. Why did you choose to earn your degree online versus on-campus? (check all that
apply)
• scheduling
• instructional considerations (e.g., preferred method of instruction or
interaction)

107
• geographic considerations
• family responsibilities
• professional responsibilities
• Other: text answer
18. How many online courses have you taken previously in higher education?
• none
• one
• two
• three
• more than three
19. Rate your comfort level using the online learning technology required for this
program.
• very uncomfortable
• uncomfortable
• somewhat comfortable
• comfortable
• very comfortable
Open-ended questions:
20. How would you describe your current level of anxiety?
21. How do you feel about the value of learning statistics?
22. How do you feel about your instructor’s level of engagement with the class?
23. How do you feel about asking questions of your professor?
24. How would you describe your engagement with your classmates?

108
Appendix B: Statistical Anxiety Rating Scale-Revised (STARS-R)
Items No anxiety Strong anxiety
1. Interpreting the meaning of a table in
an academic journal article
1 2 3 4 5
2. Going to ask my statistics teacher for
individual help with material I am
having difficulty understanding
1 2 3 4 5
3. Doing the coursework for a statistics
assignment
1 2 3 4 5
4. Making an objective decision based on
empirical data
1 2 3 4 5
5. Trying to decide which type of
statistics analysis is most appropriate
for my research
1 2 3 4 5
6. Finding that another student in class
used a different approach than I did to
a statistical problem
1 2 3 4 5
7. Asking one of your lecturers for help
in understanding SPSS output
1 2 3 4 5

109
Items No anxiety Strong anxiety
8. Enrolling in a course that covers
statistics
1 2 3 4 5
9. Going over an assignment in statistics
after it has been marked
1 2 3 4 5
10. Asking a fellow student for help in
understanding SPSS output
1 2 3 4 5
11. I am a subjective person, so the
objectivity of statistics is inappropriate
for me
1 2 3 4 5
12. I have not done math for a long time. I
know I will have problems getting
through statistics
1 2 3 4 5
13. Statistics takes more time than it is
worth
1 2 3 4 5
14. I lived this long without knowing
these statistics, why should I learn it
now?
1 2 3 4 5
15. I could enjoy statistics if it were not so
mathematical
1 2 3 4 5

110
Items No anxiety Strong anxiety
16. I wish the statistics requirement would
be removed from my academic
program
1 2 3 4 5
17. Statistics teachers speak a different
language
1 2 3 4 5
18. Statisticians are more number oriented
than they are people oriented
1 2 3 4 5
Note. Per the instructions, participants were asked to please indicate their current feelings about
statistical anxiety by circling your level of anxiety or agreement with each of the below
statements. The first 10 questions ask you to indicate how much anxiety the situation would
cause you (1 = no anxiety to 5 = strong anxiety) and the remaining eight items ask you to
indicate your level of agreement with each statement (from 1 = strongly disagree to 5 = strongly
agree). From “Reducing anxiety and increasing self-efficacy within an advanced graduate
psychology statistics course,” by A.L. McGrath et al., 2015, The Canadian Journal for the
Scholarship of Teaching and Learning: 6(1) 5, (https://doi.org/10.5206/cjsotl-rcacea.2015.1.5).
Copyright 2015 by A.L. McGrath et al.

111
Appendix C: Study Participation Information Sheet
Information Sheet
You are invited to participate in a research study conducted by Neil Teixeira (principal
investigator) and Professor Corinne Hyde (faculty advisor) at the University of Southern
California because you are enrolled in an online graduate research methods course that involves
learning statistical concepts. Your participation is voluntary. You should read the information
below, and ask questions about anything you do not understand, before deciding whether to
participate.
Purpose of the Study
This survey is designed to assess your current emotions and attitudes towards learning statistics
as part of a graduate research methods class. The goal is to identify ways to help online learners
succeed in graduate research methods courses.
Study Procedures
If you volunteer to participate in this study, you will be asked to complete the following:
• Complete a short, 10 minute online survey
• Participate in either a synchronous or asynchronous discussion session (1 hour approx.)
• Complete another short, 10-15 minute survey within 24 hours of the discussion session
Submission of your survey will constitute consent to participate in this research project.

112
Potential Risks and Discomforts
There are no anticipated risks to participating in this study. There will be no impact to your
course grade for participating, or not participating.
Potential Benefits to Participants and/or to Society
The study may help current and future online students learn about statistics and research methods
with less anxiety.
Payment/Compensation for Participation
You will not be paid for participating in this research study.
Anonymity and Confidentiality
Your USC e-mail address is the only personal information requested in the survey. This
information is needed so that the principal investigator can contact you to schedule the second
phase of the study (synchronous/asynchronous discussion). Your survey responses will not be
shared with your CMGT 540 instructors. The collected data will be downloaded from Qualtrics
and securely stored and analyzed on USC computers. The data will be only be used for the
purposes of this study. Neil Teixeira, as principal investigator, and Professor Corinne Hyde, as
faculty advisor, will have access to the data associated with this study.
The members of the research team and the University of Southern California’s Human Subjects
Protection Program (HSPP) may access the data. The HSPP reviews and monitors research
studies to protect the rights and welfare of research subjects.

