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Graduate Student Journal of Psychology
Spring 2024 - Vol. 22

Copyright 2024 by the Department of Counseling and Clinical Psychology
Teachers College, Columbia University

Changes in Undergraduate Students’ Self-Efficacy
and Outcome Expectancy in an Introductory 

Statistics Course
Milushka Elbulok-Charcape1, Evan Grandoit2, Anjali Krishnan1,3, Rose Bergdoll3, Joshua Fogel4, & Laura Rabin3

1Psychology Department, City University of New York Graduate Center, 2Psychology Department, Northwestern 
University, 3Psychology Department, City University of New York Brooklyn College, & 4Management, Marketing, 

and Entrepreneurship Department, City University of New York Brooklyn College

The exploration of psychological variables that potentially impact college student performance in challenging ac-
ademic courses can be useful for understanding success in introductory statistics. Although previous research has 
examined specific beliefs that students hold about their abilities and future outcomes, the current study is novel in 
its examination of changes in both self-efficacy (SE) and outcome expectancy (OE) in relation to performance over 
the course of an undergraduate introductory psychology statistics course. These psychological variables—relating to 
one’s belief about one’s ability to accomplish a task and the anticipated outcomes—may impact student motivation 
and performance. Students’ SE, OE, and other variables related to statistics performance were measured through a 
survey administered at the beginning and end of the course. Multivariate logistic regression and McNemar tests were 
conducted to examine factors that affected changes in SE and OE as the semester progressed. Students with lower 
scores on the final exam demonstrated a decrease in both high SE and positive OE. However, higher scores on exams 
earlier in the course were associated with increased odds for high SE but not for positive OE, suggesting that SE is less 
resilient to course performance. Based on these findings, the authors recommend that statistics instructors identify 
students at risk for decreasing SE. Instructors can help foster high SE in students struggling academically by connect-
ing the course content to their everyday lives and suggesting strategies to enhance their confidence in their content 
knowledge and increase their comfort in navigating such a challenging course.
 Keywords: statistics education, research, self-efficacy, outcome expectancy, statistics

 Statistics is a mathematics-based course required 
by many undergraduate degree programs (Norcross et 
al., 2016). A statistics course is fundamental for stu-
dents because it fosters critical thinking and reasoning 
skills (Cheng et al., 2018; Vanhoof et al., 2011; Wilson, 
2013). Students of statistics courses become informed 
consumers of knowledge (Barber, 2002), and are 
equipped with the ability to evaluate and conduct anal-
yses of research. As society’s reliance on complex data 
increases, the ability to analyze large sets of data and ap-
ply statistical skills and techniques is required for those 
entering the workforce (Brown & Kass, 2009). As a 
result, enrollment in statistics courses from 2010-2015 
increased by 19% (Blair et al., 2018) and employment 
opportunities for statisticians are projected to grow by 
30% from 2018 to 2028, outpacing the rate of many 
other professions (The Bureau of Labor Statistics, 
2020). Given that statistics courses carry such weight 
for students’ academic trajectory and their eventual en-
try into the workforce, research devoted to variables af-
fecting academic performance in undergraduate intro-
ductory statistics courses has received much attention.
 Diverse student characteristics and behaviors 
have been researched in relation to statistics course 
performance. Studies focusing on the relationship 

between sex and statistics courses report that females 
hold more negative perceptions about statistics (Cen-
dales et al., 2013; Zimprich, 2012) and that statistics 
anxiety may manifest differently in females com-
pared to males. For example, procrastination—the 
tendency to delay initiation of academic tasks—and 
poorer learning strategies, such as memorization, 
were found more likely to lead to statistics anxiety 
in men (Rodarte-Luna & Sherry, 2008). Students 
with more experience and past achievements in sta-
tistics or related fields (e.g., mathematics) have higher 
achievement in statistics courses (Johnson & Kuen-
nen, 2006; Ramirez et al., 2012). Academic procras-
tination (Schraw et al., 2007), is negatively correlated 
with statistics engagement (Onwuegbuzie, 2004) and 
academic performance (Goroshit, 2018; Paechter et 
al., 2017; Rodarte-Luna & Sherry, 2008; Wang & En-
glander, 2010). Research on help-seeking behavior, 
the tendency to seek academic assistance (Newman, 
2002), shows that the absence of help-seeking behav-
ior may be related to predictors leading to lower sta-
tistics performance (Rodarte-Luna & Sherry, 2008). 
 Psychological factors such as motivation, student 
beliefs, attitudes, and emotions are particularly im-
portant as they can impact academic performance in 



