Personalised Learning. Diverse Goals. One Heart. FULL PAPERS
Online Versus Face-to-Face: A Quantitative Study of
Factors Influencing Students’ Choice of Study Mode using
Chi-Square Test and Binary Logistic Regression
Sze-Kiu Yeung Wee Leong Lee
Singapore University of Social Sciences Singapore University of Social Sciences
Singapore Singapore
For online learning in the January 2019 semester, students at the Singapore University of Social
Sciences were able to choose whether they want to study in virtual or face-to-face mode in two
courses. Virtual refers to full online learning whereby students study, in a six-week term,
without the need to meet face-to-face with the instructor while face-to-face refers to blended
e-learning whereby students received either six or three face-to-face lessons with e-learning
resources. In full online mode, students will meet the instructor virtually via video
conferencing on a weekly basis. Data were obtained to find out which variables actually had
an effect of students’ choice of learning mode. 370 students were analysed and the variables
including gender, marital status, race, nationality, course, qualification, school, programme,
intake, age (now), age (joint) and cumulative grade point average (CGPA) were examined.
Each variable was compared with the students’ mode of study in order to identify if they are
dependent (e.g. gender versus study mode, race versus study mode, etc.) based on a chi-square
test. The significant variables were further investigated using a binary logistic regression
model. It was found that qualification, intake and CGPA were found to be significant for
students’ choice of learning.
Keywords: Virtual, Face-to-face, Mode of study, Chi-square test, Binary logistic regression
Introduction
Two courses, Customer Relationship Management (BUS354) and Starting and Managing a Business (BUS357),
offered by the School of Business provided two modes of study for students to undertake in the semester of
January 2019. One was virtual whereby the students learned online with virtual face-to-face interactions with their
instructors and peers while the other being physical face-to-face based on the blended e-learning approach of
combining either six or three face-to-face lessons with e-learning contents over a term of six weeks. Students
taking these courses were studying part-time taking classes in the evening and they had the option to choose their
mode of study. All students taking these courses will take a common examination at the end, but the continuous
assessment components will be different. Data from the Student Information Systems provided students’
background information including demographic and academic details. 370 students were analysed and the
variables that was extracted would include gender, marital status, race, nationality, course, qualification, school,
programme, intake, age (now), age (joint) and cumulative grade point average (CGPA). The purpose of this paper
was to find out from the data if there were significant variables that influenced students’ choice of study. Insights
drawn from this study will be helpful in planning for course offering in various modes. We believed the statistical
analyses of the chi-square test and the binary logistic regression would be appropriate to obtain the findings for
this study.
Literature Review
Online and face-to-face learning have been studied widely. Researchers had found that for online students they
are usually older, have full or part-time work, requires commuting to the campus, have family obligations and
have taken online courses before. Cleveland, Dutcher & Epps (2015) explained in their study that “online students
tend to be older, part or full time workers, and returning to school after being in the working world” while the
students in their survey who took the face to face “tended to be the more traditional college student: younger,
often directly out of high school” (p. 128). On the other hand, face-to-face students are usually freshmen and they
like to seek interactions with their instructors and classmates in the physical classroom. Dendir (2016) found that
“the average online student was a sophomore, whereas the typical face-to-face student was a freshman …. a closer
look at the data shows that 83% of the sample in the face-to-face section were freshmen, whereas about 77% in
the online sample were sophomore and above …. a majority of the online students (58%) had prior experience
with online courses” (p. 62). The key to online study is the flexibility and convenience to learn at the students’
own pace and when they are most productive as pointed out by Jaggers (2014) “that convenience and flexibility
ASCILITE 2019 Singapore University of Social Sciences 340
, Personalised Learning. Diverse Goals. One Heart. FULL PAPERS
are key factors that entice students to enroll in online coursework” (p. 27). In terms of student characteristics, it
was found that “student age, percentage female, race and grade point average (GPA)” had no differences by the
mode of delivery (Parcel, Radu & Gonzales, 2018, p.4). This study, based on two courses that allowed students
to choose between either online or face-to-face mode, attempts to determine which independent variables affected
students’ choice of learning.
Research Question
Based on the independent variables obtained for this study, would there be a significance between each
independent variable compared to the mode of learning (i.e. the dependent variable) for the students who studied
in BUS354 and BUS357? Would there also be interactions between these independent variables?
Chi-square Test and the Binary Logistic Regression Model
The use of the chi-square test and the binary logistic regression model as statistical tests came about from papers
discussing the analysis of dependent variable in binary form. They included the studies of integrated pest
management (IPM) adoption (Talukder, Sakib & Islam, 2017), drivers’ reactions in car crashes (Al-Taweel,
Young & Sobhani, 2016) and stillbirths in Ethiopia (Berhie & Gebresilassie, 2016). These papers analysed the
binary nature of the dependent variable (see Table 1) against a range of independent variables.