113
Participation and Withdrawal
Your participation is voluntary. Your refusal to participate will involve no penalty or loss of
benefits to which you are otherwise entitled. You may withdraw your consent at any time and
discontinue participation without penalty. You are not waiving any legal claims, rights or
remedies because of your participation in this research study.
Investigator’s Contract Information
If you have any questions or concerns about the research, please feel free to contact Neil
Teixeira, principal investigator, at teixeira@usc.edu.
Rights of Research Participant – IRB Contact Information
If you have questions, concerns, or complaints about your rights as a research participant you
may contact the IRB directly at the information provided below. If you have questions about the
research and are unable to contact the research team, or if you want to talk to someone
independent of the research team, please contact the University Park IRB (UPIRB), Office of the
Vice Provost for Research Advancement, Credit Union Building, 3720 South Flower Street,
CUB # 301 Los Angeles, CA 90089-0702, (213) 821-5272 or upirb@usc.edu. Please reference
code UP-17-00671.

Abstract (if available)

Abstract This dissertation examines the impact of synchronous, web camera-enabled sessions and cooperative learning strategies on reducing statistics anxiety among graduate students in an online research methods course. Employing a quasi-experimental, mixed-methods design, the study involved 61 students from an online master's program in communication management. Quantitative analysis using the Statistics Anxiety Rating Scale Revised (STARS-R) indicated a significant reduction in statistics anxiety for students participating in synchronous sessions, while cooperative learning strategies showed no significant effect. Qualitative data revealed that instructor availability and approachability positively influenced students' willingness to seek help. The findings suggest that real-time interaction and immediate feedback in synchronous sessions effectively mitigate statistics anxiety. The study highlights the importance of instructor engagement in online learning environments and suggests further research to explore the specific conditions under which cooperative learning might reduce anxiety. Implications for practice include integrating synchronous sessions and enhancing instructor presence in online courses.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Perspectives of learning in synchronous online education

PDF

Validation matters - student experiences in online courses: a mixed method study

PDF

A qualitative analysis of student-athletes' engagement in synchronous, virtual learning environments at the secondary level

PDF

Online student attrition in blended learning programs

PDF

What are teacher beliefs about social emotional learning in a synchronous webcam-enabled online higher education learning environment?

PDF

Asynchronous online education and introductory statistics: The role of learner characteristics

PDF

Asynchronous online education and introductory statistics: The role of learner characteristics

PDF

Towards critical dialogue: an action research project building an awareness of an administrative team member’s role, identity, and deficit thinking

PDF

Sense of belonging in an online high school: looking to connect

PDF

Bridging gaps, building futures: a meta-analysis of collaborative learning and achievement for Black and Latinx students

PDF

Student engagement in online learning: examining undergraduate student engagement in online learning communities to improve instruction

PDF

The racialized experiences of Black students in online master’s degree programs at a Predominately White Institution

PDF

Creating effective online educational content: an evaluation study of the influences affecting course developers’ abilities to create online content with engaging learning strategies

PDF

African-American/Black students’ experience and achievement in asynchronous online learning environments at a community college

PDF

Reimagining professional learning for early childhood educators of color

PDF

Relationships between a community college student’s sense of belonging and student services engagement with completion of transfer gateway courses and persistence

PDF

Culturally relevant pedagogy in Khan Academy’s Algebra 1 course

PDF

An exploration of student experiences in a preparation program for online classes in the California community college system

PDF

A comparative study of motivational predictors and differences of student satisfaction between online learning and on-campus courses

PDF

Transformative learning: action research disrupting the status quo in literature in classrooms

Asset Metadata

Creator Teixeira, Neil Patrick (author)

Core Title Reducing statistics anxiety among learners in online graduate research methods courses

School Rossier School of Education

Degree Doctor of Education

Degree Program Educational Leadership

Degree Conferral Date 2024-08

Publication Date 08/07/2024

Defense Date 07/30/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag Distance education,graduate students,math anxiety,mixed methods,OAI-PMH Harvest,online learning,research methods learning,Stars,STARS-R.,statistics anxiety,statistics learning

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hyde, Corinne (committee chair), Baroffio, Daniela (committee member), Lyons-Moore, Akilah (committee member), Tran, Binh (committee member)

Creator Email neil.teixeira@gmail.com,teixeira@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113998TEA

Unique identifier UC113998TEA

Identifier etd-TeixeiraNe-13361.pdf (filename)

Legacy Identifier etd-TeixeiraNe-13361

Document Type Dissertation

Format theses (aat)

Rights Teixeira, Neil Patrick

Internet Media Type application/pdf

Type texts

Source 20240813-usctheses-batch-1195 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu