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Integrated Primary & Secondary Research

2 Pros and Cons of Research

Advantages and Disadvantages of Primary Research

If enough information cannot be found through internal or external secondary data to solve the NPO’s problem, the organization will then proceed to design a study wherein they will collect Primary Data ;  information collected for a particular research question or project.

Primary research often is richer and more directly useful, but it also has its downsides. The term primary research is widely used in Academic Research , Market Research, and Competitive Intelligence.  Primary research aids organizations with obtaining information directly from sources themselves, instead of relying on the research of others.

Displayed in the table below is a detailed pro and cons list on Primary Research. It lists several advantages and disadvantages and provides brief information on each component.

Advantages and Disadvantages of Secondary Research

Like primary research, secondary research offers pros and cons. Similar to the table provided above, lays a table below displaying detailed pros and cons list on Secondary Research. It lists several advantages and disadvantages and provides brief information on each component.

Attribution

This page contains material taken from:

Learning, L. (2020). Introduction to Sociology. Retrieved July 27, 2020, from https://courses.lumenlearning.com/sociology/chapter/research-methods/

Primary Market Research. (2020). Retrieved July 23, 2020, from http://kolibri.teacherinabox.org.au/modules/enboundless/www.boundless.com/business/concepts/primary-market-research-0-8219/index.html

Sagepub (2006). Research in Nonprofit Organizations. Retrieved from https://us.sagepub.com/sites/default/files/upm-binaries/9066_WymerCh3.pdf

Types of Data. (2020). Retrieved July 23, 2020, from https://2012books.lardbucket.org/books/advertising-campaigns-start-to-finish/s08-01-types-of-data.html

New information the organization gathers directly from respondents they interact with, surveys, or alternative research methods.

An Open Guide to Integrated Marketing Communications (IMC) Copyright © 2023 by Andrea Niosi and KPU Marketing 4201 Class of Summer 2020 is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Conducting Research: Advantages and Disadvantages Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Even though some educators see researching as a compulsory task to obtain certain status in academia, it is important for the development of the entire society. Researchers identify issues to be addressed and provide effective solutions to the problems outlined. Conducting research is a certain kind of participation in the ongoing debate on important issues. Admittedly, debate and discussion lead to solutions and ideas which improve practice. Nonetheless, conducting research is also associated with certain limitations. It is possible to focus on three major advantages and three disadvantages of researching to reveal its importance for the development of any area of human activity, especially when it comes to education.

One of the major advantages of conducting research is the chance to participate in a broader discussion of particular issues. With the help of research, people accumulate knowledge and each researcher adds to his/her knowledge (Creswell, 2008). Every educator researches specific issues he/she encounters. It is necessary to note that research is based on data. Therefore, educators do not only share their opinions, but they provide specific data which justify their conclusions or refute some theories. Accumulation of knowledge is of paramount importance for educators as it helps them develop efficient tools to address existing issues and even foresee upcoming problems.

Another advantage of conducting research is that it provides practitioners with particular tools to address their issues. Creswell (2008, p. 4) stresses that research “suggests improvements for practice”. Therefore, educators (as well as practitioners in other fields) may find many effective solutions to their problems or develop new tools to address issues. Admittedly, this positively affects the development of every field as sharing particular tools helps practitioners develop their strategies to address a variety of issues (McKay, 2007). Thus, Koshy (2005) notes that the use of action research enables practitioners (especially novice educators) to outline an issue and develop specific strategies to address it. In other words, educators outline problems and solutions applicable in particular situations.

Another important advantage of conducting research is that it can be generalized and transformed into policies that can be applicable and beneficial in a variety of situations. Creswell (2008) claims that the ongoing debate and several tools provided by practitioners enable policymakers to come up with appropriate policies which will positively affect the entire system. Therefore, it is possible to note that conducting research helps a practitioner be aware of the ongoing debate on issues. It also helps educators develop particular tools to address every issue. Finally, it helps practitioners bring the issue to the fore and help policymakers work out effective strategies which may make a difference.

As has been mentioned above, conducting research is also associated with disadvantages. It is necessary to note that the disadvantages do not belittle the importance of conducting research. However, it is necessary to understand these disadvantages to be able to diminish the negative effects of these limitations on particular research. One of the major disadvantages is the amount of time necessary for conducting research. Creswell (2008) claims that accumulating knowledge often takes a lot of time. Some research requires years to obtain the necessary amount of information to come up with a plausible solution. This is a significant disadvantage as the contemporary world is rapidly changing and it is important to react quickly to the changing circumstances. Data analyzed and generalized may become outdated and inapplicable.

Another disadvantage is that any research is associated with certain limitations. Thus, the number of participants and amount of information gathered may be insufficient. This may lead to implausible conclusions and erroneous findings (McKay, 2007). Admittedly, it is almost impossible to obtain comprehensive data within a single survey. It is necessary to conduct several surveys to acquire the necessary amount of data. It is necessary to note that some researchers do not understand the importance of thorough analysis and comprehensive data and tend to obtain quite an insufficient amount of information. This explains the number of surveys that provide contradictory data. This adds controversy to the research. Admittedly, this negatively affects the accumulation and sharing of knowledge.

Apart from the analysis of insufficient data, conducting research is also associated with certain biases. Creswell (2008) states that some researchers may put inadequate or vague questions. This distorts data as participants tend to understand such questions differently. Some researchers may also reveal their findings inarticulately which also leads to a lot of controversies (Creswell, 2008). Researchers may use inappropriate statistical tools which also results in distorted data and controversial conclusions (Opdenakker, 2006). Besides, it is impossible to ensure unbiased research as researchers have a certain background that affects the way they perceive information. More so, some researchers do not focus on the unbiased presentation of data, which also adds controversy to the research.

In conclusion, I would like to add that all these advantages and disadvantages are manifested in every educator’s life as well as on a global scale. Being a practitioner, I understand that research will help me gain knowledge on a variety of topics. It will also help me find new ways to handle problems. I will be able to learn more about solutions found by others. This will enable me to adjust these solutions to particular situations. Admittedly, this will positively affect my development as a practitioner. However, I also understand that I will face certain difficulties as conducting research has several disadvantages. I will have to invest a lot of time as conducting research is a very time-consuming activity. I will need to collect a sufficient amount of data to be able to analyze information and come to some conclusions. I will also be attentive when using data revealed by other researchers as I will need to evaluate each survey. I will have to be attentive when implementing my research and analyzing data as I must remain unbiased. Thus, understanding the importance of conducting research and its limitations, I will be able to develop as a practitioner and contribute to the development of certain areas.

Reference List

Creswell, J.W. (2008). Educational research. Upper Saddle River, NJ: Pearson Education, Inc.

Koshy, V. (2005). Action research for improving practice: A practical guide. Upper Thousand Oaks, CA: Paul Chapman Publishing.

McKay, C.E. (2007). Evidence based practices in mental health: Advantages, disadvantages, and research considerations. CMHSR, 4 (5). Web.

Opdenakker, R. (2006). Advantages and disadvantages of four interview techniques in qualitative research. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 7 (4). Web.

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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Secondary Research Advantages, Limitations, and Sources

Summary: secondary research should be a prerequisite to the collection of primary data, but it rarely provides all the answers you need. a thorough evaluation of the secondary data is needed to assess its relevance and accuracy..

5 minutes to read. By author Michaela Mora on January 25, 2022 Topics: Relevant Methods & Tips , Business Strategy , Market Research

Secondary research is based on data already collected for purposes other than the specific problem you have. Secondary research is usually part of exploratory market research designs.

The connection between the specific purpose that originates the research is what differentiates secondary research from primary research. Primary research is designed to address specific problems. However, analysis of available secondary data should be a prerequisite to the collection of primary data.

Advantages of Secondary Research

Secondary data can be faster and cheaper to obtain, depending on the sources you use.

Secondary research can help to:

• Answer certain research questions and test some hypotheses.
• Formulate an appropriate research design (e.g., identify key variables).
• Interpret data from primary research as it can provide some insights into general trends in an industry or product category.
• Understand the competitive landscape.

Limitations of Secondary Research

The usefulness of secondary research tends to be limited often for two main reasons:

Lack of relevance

Secondary research rarely provides all the answers you need. The objectives and methodology used to collect the secondary data may not be appropriate for the problem at hand.

Given that it was designed to find answers to a different problem than yours, you will likely find gaps in answers to your problem. Furthermore, the data collection methods used may not provide the data type needed to support the business decisions you have to make (e.g., qualitative research methods are not appropriate for go/no-go decisions).

Lack of Accuracy

Secondary data may be incomplete and lack accuracy depending on;

• The research design (exploratory, descriptive, causal, primary vs. repackaged secondary data, the analytical plan, etc.)
• Sampling design and sources (target audiences, recruitment methods)
• Data collection method (qualitative and quantitative techniques)
• Analysis point of view (focus and omissions)
• Reporting stages (preliminary, final, peer-reviewed)
• Rate of change in the studied topic (slowly vs. rapidly evolving phenomenon, e.g., adoption of specific technologies).
• Lack of agreement between data sources.

Criteria for Evaluating Secondary Research Data

Before taking the information at face value, you should conduct a thorough evaluation of the secondary data you find using the following criteria:

• Purpose : Understanding why the data was collected and what questions it was trying to answer will tell us how relevant and useful it is since it may or may not be appropriate for your objectives.
• Methodology used to collect the data : Important to understand sources of bias.
• Accuracy of data: Sources of errors may include research design, sampling, data collection, analysis, and reporting.
• When the data was collected : Secondary data may not be current or updated frequently enough for the purpose that you need.
• Content of the data : Understanding the key variables, units of measurement, categories used and analyzed relationships may reveal how useful and relevant it is for your purposes.
• Source reputation : In the era of purposeful misinformation on the Internet, it is important to check the expertise, credibility, reputation, and trustworthiness of the data source.

Secondary Research Data Sources

Compared to primary research, the collection of secondary data can be faster and cheaper to obtain, depending on the sources you use.

Secondary data can come from internal or external sources.

Internal sources of secondary data include ready-to-use data or data that requires further processing available in internal management support systems your company may be using (e.g., invoices, sales transactions, Google Analytics for your website, etc.).

Prior primary qualitative and quantitative research conducted by the company are also common sources of secondary data. They often generate more questions and help formulate new primary research needed.

However, if there are no internal data collection systems yet or prior research, you probably won’t have much usable secondary data at your disposal.

External sources of secondary data include:

• Published materials
• External databases
• Syndicated services.

Published Materials

Published materials can be classified as:

• General business sources: Guides, directories, indexes, and statistical data.
• Government sources: Census data and other government publications.

External Databases

In many industries across a variety of topics, there are private and public databases that can bed accessed online or by downloading data for free, a fixed fee, or a subscription.

These databases can include bibliographic, numeric, full-text, directory, and special-purpose databases. Some public institutions make data collected through various methods, including surveys, available for others to analyze.

Syndicated Services

These services are offered by companies that collect and sell pools of data that have a commercial value and meet shared needs by a number of clients, even if the data is not collected for specific purposes those clients may have.

Syndicated services can be classified based on specific units of measurements (e.g., consumers, households, organizations, etc.).

The data collection methods for these data may include:

• Surveys (Psychographic and Lifestyle, advertising evaluations, general topics)
• Household panels (Purchase and media use)
• Electronic scanner services (volume tracking data, scanner panels, scanner panels with Cable TV)
• Audits (retailers, wholesalers)
• Direct inquiries to institutions
• Clipping services tracking PR for institutions
• Corporate reports

You can spend hours doing research on Google in search of external sources, but this is likely to yield limited insights. Books, articles journals, reports, blogs posts, and videos you may find online are usually analyses and summaries of data from a particular perspective. They may be useful and give you an indication of the type of data used, but they are not the actual data. Whenever possible, you should look at the actual raw data used to draw your own conclusion on its value for your research objectives. You should check professionally gathered secondary research.

Here are some external secondary data sources often used in market research that you may find useful as starting points in your research. Some are free, while others require payment.

• Pew Research Center : Reports about the issues, attitudes, and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis, and other empirical social science research.
• Data.Census.gov : Data dissemination platform to access demographic and economic data from the U.S. Census Bureau.
• Data.gov : The US. government’s open data source with almost 200,00 datasets ranges in topics from health, agriculture, climate, ecosystems, public safety, finance, energy, manufacturing, education, and business.
• Google Scholar : A web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.
• Google Public Data Explorer : Makes large, public-interest datasets easy to explore, visualize and communicate.
• Google News Archive : Allows users to search historical newspapers and retrieve scanned images of their pages.
• Mckinsey & Company : Articles based on analyses of various industries.
• Statista : Business data platform with data across 170+ industries and 150+ countries.
• Claritas : Syndicated reports on various market segments.
• Mintel : Consumer reports combining exclusive consumer research with other market data and expert analysis.
• MarketResearch.com : Data aggregator with over 350 publishers covering every sector of the economy as well as emerging industries.
• Packaged Facts : Reports based on market research on consumer goods and services industries.
• Dun & Bradstreet : Company directory with business information.

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Reading on Paper Versus Screens: What’s the Difference?

• Published 28 Jul 2020
• Author Kerry Benson
• Source BrainFacts/SfN

During the coronavirus pandemic, students worldwide shifted from the classroom to remote, online learning. Many swapped hard copy textbooks and worksheets for websites and other digital resources. Digital books have been with us for a decade — but how well are we absorbing it all?

Turns out print is easier to comprehend than digital text.

“[Print reading] is kind of like meditation — focusing our attention on something still,” says Anne Mangen, a literacy professor at the University of Stavanger in Norway. “And it’s a whole different kind of immersion than responding to [digital] stimuli. I think it’s healthy for us as human beings to sit down with something that doesn’t move, ping, or call on our attention.”

Print is visually less demanding than digital text. It provides spatial and tactile cues to help readers process words on a page. Mindset may also be a factor. If people associate screen time with casual web-surfing they may rush through without fully absorbing the text.

Do you think you’re the exception? Most people do. Studies found digital reading breeds overconfidence.

“We read digital [text] more quickly, [so] we think we must understand it better,” explains Lauren Singer Trakhman, who studies reading comprehension at the University of Maryland, College Park. “It’s one of the best parts of our digital world — everything is at our fingertips and we can get the headlines in a second — but it may also be one of the pitfalls. Everything’s so quick and accessible that we may not be truly digesting [what we read] anymore.”

Both scientists agree digital is fine to scan news headings for main ideas, but longer, complicated texts are best read in print, especially to retain the details.

TL;DR: Digital Reading Equals Shallower Processing

In 2016, Singer Trakhman examined undergraduates’ reading comprehension after they read digital and print versions of articles. Format didn’t affect their grasp of the main idea, but students missed details when reading on screens.

Digital reading impairs comprehension, particularly for longer, more complex texts, says Mangen. This may be because of the shallowing hypothesis — constant exposure to fast-paced, digital media trains the brain to process information more rapidly and less thoroughly.

“There’s not much [neuroscientific research] on the reading of actual texts,” Mangen says. However, existing research does offer some clues. In a 2009 study , the marketing research company Millward Brown found the brain processes physical and digital materials differently. Participants viewed advertisements on a screen and on a printed card while undergoing an fMRI scan. Print materials were more likely to activate the medial prefrontal cortex and cingulate cortex , both involved in processing emotions. Reading print also generated more activity in the parietal cortex , which processes visual and spatial cues.

Keep Scrolling or Turn the Page?

Scrolling through digital text may impair comprehension by creating spatial challenges. A 2017 study found participants’ reading comprehension suffered when they scrolled through a comic book’s individual panels instead of seeing them all at once. When we read, our brains construct a cognitive map of the text, like recalling that a piece of information appeared near the top, left-hand page of a book. But imagine drawing a map of something with constantly moving landmarks, like a webpage. It’s harder to map words that aren’t in a fixed location, because we lose important “visual placeholders,” says Singer Trakhman.

Scrolling demands more from our working memory, she adds. “In our working memory, we can hold about seven items at a time, so the goal when reading is to take away as many demands as possible. When we have to remember what we just read and we don’t have spatial [cues] to help, that’s taking some of our bandwidth.”

In addition, the LED screens’ constant flickering glow creates more work for our eyes, causing visual and mental fatigue.

However, e-readers, like Kindles, don’t require scrolling and reduce eyestrain with e-ink technology. Those are likely superior to other digital-text formats, Mangen says. But they lack an important aspect of the reading experience: turning the page.

In one of Mangen’s studies , participants read a story either on a Kindle or in print and then underwent comprehension tests. The texts were identical, but Kindle readers pressed a button to progress through the book, while print readers turned pages. Print readers were more likely to accurately recall the story’s chronological order. Mangen says this may be because print provides sensorimotor cues that enhance cognitive processing. When holding a book, we receive reminders of how many pages we’ve read and how many remain. We can flip pages to reread text as needed. Some research suggests we process information more effectively when we recruit multiple senses, and multiple brain areas, during task learning — seeing the words, feeling the weight of the pages, and even smelling the paper.

What Happens Next?

Instead of getting better at digital reading, we may be getting worse. A study examining reading comprehension research between 2000 and 2017 indicates it’s harder to comprehend digital text. The researchers found print’s advantages are greater now than in 2000. In other words, this digital-reading problem isn’t going away.

“This [finding may] have to do with the shallowing hypothesis,” Mangen says. “The habits that we acquire when we read on screens are spilling over, and we’re trying to cope by reading faster and more superficially.”

Mangen and Singer Trakhman agree we shouldn’t ditch digital reading; instead we should consider the situation when choosing our reading medium.

“I’ll never say that everyone should be reading print all the time,” says Singer Trakhman. “People are always shocked to hear that I have a Kindle, and I love my Kindle. But I only use it when I’m reading for pleasure.”

To retain on-screen text information, Singer Trakham and Mangen suggest slowing down and handwriting main takeaways. (Typing works, but handwriting is likely a superior memory tool.) 

When you need a break from the digital world, don’t underestimate the power of paper and ink. Consider turning off your electronic devices, getting a book, and curling up to turn the page.

About the Author

Kerry Benson

Kerry Benson is a writer and neuroscience enthusiast who received a neuroscience degree from Connecticut College in 2016 and a master’s in science writing from Johns Hopkins University in 2018.

CONTENT PROVIDED BY

BrainFacts/SfN

Discussion Questions

1) What are the advantages of print reading over digital?

2) What areas of the brain respond when we read print text?

3) Why do researchers think it’s getting harder to comprehend digital text?

Delgado, P., Vargas, C., Ackerman, R., & Salmerón, L. (2018). Don’t throw away your printed books: A meta-analysis on the effects of reading media on reading comprehension. Educational Research Review, 25 , 23–38. doi: 10.1016/j.edurev.2018.09.003

Garland, K. J., & Noyes, J. M. (2004). CRT monitors: Do they interfere with learning? Behaviour & Information Technology, 23 (1), 43–52. doi: 10.1080/01449290310001638504

Hou, J., Rashid, J., & Lee, K. M. (2017). Cognitive map or medium materiality? Reading on paper and screen. Computers in Human Behavior, 67 , 84–94. doi: 10.1016/j.chb.2016.10.014

Lauterman, T., & Ackerman, R. (2014). Overcoming screen inferiority in learning and calibration. Computers in Human Behavior, 35 , 455–463. doi: 10.1016/j.chb.2014.02.046

Mangen, A., Olivier, G., & Velay, J.-L. (2019). Comparing Comprehension of a Long Text Read in Print Book and on Kindle: Where in the Text and When in the Story? Frontiers in Psychology, 10 , 38. doi: 10.3389/fpsyg.2019.00038

Mangen, A., Walgermo, B. R., & Brønnick, K. (2013). Reading linear texts on paper versus computer screen: Effects on reading comprehension. International Journal of Educational Research, 58 , 61–68. doi: 10.1016/j.ijer.2012.12.002

Mayer, K. M., Yildiz, I. B., Macedonia, M., & von Kriegstein, K. (2015). Visual and Motor Cortices Differentially Support the Translation of Foreign Language Words. Current Biology, 25 (4), 530–535. doi: 10.1016/j.cub.2014.11.068

Millward Brown Case Study—Using Neuroscience to Understand. (2009). Retrieved from https://www.millwardbrown.com/docs/default-source/insight-documents/case-studies/MillwardBrown_CaseStudy_Neuroscience.pdf

Singer Trakhman, L., & Alexander, P. (2016). Reading Across Mediums: Effects of Reading Digital and Print Texts on Comprehension and Calibration. The Journal of Experimental Education . doi: 10.1080/00220973.2016.1143794

Smoker, T. J., Murphy, C. E., & Rockwell, A. K. (2009). Comparing Memory for Handwriting versus Typing. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 53 (22), 1744–1747. doi: 10.1177/154193120905302218

Yan, Z., Hu, L., Chen, H., & Lu, F. (2008). Computer Vision Syndrome: A widely spreading but largely unknown epidemic among computer users. Including the Special Issue: Internet Empowerment, 24 (5), 2026–2042. doi: 10.1016/j.chb.2007.09.004

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Peer-reviewed

Research Article

Disadvantages in preparing and publishing scientific papers caused by the dominance of the English language in science: The case of Colombian researchers in biological sciences

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Catalan Philology, University of Barcelona, Barcelona, Spain, Department of Integrative Biology, University of California, Berkeley, California, United States of America, Museum of Vertebrate Zoology, University of California, Berkeley, California, United States of America

• Valeria Ramírez-Castañeda

• Published: September 16, 2020
• https://doi.org/10.1371/journal.pone.0238372
• Peer Review
• Reader Comments

The success of a scientist depends on their production of scientific papers and the impact factor of the journal in which they publish. Because most major scientific journals are published in English, success is related to publishing in this language. Currently, 98% of publications in science are written in English, including researchers from English as a Foreign Language (EFL) countries. Colombia is among the countries with the lowest English proficiency in the world. Thus, understanding the disadvantages that Colombians face in publishing is crucial to reducing global inequality in science. This paper quantifies the disadvantages that result from the language hegemony in scientific publishing by examining the additional costs that communicating in English creates in the production of articles. It was identified that more than 90% of the scientific articles published by Colombian researchers are in English, and that publishing in a second language creates additional financial costs to Colombian doctoral students and results in problems with reading comprehension, writing ease and time, and anxiety. Rejection or revision of their articles because of the English grammar was reported by 43.5% of the doctoral students, and 33% elected not to attend international conferences and meetings due to the mandatory use of English in oral presentations. Finally, among the translation/editing services reviewed, the cost per article is between one-quarter and one-half of a doctoral monthly salary in Colombia. Of particular note, we identified a positive correlation between English proficiency and higher socioeconomic origin of the researcher. Overall, this study exhibits the negative consequences of hegemony of English that preserves the global gap in science. Although having a common language is important for science communication, generating multilinguistic alternatives would promote diversity while conserving a communication channel. Such an effort should come from different actors and should not fall solely on EFL researchers.

Citation: Ramírez-Castañeda V (2020) Disadvantages in preparing and publishing scientific papers caused by the dominance of the English language in science: The case of Colombian researchers in biological sciences. PLoS ONE 15(9): e0238372. https://doi.org/10.1371/journal.pone.0238372

Editor: Emmanuel Manalo, Kyoto University, JAPAN

Received: March 11, 2020; Accepted: August 5, 2020; Published: September 16, 2020

Copyright: © 2020 Valeria Ramírez-Castañeda. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files. Including complete survey questions and results.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

At the same time that scientific articles became the measure of scientific productivity, English was imposed as the language of science, culture, and the global economy [ 1 ]. As a consequence, today 98% of publications in science are written in English, especially in the areas of natural and basic sciences, establishing English as the lingua franca of science [ 1 ]. This creates a disadvantage for scientists with English as a Foreign Language (EFL) because they must publish complex texts in a foreign language to advance their careers [ 2 ]. This disadvantage gives rise to global inequalities, especially in countries where the majority of the population receives minimal English training and bilingualism with English is very low [ 3 ]. Thus, English proficiency and socioeconomic level influence scientific success, access to knowledge and expatriation, among others.

One of the most important goals for modern society is to increase scientific production from Africa, Latin America, Middle East, and developing Asia. There is a strong correlation among English proficiency, economic development, and technological innovation in terms of number of articles, number of researchers and research and development expenditure [ 4 ]. Therefore, the prevalence of the English language in the sciences deepens the inequality in knowledge production between countries with high and low English proficiency [ 5 ], maintaining the gap in scientific production between the countries of the global south or peripheral and the countries of the global north (include the G8 countries and Australia), reducing the individual scientific contributions of EFL scientists [ 6 ]. Together these factors limit the advancement of the broad scientific communities within those countries [ 7 ].

Numerous studies have identified the use of English in academia as a source of inequality and segregation in science [ 8 – 12 ]. These inequities affect the scientific community at multiple levels. In local communities of EFL countries, scientific thinking is harmed, particularly in higher education, as learning depends on cultural attitudes derived from the native language spoken by the students, and science becomes alien to their own experiences [ 13 – 15 ]. Diversity in language promotes diversity in thinking, affecting creative process and imagination; thus, the maintenance of multilingualism in science could have an impact on scientific knowledge in itself [ 14 ].

Local journals are a refuge for communication of scientific research in languages other than English, nevertheless they are often perceived as low-quality, since the most important research work is often reserved for international journals. Therefore, readers with language barriers only have access to limited studies that the researchers consider not complete, important or broad enough to be published in an international journal. Local readers often are unaware of the most significant research being conducted in their region, which has resulted in a void in information important for political decision making, environmental policies, and conservation strategies [ 16 – 18 ]. In addition, despite the importance of local knowledge, the professional success of a scientist correlates to a greater extent with their "internationalization". This constant pressure could be influencing academic migration, known as "brain drain". English learning is one of the pressure factors of migration, as it is more difficult to achieve upper English proficiency for scientists who remain in EFL countries [ 15 , 19 , 20 ].

In periphery countries there is a strong relationship between English proficiency and socioeconomic origin, thus it is important to understand the publishing costs associated with the socioeconomic origin of researchers. Among Latin America, Colombia is the second most unequal territory: in 2018 it invested only 0.24% of its GDP (Sweden investment was 2.74% of its GDP) in science, technology and innovation [ 21 ], and it has one of the lowest levels of English proficiency among the world rankings [ 4 ]. In addition, for 2019, Colombia had only 58 researchers per million inhabitants [ 22 , 23 ]. This study aims to determine if Colombian doctoral students of natural sciences face disadvantages when publishing scientific articles in English, compared to publications in their first language, and to quantify the extra work that these scientists put into writing, reading, and presenting their work in English. In addition, this study examines the impact of socioeconomic background on English proficiency and the costs it generates when publishing.

Materials and methods

In order to determine the costs of publishing in English experienced by Colombian researchers in biological sciences, 49 to academics were surveyed. These researchers completed their PhDs or are enrolled in doctoral studies and are attempting to publish. They participated in the “Implications of language in scientific publications” survey containing 44 questions in Spanish language ( S3 and S4 Files). This survey was available for two months and shared directly to researchers and on Twitter under the hashtag “#CienciaCriolla” (used between Colombian researchers). Responses were anonymous. It must be mention that the researcher’s demography in Colombia is gender, ethnic, and socioeconomic biased. Only 30.21% of natural science researchers are women [ 24 ], researchers come primarily from big cities [ 25 ], and undergrad students come mainly from middle and high socioeconomic classes [ 26 ]. Therefore, it would not have been possible to completely control for bias in who took the survey. It must be also recognized that without specific numbers for total Colombian researchers in biological sciences, 49 may not be a representative sample size from which to draw accurate statistical inferences.

Additionally, the prices offered by prestigious scientific publishers for translation (Spanish to English) and editing of scientific texts were searched to measure the economic impact in relation to a Ph.D. student salary in Colombia [ 27 – 31 ].

Survey construction

The main survey of this work, entitled “Implications of language in scientific publications,” has 44 questions divided into three sections: basic data, writing articles in English, and learning English ( S3 and S4 Files). This survey sought for the most quantitative approach as possible, however, each question is inevitably under some degree subjectivity due to human interpretation. The responses obtained were grouped for statistical analysis ( Table 1 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0238372.t001

Statistical analysis

Statistical analyses were performed in R v.3.6.1 [ 35 ] and data were plotted with the ggplot package [ 36 ]. To compare reading and writing between English and Spanish, time investment and the level of anxiety in conferences participation, an ANOVA was performed ( aov in package ‘stats’ v3.5.3). The margin of error was calculated with 95% confidence. An Analysis of Principal Components (PCA) was performed using the variables contained in the “English proficiency” and “Socioeconomic data” groups for reducing redundancy in the variables ( PCA in package FactorMiner v2.2). The proportion of variance explained by each principal component was reviewed, and only the first principal component was retained for each dataset, as it described 51% and 62% correspondingly of the total variation. Subsequently, a linear regression was executed with the intention of comparing these two variables, English proficiency PC1 vs socioeconomic status PC1 (using lm in package ‘stats’ v3.5.3).

Editing and translation service costs

In order to visualize the prices of English editing and translation services for scientific texts, information was sought in five of the most relevant scientific publishers [ 27 – 31 ]. The information and costs of these services are public and can be obtained through the web pages of publishers. All data were taken with respect to prices for a text of 3000 words, as that is the average length of a scientific article; searches were performed in October 2018.

These publishers offer two types of editing services, a three-day service (premium) and a one-week service (standard); both prices were used for the analysis. Only the prices for Spanish—English translations were used. Finally, these prices were compared with an average doctoral salary in Colombia [ 25 ], 947 US dollars or 3 million Colombian pesos (1 US dollar = 3.166 Colombian pesos, exchange price on January 31, 2019).

A total of 49 responses were obtained from Colombian doctoral students or doctorates in biological sciences whose first language is Spanish. From Colombians’ surveyed 92% (sd = 0.272) of their published scientific articles are in English and only 4% (sd = 0.2) of their publications were in Spanish or Portuguese. In addition, 43.5% of the doctoral students stated at least one rejection or revision of their articles because of the English grammar.

With regards to time investment, there was a significant increase in the time invested writing a scientific article in English in comparison to Spanish for survey participants ( Fig 1 ). The process of writing in Spanish takes on average 114.57 (sd = 87.77) labor hours, while in English, 211.4 (sd = 182.6) labor hours. On average, these scientists spend 96.86 labor hours more writing in English. However, 81.2% of the doctoral students stated that they prefer to write directly in English in comparison to writing in Spanish and then translating into English.

An ANOVA analysis was performed to compare the variables obtaining an F-value = 7.095 and p-value = 0.00951 **. The dotted line represents labor hours per month.

https://doi.org/10.1371/journal.pone.0238372.g001

The need for editing or translation of scientific texts is widespread among Colombian doctoral students. Among the respondents, 93.9% have asked for favors to edit their English and 32.7% have asked for translation favors. Regarding the use of paid services, 59.2% have paid for editing their articles and 28.6% have paid for a translation.

The Premium editing total cost and the standard translation cost represent almost a half of an average doctoral monthly salary in Colombia ( Fig 2 ).

The Y axis is the price of the service in US dollars, the X axis represents the type of service, the standard or premium service corresponds to the delivery days. The dotted line represents an average Ph.D salary in Colombia ($ 947).

https://doi.org/10.1371/journal.pone.0238372.g002

Reading comprehension is also affected by the language of the text ( Fig 3 ). However, only 18% of respondents prefer to read scientific articles in Spanish than in English. On the other hand, neither the interpretation of figures nor the understanding of scientific terminology is affected by the reading language.

A Poisson regression was used to analyze these discrete ordinal variants (Qualification from 1 to 5). A Chi-squared test was performed between languages for each category: interpretation of figures (Z-value = 0.756, Pr (Chi) = 0.09754), understanding of scientific terminology (z-value = 0.143, Pr (Chi) = 0.4619) and reading comprehension (z-value = 1.427, Pr (Chi) = 0.01209 *).

https://doi.org/10.1371/journal.pone.0238372.g003

To analyze the difficulty of writing scientific articles in two languages, survey participants were also asked how they found it difficult to write different sections of articles: introduction, methods, results, discussion, and conclusions. In all cases, survey participants found the discussion was the most difficult section to write, while the methods were perceived as "easier" ( Fig 4 ). Overall, all sections except methods are perceived as significantly "more difficult" to write in English than in the participant’s first language.

A Poisson regression and Chi-square test was carried out: Introduction (z-value = 9.325, Pr (Chi) = 0.0158 *), methods (z-value = 3.046, Pr (Chi) = 0.07057), results (z-value = 4.899, Pr (Chi) = 0.04397 *), discussion (z-value = 11.732, Pr (Chi) = 0.02384 *), and conclusion (z-value = 7.688, Pr (Chi) = 0.03956 *).

https://doi.org/10.1371/journal.pone.0238372.g004

With regard to the use of English in oral presentations at international events and conferences, 33% of respondents stated that they have stopped attending due to the mandatory use of English in oral presentations. Additionally, greater anxiety was perceived when presenting papers orally in English than in Spanish ( Fig 5 ).

A Poisson regression was used to analyze discrete ordinal variants (Anxiety level from 1 to 5). A Chi-square test was carried out (z-value = 8,882, Pr (Chi) = 0.005419 **).

https://doi.org/10.1371/journal.pone.0238372.g005

In order to determine whether or not the socioeconomic origin of doctoral students affects their proficiency in English and in turn increases the costs of publishing in English, an analysis of principal components was used reduce survey data related to socioeconomic background or English proficiency into single variables because both represent more than the 50% of the whole variance. For the following analyzes: 1) English proficiency is represented by PC1_English_proficiency, which explains 51% of the variance of the survey variables that are related to this subject (see methods ), 2) the socioeconomic status is represented by PC1_Socioeconomic_status, which represents 62% of the variance of the variables of the survey that were related to this denomination (see methods ). The socioeconomic status explains 15% of the English proficiency of researchers ( Fig 6 ), which means that family and economic resources are partly translated into more proficient English.

Principle components representing socioeconomic status and English proficiency are significantly correlated (R2 = 0.1548, adjusted R2 = 0.1368, F = 8.605, p-value = 0.005168 **).

https://doi.org/10.1371/journal.pone.0238372.g006

Many of the factors relating to publishing in English assessed in our study represent substantial costs in time, finances, productivity, and anxiety to Colombian researchers. Interestingly, the researchers appear to prefer to read and write articles in English and the scientific terminology do not represent an additional cost for Colombian researchers. In addition, a correlation between the socioeconomic status and English proficiency was found, suggesting an intersectional effect of language in science. These results can be extrapolated to understand costs of the English hegemony to all South American researchers, that in part contributes to a global gap between native English-speaking scientists (NES) and EFL scientists. This gap makes apparent the necessity of recognizing and protecting multilingualism in science. Although having common language is important for science communication, this effort should involve different actors in the research community and not only EFL researchers’ effort.

Our results show that several factors could lead to disadvantages of EFL researchers. The time investment in writing an article in English, for example, increases on average by 96.86 labor hours. This variable was not directly measured; it is based on the subjective perception of time of each person. However, as Guardiano and collaborators [ 37 ] suggest, this extra cost affects the time spent on scientific tasks, decreasing the scientific productivity of researchers. Regarding the economic costs, between 50% and 30% of respondents have hired services to correct or translate scientific texts. To contextualize the cost of these services, a doctoral student should invest one-quarter to one-half doctoral monthly salary per article. It should be taken into account that scholarships and financing opportunities for doctoral students in the country are scarce [ 38 ], and not all of them have access to the forgivable loans provided by governmental institutions. More than 90% of researchers have asked for English-editing favors, but favors are unpaid labor that may have subsequent costs. The cost of this favor particularly leans on the weakest in the relationship, in this case, the EFL researchers because their career depends on publishing in a second language. Therefore, ensuring a permanent source of “favors” is essential for an EFL researcher that is willing to negotiate for “help” by reinforcing dependence with research groups or scientists in NES countries [ 8 ]. Romero-Olivares [ 39 ] exemplified this point by showing a reviewer comment “The authors need a native English-speaking co-author to thoroughly revise the grammar of this manuscript.”, or as Ordoñez-Matamoros et al [ 40 ] mention for Colombian researchers “co-authoring with partners located in foreign countries tend to publish their work in journals of higher impact factor and receive more citations per article than those not co-authoring with partners located overseas”.

Around 80% of the respondents prefer to read and write scientific content directly in English. However, this result could be interpretable as “obligation” rather than as "preference" because of the monolingualism of scientific readings and the pressure to publish in international journals, and therefore in English [ 37 ]. A scientist’s preference for reading and writing in English could also be due to the prevalence of English as the source for scientific words and phrases, as well as the scientist’s need to improve their own English in order to overcome these other barriers [ 41 ]. The preference of writing directly in English and not translating may be related to the higher cost of translation in comparison with the revision service ( Fig 2 ). Additionally, scientists are more likely to request a favor for English editing than for a translation [ 37 ]. Strong feelings of insecurity or an "inferiority complex" generated by scientific writing in English is one of the most important segregation factors mentioned by EFL speaking researchers and increase the need of constant editing or correction [ 8 , 10 , 42 ]. This difficulty or insecurity is augmented in the introduction and discussion sections of an article [ 12 , 43 – 46 ]. However, the “materials and methods” section in an article and understanding scientific terminology are equally understood and used in both languages by the respondents, possibly because most words and expressions in modern science are coined in English [ 47 ].

In this study, 43.5% of surveyed researchers reported suffering from rejection or revisions because of aspects related to grammar or style in English writing. Coates [ 48 ] shows that there is a greater probability of manuscript rejection by a journal if there are grammatical errors, but Lindsey and Crusan [ 49 ] found that seems to be the ethnicity of the EFL researchers but not the grammar that is influencing the text evaluation. Some critical voices disagree with the reviewers’ bias hypothesis [ 50 ]. This subject is still under controversy, and in this paper, without comparing this trend with native speakers, it is not possible to conclude that rejection because of English writing is worse for EFL researchers. To start to unravel this bias hypothesis, it will be necessary to gather primary data about correlations between the quality of the article and impressions from reviewers on the writing of EFL researchers (with and without ethnicity information). Nevertheless, understanding reviewer comments is more difficult for a EFL speaking author, since these frequently contain expressions, euphemisms, or colloquialisms that are not easily interpreted by EFL speakers [ 51 , 52 ]. For this reason, several authors call on reviewers to write comments that contribute and guide the use of English, and that does not discourage or criticize EFL authors for the lack of mastery of the language [ 39 , 42 , 53 ]. On the other hand, “not every native English speaker is competent to solve peculiarities in the grammar and style of the “good” use of academic English”, therefore, all scientists have been pressured to use editing services [ 54 ]. In other words, it is questionable to judge or reject innovations or scientific research by linguistic factors or with the excuse of linguistic factors. If a particular research is important for the scientific community, the journal or other resources must assume the cost and effort of translation or editing services, shifting the costs from individual scientists to the publishers or the community.

It was expected that additional costs for Colombian researchers would be found, since similar findings have been reported from other EFL speaking countries in the world [ 11 , 12 , 37 , 43 , 55 , 56 ]. Despite the lack of specific studies on this subject across Latin America, a few exceptions showed similar results: “Regression analysis established that variables of science writing burden contribute to a sense that English is a barrier to scientific writing” [ 11 , 12 ]. Additionally, opinion pieces from Latin-American researchers also agree about the linguistic barrier in science [ 39 , 57 ]. It is possible to assume that these results can be extrapolated to other countries bordering Colombia, given the similarity in proficiency and access to English, shared first language, low state investment in science and technology, and parallel political history with the US and Europe [ 11 , 58 , 59 ]. The results could even be extrapolated to other peripheral countries of the world, as Hanauer et al. [ 12 ] found similar disadvantages over doctoral students from two countries on different continents, Mexico, and Taiwan.

In this study we not only explore the impact that English proficiency has on doctoral students or post-doctoral researchers, but how those impacts are influenced by the researcher's socioeconomic origin. A positive relationship (R 2 = 0.14) was found between English proficiency and socioeconomic status, which is supported by previous studies [ 60 ], hence maintaining in science the patterns of social segregation at national and global levels. This low correlation could be explained by a pre-existing socioeconomic bias in Colombia where most undergrad students come from middle and high socioeconomic classes [ 25 , 26 ]. Another fact that could affect this percentage is the PCA analysis because English proficiency was calculated taking into account years living in English-speaking countries and the percentage of English spoken every day. Therefore, if the researcher lives outside Colombia and speaks English every day the score is higher.

This low correlation, could be explained by the pre-existing socioeconomically biased in Colombia where most undergrad students come from middle and high socioeconomic classes [ 25 , 26 ]. Another fact that could affect this percentage is the PCA analysis because the English proficiency was calculated taking into account years living in English-speaking countries and the percentage of English spoke every day. Therefore, if the researcher lives outside Colombia and speaks English every day the score is higher.

This study finds that the system within science that denotes English as the lingua franca reinforces inequities between scientists from NES and EFL speaking countries, as well as socioeconomic inequities within countries that primarily speak a language other than English. Globalizing science, so far, has meant offering greater advantages to English speakers at the expense of another scientists’ prosperity in the world. Science at present, due to different pressures, opts for English as the only language acceptable for scientific communication, however, some researchers still value the protection of multilingualism in science [ 44 , 61 ]. Defending multilingualism as an alternative in science would promote the reduction of international and social inequities, which would ultimately boost what Segatto [ 62 ] has called "a radically plural world". The homogenization of language in science with the excuse of “integration” is an expression of the elimination of diversity, and this can have consequences not only on the human diversity that makes science but on the diversity of scientific questions that arise [ 17 ].

The convenience of a common language in science must be recognized; however, it is essential that solutions to this problem involve scientists from a variety of backgrounds through a bilateral effort (EFL speaking scientists and NES speaking scientists) [ 10 , 16 ]. Although research is a collective process, the proposed solutions so far have leaned on individual investment, which creates barriers to performing science that more greatly affect researchers of lower socioeconomic backgrounds. Universities, publishers, translation technology, conferences, among others, must also commit to generate ideas for change [ 17 , 37 ]. One potential approach would be to increase the perceived value of publishing in regional or smaller journals regardless of impact factors (IFs), in order to reduce the pressure to publish in the most prestigious and monolingual journals [ 6 , 63 ]. Publishing in high IFs journals is a symbolic capital that delineates what should be “desired” as the maximum “goal” of any scientist. In terms of self-identification, not being able to publish in these journals increases the feeling of incompetence and insignificancy [ 64 ]. The value given to these IFs journals is supported by the idea that the most important and novel studies in academia are published there, however, an increasing number of voices have highlighted the relative value of scientific advances. For example, differential importance between countries or local communities [ 18 ], the influence of trends and use of novel technologies in determining research value (e.g. genetic or genomic data) [ 65 ], and devaluation of important but not modern topics in biology, such as natural history and taxonomy [ 66 – 68 ]. Implementing changes in this regard must be a collective effort as we need to rethink the value of scientific publishing. Elife journal is one example of reevaluating standards in a scientific journal [ 64 ]. Other ideas such as encouraging researchers either from the global south or global north who work in the global south to publish in local journals, could be also implemented.

Other alternatives include supporting journals that accept papers in several languages, promoting the inclusion of other languages in journals at the international level, incorporating revision or translation services in all fees paid to publish an article and providing these services to all scientists at no additional charge to them, establishing multilingual annual or periodic editions in renowned journals, among others [ 37 , 57 ]. Proposals for universities and conferences include aids such as English tutoring for academic purposes [ 69 ], retaining in international conferences a space for presenting in local languages [ 17 ], using methodologies such as simultaneous translation in conferences, and generating exchange spaces in other languages, among others. Finally, it would be helpful to strengthen public available technologies such as Google Translate that allow simultaneous written translation [ 17 ]. In the future, more alternatives will arise, and it will be essential to analyze and monitor them to investigate their reception at the editorial and scientific level.

