scholarly articles design research

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved March 18, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, what is your plagiarism score.

Academic stress and cyberloafing among university students: the mediating role of fatigue and self-control

• Gabriel E. Nweke 1 ,
• Yosra Jarrar 2 &
• Ibrahim Horoub   ORCID: orcid.org/0000-0002-3104-2831 3  

Humanities and Social Sciences Communications volume  11 , Article number:  419 ( 2024 ) Cite this article

20 Accesses

Metrics details

• Health humanities

This study aims to fill a gap in existing literature by investigating the relationship between academic stress and cyberloafing behavior among university students. By examining 415 final-year undergraduate students from various faculties at Girne American University, the research utilizes a correlational design to analyze the impact of academic stress on cyberloafing, considering the mediating effect of fatigue and the moderating influence of self-control. The findings reveal a significant positive association between academic stress and cyberloafing, with fatigue partially mediating this relationship and self-control moderating the influence. This research offers a novel perspective on understanding and addressing cyberloafing in educational settings, thereby contributing to the existing body of knowledge on this topic. The study’s methodology and findings provide valuable insights into the complex interplay of academic stress, fatigue, self-control, and cyberloafing, offering implications for educational institutions in addressing and mitigating cyberloafing behaviors among students.

Similar content being viewed by others

Implications of Tamarkoz on stress, emotion, spirituality and heart rate

Nasim Bahadorani, Jerry W. Lee & Leslie R. Martin

Neural activation during emotional interference corresponds to emotion dysregulation in stressed teachers

Samuel Fynes-Clinton, Chase Sherwell, … Annemaree Carroll

Revisiting effects of teacher characteristics on physiological and psychological stress: a virtual reality study

Lisa Bardach, Yizhen Huang, … Dirk Richter

Introduction

The abundant amount of internet services and the proliferation of mobile technologies have greatly impacted various aspects of our lives. People utilize the advancements in mobile internet technologies in all environments for activities such as entertainment, posting updates, research, shopping, visual communication, texting, gaming, online gambling, and internet surfing. While these activities can be beneficial in certain aspects of life activities and learning, uncontrolled and excessive use of internet mobile technologies can be detrimental and counterproductive (Metin-Orta and Demirutku, 2020 ; Venkatesh et al., 2012 ; Yılmaz et al., 2015 ). In academic settings, there is no doubt that the use of mobile and internet technologies has greatly improved the instructor–student relationship, easy access to various databases, and overall quality of education. However, the widespread use of technology in the classroom has been shown to have negative unintended consequences, including compulsive use of media, resulting in cyberloafing or cyberslacking (Alyahya and Alqahtani, 2022 ).

Cyberloafing is a modern-day concept that represents aspects of the dark or distracting side of technology and its uses. The term cyberloafing was operationalized by Lim ( 2002 ), who defined it as a voluntary personal act of browsing and surfing the internet for entertainment and social interaction during work hours. Cyberloafing is referred to as the use of mobile devices and internet services to engage in activities that are outside the scope of the required task during work/class time (Ravizza et al., 2014 ). In an educational environment, cyberloafing is the use of internet services during lecture time for extraneous issues and engaging in activities not relevant to the class requirements (Twum et al., 2021 ). Cyberloafing activities in the classroom primarily involve exchanging emails and text messages, online gaming, watching movies, and social media use (Hardiani et al., 2018 ; Khansa et al., 2017 ; Olmsted and Terry, 2014 ).

Cyberloafing is currently prevalent in universities due to the availability of mobile devices and easy access to Internet services. A large proportion of students in TRNC use their smartphones during class, with the majority admitting using the smartphone for non-learning activities (Ozdamli and Ercag, 2021 ). Previous studies have reported that cyberloafing is associated with reduced classroom attention (Taneja et al., 2015 ), low academic engagement (Ravizza et al., 2014 ), instructor-student conflict (Hembrooke & Gay, 2003 ) and poor academic performance (Dursun et al., 2018 ; Flanigan and Babchuk, 2015 ; Ravizza et al., 2014 ). Additionally, cyberloafing contributes greatly to the development of smartphone addiction among students (Doush and Alhami, 2018 ; Gokcearslan et al., 2016 ; Nwakaego and Angela, 2018 ).

Taking into consideration these negative impacts of cyberloafing, particularly on the quality of education and student mental health, it is imperative that researchers try to understand various situational and dispositional factors that influence cyberloafing behaviors in the classroom. Past studies on students’ cyberloafing behaviors in educational settings have mostly focused on factors such as age, gender, computer skills, and access to the internet in the classroom as predictors of cyberloafing but, within the current stage of these factors, the role of student academic activities workloads or academic stress has not been examined in relation to cyberloafing.

Academic stress is the emotional, physical, and psychological strain a student experiences that can be attributed to the academic demands and requirements (Rahmawati, 2012 ). Heavy course loads, assignments, exams, time management, competitions among students, teacher competency, and lack of resources constitute stress and can trigger cyberloafing in students (Hibrian et al., 2022 ). Cyberloafing behaviors, and surfing the internet, is used by students to ameliorate the impact of academic stress in the classroom. Furthermore, aspects including fatigue and self-control, mostly contained separately on each of the main variables, have the power to connect either. As such, this paper plans on examining the effect of academic stress on cyberloafing behavior, while also understanding the mediating role of fatigue between both, in addition to the moderating role of self-control within the academic or non-academic setting.

Theoretical framework

Through the application of a study towards which academic stress influences or causes the act of cyberloafing, an analysis towards the type of behavior, the causes behind the action, and how both interconnects should be deduced through the theoretical framework. An applicable theory, that of which might direct itself more towards cyberloafing, is the Theory of Planned Behavior. Coined in the 1980s by Icek Ajzen, the psychological theory describes the behaviors of individuals to be primarily influenced by belief, that of which comprises the three core components: attitude, subjective norms, and perceived behavioral control, in order to produce the outcome of the “planned behavior” (Askew et al., 2014 ). The theory was further expanded due to a need for understanding towards the motivations and intentions behind any and all behaviors (Conner and Armitage, 1998 ). Within the scope of cyberloafing, a connection was placed towards the effect of self-efficacy, or its placement as a self-determinant factor, to it application through cyberloafing behavior at locations such as the workplace, with the subjective norms and control focusing on the need or desire of cyberloafing as a whole (Askew et al., 2014 ). In such a case, a further relationship can be hypothesized towards the planned behavior, cyberloafing, occurring from the cause of academic stress, moderated by self-control, in the theory’s case behavioral control, and mediated by fatigue. If applied within that sense, the theory of planned behavior can then prove that in most cases, within the norms of technology access and expansion, cyberloafing has become a subjective norm that is enhanced within academic stress, where students intentionally focus on cyberloafing as a means to assist their ongoing stress. Yet, criticism towards the theory follows its inadequate portrayal of rationalism or logical behavior, that of which Ajzen has argued for the assumptions, stating that it could be irrational, untrue, or unreasonable (Barber, 2011 ). In addition, lack of application towards experimental studies places the need to further understand the interconnectedness of the theory towards concepts including education and technology, within this case academic stress and cyberloafing, which will be further analyzed within this paper.

Literature review

In examining the connection between academic stress and cyberloafing, within the mediating role of fatigue and the moderating role of self-control, several studies were identified to understand the background behind the variables studies and examine the research within the methods and findings further on, while also breaking down the hypotheses of the study.

Relations between academic stress and cyberloafing

In presenting a background to the study, a 2021 paper by Chen et al., examining university students within the Hubei province, found a connection between academic stress, that of which was defined as the psychological strain students experience due to sensory overload, with that of cyberloafing as a method of maladaptive coping mechanisms. In placing a connection between both, the study places a central factor to the experiment, however, crossed out its potential mediating and moderating factors of fatigue and self-control, while focusing towards one specific province. Moreover, a 2019 study found that academic stress directly has an effect towards cyberloafing, most prominently through smartphone addiction as the concentrated platform. Curating its study within the post-graduate students of Universitas Negeri Jakarta at an older age range, the study concluded that higher levels of academic stress increased phone addiction, creating a connection and identifying a prominent source (Hamrat et al., 2019 ). Moreover, Zhou et al., 2021 study, focusing on examining academic stressors and cyberloafing within college students amidst the moderating role of self-control, also concluded a positive influence, excluding the possibility of fatigue as a mediator, but extending cyberloafing’s role as a form of cure for their stress. As such, through the literature reviewed, the following hypothesis can be compromised for the relationship between academic stress and cyberloafing within the study:

H 1 . Academic stress would have a positive influence on cyberloafing .

Academic stress, fatigue, and cyberloafing

While studies showed a constant relationship between academic stress and cyberloafing, a further evaluation towards the role of fatigue was also observed. This is supported by Yogisutanti et al. ( 2020 ), who observed that individuals experiencing heightened work-related stress were more prone to becoming fatigued and may seek extraneous activities for relief. The premise of relief through this study, could be further disguised as that of cyberloafing. In addition, Hibrian et al.’s ( 2022 ) study found that the amount of stress individuals go through on one day significantly impacts their fatigue levels the following day. Furthermore, when students are fatigued, they struggle to concentrate on their tasks, making them more prone to distractions like cyberloafing during academic activities. Akbulut et al. ( 2017 ) paper similarly noted that fatigue hampers individuals’ ability to focus on their primary objectives, making them more susceptible to online distractions, such as cyberloafing, regardless of their gender or social status. A study on Iranians found that individual levels of work-related fatigue had a notable impact on cyberloafing activities and greater fatigue levels were associated with a higher likelihood of engaging in cyberloafing (Aghaz and Sheikh, 2016 ). Similarly, Ghani et al. ( 2018 ) found that high level stress, and fatigue are associated with low productivity, reduced attention and cyberloafing behaviors among Government Servants in Malaysia. It has also been proposed that stress and fatigue are linked to cyberloafing, but it remains unclear if fatigue acts as a potential mechanism connecting stress to cyberloafing in particular. As such, the following hypothesis is proposed based on that of which was examined above:

H 2 . Fatigue would mediate the relationship between academic stress and cyberloafing .

Academic stress, self-control, and cyberloafing

One factor that has been shown to play a significant role in mitigating the link between stress and cyberloafing is self-control (Li et al., 2023 ; Zhou et al., 2021 ). Self-control is defined as the capacity to override or alter one’s internal reactions and to halt undesirable behavioral inclinations, such as impulsive actions (Zhang et al., 2015 ). Self-control refers to individuals’ capacity to consciously manage their actions, resisting impulses, habits, or automatic responses. Extensive research has consistently shown that self-control is predictive of positive adjustment, optimal performance, and academic success, as proved by Duckworth et al. ( 2019 ) study and Li et al.’s ( 2023 ) paper. According to a 2011 examination of self-control within individuals, strong self-control was correlated with responsibility, discipline and drive (Zettler, 2011 ). Given that cyberloafing frequently has detrimental effects on academic performance and goal attainment and can be considered a form of deviant behavior in the educational setting, it is plausible to assume that individuals with higher inherent self-control would engage in less cyberloafing during their daily academic tasks. That is supported by the 2021 paper, which in connecting self-control, cyberloafing and the big five personality traits, found that students with strong self-control, who are predisposed to setting ambitious goals and striving for achievement, are likely to be less susceptible to the influence of daily academic stressors. This is because they find it easier to establish effective and consistent study routines, and they are less likely to engage in cyberloafing during class time. On the other hand, the study concluded that individuals with low self-control facing the same level of academic stressors may be more prone to engage in cyberloafing because they struggle to resist the temptation of online distractions during academic tasks (Liani et al., 2021 ). In addition, Zhou et al.’s paper, through the strength model of self-control, found that higher levels of self-control generally imply greater cognitive resources and a reduced likelihood of being depleted by academic stressors (Zhou et al., 2021 ). Therefore, high self-control may act as a natural safeguard against the impact of academic stressors on cyberloafing. Based on the literature reviewed, we proposed that self-control may act as a moderator in the relationship between academic stress and cyberloafing among university students.

H 3 . Self-control would moderate the relationship between academic stress and cyberloafing .

Through observing and correlating the literature reviewed in the past, this study can therefore identify and isolate the necessary factors to be examined within the study, while also showcasing its effects within the scope of the target participants as well as the findings presented later on.

The present study

This study aimed to explore (a) whether the level of academic stress is associated with cyberloafing behavior among university students, (b) whether fatigue would mediate the relationship between academic stress and cyberloafing, (c) and whether the direct path between academic stress and cyberloafing would be moderated by individuals’ level of self-control.

As an integrated model (see Fig. 1 ), the present study was guided by the following hypotheses:

Conceptual model depicting the relationship between academic stress and cyberloafing with fatigue as a mediator and self-control as a moderator.

H 1 . Academic stress would have a positive influence on cyberloafing.

H 2 . Fatigue would mediate the relationship between academic stress and cyberloafing.

H 3 . Self-control would moderate the relationship between academic stress and cyberloafing.

Methodology

Participants and procedure.

Data collection took place manually, located in the classrooms during the spring semester (April through June) of 2023 at the Girne American University, Cyprus. The study included 424 final-year undergraduate students from five faculties and departments, including Architecture, Communication, Education, Engineering, and Humanities, all of which constituted within the university. The inclusion criteria were being adapted in the final year of study, requested graduation projects or dissertation, to ensure relative similar academic stressors. These participants were selected through convenience sampling.

After removing nine students who did not respond to substantial part of the questions, the remaining sample, 415, had an average age of 23.42 years (with a standard deviation of 1.34), and 56.25% of them identified as female. Utilizing simulation tool by the MARlab Power Analysis for Indirect Effects as outlined by Schoemann et al. ( 2017 ), and applying a significance level of α  = 0.05, medium effect size of 0.30 with a power of 0.80, the achieved sample size of N  = 415 is deemed sufficient for testing the research hypotheses.

The sample composition consisted of 62 Communication (14.94%), 59 Humanities (14.22%), 82 Engineering (19.76%), 97 Architectures (23.37%), and 115 Education (27.71%).

This study received approval from the Research Ethics Committee of Girne American University. Before collecting the data, participants were given written informed consent forms that explained the aim of the study and its significance. They were informed that their responses would be kept confidential with no identifying information recorded, and their answers would be solely used for research purposes. Participants were also told they could withdraw from the study at any time without any consequences. Subsequently, participants were invited to give brief demographic information and complete a series of questionnaires. It took the participants ~20 min to complete all the questionnaires.

Fatigue Assessment Scale (FAS)

Fatigue was measured by the Fatigue Assessment Scale (FAS) developed by Michielsen et al., ( 2003 ) to assess the level of mental and physical fatigue. Fatigue Assessment Scale (FAS) contains 10 self-report items with a response ranging from 1- never to 5- always. Five questions assess physical fatigue, while five questions (specifically questions 3 and 6–9) pertain to mental fatigue. Respondents were required to provide an answer to each question, even if they were not currently experiencing any symptoms. Total score for the Fatigue Assessment Scale (FAS) was computed by summing the scores for all questions. The total FAS score ranges from 10 to 50, with a score below 22 indicating an absence of fatigue and a score of 22 or higher indicating the presence of fatigue. The scale developers reported acceptable internal consistency and validity (Michielsen et al., 2003 ). The FAS has a Cronbach alpha of 0.90 for this study.

Perception Scale Academic Stress (PAS)

Academic stress was measured with the Perception Scale Academic Stress (PAS), developed by Bedewy and Gabriel ( 2015 ). It is an 18-item scale created to ascertain how individuals perceive academic stress and identify its primary sources, namely academic expectations, workload, examinations, and students’ self-perceptions in academics (Bedewy and Gabriel, 2015 ). The PAS is re-structured so that a higher score signifies higher levels of stress, with scores ranging from 18 to 90. The developers reported a high reliability and validity for this scale. The Cronbach Alpha in this study is 0.78

Cyberloafing Activities Scale (CAS)

Cyberloafing was assessed using the Cyberloafing Activities Scale (CAS), a 30-item scale developed by Akbulut et al. ( 2016 ), to evaluate classroom cyberloafing activities. This scale encompassed five distinct factors: “sharing” (9 items, e.g., “I share content on social networks”), “shopping” (7 items, e.g., “I visit online shops for used products”), “real-time updating” (5 items, e.g., “I read tweets”), “accessing online content” (5 items, e.g., “I watch videos online”), and “gaming/gambling” (4 items, e.g., “I play online games”). Respondents indicated the frequency of their engagement in these behaviors during classroom activities using a 5-point scale. Cronbach’s alpha was 0.85 in this study.

The Self-Control Scale (SCS)

The Self-Control Scale (SCS) measured the individual differences in self-control during class activities, and under stressful conditions. The scale evaluates an individual’s capacity to manage their impulses, modify their emotions and thoughts, and prevent unwanted behavioral tendencies from being acted upon. The scale was adapted from Tangney et al. ( 2004 ) and includes 36 items, e.g., “I am good at resisting temptation”. Respondents rated their alignment with a series of statements on a 5-point scale, and the cumulative scores yield a total self-control score, where higher values signify stronger self-control. The Cronbach’s alpha of this scale for this study was 0.78.

Data analysis

Data analysis was conducted using SPSS 23.0 and JAMOVI 2.3.2.1.0 statistical package. Specifically, SPSS 23.0 was employed to generate descriptive statistics, assess questionnaire reliability, check the assumptions and perform correlational analyses while GLM mediation models in JAMOVI were utilized for mediation and moderation analyses. Prior to testing the models in the JAMOVI library, we checked the data for linearity, normality, outliers and multicollinearity, and no violation was recorded. The Through visual inspection of the scatter plots or linearity residual plots (Pallant & Manual, 2013 ), linearity was ascertained. Kolmogorov-Smirnov test for normality indicated that all constructs had a p-value greater than 0.05, and the skewness-kurtosis values fell within the range of ±1.5, suggesting normal distribution of the data (Tabachnick et al., 2013 ). In addition, a comprehensive collinearity diagnostics, assessing variance inflation factor (VIF) values following the criteria of Hair et al. ( 2017 ) with a threshold of 5, revealed that all constructs exhibited VIF values ranging from 1.801 to 3.102. This implies the absence of multicollinearity in the study.

Descriptive statistics and correlations matrix

Table 1 displays the descriptive statistics and Pearson correlation coefficients for the variables under examination. The average scores for cyberloafing, self-control, and academic stress were above the mean (3.634, 3.198, and 3.111, respectively), while fatigue exhibited a below-average mean score (2.317). Cyberloafing is positively correlated with academic stress and fatigue, whereas psychological cyberloafing is negatively correlated with self-control. Additionally, there is a negative correlation between academic stress and self-control. Surprisingly, the correlation between fatigue and fatigue is negative.

Our findings suggested that individuals who experienced higher academic stress are more prone to indulge in cyberloafing behaviors. Similarly, those with high levels of fatigue also exhibited increased cyberloafing behaviors. Additionally, individuals low in self-control tend to engage more in cyberloafing activities (Table 2 ).

Mediating role of fatigue

Correlation analysis revealed significant and positive associations among academic stress, fatigue, and cyberloafing, which set the stage for examining the mediating role of fatigue. To test Hypothesis 1, a construction of the general linear model was held, using the medmod jamovi package, treating academic stress as a predictor, cyberloafing as the outcome variable, and fatigue as a mediator. The direct and indirect paths between academic stress and cyberloafing are detailed in Table 3 . As shown in Table 3 (direct path), our results showed that academic stress positively contributed to cyberloafing ( β  = 0.109, p  < 0.05). This supports Hypothesis 1, suggesting that higher academic stress was associated with higher cyberloafing activities among students. To examine Hypothesis 2, a mediation analysis was conducted to examine the mediating effect of fatigue on academic stress and cyberloafing. The total effect of the model was found to be significant, ( β  = 0.133, z  = 2.72, CI [0.037, 0.229], p  < 0.05). Notably, there was a statistically significant direct effect ( β  = 0.104, z  = 2.20, CI [0.011, 0.197], p  < 0.05.), and the Bootstrap 95% confidence intervals for the indirect effect of fatigue did not include zero ( β  = 0.029, z  = 2.08, p  = 0.038). The finding suggests that fatigue partially mediates the relationship between academic stress and cyberloafing. Therefore, Hypothesis 2, stating that fatigue would mediate the relationship between academic stress and cyberloafing, is supported.

Moderating the impact of self-control

To test hypothesis 3 a moderation test was conducted, with academic stress as the predictor, cyberloafing as the outcome, and self-control as a moderator. As shown in Table 4 , the interaction effect of academic stress and self-control negatively and significantly explained cyberloafing ( β  = −0.011, z  = −2.93, CI [−0.028, 0.11], p  = 0.037).

These findings strongly supported Hypothesis 3, which proposed that self-control moderates the link between academic stress and cyberloafing. To further elucidate this interaction effect, we conducted separate analyses, conducting the simple slope estimates, and by plotting academic stress against cyberloafing for individuals with low (M - SD) and high (M + SD) levels of self-control.

In examining simple slopes, it was observed that the relationship between academic stress and cyberloafing was notably weaker but still statistically significant among individuals with high self-control ( B  = −0.19, Bse = 0.067, z  = −2.87, p  < 0.005), compared to those with low self-control ( B  = −0.31, Bse = 0.076, z  = −4.65, p  < 0.001). Individuals possessing lower self-control and facing elevated academic stress tended to exhibit a higher tendency for cyberloafing compared to their counterparts with greater self-control.

Figure 2 indicated that participants who reported lower than average levels of self-control experienced a more pronounced impact of academic stress on cyberloafing compared to those with average or higher than average levels of self-control.

Self-control moderating the relationship between academic stress and cyberloafing.

In summary, the study found that fatigue mediated the relationship between academic stress and cyberloafing. Moreover, participants’ self-control level played a moderating role in the academic stress-cyberloafing relationship.

Building on previous research, this cross-sectional study investigated the connection between academic stress and cyberloafing among final year students at Girne American University, TRNC. It also examined how fatigue mediates this relationship and how self-control moderate it. The findings indicated that academic stress, fatigue, and self-control play vital roles in cyberloafing behavior in the classroom.

Within the scope of the theoretical framework, which of which was noted as the theory of planned behavior, the findings, which proved a positive relationship between academic stress and cyberloafing, further managed to prove its correlation towards the theory further. When faced with low self-control, students are more likely to feel academically stressed, leading to that of cyberloafing. Through the use of cyberloafing as a withdrawal action, that of which was studied prior by Askew et al. ( 2014 ), it is therefore considered an intentional behavior occurring as a result of the combination of attitude, both within academic stress and self-control, along with the mediating role of fatigue at hand. As such, considering the behavioral perceptions of students, the act of cyberloafing, influenced by academic stress, can therefore be considered a “planned behavior” in support of the theory. Moreover, looking towards the social norms of technological use, the subjectivity of the norm to gravitate towards cyberloafing can also be considered when discussing this study. Implications analyzed from the theory can contradict cyberloafing as a planned behavior, rather as a natural result, however when looking into several other outcomes of academic stress, that of which can be predicted to be consultation, isolation, etc. The use of cyberloafing in specific modifies it to be that of an intentional behavior, rather than a natural outcome. Furthermore, the role of self-efficacy through academic stress and self-control as a whole play a crucial part in deducing the role of the theory within cyberloafing, but nonetheless can be carried towards different cases, demographics, etc.

Relationship between academic stress and cyberloafing

The first hypothesis result revealed a direct link between academic stress and cyberloafing. Academic stress positively impacted cyberloafing behaviors among the students. Our findings showed that students tend to engage in cyberloafing behaviors during lectures, such as accessing social media and watching online videos, particularly when they are stressed about their academic performance or when faced with a high workload. Academic stress is an integral part of students’ lives, encompassing various facets as categorized by Deng et al. ( 2022 ), including pressure from instructors, test result pressure, exam-related stress, group work pressure, peer pressure, time management, and internal stress. Some individuals cope with this stress by disengaging from stress-inducing activities (Zhou et al., 2021 ). In our study’s context, students experiencing stress tend to turn to the internet and games for entertainment purposes (Liang et al., 2022 ), with cyberloafing serving as a means for students to relieve academic pressure.

Our study’s findings are consistent with Chen et al. ( 2021 ), which explored related variables and identified a strong connection between perceived stress and cyberloafing and maladaptive coping mechanisms among university students within Hubei province, China. In addition, relating back to the study of Hamrat et al. ( 2019 ), which identifies the significance of academic stress’s direct impact on cyberloafing activities and smartphone addiction among post-graduate students at Universitas Negeri Jakarta, smartphone addiction can be also placed as a probable factor to the results discovered within this study. Based on our findings, it is evident that academic stress efficiently and positively influences cyberloafing behavior, corroborating the conclusions of Zhou et al. ( 2021 ), who argued that academic stress plays a pivotal role in driving cyberloafing behavior and that cyberloafing serves as a means for students to alleviate boredom, sensory overload, and stress. Consequently, the first hypothesis of this study is supported.

Mediating role of fatigue in the relationship between academic stress and cyberloafing

The results of the second hypothesis show that academic stress has a positive and significant impact on fatigue, suggesting that high levels of academic stress deplete energy levels and lead to fatigue, which is consistent with the findings of past research. In specific, basing off Yogisutanti’s et al. ( 2020 ) study, the method of “relief” can be explored as cyberloafing, in which sense fatigue would also play a role within the equation. In addition, Hibrain’s et al. ( 2022 ) study could also be analyzed in correlation within the study as fatigue was considered an essential variable discovered within the results, and although placing a partial role, was still confirmed within the participants, as such curating similar results. Moreover, in support of Akbulut et al. ( 2017 ) paper, the study can conclude that fatigue, in shifting focus of students from their academic objectives, can direct them towards cyberloafing, placing it as a mediator, which was highlighted within this paper as well. In comparing countries, the study found similar results to that of Aghaz and Sheikh in Iran and Ghani, to which in this case, Girne University’s students were also exposed to cyberloafing after excessive fatigue caused by academic stress, supporting their stances on the topic as a whole. As such, although placing a partial role within its correlation, fatigue as a mediator does show strong potential and observation within more concentrated analysis, but nonetheless shows a positive relationship towards both main variables.

Moderating the role of self-control in the relationship between academic stress and cyberloafing

We investigated the interaction between academic stress and self-control in predicting cyberloafing behaviors among students. Our initial findings revealed a significant positive relationship between academic stress and cyberloafing, and a significant negative relationship between academic stress-cyberloafing and self-control, suggesting that low self-control is associated with high academic stress and high cyberloafing activities. This is in line with Zhou et al. ( 2021 ) study that found Individuals with low levels of trait self-control were more susceptible to the impact of daily academic stressors when it came to engaging in cyberloafing. The moderation analysis showed that adding self-control to the relationship between academic stress and cyberloafing changed the dynamics. In this study, self-control moderated the positive relationship between academic stress and cyberloafing by altering the strength, but not the direction of the relationship. For both high and low levels of self-control, the positive relationship between academic stress and cyberloafing was maintained but the strength of the relationship was much higher for low self-control. As such, through cyberloafing, the lower levels of self-control are separated from the drive, discipline, and responsibility found within higher levels of self-control, as discussed in Zettler’s study, further enhanced by the high levels of academic stress, which serves as a blockade to such factors. In addition, through the results, the study can also connect towards the 2021 paper by Liani et al. in which students with stronger self-control would be less likely to engage in cyberloafing or distracting activities. All in all, connecting towards previous studies, this paper shows a more prominent and direct role of self-control as a moderator between cyberloafing and academic stress, further proving its significance amongst students.

This study identified academic stress, fatigue, and self-control as significant factors influencing university students’, in TRNC, cyberloafing behaviors. The research on academic stress and cyberloafing behavior among university students yielded significant findings. Firstly, it established that higher levels of academic stress are positively associated with an increased frequency of cyberloafing activities in the classroom. Secondly, the study revealed that fatigue plays a mediating role in the relationship between academic stress and cyberloafing, indicating its influence on students’ online behaviors. Lastly, the research demonstrated that the link between academic stress and cyberloafing is moderated by self-control, with individuals exhibiting low self-control being more prone to engaging in cyberloafing, particularly when experiencing high levels of academic stress. As such, the importance of identifying the cause of a common action such as cyberloafing, in addition to the mediating and moderating factors of such activities can enhance the solutions projected to prevent cyberloafing, and assist in boosting self-control, decreasing fatigue and academic stress, to further support productive support, especially within the educational field, later on expanding to that of the work field and other communities with similar causes.

Limitations and recommendations

Limitations of this study include the isolation of participants from one university in TRNC, Girne American University, which may restrict the generalizability of our findings. As such, the study recommends further research including participants from different age ranges, educational facilities, and locations. In addition, this study explored the mediating roles of fatigue and the moderating role of self-control through a cross-sectional correlational design, preventing the results from establishing causal relationships between variables. The use of time series analysis and longitudinal studies could overcome this limitation, and is further recommended moving forward. Finally, all the data in our study were collected through self-reporting questionnaires, academic stress, self-control, and fatigue were assessed using scales that measured the participant’s perception of the variables which may lack objectivity, and cyberloafing was assessed by relying on the participants’ self-reported frequency. Future research recommends employing more objective measures, such as monitoring devices, to assess student cyberloafing frequency and duration.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Aghaz A, Sheikh AY (2016) Cyberloafing and job burnout: an investigation in the knowledge-intensive sector. Comput Hum Behav 62:51–60

Article   Google Scholar  

Akbulut Y, Dönmez O, Dursun ÖÖ (2017) Cyberloafing and social desirability bias among students and employees. Comput Hum Behav 72:87–95

Akbulut Y, Dursun ÖÖ, Dönmez O, Şahin YL (2016) In search of a measure to investigate cyberloafing in educational settings. Comput Hum Behav 55:616–625

Alyahya S, Alqahtani A (2022) Cyberloafing in educational settings: a systematic literature review. Int J Interact Mob Technol 16(16):113–141. https://doi.org/10.3991/ijim.v16i16.32285

Askew K, Buckner JE, Taing MU, Ilie A, Bauer JA, Coovert MD (2014) Explaining cyberloafing: the role of the theory of planned behavior. Comput Hum Behav 36:510–519

Barber JS (2011) The Theory of Planned Behaviour: considering drives, proximity and dynamics. Vienna Yearb Popul Res/Vienna Inst Demogr, Austrian Acad Sci 9:31

Bedewy D, Gabriel A (2015) Examining perceptions of academic stress and its sources among university students: the Perception of Academic Stress Scale. Health Psychol Open 2(2):2055102915596714

Article   PubMed   PubMed Central   Google Scholar  

Chen Y, Chen H, Andrasik F, Gu C (2021) Perceived stress and cyberloafing among college students: the mediating roles fatigue and negative coping styles. Sustainability 13(8):4468. https://doi.org/10.3390/su13084468

Conner M, Armitage CJ (1998) Extending the theory of planned behavior: a review and avenues for further research. J Appl Soc Psychol 28(15):1429–1464

Deng Y, Cherian J, Khan NUN, Kumari K, Sial MS, Comite U, Gavurova B, Popp J (2022) Family and academic stress and their impact on students’ depression level and academic performance. Front Psychiatry 13:869337. https://doi.org/10.3389/fpsyt.2022.869337

Doush IA, Alhami I (2018) Evaluating the accessibility of computer laboratories, libraries, and websites in Jordanian Universities and Colleges. Int J Inf Syst Soc Change 9(2):44–60

Dursun OO, Donmez O, Akbulut Y (2018) Predictors of cyberloafing among preservice information technology teachers. Contemp Educ Technol 9(1):22–41. https://doi.org/10.30935/cedtech/6209

Duckworth AL, Taxer JL, Eskreis-Winkler L, Galla BM, Gross JJ (2019) Self-control and academic achievement. Annu Rev Psychol 70:373–399. https://doi.org/10.1146/annurev-psych-010418-103230

Article   PubMed   Google Scholar  

Flanigan AE, Babchuk WA (2015) Social media as academic quicksand: a phenomenological study of student experiences in and out of the classroom. Learn Individ Differ 44:40–45. https://doi.org/10.1016/j.lindif.2015.11.003

Hair J, Hollingsworth CL, Randolph AB, Chong, AYL (2017) An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems 117(3):442–458

Hamrat N, Hidayat DR, Sumantri MS (2019) Dampak stres akademik dan cyberloafing terhadap kecanduan smartphone [The impact of academic stress and cyberloafing on smartphone addiction]. J EDUCATIO: J Pendidik Indones 5(1):13–19. https://doi.org/10.29210/120192324

Hardiani WA, Rahardja E, Yuniawan A (2018) Effect of role conflict and role overload to burnout and its impact on cyberloafing. J Bisnis Strateg 26(2):89–99

Hembrooke H, Gay G (2003) The laptop and the lecture: the effects of multitasking in learning environments. J Comput High Educ 15:46–64. https://doi.org/10.1007/BF02940852

Hibrian H, Baihaqi M, Ihsan H (2022) Relationship between academic stress and cyberloafing behavior among Psychology Department Students at Universitas Pendidikan Indonesia. PSIKOPEDAGOGIA J Bimbing Dan Konseling 10(2):89–93. https://doi.org/10.12928/psikopedagogia.v10i2.17602

Ghani FA, Muslim NA, Rasli MA, Bhaskaran KN, Rashid RE, Kadir SL (2018) Problematic usage of digital technologies at workplace: a study on job stress and cyberloafing behaviour among government servants in Malaysia. Glob Bus Manag Res Int J 10:754

Google Scholar  

Gokcearslan Ş, Mumcu FK, Haşlaman T, Cevik YD (2016) Modelling smartphone addiction: the role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Comput Hum Behav 63:639–649. https://doi.org/10.1016/j.chb.2016.05.091

Khansa L, Barkhi R, Ray S, Davis Z (2017) Cyberloafing in the workplace: mitigation tactics and their impact on individuals’ behavior. Inf Technol Manag, 1–19

Li Q, Ren X, Zhou Z, Wang J (2023) Reciprocal relationships between self-control and self-authenticity: a two-wave study. Front Psychol 14:1207230. https://doi.org/10.3389/fpsyg.2023.1207230

Liang X, Guo G, Gong Q, Li S, Li Z (2022) Cyberloafing to escape from the “Devil”: investigating the impact of abusive supervision from the third-party perspective. Front Psychol 12:722063. https://doi.org/10.3389/fpsyg.2021.722063

Liani L, Baidun AD, Rahmah M (2021) The influence of big five personality trait and self-control on cyberloafing. In Proceedings of the 9th international conference on Cyber and IT Service Management (CITSM), Bengkulu, Indonesia, 2021, pp 1–5, https://doi.org/10.1109/CITSM52892.2021.9588899

Lim VK (2002) The IT way of loafing on the job: cyberloafing, neutralizing and organizational justice. J Organ Behav 23(5):675–694. https://doi.org/10.1002/job.161

Metin-Orta I, Demirutku K (2020) Cyberloafing behaviors among university students and its relation to Hedonistic-Stimulation value orientation, cyberloafing attitudes, and time spent on the Internet. Curr Psychol, 1–12. https://doi.org/10.1007/s12144-020-00932-9

Michielsen HJ, De Vries J, Van Heck GL (2003) Psychometric qualities of a brief self-rated fatigue measure the fatigue assessment scale. J Psychosom Res 54:345–352

Nwakaego FO, Angela OI (2018) Influence of cyberloafing on library and information studies students at the University of Ibadan, Nigeria. J Educ Res Rev 6(3):54–60. https://doi.org/10.30918/AERJ.63.18.039

Olmsted NM, Terry CP (2014) Who’s texting in class? A look at behavioral and psychological predictors. Psi Chi J Psychol Res 19(4):183–192. https://doi.org/10.24839/2164-8204.JN19.4.18

Ozdamli F, Ercag E (2021) Cyberloafing among university students. TEM J 10(1):421–426. https://www.proquest.com/scholarly-journals/cyberloafing-among-university-students/docview/2702220573/se-2

Pallant J, Manual SS (2013) A step by step guide to data analysis using IBM SPSS. Australia: Allen & Unwin. 10(1):1753–6405

Rahmawati DD (2012) Pengaruh self-efficacy terhadap stres akademik pada siswa kelas I rintisan sekolah bertaraf internasional (RSBI) di SMP Negeri 1 Medan [The effect of self-efficacy on academic stress in class I International Standard School Pilots (RSBI) at Public Junior High School 1 Medan]. [Undergraduate Thesis, North Sumatera University]. North Sumatera Utara Digital Archive. https://l24.im/MRb0i

Ravizza SM, Hambrick DZ, Fenn KM (2014) Non-academic internet use in the classroom is negatively related to classroom learning regardless of intellectual ability. Comput Educ 78:109–114. https://doi.org/10.1016/j.compedu.2014.05

Schoemann AM, Boulton AJ, Short SD (2017) Determining power and sample size for simple and complex mediation models. Social Psychological and personality Science, 8(4):379–386

Tabachnick BG, Fidell LS, Ullman JB (2013) Using multivariate statistics vol. 6. Boston, MA: pearson, pp 497–516

Taneja A, Fiore V, Fischer B (2015) Cyber-slacking in the classroom: potential for digital distraction in the new age. Comput Educ 82:141–151. https://doi.org/10.1016/j.compedu.2014.11.009

Tangney JP, Baumeister RF, Boone AL (2004) Self-Control Scale (SCS) [Database record]. APA PsycTests. https://doi.org/10.1037/t19593-000

Twum R, Yarkwah C, Nkrumah IK (2021) Utilisation of the internet for cyberloafing activities among university students. J Digit Educ Technol 1(1):ep2101. https://doi.org/10.21601/jdet/10912

Venkatesh V, Thong JY, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quart, 36(1):157–178. Retrieved on 27 November 2022 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2002388

Yılmaz FGK, Yılmaz R, Ozturk HT, Sezer B, Karademir T (2015) Cyberloafing as a barrier to the successful integration of information and communication technologies into teaching and learning environments. Comput Hum Behav 45:290–298

Yogisutanti G, Hotmaida L, Gustiani Y, Panjaitan SW, Suhat S (2020) Teaching under stress and fatigue: can affect the performance? Health Sci J 12:112–123

Zettler I (2011) Self-control and academic performance: two field studies on university citizenship behavior and counterproductive academic behavior. Learn Individ Differ 21(1):119–123. https://doi.org/10.1016/j.lindif.2010.11.002

Zhang H, Zhao H, Liu J, Xu Y, Lu H (2015) The dampening effect of employees’ future orientation on cyberloafing behaviors: the mediating role of self-control. Front Psychol 6:1482. https://doi.org/10.3389/fpsyg.2015.01482

Zhou B, Li Y, Tang Y, Cao W (2021) An experience-sampling study on academic stressors and cyberloafing in college students: the moderating role of trait self-control. Front Psychol 12:514252. https://doi.org/10.3389/fpsyg.2021.514252

Download references

Author information

Authors and affiliations.

Faculty of Humanities, Girne American University, Girne, Cyprus

Gabriel E. Nweke

Mohammed Bin Rashid School for Communication, American University in Dubai, Dubai, UAE

Yosra Jarrar

Faculty of Arts, Bethlehem University, Bethlehem, Palestine

Ibrahim Horoub

You can also search for this author in PubMed   Google Scholar

Contributions

Gabriel E. Nweke : Conceptualization, Software, Writing—Original Draft Preparation. Yosra Jarrar : Methodology, Investigation, Writing—Review & Editing. Ibrahim Horoub: Investigation, Resources, Data Curation.

Corresponding author

Correspondence to Ibrahim Horoub .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This study received approval from the Research Ethics Committee of Girne American University.

Informed consent

Participants were given written informed consent forms that explained the aim of the study and its significance.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Nweke, G.E., Jarrar, Y. & Horoub, I. Academic stress and cyberloafing among university students: the mediating role of fatigue and self-control. Humanit Soc Sci Commun 11 , 419 (2024). https://doi.org/10.1057/s41599-024-02930-9

Download citation

Received : 28 October 2023

Accepted : 04 March 2024

Published : 15 March 2024

DOI : https://doi.org/10.1057/s41599-024-02930-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Open access
• Published: 15 March 2024

Perceptions of students and faculty on NCAAA-accredited health informatics programs in Saudi Arabia: an evaluative study

• Haitham Alzghaibi 1 , 2  

BMC Medical Education volume  24 , Article number:  296 ( 2024 ) Cite this article

15 Accesses

Metrics details

As the healthcare sector becomes increasingly reliant on technology, it is crucial for universities to offer bachelor’s degrees in health informatics (HI). HI professionals bridge the gap between IT and healthcare, ensuring that technology complements patient care and clinical workflows; they promote enhanced patient outcomes, support clinical research, and uphold data security and privacy standards. This study aims to evaluate accredited HI academic programs in Saudi Arabia.

This study employed a quantitative, descriptive, cross-sectional design utilising a self-reported electronic questionnaire consisting of predetermined items and response alternatives. Probability-stratified random sampling was also performed.

The responses rates were 39% ( n =  241) for students and 62% ( n =  53) for faculty members. While the participants expressed different opinions regarding the eight variables being examined, the faculty members and students generally exhibited a strong level of consensus on many variables. A notable association was observed between facilities and various other characteristics, including student engagement, research activities, admission processes, and curriculum. Similarly, a notable correlation exists between student engagement and the curriculum in connection to research, attrition, the function of faculty members, and academic outcomes.

While faculty members and students hold similar views about the institution and its offerings, certain areas of divergence highlight the distinct perspectives and priorities of each group. The perception disparity between students and faculty in areas such as admission, faculty roles, and internships sheds light on areas of improvement and alignment for universities.

Peer Review reports

Introduction

Health informatics (HI) is a multifaceted field dedicated to the collection, storage, retrieval, and utilisation of health data to enhance healthcare quality [ 1 , 2 , 3 ]. It combines methodologies from information science, computer science, and healthcare to improve healthcare delivery in various ways, such as electronic medical records, imaging, and decision support systems [ 2 ]. Offering bachelor's degree programs in HI is crucial for universities as the healthcare sector becomes increasingly reliant on technology [ 4 , 5 , 6 ]. HI professionals bridge the gap between IT and healthcare, ensuring that technology complements patient care and clinical workflows [ 7 , 8 ]. These experts promote enhanced patient outcomes, support clinical research, and ensure that data security and privacy standards are met. With the growth of telemedicine and global health challenges, these professionals design and manage systems that cater to diverse populations and adapt to constantly evolving technological developments [ 4 , 5 , 6 ]. HI is an interdisciplinary field that combines elements of medicine, IT, management, and social sciences; an HI degree offers graduates a comprehensive understanding of modern healthcare challenges. As the healthcare industry continues its digital transformation, there is an indispensable need for a workforce trained in HI to ensure cost efficiency, address global health crises, and future-proof the healthcare sector for new technological developments [ 7 ]. However, despite the significance of this topic, no research has evaluated accredited HI programs in Saudi Arabia.

Increasing interest in the domain of human–computer interaction (HCI) and the need for individuals who are proficient in this discipline have led to a proliferation of educational prospects, including degrees at various levels [ 1 , 5 , 9 ]. The growing importance of the field led the International Medical Informatics Association (IMIA) to revise the framework of HI and medical informatics education in 2010 with the goal of addressing the educational requirements of a diverse group of healthcare professionals from many different fields, such as medicine, nursing, healthcare management, dentistry, pharmacy, public health, health record administration, and informatics. The HI competences framework was designed to fulfil the needs of specialized initiatives in the fields of biomedical research and HI [ 10 ]. HI education in Saudi Arabia has undergone a significant transformation as the country transitioned into a modernised healthcare system in the late twentieth century [ 11 ]. While the system was initially manual and paper-based, the country quickly recognised the need for digitised healthcare. Specialised HI programs were then established in universities such as King Saud University and King Faisal Specialist Hospital and Research Centre. The Saudi Association for Health Informatics (SAHI) was formed to further facilitate professional collaboration, and several educational institutions now offer comprehensive programs in the field. This highlights Saudi Arabia’s commitment to integrating technology into its healthcare system [ 12 ]. The bachelor of HI degree offered at Saudi universities are four-year programs consisting of courses in healthcare informatics, healthcare administration, and clinical informatics [ 6 ]. During their studies, students also have the opportunity to specialise in a particular field of HI that best represents their interests and helps them achieve their desired career goals. Students gain a strong understanding of information systems and HI as well as how to apply these concepts to healthcare. Finally, Saudi governmental universities are entirely supervised, monitored, and regulated by the Saudi Ministry of Education (MoE) [ 3 , 13 ].

The Saudi National Commission for Academic Accreditation and Assessment (NCAAA) plays a pivotal role in ensuring the quality of higher education in Saudi Arabia, especially concerning bachelor’s degrees [ 14 , 15 , 16 ]. It establishes and maintains quality standards for academic institutions and their programs, offers accreditation to the programs which meet their standards, and continuously assesses institutions to promote ongoing improvement. The NCAAA also provides guidance, resources, and workshops to institutions, aiming to elevate the international recognition of Saudi academic credentials [ 16 , 17 ]. Through its rigorous accreditation and assessment processes, NCAAA assures the public, students, and employers that the education offered by accredited institutions is high quality and relevant to current needs [ 14 , 17 ]. By setting high standards, especially in research and innovation, the NCAAA supports Saudi Arabia's ambition of evolving into a knowledge-based economy and ensures that Saudi graduates remain competitive on the global stage [ 15 , 16 ].

Aim of the study

This study aims to evaluate bachelor’s degrees in HI programs accredited by the NCAAA in Saudi Arabia.

Study objectives

This study measured the level of satisfaction of both students and faculty members, compared students’ and faculty members’ perceptions toward bachelor’s degrees in HI, and determined the level of quality of HI programs accredited by the NCAAA.

This study employed a quantitative, descriptive, cross-sectional design, utilising a self-reported electronic questionnaire consisting of predetermined items and response alternatives [ 18 , 19 , 20 ]. To accomplish the objectives of this study, a self-designed questionnaire was created using Google Forms. The questionnaire used in this study was divided into three sections. The first section outlined the purpose of the study, emphasised the importance of participation, and informed the respondents of their ability to withdraw from the study at any time. Additionally, the section provided information regarding how the respondents’ information would be used and how their confidentiality would be maintained. The second section of the questionnaire gathered demographic data about the participants, including their gender, age, and the name of the university they attended. The third section of the questionnaire comprises eight primary categories. Each category reflects a crucial factor for assessing bachelor’s programs.

These factors were derived from previously published academic work regarding similar topics. Categories included facilities [ 21 , 22 ], students’ involvements [ 23 ], curriculum [ 22 , 24 , 25 ], research [ 22 , 26 ], admission [ 27 , 28 , 29 ], roles of faculty staff [ 22 , 30 , 31 , 32 ], outcome [ 15 , 33 , 34 ], and internship [ 35 , 36 , 37 ]. For a comprehensive list of these aspects, please refer to Appendix 1 . In addition, the factors were selected specifically to match NCAAA criteria [ 15 , 38 ]. The participants were instructed to assess each item using a rating scale ranging from ‘1 = strongly disagree’ to ‘5 = strongly agree’. Despite the distribution of the two data-gathering instruments, these instruments exhibited a high degree of similarity. Students and faculty members completed the same questionnaires with the exception of the demographic information, which had been included to ensure that the survey was suitable for the specific populations being surveyed. Students were asked about their academic level while faculty members were surveyed regarding their professional status.

Data collection instrument validity

The initial instrument underwent a comprehensive evaluation process by expert panels comprising external specialists, such as the academic department head, heads of student affairs and academic departments, and faculty members. The primary objective of the expert panel was to analyse the content of the questionnaire, determine its relevance to the intended population, and assess the clarity and comprehensibility of the questions. In response to the input received, the study incorporated demographic questions regarding the participants’ gender and age. Modifications were also made to the Likert-scale responses, reducing the number of response options from seven to five.

After receiving assessments and input from expert panels, a pilot study was conducted with a limited number of students ( n =  9) and faculty members ( n =  4). These participants were selected based on several factors, including diverse positions, gender, and study level. The objective of the pilot study was to obtain feedback regarding the quality of the questionnaire, including assessments about readability, comprehensiveness, appropriateness, and clarity. Participants were also asked to offer recommendations for enhancing the questionnaire.

Every individual involved in the pilot project successfully completed the initial version of the questionnaire and offered their observations and input on many aspects, such as the method, duration of questionnaire administration, and comprehensibility of the questions. The pilot study’s findings suggested that there was no need to include or exclude any questions. In general, the questionnaire was perceived as relatively clear and straightforward to administer. The user’s text was revised to enhance clarity and readability, with minor spelling, grammatical, and numbering corrections made based on the input. The pilot study was conducted during two weeks in January 2023. The input provided by the volunteers was integrated into the final iteration of the questionnaire as the necessary adjustments were modest.

Data collection process

After both data collection instruments were modified and validated, Google Forms links were sent to the academic department heads at universities that met the inclusion criteria described below. The department heads were asked to share the links with their students and faculty members. The questionnaire was distributed during the week of February 06, 2023. Two reminder emails were sent to the departments heads during the week commencing 20 February 2023 and the week commencing 27 February 2023.

Population and sampling

In this study, the targeted population consisted of all undergraduate students ( n =  614) and faculty members ( n =  85) enrolled in HI programs at Saudi universities that met the study’s inclusion criteria. Probability stratified random sampling were utilized [ 39 , 40 ], so the inclusion criteria were Saudi universities offering HI programs at the bachelor’s degree level with internship programs. Male and female students and faculty members from all academic levels and positions were eligible to participate in the study. Those in medical informatics and biomedical informatics programs were excluded, as were postgraduate HI degree programs, bachelor’s degree in HI programs without internships, and programs that had not yet obtained NCAAA accreditation. A purposive, total population sampling method was used [ 39 , 40 ]. Seven Saudi universities met these inclusion criteria. Using the selected sampling technique, questionnaires were distributed to 614 students and 85 faculty members at these universities.

Data analysis

The questionnaire data were categorised into numerical groupings and subsequently input into IBM SPSS, Version 29. Cronbach's alpha was used to conduct an initial reliability test. Subsequently, an initial descriptive analysis was performed using the data obtained from the questionnaire. Furthermore, inferential statistics were used to ascertain any noteworthy disparities among the groups or associations between variables.

Cronbach’s alpha showed that the data collection instrument was statistically reliable (a = 0.86). The response rates were 39% ( n =  241) for students and 62% ( n =  53) for faculty members.

Table 1 presents a detailed breakdown of the faculty members and students based on various attributes. The majority of faculty members were men (58%, n =  31), but the distribution was more even in students. The highest percentage of students (69%, n =  166) were between 18 and 20, while the faculty had a more dispersed age range.

Table 2 presents a comprehensive overview of the feedback provided on various aspects of a higher educational institution, grouped as admissions, facilities, research, faculty staff roles, curriculum, outcomes, and internships.

Table 3 illustrates the correlation coefficients between different educational factors for the two groups.

Table 4 presents a comparative analysis of the mean scores of various educational variables among students based on their current level of study. This interpretation focuses primarily on significance (sig.), which was provided for each variable to determine the statistically significant differences between groups.

Of the educational variables listed, two showed significant differences between student groups. Curriculum has a p -value of 0.001, indicating a highly significant difference between the groups. Similarly, Outcome stands out with a p -value of 0.000, suggesting an extremely significant variation among the study levels.

Table 5 elucidates a comparative assessment of the mean scores for Admission, Roles of Faculty Staff, Outcome, and Curriculum between faculty members and students in relation to an internship program.

Roles of Faculty Staff and Outcome for students exhibit significant differences with p -values of 0.001 and 0.000, respectively, suggesting notable variations in perceptions between the two groups regarding these aspects of the internship program. These values were well below the 0.05 threshold, underscoring their statistical relevance.

This study aimed to provide a comprehensive examination of accredited HI academic programs in Saudi Arabia. While the literature has rarely expounded on HI in Saudi Arabian universities, especially those accredited by the NCAAA, little is known about HI programs in Saudi Arabian universities, especially bachelor’s degrees.

The assessed criteria included several academic factors, such as student involvement, academic outcomes, and research, as well as logistical factors, including facilities, admission processes, personnel, and internships. Initially, students expressed a high level of satisfaction towards their academic programs. There were similarities between the attitudes expressed by students in this study and those described by Khan et al. [ 41 ]. This outcome is contrary to those of Rawas and Yasmeen [ 42 ] and Al-Natour [ 43 ], who found that students were less satisfied with their academic programs. The results of this study are mostly in accordance with the current literature on how HI has facilitated the electronic management of health information [ 1 , 5 ], whereas the results indicating computational emphasis match the current literature [ 1 ], as HI programs offered in Saudi Arabian universities were found to have varying degrees of satisfaction among students. When looking at individual variables, the results of this study seem to be consistent with other studies that found a very high level of satisfaction among students towards research, the role of faculty members, and facilities [ 44 ].

A positive correlation was observed between college facilities and other academic and logistical characteristics. This finding suggests that students perceive an enhancement in their academic abilities when universities offer improved facilities. This study supports evidence from previous observations (e.g. [ 45 ]). Furthermore, a noteworthy association was observed among the eight factors as well as between each academic variable and each logistic variable. This suggests that the variables are interconnected and cannot be viewed in isolation. Additionally, this observation suggests that alterations in any of the factors could impact the remaining variables, potentially leading to cumulative effects that could be either advantageous or detrimental to the program. This implies that universities must not focus solely on academic brilliance or logistical aspects of their degree programs; instead, they must consider the integration of all factors to achieve a comprehensive level of student satisfaction. These findings match those observed in earlier studies [ 46 , 47 ].

This study also aimed to compare the perceptions of faculty members and students. In general, both faculty members and students had high evaluations for many of the categories presented. For instance, both groups highly regarded the research support and facilities offered by the institution. However, noticeable differences are observed in certain areas. In the Admission category, students rated the statement ‘The period of study in your college is longer than other colleges’, much higher than faculty, indicating that students might feel the duration of their program is longer in comparison to similar programs. However, the faculty members showed more confidence in the balance of admissions between different disciplines.

Second, regarding the Roles of Faculty Staff and Facilities, students consistently gave higher ratings in most areas compared to faculty members. For example, students felt more strongly that the faculty emphasised the use of multiple sources in their curricula and that they linked the curriculum with the reality of society and culture. Interestingly, students also rated the faculty staff’s experience of delivering courses in a simple way higher than the faculty members did themselves. This suggests that students valued and recognised the teaching methods and efforts of the faculty staff.

Lastly, in the Internship category, there was a significant difference in perception regarding the internship duration. While faculty members found the duration to be much shorter (rating it at 2.32), students rated it much higher (3.80), suggesting that they felt it was relatively longer or more adequate. Moreover, both groups highly regarded the internship program's ability to reflect all taught courses in the bachelor’s degree in HI, with students rating it slightly higher. In addition, the results showed that first-year students had higher beliefs about curricula than third-year students, which might be because third-year students have started being introduced to the work field through internships and have realised that the curriculum is fully sufficient for real-world application. This could also explain why first-year students had higher expectations of outcomes than third-year students, as they believed that their undergraduate years would be sufficient to fully equip them for the work field. This finding is contrary to those of previous studies which suggested that internships show no significant differences among students [ 48 , 49 ].

This study offers an in-depth understanding of HI programs in SA by drawing insights from two distinct populations. This introduces a valuable tool for assessing bachelor's degrees, particularly those in the health domain, using a central internship component. Moreover, it can serve as a beneficial guide for educational policymakers, program tutors, and curriculum developers. This study further highlights various indicators that pinpoint the strengths and shortcomings of HI programs. Moreover, a strength of this study is that it includes the perceptions of both students and faculty members. However, this study encountered some limitations, including the use of self-reported methods. While these methods may have been the only accessible tool for data collection, they constitute a potential threat to the internal validity of the study, as Heppner and Wampold [ 50 ] showed. With self-report methods, participants’ responses could be biased, or they may become ashamed and not provide accurate information. For instance, students might show a social desirability bias when asked about the effectiveness of an educational program, and they might exaggerate the benefits of the programs if they felt they were not able to comprehend some of the program courses. In some instances, students might also guess the study’s objectives and provide skewed information that could either confirm or challenge the researcher’s hypothesis.

Suggestions for future research include controlling the independent variables of the study through semi-structured one-on-one interviews that address variables such as student involvement and academic outcomes without ascribing any sense of liability or responsibility to students or staff, which could make it easier for them to provide their honest inputs. An inductive thematic analysis could be introduced in addition to this quantitative cross-sectional study as a mixed-methods research design would provide more insight into both the qualitative and quantitative aspects of the research question. Moreover, future research may need to compare HI programs that are and are not yet accredited by the NCAAA.

Conclusions

This study offers a comprehensive analysis of HI programs accredited by the NCAAA at Saudi Arabian universities, illuminating the perceptions of both faculty members and students. Highlighting the significance of intertwined academic and logistic factors in shaping student satisfaction, this study emphasises the importance of considering both realms for holistic educational success. Notably, the perception disparity between students and faculty in areas such as Admission, Faculty Roles, and Internships sheds light on areas for improvement and alignment. The study also evaluated other factors, including Facilities, Curriculum, Research, Student Involvement, and Outcomes. While the results align with the current literature, the self-reported methodology employed poses inherent biases, potentially affecting the study's internal validity. Recommendations for future work emphasise the adoption of mixed methods, in-depth interviews, and comparisons between accredited and non-accredited HI programs to ensure a richer, multidimensional understanding of HI education in Saudi Arabia.

Availability of data and materials

The datasets used and analysed in the current study are available from the corresponding author upon reasonable request.

Abbreviations

Health Informatics

International Medical Informatics Association

Ministry of Education; SPSS, Statistical Package for Social Sciences

Dalrymple PW. Data, information, knowledge: The emerging field of health informatics. Bull Am Soc Inf Sci Technol. 2011;37(5):41–4.

Article   Google Scholar  

Hasman A, Mantas J. IMIA Accreditation of Health Informatics Programs. Healthc Inform Res. 2013;19(3):154–61.

Article   PubMed   PubMed Central   Google Scholar  

Alkhazim MA. Higher Education in Saudi Arabia: Challenges, Solutions, and Opportunities Missed. High Educ Pol. 2003;16(4):479–86.

Alshammari F. Factors influencing decisions to enroll in the health informatics educational programs. Inform Health Soc Care. 2016;41(2):177–91.

PubMed   Google Scholar  

Bichel-Findlay J, Koch S, Mantas J, Abdul SS, Al-Shorbaji N, Ammenwerth E, et al. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision. Int J Med Informatics. 2023;170:104908.

Almalki M, Jamal A, Househ M, Alhefzi M. A multi-perspective approach to developing the Saudi Health Informatics Competency Framework. Int J Med Informatics. 2021;146:104362.

Monkman H, Mir S, Borycki EM, Courtney KL, Bond J, Kushniruk AW. Updating professional competencies in health informatics: A scoping review and consultation with subject matter experts. Int J Med Informatics. 2023;170:104969.

Zakaria N, Zakaria N, Alnobani O, AlMalki M, El-Hassan O, Alhefzi MI, et al. Unlocking the eHealth professionals’ career pathways: A case of Gulf Cooperation Council countries. Int J Med Informatics. 2023;170:104914.

Jaspers MW, Mantas J, Borycki E, Hasman A. IMIA Accreditation of Biomedical and Health Informatics Education: Current State and Future Directions. Yearb Med Inform. 2017;26(1):252–6.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Mantas J, Ammenwerth E, Demiris G, Hasman A, Haux R, Hersh W, et al. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics. First Revision Methods Inf Med. 2010;49(2):105–20.

Article   PubMed   Google Scholar  

Almalki M, Househ M, Alhefzi M. Developing a Saudi Health Informatics Competency Framework: A Comparative Assessment. Stud Health Technol Inform. 2019;264:1101–5.

Altuwaijri MM. Supporting the Saudi e-health initiative: the Master of Health Informatics programme at KSAU-HS. East Mediterr Health J. 2010;16(1):119.

Article   CAS   PubMed   Google Scholar  

Prokop M. Saudi Arabia: The politics of education. Int Aff. 2003;79(1):77–89.

Al Mohaimeed A, Midhet F, Barrimah I, Saleh MN. Academic accreditation process: experience of a medical college in saudi arabia. Int J Health Sci (Qassim). 2012;6(1):23–9.

Saeed S, Almuhaideb AM, Bamarouf YA, Alabaad DA, Gull H, Saqib M, et al. Sustainable Program Assessment Practices: A Review of the ABET and NCAAA Computer Information Systems Accreditation Process. Int J Environ Res Public Health. 2021;18(23):12691.

Bougherira MR, Elasmar MH. Impact of academic accreditation on teaching and learning: faculty members’ perceptions. J Furth High Educ. 2023;47(2):167–81.

Alkhateeb FM, Arkle S, McDonough SLK, Latif DA. Review of National and International Accreditation of Pharmacy Programs in the Gulf Cooperation Council Countries. Am J Pharm Educ. 2018;82(10):5980.

Bowling A. Research methods in health : investigating health and health services / Ann Bowling. 3rd edition ed. Maidenhead, Berkshire, England: McGraw Hill/Open University Press; 2009.

Dawson C. Introduction to Research Methods: A Practical Guide for Anyone Undertaking a Research Project: How To Books; 2009.

Offredy M, Vickers P. Developing a Healthcare Research Proposal: An Interactive Student Guide: Wiley 2010.

Samara A, Nistrup A, Al-Rammah TY, Aro AR. Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students. Int J Women’s Health. 2015;7:279–86.

Brito RM, Rodríguez C, Aparicio JL. Sustainability in Teaching: An Evaluation of University Teachers and Students. Sustainability. 2018;10(2):439.

Balva D, Page DT, Collardeau F, Gómez Henao JA, Flores-Camacho AL. International Capacity Building in Psychological Science: Reflections on Student Involvement and Endeavors. Trends in Psychology. 2022.

Harris L, Driscoll P, Lewis M, Matthews L, Russell C, Cumming S. Implementing curriculum evaluation: case study of a generic undergraduate degree in health sciences. Assess Eval High Educ. 2010;35(4):477–90.

Gamble AS. Simulation in undergraduate paediatric nursing curriculum: Evaluation of a complex ‘ward for a day’ education program. Nurse Educ Pract. 2017;23:40–7.

Aglen B. Pedagogical strategies to teach bachelor students evidence-based practice: A systematic review. Nurse Educ Today. 2016;36:255–63.

Majdzadeh R, Nedjat S, Keshavarz H, Rashidian A, Eynollahi B, Larijani B, et al. A new experience in medical student admission in Iran. Iran J Public Health. 2009;38(Suppl 1):36–9.

Google Scholar  

de Boer T, Van Rijnsoever FJ. One field too far? Assessment & Evaluation in Higher Education. 2022:1–14.

Ortega KH, Burns SM, Hussey LC, Schmidt J, Austin PN. Predicting success in nurse anesthesia programs: an evidence-based review of admission criteria. Aana j. 2013;81(3):183–9.

Wilson Kasule G, Wesselink R, Noroozi O, Mulder M. The current status of teaching staff innovation competence in Ugandan universities: perceptions of managers, teachers, and students. J High Educ Policy Manag. 2015;37(3):330–43.

Tovar E. The Role of Faculty, Counselors, and Support Programs on Latino/a Community College Students’ Success and Intent to Persist. Community Coll Rev. 2015;43(1):46–71.

Bedggood RE, Donovan JD. University performance evaluations: what are we really measuring? Stud High Educ. 2012;37(7):825–42.

Bhuyan MH, Khan SSA. Assessing and evaluating the course outcomes of electrical circuit course for bachelor of science in electrical and electronic engineering program. Int J Educ Pedagogical Sciences. 2020;14(12):1163–71.

Coates H, Richardson S. An international assessment of bachelor degree graduates&apos; learning outcomes. 2012.

Warinda T. Accounting students’ evaluation of internship experiences from a skills perspective. Int J Asian Soc Sci. 2013;3(3):783–99.

Ebner K, Soucek R, Selenko E. Perceived quality of internships and employability perceptions: the mediating role of career-entry worries. Educ Train. 2021;63(4):579–96.

Phoebe WK. Determinants of internship effectiveness for university students in Hong Kong. Unpublished bachelor’s thesis) Hong Kong Baptist University, Hong Kong. 2010.

NCAAA. national center for academic accreditation and evaluation criteria 2023 [Available from: https://etec.gov.sa/en/ncaaa/services .

Thompson SK. Sampling: Wiley. 2012.

Book   Google Scholar  

Bryman A. Social Research Methods: OUP Oxford. 2012.

Khan SQ, Al-Shahrani M, Khabeer A, Farooqi FA, Alshamrani A, Alabduljabbar AM, et al. Medical students’ perception of their educational environment at Imam Abdulrahman Bin Faisal University, Kingdom of Saudi Arabia. J Family Community Med. 2019;26(1):45–50.

Rawas H, Yasmeen N. Perception of nursing students about their educational environment in College of Nursing at King Saud Bin Abdulaziz University for Health Sciences. Saudi Arabia Med Teach. 2019;41(11):1307–14.

Al-Natour SH. Medical Students’ Perceptions of their Educational Environment at a Saudi University. Saudi J Med Med Sci. 2019;7(3):163–8.

Malechwanzi JM, Lei H, Wang L. Students’ Perceptions and Faculty Measured Competencies in Higher Education. Int J Higher Educ. 2016;5(3):56–69.

El Achi D, Al Hakim L, Makki M, Mokaddem M, Khalil PA, Kaafarani BR, et al. Perception, attitude, practice and barriers towards medical research among undergraduate students. BMC Med Educ. 2020;20(1):195.

Algahtani H, Shirah B, Bukhari H, Alkhamisi H, Ibrahim B, Subahi A, et al. Perceptions and attitudes of different healthcare professionals and students toward interprofessional education in Saudi Arabia: a cross-sectional survey. J Interprof Care. 2021;35(3):476–81.

Aldowsari MK, Al-Ahmari MM, Aldosari LI, Al Moaleem MM, Shariff M, Kamili AM, et al. Comparisons Between Preclinical and Clinical Dental Students’ Perceptions of the Educational Climate at the College of Dentistry, Jazan University. Adv Med Educ Pract. 2021;12(null):11–28.

Alnajjar H, Bayoumy HMM, Rawas H, de Beer J. Assessing the effectiveness of two internship clinical training programs: Impact on the perception of competency enhancement and student satisfaction. Saudi J Health Sci. 2019;8(2):75–80.

Altuwayrib S, Win K. Internship Programs for Undergraduate Health Informatics Students in Saudi Arabia. 2019.

Heppner PP, Wampold BE, Owen J, Thompson, Wang KT. Research Design in Counseling: Cengage Learning. 2015.

Download references

Acknowledgements

Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project

This project was fully funded by Qassim University.

Author information

Authors and affiliations.

Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia

Haitham Alzghaibi

Institute of Health Informatics, University College London, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

H.A. Wrote the entire manuscript.

Corresponding author

Correspondence to Haitham Alzghaibi .

Ethics declarations

Ethics approval and consent to participate.

All methods in this study were performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Qassim University No. 23–32-10. All the participants provided informed consent for participation. In the questionnaire-based study, all participants were informed of the voluntary nature, confidentiality, and aim of the study and the nature of their participation before participating.

Consent for publication

Not applicable.

Competing of interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Alzghaibi, H. Perceptions of students and faculty on NCAAA-accredited health informatics programs in Saudi Arabia: an evaluative study. BMC Med Educ 24 , 296 (2024). https://doi.org/10.1186/s12909-024-05065-2

Download citation

Received : 02 May 2023

Accepted : 16 January 2024

Published : 15 March 2024

DOI : https://doi.org/10.1186/s12909-024-05065-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health informatics
• Bachelor’s in health informatics
• Saudi universities
• Curriculums
• Health information management

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Crit Care Med
• v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

Source of support: Nil

Conflict of interest: None