Table 1: Dependent Variables in Binary Format
Dependent Variable Category
IPM Adoption 1: Yes, 0: No
Drivers’ Reactions 1: Drivers take Reactions, 0: Drivers do not take Reactions
Experienced Stillbirth 1: Yes, 0: No
Given that all the independent variables are in categorical format, the use of the chi-square test to determine the
significance of the variables with the binary dependent variable made statistical sense. To further identify the
levels of each independent variable such that there is significance associated with the dependent variable, these
papers suggested the use of the binary logistic regression model. For example, are there significance associations
between IPM adoption and different regions (Talukder et al., 2018), divers’ reactions and crash type (Al-Taweel
et al., 2016) and experiencing stillbirth and maternal age (Berhie & Gebresilassie, 2016). The key question for
each study was to determine if there was IPM adoption, drivers take reactions or experiencing stillbirth among
different levels of independent variables. From these studies, it was established that a consistent statistical
approach using the chi-square test and the binary logistic regression model to determine if students’ choice of
learning (virtual or face-to-face) was significant against a selection of independent variables would be valid.
Details about the binary logistic regression model are explained in the journals from Peng, Lee & Ingersoll (2002)
and Sperandei (2013).
Interpreting Results from the Binary Logistic Regression Model
In terms of interpreting the results of the binary logistic regression model, an understanding on the use of the odds
ratio (OR) is important (Strand, Cadwallader & Firth, 2011). By definition, an OR ‘compares the odds of success
(or failure) for a particular group to a base (reference) category for that variable’ (Strand el al., 2011, p. 18). For
example, if we evaluate ethnicity and higher academic results according to Table 2, we note that White British
students have been selected as the reference category. Indian students are 1.58 times more likely than White
British students to achieve higher academic results or they are 58% more likely to achieve higher academic results
than White British students. Conversely for Black Caribbean students the OR is 0.53, so Black Caribbean students
are less likely to achieve higher academic results compared to White British students. In percentage terms they
are 47% less likely to achieve higher academic results. What this means is that Indian students are more likely
while Black Caribbean are less likely compared to White British students on achieving higher academic results.
In SPSS, OR is represented by the ‘Exp(B)’ ratio.
ASCILITE 2019 Singapore University of Social Sciences 341
Online Versus Face-to-Face: A Quantitative Study of
Factors Influencing Students’ Choice of Study Mode using
Chi-Square Test and Binary Logistic Regression
Sze-Kiu Yeung Wee Leong Lee
Singapore University of Social Sciences Singapore University of Social Sciences
Singapore Singapore
For online learning in the January 2019 semester, students at the Singapore University of Social
Sciences were able to choose whether they want to study in virtual or face-to-face mode in two
courses. Virtual refers to full online learning whereby students study, in a six-week term,
without the need to meet face-to-face with the instructor while face-to-face refers to blended
e-learning whereby students received either six or three face-to-face lessons with e-learning
resources. In full online mode, students will meet the instructor virtually via video
conferencing on a weekly basis. Data were obtained to find out which variables actually had
an effect of students’ choice of learning mode. 370 students were analysed and the variables
including gender, marital status, race, nationality, course, qualification, school, programme,
intake, age (now), age (joint) and cumulative grade point average (CGPA) were examined.
Each variable was compared with the students’ mode of study in order to identify if they are
dependent (e.g. gender versus study mode, race versus study mode, etc.) based on a chi-square
test. The significant variables were further investigated using a binary logistic regression
model. It was found that qualification, intake and CGPA were found to be significant for
students’ choice of learning.
Keywords: Virtual, Face-to-face, Mode of study, Chi-square test, Binary logistic regression
Introduction
Two courses, Customer Relationship Management (BUS354) and Starting and Managing a Business (BUS357),
offered by the School of Business provided two modes of study for students to undertake in the semester of
January 2019. One was virtual whereby the students learned online with virtual face-to-face interactions with their
instructors and peers while the other being physical face-to-face based on the blended e-learning approach of
combining either six or three face-to-face lessons with e-learning contents over a term of six weeks. Students
taking these courses were studying part-time taking classes in the evening and they had the option to choose their
mode of study. All students taking these courses will take a common examination at the end, but the continuous
assessment components will be different. Data from the Student Information Systems provided students’
background information including demographic and academic details. 370 students were analysed and the
variables that was extracted would include gender, marital status, race, nationality, course, qualification, school,
programme, intake, age (now), age (joint) and cumulative grade point average (CGPA). The purpose of this paper
was to find out from the data if there were significant variables that influenced students’ choice of study. Insights
drawn from this study will be helpful in planning for course offering in various modes. We believed the statistical
analyses of the chi-square test and the binary logistic regression would be appropriate to obtain the findings for
this study.