Supporting information

S1 file. complete article in spanish..

https://doi.org/10.1371/journal.pone.0238372.s001

S2 File. Survey questions in Spanish.

Questions in Spanish (original language) of the survey “Implications of language in scientific publications”.

https://doi.org/10.1371/journal.pone.0238372.s002

S3 File. Survey questions in English.

Questions in English of the survey “Implications of language in scientific publications”.

https://doi.org/10.1371/journal.pone.0238372.s003

S4 File. Raw data.

Raw data obtained from the Survey in Spanish (original language).

https://doi.org/10.1371/journal.pone.0238372.s004

S5 File. Theorical framework.

Short explanation of English as lingua franca in Science, English as a foreign language in Colombia and Implication of English in Science (in English and Spanish).

https://doi.org/10.1371/journal.pone.0238372.s005

Acknowledgments

Thanks to the researchers who completed the surveys or helped to share the survey. To Maria Carme Junyent Figueras for being the master thesis director that leads to this paper. To Pere Francesch Rom, Henry Arenas, Prof. Francesc Bernat, Prof. David Bueno and Prof. Avel·lí for editing and making suggestions on the original manuscript in Spanish. To the developers of Google Translate for creating a free powerful tool to translate in the first place the manuscript. To Rebecca Tarvin, Danny Jackson and Tyler Douglas and for editing and commenting on the manuscript in English.

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Types of Interviews in Research | Guide & Examples

Published on March 10, 2022 by Tegan George . Revised on June 22, 2023.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure.

• Structured interviews have predetermined questions asked in a predetermined order.
• Unstructured interviews are more free-flowing.
• Semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research .

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, other interesting articles, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate   research biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyze because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant.

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered “the best of both worlds.”

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalizability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before.
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualize your initial hypotheses.
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions.

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behavior, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organize than experiments or large surveys . However, their small size leads to low external validity and the temptation as a researcher to “cherry-pick” responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic.
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a “yes” or “no.”
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas.

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Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilize this research method.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of 4 types of interviews .

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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Benefits and harms of social media use: A latent profile analysis of emerging adults

Brian taehyuk keum.

1 Department of Social Welfare, University of California Los Angeles, 337 Charles E. Young Drive, Los Angeles, CA 90095 USA

Yu-Wei Wang

2 University of Maryland, College Park, MD USA

Julia Callaway

3 University of Southern Denmark, Odense, Denmark

Israel Abebe

Seini o’connor.

4 Refugees As Survivors New Zealand, Auckland, New Zealand

Associated Data

The datasets generated during and/or analyzed during the current study are not publicly available due to confidentiality and privacy reasons set forth by the Institutional Review Board.

Not applicable.

The rise in social media use among emerging adults in the United States has been well-documented, but researchers are still working on identifying how the type—not just the frequency—of use impacts psychological well-being. We identified “profiles” of social media use among young adults based on the frequency and purposes of use, and examined their associations with benefits and harms to psychosocial well-being, using data from 2828 incoming undergraduate students ( M age  = 18.29 years; age range: 17 to 25 years). Using Latent Profile Analysis, we identified three unique profiles of individuals who used social media with varying levels of intensity across different purposes: Active Users (32.4%), Passive Users (25.3%), and Average Users (42.4%). Each profile was associated with varying levels of beneficial and harmful psychosocial outcomes. Compared to Average Users, (a) Active Users reported significantly better psychosocial well-being, but also more harmful outcomes; and (b) Passive Users experienced significantly lower levels of perceived social media benefits and social connectedness, while also reporting less problematic social media use and social media stress. Implications of these findings for research and practice are discussed.

Emerging adulthood—defined broadly as the period of time from the late teens through the 20s, with a particular focus on ages 18-25 (Arnett, 2000 )—represents an important stage for the continuation of identity and psychological development. With the ubiquitous presence of social media in young adults’ lives (Pew Research Center, 2018 ), a growing body of research has documented the influence of social media on this developmental process (Mazalin & Moore, 2004 ; Subrahmanyam et al., 2008 ). Researchers have identified broad patterns of social media use—such as active, passive, and problematic use (Marino et al., 2018 ; Verduyn et al., 2017 )—and both harms and benefits associated with using social media (e.g., Verduyn et al., 2017 ). Others have uncovered specific patterns of social media use among adolescents (e.g., Bányai et al., 2017 ; Kurek et al., 2017 ) to identify groups of individuals that seem to be at greater mental health risk.

However, questions remain about the patterns of social media use among emerging adults that may lead to benefits or vulnerability to harmful psychological effects. Also, it is unclear whether the mixed research findings related to patterns of social media use and psychosocial outcomes were associated with “what” people use social media for. The majority of research on social media use has focused on frequency and amount of use and negative psychosocial outcomes (e.g., loneliness, distress), with less attention paid to the purpose of use and beneficial outcomes (e.g., social connection, meaning in life). To address this gap and to examine both the quantity and type of social media use in a comprehensive way, we used a novel approach to examine social media use patterns (“profiles”) of emerging adults based on the frequency and purpose of social media use and their differential associations with psychosocial benefits and harms.

Benefits and Patterns of Social Media Use

The Uses and Gratifications Theory on social media use (Katz et al., 1973 ; Ruggiero, 2000 ) suggest people use social media to fulfill their needs and motivation for gratification.

According to this theory, one major motivator for use and gratification is to connect with others online efficiently, selectively, and meaningfully, in order to fulfill their social needs and approval (Urista et al., 2009 ). There are a plethora of social media platforms that help individuals form their social capital, and their social media use is largely dictated by this process. Given the importance of social connection in the survival of human beings (Holt-Lunstad et al., 2010 ), social media has been noted as an important tool for social bonding and network building. For example, studies have found that online-mediated social connections promoted benefits and well-being among cybervictims (McLouglin et al., 2018 ) and buffered anxiety and isolation due to the COVID-19 pandemic (Stuart et al., 2021 ).

In fact, research has identified a range of social, developmental, and emotional benefits associated with social media use (Anderson & Jiang, 2018 ; Duggan et al., 2015 ; GLSEN et al., 2013 ). A recent study by the Pew Research Center on teenagers’ habits and experiences of using social media indicated that a majority felt more connected to their friends, interacted with a more diverse group of people, and felt supported when they used social media (Anderson & Jiang, 2018 ). Gender Minority groups (e.g., lesbians, gay, bisexual, and transgender youth) may particularly benefit from the use of social media and other online resources that provide information, support, and feelings of meaningfulness, as well as those social media outlets that may assist in identity development and civic connectedness (GLSEN et al., 2013 ). Additionally, researchers have noted the potential of social media platforms to provide a prompt for reminiscence and increased meaning in life and have demonstrated how deeper engagement with personal social media content can facilitate connections with others, enhance self-knowledge, and increase a sense of connection between present and past selves (Thomas & Briggs, 2016 ). Research with young adults in Australia identified “Facebook connectedness” to be distinct from other forms of social connectedness, and to be significantly associated with lower depression and anxiety and greater life satisfaction (Grieve et al., 2013 ).

On the contrary, some researchers have linked social media use to harmful and deleterious effects on mental health (e.g., Marino et al., 2018 ; Twenge et al., 2018 ). While the Uses and Gratifications Theory (Katz et al., 1973 ; Ruggiero, 2000 ) suggests that social connection may be a major motivator of social media use, those who use social media for connection may also experience social isolation and comparison, which can in turn yield harmful outcomes such as loneliness and self-negativity (Primack et al., 2017 ). Comprehensive reviews of the literature documented the negative associations between social media use and various measures of psychological well-being (Frost & Rickwood, 2017 ; Verduyn et al., 2017 ). Specifically, across dozens of cross-sectional and longitudinal studies, researchers identified a pattern of small to medium positive associations between higher levels of social media use and harmful psychological outcomes (i.e., anxiety, depression, and distorted body image), and negative relationships between levels of social media use and subjective well-being.

These seemingly conflicting findings may be explained by psychosocial variables associated with more passive or active forms of engagement. The association between social media use and well-being appears to vary with the pattern or type of social media use. Some studies suggested that passive patterns of using social media (such as browsing others’ profiles or scrolling through feeds) were associated more consistently with harmful outcomes, whereas active patterns of social media use (such as self-disclosing online, engaging with others, and sharing links) were more consistently related to beneficial outcomes (Frost & Rickwood, 2017 ; Verduyn et al., 2017 ). Other studies suggested that harmful outcomes were associated with more intense patterns of social media use that promote upward social comparison, envy, brooding, and more negative and emotional self-disclosures (which may be more likely when users are only “looking” at others’ lives and not interacting with them), whereas beneficial outcomes were associated with patterns of use that promote perceived social support, social capital, and social connectedness (Frost & Rickwood, 2017 ; for a summary, see Verduyn et al., 2017 ). Marino et al. ( 2018 ) further proposed that “problematic” patterns of use—characterized not only by a high frequency of social media use but also addiction-like symptoms and struggles with self-regulation—led to poor psychological outcomes. These researchers found that psychological distress was significantly higher among young adults across 23 independent samples who exhibited “problematic” patterns of Facebook use.

