A Comparison of the Different Engineering Majors in Relation to Perceived Social Media Addiction
Fatima Zehra Allahverdi1*
1 Department of Psychology, Social Sciences University of Ankara, Ankara, Turkey
*Corresponding Author: [email protected]
Accepted: 15 July 2021 | Published: 1 August 2021
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Abstract: The current study took place in the Gulf region of the Middle East at a public university. The study examined gender (Male, Female), the majors among Engineering students, and age (Freshman, Sophomore, Junior, Senior) in relation to perceived social media addiction which was measured on a zero to one hundred scale. A hierarchical regression analysis was conducted and revealed insignificant results in terms of the seven Engineering majors, however, significant differences were found between age and gender. Female students believed they were addicted about 10% more than males. Freshman and Sophomore students believed they were addicted about 10% more than Juniors and Seniors.
Keywords: Perceived social media addiction, engineering majors, age, gender
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1. Introduction
The internet has permeated our lives in recent years (Grau, Kleiser, & Bright, 2019). Most of what we do, including communication, is now through online platforms and systems. Among such platforms are Instagram, Twitter, and Facebook (Okdie & Ewoldsen, 2018). With the increase of such systems, so has the percentage of people being involved. A large portion of university students, around 65%, prefer to spend more than three hours online (Cotton, 2008).
Addiction can begin with “seemingly benign behaviors that through psychological, biophysical and/or environmental triggers become harmful and morph into an addiction” (Grover et al., 2011, p.1). An addiction based on consumption is composed of four stages: non-use, non- addictive use, near-addiction, and addiction. The phases are represented by time spent and frequency of engagement, self-control, and the level of negative consequences (Grau et al., 2019; Martin et al., 2013). Negative consequences may either be economic, physical, psychological, or social. For instance, social media addiction can cause burnout which negatively relates to job performance or in other words has a negative economic consequence (Zivnuska, Carlson, Carlson, Harris, & Harris, 2019). The current study looked at the self- perceived addiction of individuals. Symptoms of internet addiction are comparable to that of low self-esteem, anxiety, disinhibition, depression, and hostility (Widyanto & Griffiths, 2006).
The need to interact is one of the main reasons people use social media (Palfrey & Gasser, 2008). Although previous research focuses on internet addiction, recent research has begun to focus on social media addiction (Grau et al., 2019). Social media addiction has become a major concern within the current technological world (Kircaburun, 2016). Other research has found that 76 percent of the users of the internet visit Facebook daily; in fact, 55 percent of the visiting individuals visit Facebook several times a day (Greenwood et al., 2016).
Research has also found that females are more prone to social media addiction (Kuss &
Griffiths, 2017; Karadag et al., 2015). However, other researchers such as Erfanmanesh and Hossseini (2015) refer to how men are more susceptible to social media addiction compared to women. Therefore, the current study also included gender as a variable in predicting perceived social media addiction.
2. Purpose and Methodology
The current study aimed to examine the predictability of phubbing frequency, gender, age, and the majors of Engineering on perceived social media addiction. The population examined were university students in the Gulf region of the Middle East. Gender was included in the study, due to varied results by researchers (Erfanmanesh & Hossseini, 2015; Kuss & Griffiths, 2017).
How is the percentage of self-perceived social media addiction a function of the following factors: gender differences, age, and majors among Engineering Students?
The current study utilized a multiple regression analysis approach. Linear relationships and the strength of relationships can be identified using scatterplots and correlations. However, regression quantitatively identities how variables are related. "There are two potential objectives of regression analysis: to understand how the world operates and to make predictions" (Albright & Winston, 2010, p.531). Therefore, it provides the researcher with a formula of the relationship, allowing the prediction of future cases.
The study included university students from the College of Engineering. The language of instruction was English. The total number of students surveyed was 204. The Engineering majors included in the study were as follows: Mechanical Engineering, Chemical Engineering, Petroleum Engineering, Industrial Engineering, Electrical Engineering, Computer Engineering, and Civil Engineering. Table 1 provides a breakdown of the percent of participating students by major.
There were more females (2/3) compared to males (1/3) in the study. This was because the vast majority of the Engineering students were females. The survey was given to students from all years. Table 2 provides a breakdown of the percent of participating students by year.
Freshman year corresponds to about the age18-19, Sophomore year corresponds to about the age 20-21, Junior year corresponds to about the age 22-23, and Senior year corresponds to about the years 24-26. Therefore, it is drastically different from other countries, including the United States.
Table 1: Percent of participating students by major
Engineering Major % of survey students
Chemical Engineering 9.1%
Computer Engineering 12.4%
Electrical Engineering 11.3%
Industrial Engineering 28.3%
Mechanical Engineering 21.5%
Civil Engineering 12.3%
Table 2: Percent of participating students by Year
Year % of survey students
Freshman 17.6%
Sophomore 21.5%
Junior 20.5%
Senior 40.4%
3. Measure and Findings
Participants had to circle either male or female for gender. Due to the culture of the country surveyed, individuals officially referred to themselves as the sex they were born with. For major, participants had to write down their Engineering major. For the year, participants had to circle whether they were Freshman, Sophomore, Junior, or Senior. Perceived social media addiction was assessed with the following question, "To what extent do you feel addicted to social media?", using a numerical scale from zero to 100.
Before analyzing gender and year, Engineering majors were separately considered utilizing a multiple regression to determine if perceived social media addiction was a function of major.
However, it was found that Engineering majors were not significant in the model.
Hierarchical multiple regression was utilized. The independent variables of gender and year were examined as predictors of perceived social media addiction. The assumptions of normality, linearity and homoscedasticity were all met. Moreover, no outliers were found and multicollinearity was not a problem. For the reliability of the equation, there needs to be fifteen subjects for each variable. This was met in the current study.
The average addiction perception of social media was (x̅ = 66.13) with a standard deviation of (sdv=24). Of the 204 students, 100 of them perceived themselves as addicted to social media between 75%-100%. Of the 204 students, 37 of them perceived themselves as addicted to social media 100%.
Table 3 provides the model summary for the regression analysis. Table 4 provides the coefficients for the final model. Due to the listwise removal of missing data in SPSS, the number of participants for the regression model was reduced to 187. As seen in Table 4, regression results indicate that the overall model predicts perceived social media addiction, R2
= 0.21, R2adj = 0.17, F (1, 180), p < .01, with the independent variables accounting for 21%
of the variance in the dependent variable. Table 5 indicates the coefficients of the model.
The data was then used to create a multiple regression formula:
Addiction Perception = 61.4 – 8.03 Gender (Male) -2.52 Year(Sophomore) -13.79 Year(Junior) -9.40 Year(Senior)
It should be noted that for the base of the regression formula, the gender is female, and the year is Freshman. Therefore, students in this category had an average of 61.4% perceived social media addiction. If for instance, the gender was male, the year was Freshman, the perceived social media addiction would be 53.37.
To validate the formula, the data was split into two. A little more than two-thirds of the data was used to run a regression model and the remaining was used to cross-validate the regression equation.
Table 4: Model Summary
Steps R R2 R2adj R2 Change F Change p df1 df2
Year, Gender 0.46 0.21 0.17 0.03 6.1 <.01 1 180
Table 5: Coefficients for Final Model
Model Beta T Model
Year (Se) -12.71 -0.26 -2.74***
Year (J) -18.37 -0.31 -3.51****
Year (So) -6.19 -0.11 -1.19
Gender (M) -8.23 -0.17 -2.47**
* 01 **0.05 ***0.01
4. Conclusion
The current research asked whether perceived social media addiction was a function of gender differences, age, and majors among Engineering students. Engineering majors were not found to be significant. Therefore, it was removed from the analysis.
Among the surveyed students, 100 of them perceived themselves as addicted to social media between 75%-100% and 37 of them perceived themselves as addicted to social media 100%.
A hierarchical regression analysis was conducted to determine the amount of variance the independent variables explained in the dependent variable, perceived social media addiction.
Significant differences were observed between genders and age. Female students believed they were addicted about 10% more than males. Freshman and Sophomore students believed they were addicted about 10% more than Juniors and Seniors.
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