38

ELBULOK-CHARCAPE ET AL.

statistics courses. Undergraduate students who were 
motivated had better exam grades and valued statistics 
more than those not motivated (Budé et al., 2007). 
Students’ beliefs and attitudes about their knowledge 
of statistics do not solely impact their course perfor-
mance (Chiesi & Primi, 2009; Dempster & McCorry, 
2009; Ramirez et al., 2012), but can also disrupt learn-
ing and application of statistical concepts outside of 
the classroom (Gal & Ginsberg, 1994). Even in class-
es utilizing diverse teaching styles (e.g., lecture-based 
versus active learning), ambivalent attitudes toward 
statistics remain fairly stable throughout the semester 
(Bateiha et al., 2020). Finally, some emotional states 
such as fear (Slootmaeckers et al., 2014) and anxiety 
(Cui et al., 2019; Davis & Mirick, 2015; Esnard et 
al., 2021; Trassi et al., 2022) towards statistics nega-
tively affect student academic performance in statis-
tics. These emotional states impact cognitive factors, 
which ultimately alter students’ expectations of statis-
tics courses, effort exerted, and academic performance. 
Specific cognitive variables and their positive relation-
ship to academic performance in statistics includ-
ing self-regulation (Acee & Weinstein, 2010; Dunn, 
2014), the use of cognitive and meta-cognitive strate-
gies (Rodarte-Luna & Sherry, 2008; Trogden & Royal, 
2019), and analytical skills (Miller, 2019) have been in-
vestigated. Among these cognitive variables, one of par-
ticular importance is students’ self-efficacy in statistics.
 Self-efficacy is defined as an individual’s perceived 
ability to accomplish a designated task (Bandura, 
1977) and is believed to be domain-specific (Bandu-
ra, 1997; Pajares, 2009). Self-efficacy as a predictor of 
academic success in statistics has yielded conflicting 
findings. While most studies (Finney & Schraw, 2003; 
Mantooth et al., 2020; McGrath et al., 2015) report 
high self-efficacy change to be a strong predictor of ac-
ademic success in statistics, other studies failed to find 
a significant association between increased self-effica-
cy and success in statistics (Mihai-Bogdan et al., 2015; 
Olani et al., 2011; Walker & Brakke, 2017). In one 
study, student self-efficacy for statistics increased as the 
semester progressed (Walker & Brakke, 2017). Howev-
er, self-efficacy alone does not contribute to achieve-
ment in a statistics course (Franceschini et al., 2014).
 Outcome expectancy refers to beliefs about an-
ticipated outcomes of action (Bandura, 1986; 1997); 
these expectancies are mechanisms thought to in-
fluence performance and behavior (Bandura, 1986). 

Self-efficacy is also positively associated with outcome 
expectancy (Williams, 2010). Many studies exploring 
outcome expectancy focus on risky behavior such 
as smoking (Glock et al., 2012; Pokhrel et al., 2014; 
Urbán, 2010), drinking (Lopez-Vergara et al., 2012; 
Read, 2012), safe sex (Newby et al., 2013), and gam-
bling (St-Pierre et al., 2013). One study focusing on 
academic aspects of outcome expectancy found that 
increasing high self-efficacy and outcome expectancy 
in low-performing students improved undergraduate 
students’ English course performance (Elborolosy & 
Al Thenyan, 2020). Another study on high school 
students enrolled in science and engineering-technol-
ogy courses reported that students’ self-efficacy and 
outcome expectancy directly impacted their course 
performance (Han et al., 2021). Notably, student 
course performance was also impacted by the teachers’ 
self-efficacy and outcome expectancy regarding their 
own teaching ability—suggesting that student per-
formance in other Science, Technology, Engineering, 
and Mathematics (STEM) courses, such as statistics, 
could also be enhanced by improving their confidence 
in their own ability and outcome expectation related 
to those courses. Another study found that Chinese el-
ementary school students’ increased STEM stereotype 
beliefs negatively predicted their STEM self-efficacy 
and positive outcome expectancy. Additionally, their 
STEM self-efficacy and positive outcome expectancy 
predicted their STEM career interest (Luo et al., 2021). 
 Three studies have focused on outcome expectan-
cy concerning statistics courses, although they did not 
study change in outcome expectancy. In one study, 
outcome expectancy was influenced by perceived con-
trollability and perhaps unsurprisingly, negative out-
come expectancy had a negative association with ef-
fect on first-year students enrolled in a health sciences 
statistics course (Budé et al., 2007). In another study, 
significant predictors of achievement in a psychology 
statistics course were past academic performance in 
statistics and positive expectancy (Hood et al., 2012). 
Finally, self-efficacy has been found to directly affect 
expectations for performance in statistics (Esnard 
et al., 2021). Further understanding the association 
of change in outcome expectancy with other vari-
ables and statistics course performance is warranted. 
 While numerous studies focus on self-efficacy 
in relation to undergraduate introductory statistics 
courses, few focus on outcome expectancy about this 



39

CHANGES IN SE AND OE IN A STATISTICS COURSE

 course, and the authors are not aware of any study that 
focuses on both psychological constructs. Therefore, 
the current study tracks changes in undergraduate 
students’ academic self-efficacy and outcome expec-
tancy while enrolled in an undergraduate introduc-
tory psychology statistics course. We study how these 
constructs change according to students’ academic 
performance over the course of an academic semester. 
We include other relevant variables shown to impact 
academic performance in a statistics course of sex, 
college year, help-seeking behavior, and academic pro-
crastination as covariates. We predict that students’ 
outcome expectancy will be more resilient to change 
than their self-efficacy based on their performance in 
a statistics course and associated covariates. We pre-
dict that self-efficacy will change more than outcome 
expectancy, due to self-efficacy previously being asso-
ciated with course performance (Hii et al., 2013; Mc-
Grath et al., 2015; Waples, 2016). If our prediction 
is accurate, these results will support the conclusion 
that self-efficacy and outcome expectancy are not 
uniformly affected throughout a statistics course, in 
which students can begin the course with many pre-
conceived fears and anxiety about the topic (Cui et al., 
2019; Davis & Mirick, 2015; Esnard et al., 2021; Sloot-
maeckers et al., 2014). As such, the use of different 
interventions to address these constructs in students 
enrolled in statistics courses would be warranted.

Methods
Participants
 Participants were undergraduate students en-
rolled in introductory psychology statistics courses at 
an urban public college that is part of a large public 
university system in the Northeast United States. All 
students who attended class during the first week of 
the semester were invited to participate. The study was 
conducted in three introductory psychology statistics 
course sections, each from a different academic semes-
ter. Participation entailed completion of study pre- 
and post-questionnaires at week 1 (beginning) and 
week 15 (end) of the semesters. Participants were not 
compensated nor were they penalized for non-partici-
pation. There were 341 students initially enrolled, and 
286 students participated for an 83.9% response rate. 
The study was ethically conducted and received IRB 
approval. All participants provided informed consent.

Procedure
 The course instructor and two research as-
sistants created the self-report questionnaire used 
in the current study, which inquired about stu-
dent demographics, academic self-efficacy, aca-
demic outcome expectancy, academic help-seek-
ing behavior, and academic procrastination.
Psychological Measurements
 Academic self-efficacy was assessed by self-report 
response to the statement, “I am [was] quite capable 
of mastering the material in this class.” Academic 
outcome expectancy was assessed by self-report re-
sponse to the statement, “I will [would] never [be 
able to] do well in this class” where disagreeing indi-
cates positive outcome expectancy. The past tense 
of the verb [words included in brackets] was used 
for the follow-up questionnaire. Both statements 
utilized a 4-point Likert-type scale with response 
options of 1 = strongly agree, 2 = somewhat agree, 
3= somewhat disagree, and 4 = strongly disagree.
 The self-efficacy item used in this study was influ-
enced by the statistics self-efficacy assessment developed 
by Hall and Vance (2010), for which they reported a 
reliability coefficient of 0.92. The statistics self-efficacy 
item used in this study was inspired by the question in 
their assessment “How confident are you with solving 
statistical problems?”. To reduce the time and effort 
for students to respond to the entire survey used in this 
study, the full statistics self-efficacy assessment by Hall 
and Vance (2010) was not adapted. Additionally, the 
number of scale responses for the adapted self-efficacy 
item was changed from five to four so the response va-
lence would not be obscured by a neutral answer option.
 The outcome expectancy item used in this study 
was adapted from the statistics outcome expectan-
cy assessment created by Hood et al. (2012). Their 
assessment question, “I expect to do well in this re-
search methods and statistics course”, inspired the 
outcome expectancy item included in this study. To 
reduce the time and effort for students to respond to 
the entire survey used in this study, the full statistics 
outcome expectancy assessment by Hood et al. (2012) 
was not adapted. The response scale for the adapted 
outcome expectancy item was reduced from a 7-point 
to a 4-point response scale to match the self-efficacy 
item. Hood et al. (2012) reported a Cronbach’s al-
pha of 0.69 for their outcome expectancy assessment.
 Variability in participants’ responses to the ac-



40

ELBULOK-CHARCAPE ET AL.

ademic self-efficacy and outcome expectancy scales was 
low.  For the self-efficacy scale given at the beginning of 
the course, 28.3% of participants selected “strongly agree,” 
64.7% of participants selected “somewhat agree,” 6.6% of 
participants selected “somewhat disagree,” and 0.3% of 
participants selected “strongly disagree.” For the self-ef-
ficacy scale given at the end of the course, 23.1% of par-
ticipants selected “strongly agree,” 53.8% of participants 
selected “somewhat agree,” 19.6% of participants selected 
“somewhat disagree,” and 3.5% of participants “strongly 
disagree.” For the outcome expectancy scale given at the be-
ginning of the course, 1% of participants selected “strong-
ly agree,” 3.5% of participants selected “somewhat agree,” 
33.9% of participants selected “somewhat disagree,” and 
61.5% of participants “strongly disagree.” For the out-
come expectancy scale given at the end of the course, 2.1% 
of participants selected “strongly agree,” 15.4% of partic-
ipants selected “somewhat agree,” 35.7% of participants 
selected “somewhat disagree,” and 46.9% of participants 
“strongly disagree.” Due to this limited variability, respons-
es for both scales were categorized into agree or disagree. 
 Help-seeking behavior was assessed by self-report 
responses to the following two questions on the pre-and 
post-questionnaire: “If you did not understand something 
in class or got stuck when working on problems outside 
of class, how likely were you to: (1) Attend a peer tutor 
session; and (2) Go to the learning center.” Both state-
ments utilized a 4-point Likert-type scale with response 
options of 1 = very likely, 2 = somewhat likely, 3 = some-
what unlikely, and 4 = never would. Due to the limited 
variability, responses for those who responded positive-
ly to either question (i.e., very likely or somewhat likely) 
were categorized as self-reported help-seekers while those 
who endorsed negative responses (i.e., somewhat unlike-
ly or never would) were categorized as non-help-seek-
ers. Kuder-Richardson 20 for the baseline questionnaire 
was 0.78 and for the follow-up questionnaire was 0.73.
 Academic procrastination was assessed by self-report 
responses to the following four questions on the pre-and 
post-questionnaire: With regard to academic tasks (e.g., 
reading for class, completing homework assignments, pre-
paring for exams): (1) To what degree did you tend to de-
lay or procrastinate?; (2) To what degree did you typically 
have to rush to complete a class-related task on time?; (3) 
How often did you begin assignments shortly after they 
are assigned? (reverse coded); and (4) To what degree was 
procrastination on academic tasks a problem for you? All 
statements utilized a 5-point Likert-type scale with re-

sponse options of: Item 1: 1 = never procrastinate, 
2 = almost never, 3 = sometimes, 4 = nearly always, 
and 5= always procrastinate; Item 2: 1 = never rush, 
2 = almost never, 3 = sometimes, 4 = nearly always, 
5 = always rush; Item 3: 1 = never begin shortly after 
they are assigned, 2 = almost never, 3 = sometimes, 
4 = nearly always, 5 = always begin shortly after 
they are assigned; and Item 4: 1 = not at all a prob-
lem, 2 = a small problem, 3 = a moderate problem, 
4 = a large problem, and 5 = a very large problem. 
Scores on the four items were summed to create a 
total academic procrastination score, with higher 
scores indicating a greater tendency to procrastinate. 
Cronbach alpha for the baseline questionnaire was 
0.79 and for the follow-up questionnaire was 0.82. 
Course Performance Measurements
 Students attended bi-weekly lectures taught by 
an instructor for 75 minutes per lecture and weekly 
laboratory sessions led by graduate student instruc-
tors for 110 minutes per session. During lecture, the 
instructor taught students how to use and compute 
various types of analyses. Each of the three exams, 
administered during the lecture portion of the class, 
was semi-cumulative, covered approximately one-
third of the course material, and was graded out of 
100 possible points. The first examination consisted 
of multiple-choice questions that tested descriptive 
statistics, z-scores, correlation, regression, funda-
mentals of the normal curve, and basic probability 
theory. The second examination consisted of mul-
tiple-choice questions and one hypothesis testing 
procedure (i.e., complete a t-test by hand) on the 
principles and steps of hypothesis testing using sin-
gle sample t-tests, dependent mean t-tests, z-tests, 
decision errors, effect size, power, and computation 
of confidence intervals. The third examination con-
sisted of multiple-choice questions and one hypoth-
esis testing procedure covering independent mean 
t-tests, analysis of variance, chi-square tests, rank-or-
der tests, and specific advanced statistical procedures 
(e.g., hierarchical multiple regression, factor anal-
ysis, structural equation modeling). Examinations 
were graded objectively by the lecturing professor 
and a graduate student instructor. Partial credit 
for hypothesis testing responses was possible and 
awarded according to an objective scoring rubric.
 In the laboratory sessions, graduate student 
instructors reviewed lecture material, demonstrat-



41

CHANGES IN SE AND OE IN A STATISTICS COURSE

ed the use of statistical analysis using IBM SPSS Sta-
tistics (IBM Corporation, 2021), and administered 
weekly quizzes. Additionally, two undergraduate 
peer tutors, who had previously taken the course and 
earned high marks, were available for voluntary tu-
toring for approximately 4-6 hours per week to rein-
force course concepts and prepare students for exams. 
The laboratory grade was computed based on average 
scores of weekly homework assignments (60%), mul-
tiple-choice quizzes (15%), and attendance/participa-
tion (25%). Homework assignments and laboratory 
quizzes were graded objectively by a graduate student 
instructor according to a detailed scoring rubric. 
Students’ final course grades were calculated based 
on scores on the three in-class exams (each worth 
23%), overall lab grade (26%), attendance during lec-
tures (2%), and a brief in-class presentation (3%).
Statistical Analyses
 Descriptive statistics were calculated for the vari-
ables with means and standard deviations for the con-
tinuous variables and frequency and percentage for the 
categorical variables. The McNemar test was performed 
to compare the self-efficacy and outcome expectancy 
variables from baseline to follow-up on the third exam 
because it was the performance measure closest in time 
to follow-up. Multivariate logistic regression was per-
formed to examine the factors that affected the self-effi-
cacy and outcome expectancy variables. IBM SPSS Sta-
tistics version 28 (IBM Corporation, 2021) was used 
for all the analyses. All p-values were two-tailed. Of the 
286 participants, nine total students were not included 
in regression analyses. One student was not included 
because of missing laboratory quiz grades. Six other 
students were excluded because they did not respond 
to all the procrastination questions in the post-ques-
tionnaire and two other students were excluded be-
cause they did not indicate their current year in college.

Results
 Table 1 shows sample descriptive statistics. More 
than three-quarters (76.6%) of participants identified 
as female, which could indicate overall increased neg-
ative outcome expectancy for course outcomes (van 
Es & Weaver, 2018). College year was almost equally 
distributed with approximately one-third for each cat-
egory, with third-year students representing the larg-
est category. The fourth and other category included 
fourth-year students (n=64), students in a year greater 

than their fourth year (n=11), those obtaining their 
second bachelor’s degree/non-graduate post-baccalau-
reate status (n=2), graduate students (n=2), non-de-
gree-seeking students (n=3), and unknown (n=1). 
Toward the end of the semester (i.e., week 15), ap-
proximately two-thirds of students reported that they 
were likely to engage in help-seeking behavior when 
they got stuck or did not understand course content. 
Mean exam scores were slightly above 80 for Exam 1, 
approximately 75 for Exam 2, and slightly above 70 
for Exam 3. Mean lab quiz scores were slightly above 
80. Mean procrastination scores were approximately 
10.4 (lowest score was 4 and highest score was 20), 
which was slightly above the midpoint of possible 
scores, slightly tilting in the direction of higher pro-
crastination. Self-efficacy lowered from slightly above 
90% of participants at baseline endorsing a statement 
of feeling capable of mastering course material to ap-
proximately three-quarters feeling the same way to-
wards the end of the course. Additionally, outcome 
expectancy shifted negatively with about 95% of par-
ticipants expecting to do well at the beginning of the 
course to slightly above 80% by the end of the course.
 Table 2 shows McNemar test comparisons from 
baseline to follow-up for agreeing with the statistics 
self-efficacy item, “I am [was] quite capable of mas-
tering the material in this class.” For the entire sam-
ple, for those who scored below 70, and those who 
scored below 80 on Exam 3, there was a statistically 
significant percentage decrease in statistics self-effi-
cacy from baseline to follow-up. There were no sta-
tistically significant percentage decreases in report-
ed statistics self-efficacy from baseline to follow-up 
for those who scored 80-89.9 or 90-100 on Exam 3.
 Table 3 shows McNemar test comparisons from 
baseline to follow-up for disagreeing with the out-
come expectancy item “I will [would] never [be able 
to] do well in this course.” In both the entire sample 
and those who scored below 70 on Exam 3, there was 
a statistically significant percentage decrease in posi-
tive outcome expectancy from baseline to follow-up. 
There were no statistically significant percentage 
decreases from baseline to follow-up for those who 
scored from 70-79.9, 80-89.9, or 90-100 on Exam 3.
 Table 4 shows multivariate logistic regression anal-
yses for follow-up of agreeing with the self-efficacy item 
“I was quite capable of mastering the course material in 
this class.” Higher scores on the first two exams were each 



42

ELBULOK-CHARCAPE ET AL.

statistically significantly associated with increased odds 
for high self-efficacy. The other variables were not sta-
tistically significantly associated with high self-efficacy.
 Table 5 shows multivariate logistic regres-
sion analyses for follow-up of positive out-
come expectancy. No variables were significant-
ly associated with positive outcome expectancy.

Discussion
 In the current study, both high self-efficacy and 
positive outcome expectancy significantly decreased 
from baseline to follow-up in univariate analyses for 
lower-performing students on the third (and final) 
exam of the semester. In our multivariate analyses, we 
found that test performance on Exams 1 and 2 was 
significantly associated with increased odds of high 
self-efficacy. Positive outcome expectancy, however, 
was not associated with any of the aforementioned 
variables; thus, as predicted, outcome expectancy 
was less susceptible to change throughout the course.
 As reported, higher scores on the first two exams 
were associated with high self-efficacy. This is consis-
tent with previous findings of a moderate correlation 
between self-efficacy and course performance in sta-
tistics (Hii et al., 2013; McGrath et al., 2015; Waples, 
2016). Importantly, we found that as performance 
decreased so did self-efficacy for the course. This was 
expected, as student performance is positively con-
nected to student confidence (Sucuoğlu, 2018). We 
did not find a significant association between Exam 3 
performance and increased odds for high self-efficacy. 
We speculate that students adjusted their self-efficacy 
based on performance on the earlier exams and by the 
final exam (Exam 3), their self-efficacy was less suscep-
tible to change. In light of this finding, instructors 
could track critical points where students’ self-efficacy 
decreases, discuss self-efficacy directly at the outset of 
the course, and provide explicit strategies to enhance 
self-efficacy in those who are ambivalent or struggling 
in an introductory statistics course. After all, stu-
dents with higher self-efficacy are more likely to con-
tinue engaging with statistics potentially leading to 
improved statistics performance (Gopal et al., 2018).
 In our univariate analyses, we found a similar 
pattern for high self-efficacy and positive outcome ex-
pectancy. Students who obtained lower Exam 3 scores 
from 0-69.9 showed decreases in both high self-effica-
cy and positive outcome expectancy from baseline to 