Groups of Individuals with Distinct Patterns of Social Media Use

Noting the importance of differentiating between types of social media use, a growing number of studies examined psychosocial factors associated with different levels and patterns of use among individuals. For example, Wilson et al. ( 2010 ) examined whether social media use among 201 university students was predicted by their scores on the NEO Five-Factor Personality Inventory. They found that extroverted and less conscientious students reported significantly greater social media use and addictive inclinations. Another study showed that individuals with greater social comparison tendencies reported greater social media use (Tandoc et al., 2015 ). Additionally, one study demonstrated that negative collective self-esteem was associated with online social compensation among first-year college students—those who felt negatively about their social group used social media to connect with other group members and to feel better about themselves (Barker, 2009 ). As for other psychosocial factors, Caplan ( 2007 ) found that both self-reported loneliness and social anxiety among 343 undergraduate students were significantly associated with a preference for online interactions. In this study, they also found that social anxiety explained more variance in predicting preference for online interactions, which in turn significantly predicted greater problems in keeping up with their school, work, and social engagements. Overall, a wide range of personal characteristics and individual tendencies seem to differentiate individuals’ social media use patterns.

Against this backdrop, researchers have employed statistical analyses to identify different clusters of internet and social media use behaviors and examine whether certain patterns are associated with greater benefits or harms. For example, Eynon and Malmberg ( 2011 ) conducted Latent Profile Analysis on 1069 children and teenagers in the United Kingdom (ages 8-19) and found that, based on internet use, individuals clustered into peripheral, normative, and all-rounder/active participator groups. Using a latent segmentation approach, Alarcón-del-Amo et al. ( 2011 ) found four groups (introvert, novel, versatile, and expert-communicator) that differed in the frequency of social media use among 399 internet users (ages 16-74). Both studies found nuanced patterns of social media use that seem to be distinguished by the activities performed on social media and frequency of use. While these patterns are informative in understanding different themes of social media behaviors, they do not account for the purpose or motivation behind these behaviors that could further provide greater nuance in profiling social media use.

Beyond the level of use, other studies have examined contextual patterns of use or patterns in relation to psychological outcomes. For example, Bányai et al. ( 2017 ) used Latent Profile Analysis on data from 5961 adolescents in Europe regarding their social media addiction and found that about 4.5% were at risk for social media addiction, while 17.2% and 78.3% were at low- and no-risk, respectively. Using a similar methodology, Kurek et al. ( 2017 ) found four clusters of information and communication technology use among 933 adolescents: average use, elevated use (of all forms of technology), high video game-low social media use, and high social media and internet use. As expected, they found that adolescents in the elevated and high video game groups reported poorer identity such as false self-perception and lower self-image satisfaction, compared with the average use group. Kurek et al. ( 2017 ) also found significant relationships between the elevated and the high social media/internet use groups and self-reported problem behaviors (e.g., with friends), relative to the average group. Using a similar approach, Ilakkuvan et al. ( 2019 ) conducted a Latent Profile Analysis with 1062 young adults who used social media and found five classes: low users (lower use of social media compared to full sample), high users (higher use of social media compared to full sample), professional users (high use of professional social media such as Linkedin, low use of creative social media such as Vine), creative users (high use of vine and Tumblr, low use of Linkedin), and mainstream users (high use of Facebook and YouTube, and average use of other social media). Compared to high users, creative users had higher odds of using substances and lower odds of depressive symptoms, mainstream users had higher odds of using substances socially (alcohol and hookah), professional users had higher odds of using alcohol, cigarettes, and cigars, and low users had higher odds of using other drugs (e.g., cocaine and heroin). Altogether, findings in these studies suggested that certain patterns of use may be more likely to be associated with harmful outcomes.

The Present Study

Based on our review, we aimed to fill two gaps in the literature. First, whereas most studies have examined social media use patterns based on the frequency of use of different social media platforms (e.g., Fardouly & Vartanian, 2015 ; Manago et al., 2015 ), we developed and employed more comprehensive social media use items differentiating patterns of use by frequency and purpose (e.g., staying connected with friends and family; networking; meeting new people; expressing ideas). Building on the Uses and Gratifications Theory (Katz et al., 1973 ; Ruggiero, 2000 ), this approach allows for a reflection of the driving forces behind certain social media activities and provides context for explaining the profiles that emerge from our study. Secondly, while most studies have focused on the psychological harms in relation to patterns of social media use, we also tested different social media patterns in relation to psychological benefits . As reviewed above and based on the Uses and Gratifications Theory that suggests motivation for social media use to fulfill social needs (Katz et al., 1973 ; Ruggiero, 2000 ), social media use presents a plethora of potential benefits in terms of social connectedness, online support, life satisfaction, and the development of one’s identity and sense of self. Therefore, we believe it is equally important to understand, promote, and capitalize on these beneficial outcomes.

We conducted Latent Profile Analysis (LPA) to uncover different types of social media users among emerging adults based on items regarding purposes of social media use. We then examined whether there may be significant differences among the groups regarding the benefits and harms associated with social media use. Beneficial outcomes include factors such as perceived social media-related benefits, satisfaction with life, social connectedness, and a sense of meaning in life. Harmful outcomes include factors that indicate poorer psychological well-being, such as social media-related stress, problematic social media use, and lack of social comfort on social media. Our hypotheses on these associations were contingent on the different profiles that emerged from our sample. However, based on previous research on adolescents and adults, we hypothesized that there would be higher levels of both harmful and beneficial outcomes associated with more active users, while the reverse may be true for passive users.

Participants and Procedure

The study received Institutional Review Board approval (#316599-15). Data for the current study was drawn from archival data consisting of 2828 undergraduates (mean age = 18.3 years; age range: 17 to 25 years) who were newly entering a large Mid-Atlantic public university. The data were collected as part of an incoming student survey administered in August of 2018. We arrived at the sample size of 2828 from the original 4321 by removing cases who failed both attention-check questions (one appearing halfway and one at the end of the survey), indicated that they do not use social media, reported ages older than 25, and were missing data on the primary study variables. Because the LPA analysis strategy requires large sample sizes (Nylund-Gibson & Choi, 2018 ), we maximized the sample size possible.

Participants identified racially/ethnically as White (55.3%), Asian/Asian American (21.8%), Black/African American (6.9%), Chicano/Hispanic/Latino/a (4.2%), Middle Eastern/North African (1.6%), and Native Hawaiian/Pacific Islander (0.1%). One in 10 (10%) identified as bi- or multi-racial, selecting more than one of these racial backgrounds. Over half identified as female (53.1%), and 46% as male, while 0.9% identified as transgender, gender non-conforming, or having a different gender identity.

Social Media Use

We developed items to capture students’ frequency and type of social media use. First, we asked students to rank-order social media platforms (Facebook, Instagram, Snapchat, Tumblr, Twitter, and Other—please specify) based on how much they used these social media platforms. Second, we asked students to identify their frequency of use of social media across all of these platforms, on a 1 ( never ) to 5 ( very often ) scale, for each of the 16 different purposes represented by the following categories: social connection (e.g., “staying connected with friends and family”), support and help (e.g., “seeking advice, help, or support from others”), tangible benefits (e.g., “getting income”), entertainment (e.g., following sports/fitness), and casual use (e.g., “passing time without a particular purpose;” see Fig.  1 for all 16 purposes). Third, we asked students on a scale of 1 ( never ) to 10 ( all the time ) how often they: a) “Check your social media account(s)”, b) “Actively use your social media account(s) (e.g., posting status updates, sharing links, reacting and commenting on friends’ walls, or sending messages),” and c) “Passively use your social media account(s) (e.g., scrolling through your newsfeed, looking at friends’ pages, pictures, and status updates).” We then (a) examined the total number of distinct social media platforms they engaged with and the order of the platforms based on the frequency of use, (b) calculated the average frequency for each of the 16 purposes of social media use items, and (c) calculated a mean score for their overall, active, and passive social media use.

Plot of mean responses to 16 Purpose of Use variables, overall and by class

Benefits of Social Media Use

One-item social media benefits.

We developed a stand-alone item asking students to rate their agreement to the statement “I get the benefit, support, and help I need from social media.” Students indicated their response on a six-point Likert-type scale (1 =  “strongly disagree” to 6 =  “strongly agree” ).