Literature Review
Online and face-to-face learning have been studied widely. Researchers had found that for online students they
are usually older, have full or part-time work, requires commuting to the campus, have family obligations and
have taken online courses before. Cleveland, Dutcher & Epps (2015) explained in their study that “online students
tend to be older, part or full time workers, and returning to school after being in the working world” while the
students in their survey who took the face to face “tended to be the more traditional college student: younger,
often directly out of high school” (p. 128). On the other hand, face-to-face students are usually freshmen and they
like to seek interactions with their instructors and classmates in the physical classroom. Dendir (2016) found that
“the average online student was a sophomore, whereas the typical face-to-face student was a freshman …. a closer
look at the data shows that 83% of the sample in the face-to-face section were freshmen, whereas about 77% in
the online sample were sophomore and above …. a majority of the online students (58%) had prior experience
with online courses” (p. 62). The key to online study is the flexibility and convenience to learn at the students’
own pace and when they are most productive as pointed out by Jaggers (2014) “that convenience and flexibility
ASCILITE 2019 Singapore University of Social Sciences 340
, Personalised Learning. Diverse Goals. One Heart. FULL PAPERS
are key factors that entice students to enroll in online coursework” (p. 27). In terms of student characteristics, it
was found that “student age, percentage female, race and grade point average (GPA)” had no differences by the
mode of delivery (Parcel, Radu & Gonzales, 2018, p.4). This study, based on two courses that allowed students
to choose between either online or face-to-face mode, attempts to determine which independent variables affected
students’ choice of learning.
Research Question
Based on the independent variables obtained for this study, would there be a significance between each
independent variable compared to the mode of learning (i.e. the dependent variable) for the students who studied
in BUS354 and BUS357? Would there also be interactions between these independent variables?
Chi-square Test and the Binary Logistic Regression Model
The use of the chi-square test and the binary logistic regression model as statistical tests came about from papers
discussing the analysis of dependent variable in binary form. They included the studies of integrated pest
management (IPM) adoption (Talukder, Sakib & Islam, 2017), drivers’ reactions in car crashes (Al-Taweel,
Young & Sobhani, 2016) and stillbirths in Ethiopia (Berhie & Gebresilassie, 2016). These papers analysed the
binary nature of the dependent variable (see Table 1) against a range of independent variables.
Table 1: Dependent Variables in Binary Format
Dependent Variable Category
IPM Adoption 1: Yes, 0: No
Drivers’ Reactions 1: Drivers take Reactions, 0: Drivers do not take Reactions
Experienced Stillbirth 1: Yes, 0: No
Given that all the independent variables are in categorical format, the use of the chi-square test to determine the
significance of the variables with the binary dependent variable made statistical sense. To further identify the
levels of each independent variable such that there is significance associated with the dependent variable, these
papers suggested the use of the binary logistic regression model. For example, are there significance associations
between IPM adoption and different regions (Talukder et al., 2018), divers’ reactions and crash type (Al-Taweel
et al., 2016) and experiencing stillbirth and maternal age (Berhie & Gebresilassie, 2016). The key question for
each study was to determine if there was IPM adoption, drivers take reactions or experiencing stillbirth among
different levels of independent variables. From these studies, it was established that a consistent statistical
approach using the chi-square test and the binary logistic regression model to determine if students’ choice of
learning (virtual or face-to-face) was significant against a selection of independent variables would be valid.
Details about the binary logistic regression model are explained in the journals from Peng, Lee & Ingersoll (2002)
and Sperandei (2013).
Interpreting Results from the Binary Logistic Regression Model
In terms of interpreting the results of the binary logistic regression model, an understanding on the use of the odds
ratio (OR) is important (Strand, Cadwallader & Firth, 2011). By definition, an OR ‘compares the odds of success
(or failure) for a particular group to a base (reference) category for that variable’ (Strand el al., 2011, p. 18). For
example, if we evaluate ethnicity and higher academic results according to Table 2, we note that White British
students have been selected as the reference category. Indian students are 1.58 times more likely than White
British students to achieve higher academic results or they are 58% more likely to achieve higher academic results
than White British students. Conversely for Black Caribbean students the OR is 0.53, so Black Caribbean students
are less likely to achieve higher academic results compared to White British students. In percentage terms they
are 47% less likely to achieve higher academic results. What this means is that Indian students are more likely
while Black Caribbean are less likely compared to White British students on achieving higher academic results.
In SPSS, OR is represented by the ‘Exp(B)’ ratio.
ASCILITE 2019 Singapore University of Social Sciences 341