follow-up. Conversely, for those who obtained high-
er scores of 80 or higher on Exam 3, no statistically 
significant changes were found for high self-efficacy 
and positive outcome expectancy from baseline to fol-
low-up. Essentially, self-efficacy and outcome expec-
tancy changed in the same direction as performance on 
the final exam. These directional changes are expected 
as students’ perceived competence is based on mastery 
experiences (Bandura, 1986; 1997), including past suc-
cesses as well as failures (Fong & Krause, 2014). In our 
multivariate analysis, no variables were significantly as-
sociated with positive outcome expectancy. This adds 
to the mixed findings of previous literature reporting 
achievement (exam grades) as associated with (Hood et 
al., 2012) and not associated with outcome expectancy 
(Budé et al., 2007). However, our finding is similar to 
the finding of effort (self-reported help-seeking) and 
outcome expectancy having no significant relationship 
(Budé et al., 2007; Esnard et al., 2021; Hood et al., 2012).
Limitations
 This study has certain limitations. We combined 
first year with second year students and fourth year 
with more senior students because our samples of those 
students were much smaller compared to the number 
of third year students. Responses to the self-efficacy 
and outcome expectancy questions were also dichot-
omized because of limited variability in the responses. 
Second, self-efficacy and outcome expectancy were 
each self-assessed by only one item to reduce partici-
pant burden (as the current study was part of a larger 
study of performance in statistics courses). Lengthier 
and validated measures of statistics self-efficacy, such 
as Current Statistics Self-Efficacy (CSSE) or Self-Ef-
ficacy to Learn Statistics (SELS; Finney & Schraw, 
2003) should be used in future research to determine 
if there are similar patterns as in our study. Third, 
contextual factors such as sequence of courses, office 
hour meetings, and prerequisites were not included.
Recommendations
 Considering our findings, we recommend that 
statistics instructors identify students who are at risk 
for decreasing self-efficacy. Furthermore, instructors 
should help foster high self-efficacy in students by di-
rectly connecting course content to students’ everyday 
lives, incorporating formative low-stakes assessments 
that may help increase students’ mastery experiences 
(Zientek et al., 2019), training students to employ study 
strategies (Liao & Wang, 2018), and teaching about 



43

CHANGES IN SE AND OE IN A STATISTICS COURSE

growth mindset (Samuel & Warner, 2021). Given the 
anxiety felt by students enrolling in statistics courses, a 
brief training based on this targeted instruction could 
be offered to statistics instructors to help them compre-
hend common pitfalls and psychological variables that 
could help students navigate such a challenging course.
 Researchers found an improvement in statistics 
performance that correlated with post self-effica-
cy scores only for undergraduate students who first 
solved statistics problems in a group setting, in which 
each member provided explanations for their own 
answers, gave feedback on the answers and explana-
tions of the other group members, and then solved the 
same problems again individually with no feedback, 
compared to students who only solved the problems 
once, individually, with no feedback (Hall & Vance, 
2010). Additionally, they suggested students be pro-
vided an explanation of the course material prior to 
measuring their statistics self-efficacy so that students 
can give an accurate assessment of their ability. Based 
on their findings, we recommend that students be 
given opportunities to compare their statistics prob-
lem-solving and answer-choice rationale to their peers’ 
statistics problem-solving and answer-choice rationale 
in low-stakes assessments. It is also vital that these op-
portunities be provided numerous times throughout 
a statistics course so that changes in their self-effica-
cy and performance remain congruent and improve.
Conclusion
 In conclusion, through our multivariate analy-
ses, we found that greater performance on class exams 
early in the semester was associated with high self-ef-
ficacy. Change in positive outcome expectancy was 
not associated with exam performance. However, 
lower-performing students demonstrated decreases 
in both higher self-efficacy and positive outcome ex-
pectancy. Self-efficacy was more vulnerable to course 
performance and strategies to address this should be 
considered as self-efficacy is a predictor of academ-
ic success and achievement in statistics (Johnson & 
Kuennen, 2006; Mihai-Bogdan et al., 2015; Olani et 
al., 2011; Ramirez et al., 2012; Walker & Brakke, 2017).

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Table 1

Descriptive Statistics of Sample



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ELBULOK-CHARCAPE ET AL.

Table 2

Comparisons of High Self-Efficacy from Baseline to Follow-Up

Note. N/A= not applicable since baseline had 100% agreement.



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CHANGES IN SE AND OE IN A STATISTICS COURSE

Table 3

Comparisons of Positive Outcome Expectancy from Baseline to Follow-up

Note. N/A= not applicable since baseline had 100% agreement.



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ELBULOK-CHARCAPE ET AL.

Table 4

Logistic Regression Analyses for Follow-up of High Self-Efficacy



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CHANGES IN SE AND OE IN A STATISTICS COURSE

Table 5

Logistic Regression Analyses for Follow-up of Positive Outcome Expectancy