Satisfaction with Life Scale (SWLS; Diener et al., 1985 )

This measure has five items presenting statements that respondents are invited to agree or disagree with (on a 7-point Likert-type scale ranging from 1 = “ strongly disagree” to “ strongly agree” ). Example items include “In most ways my life is close to my ideal” and “I am satisfied with my life.” Scores on each item are averaged to produce an overall score, with higher scores indicating higher life satisfaction. It shows convergent validity with other measures of subjective well-being across two samples (Diener et al., 1985 ), such as other measures of life satisfaction ( r  = .62 to .66), single-item measures of life satisfaction as a whole ( r  = .62 to .68), semantic-differential measurements of present life ( r  = .59 to.75), and affect balance including relationships with positive affect ( r  = .50 to .51) and negative affect ( r  = −.32 to −.37). Additionally, in their review of the literature, Diener et al. ( 2013 ) reported a relationship between satisfaction with life scales, including the SWLS, and non-self-report measures of life satisfaction, such as ratings made by trained raters based on interviews conducted with the participants (Diener et al., 2013 ). Reliability estimates in past studies have been reported to be.80 or higher (Diener et al., 2013 ). For our sample, α = .87.

Social Connectedness Scale – Revised 15 Item Version (SC-15; Lee et al., 2008 )

This scale measures individuals’ sense of belonging to and being in connection with their social world. It was shortened from the 20-item Social Connectedness Scale-Revised (SCS; Lee et al., 2001 ) to remove items cross-loading across social connectedness and extraversion. Respondents are asked to rate their agreement to statements such as “I am able to relate to my peers” and “Even around people I know, I don’t feel that I really belong” (reverse scored) on a scale from 1 (“strongly disagree”) to 6 (“strongly agree”). Both versions of the scale have convergent validity and are significantly correlated with—but conceptually distinct from—extraversion; r  = .62 ( p  < .01) for the 20-item version and r  = .55 ( p  < .01) for the 15-item version (Lee et al., 2008 ). Additionally, Lee et al. ( 2008 ) found the SC-15 to be significantly correlated with life satisfaction ( r  = .50, p  < .01), affect balance ( r  = .52, p  < .01), positive affect ( r  = .40, p  < .01), and negative affect ( r  = −.41, p  < .01). They also reported good reliability in a general college sample (α = .93). For our sample, α = .94.

Meaning in Life Measure (MILM; Hill et al., 2018 )

This 8-item measure has two subscales, assessing agreement on a 1 (“ extremely disagree ”) to 9 (“ extremely agree ”) scale with items related to Experience of meaning in life (e.g., “I have something I want to accomplish in my life”) and Reflectivity about meaning in life (e.g., “There are times in my life when I think about what it all means”). Hill et al. ( 2018 ) found the measure to have good construct and concurrent validity, showing positive relationships with other measures of meaning in life ( r  = .69, p  < .001), subjective well-being ( r  = .56-.67, p  < .001), extraversion ( r  = .26, p  < .001), agreeableness ( r  = .42, p  < .001), conscientiousness ( r  = .35, p  < .001), and openness ( r  = .36, p  < .001). Hill and colleagues also reported high test-retest reliability, and good internal consistency across the total scale (α = .85) and each of the subscales (α = .82 for Experience and α = .86 for Reflectivity). For our sample, α = .87 for the full scale, α = .79 for the Experience subscale, and α = .88 for the Reflectivity subscale.

Meaning in Life Questionnaire (MLQ-Search; Steger et al., 2006 )

This measure contains 10 items assessing perceived meaning in life across two subscales: Presence and Search. For this study, we used the five-item Search subscale, which asks participants to rate a series of statements (e.g., “I am always looking for something that makes my life feel meaningful”) on a scale of 1 (absolutely untrue) to 10 (absolutely true). The “Presence” subscale overlaps with the “Experience” subscale in the MILM, and therefore was not included in the survey. Steger and colleagues reported good convergent validity for the Search subscale, showing significant correlations with fear ( r  = .25, p  < .005), shame ( r  = .19, p  < .05), sadness ( r  = .26, p  < .005), neuroticism ( r  = .20, p  < .05), and depression ( r  = .36, p  < .005). They also reported good test-retest reliability and high internal consistency of the Search subscale (α = .87) in their undergraduate student samples. For our sample, α = .94.

Harms of Social Media Use

One-item social media stress.

We developed a stand-alone item asking students to rate their agreement to the statement “social media brings additional stress to my life.” Students indicated their response on a six-point Likert-type scale (1 = “strongly disagree” to 6 = “strongly agree”).

Emotional Thermometers (Mitchell, 2007 ; Mitchell et al., 2010 )

This measure asked participants to indicate their level of depression, distress, anxiety, and anger on visual thermometers with “temperature” levels ranging from 0 to 10, where 0 = “none” and 10 = “extreme.” Participants marked the number that best described how much emotional upset they had been experiencing in the week prior to taking the survey, including the day they completed the survey. The thermometers have been validated against other diagnostic measures in a clinical sample and found to have adequate specificity and sensitivity. Specifically, Mitchell ( 2007 ) reported in a meta-analysis the pooled sensitivity (77%) and specificity (66%) of the Distress thermometer against various validated measures in ten different studies. Additionally, Mitchell and colleageus (Mitchell et al., 2010 ) reported that as compared to the Hospital Anxiety and Depression Scale (HADS) total score (Zigmond & Snaith, 1983 ), the Anger thermometer was optimal with 61% sensitivity and 92% specificity. As compared to the HADS anxiety scale, the Anxiety thermometer was optimal with 92% sensitivity and 61% specificity. As compared to the HADS depression scale (60% sensitivity and 78% specificity) and the DSM-IV screening for major depression (80% sensitivity and 79% specificity), the Depression thermometer was optimal.

Three Item Loneliness Scale (Hughes et al., 2004 )

This measure was developed as a shortened form of the Revised UCLA Loneliness Scale (R-UCLA; Russell et al., 1980 ) suitable for inclusion in longer surveys. The three items assess how often respondents lack companionship, feel left out, and feel isolated, measured on a 3-point Likert-type scale (1 = “hardly ever” to 3 = “often”). The average score across the three items is calculated, with higher scores indicating greater loneliness. Hughes et al. ( 2004 ) reported that the scale had acceptable internal reliability (α = .72) in older adult samples and was highly correlated with scores on the original R-UCLA scale (r = .82, p < .001) and with scores on loneliness-related items in the Center for Epidemiologic Studies–Depression Scale (CES-D; Turvey et al., 1999 ), demonstrating convergent validity. For our sample, α = .82.

Problematic Social Media Use

This measure is an adaptation of the short-form of the Problematic Internet Use Questionnaire (PIUQ-9; Koronczai et al., 2011 ), which was developed from the longer 18-item PIUQ (Demetrovics et al., 2008 ). For the purposes of our study, we changed PIUQ items referring to “the Internet” to refer to “social media.” Respondents were asked to indicate on a scale from 1 (“never”) to 5 (“always”) how frequently they engaged in problematic behaviors on three subscales: Obsession (e.g., “How often do you feel tense, irritated, or stressed if you cannot be on social media for as long as you want to?”), Neglect (e.g., “How often do you neglect household chores to spend more time on social media?”) or Lack of Control (e.g., “How often does it happen to you that you wish to decrease the amount of time spent on social media but you do not succeed?”).

In initially developing the scale through testing with an online community sample of young adults, Demetrovics et al. ( 2008 ) reported acceptable/good internal consistency across the three subscales (α ranging from .74 to .87), and high test-retest reliability (r = .903, p < .001). For the briefer version, Koronczai et al. ( 2011 ) reported good whole-scale reliability in both adolescent (α = .87) and adult (α = .84) samples. Neither scale has been clinically validated for the diagnosis of internet addiction, although Demetrovics et al. ( 2008 ) found that higher PIUQ scores were positively associated with other addictive behaviors, such as the use of slots ( F  = .131, p  = .011) and other gaming machines ( F  = 4.501, p  = .025). They also provided support for the validity of the PIUQ as a tool for assessing patterns of use by collecting corresponding data on participants’ internet habits and demographic data. For our sample, α = .86.

Online Cognition Scale-Social Comfort Subscale (Davis et al., 2004 )

The Online Cognition Scale is a 36-item questionnaire designed to measure problematic Internet use. The full OCS has four subscales (Loneliness/depression, Lack of Impulse Control, Social Comfort, and Distraction). We used only the 13-item Social Comfort subscale for this study, replacing the reference to “online” with reference to “on social media.” Higher Social Comfort is regarded in this study as a further indicator of problematic social media use. Respondents rate their agreement on a 1 (“Strongly disagree”) to 7 (“Strongly agree”) scale for items such as “I say or do things on social media that I could never do in person” and “I wish my friends and family knew how people regard me on social media,” from which an average Social Comfort score is calculated. Davis et al. ( 2004 ) reported that the Social Comfort subscale showed (a) convergent validity with related measures of rejection sensitivity ( r  = .41, p  < .001), procrastination ( r  = .23, p  < .001), loneliness ( r  = .37, p  < .001), and feelings of competency online ( r  = .62, p  < .001), and (b) good reliability (α = .87) in their sample of undergraduate students. For our sample, α = .86.

Our overall aims were to understand the distinct class profiles of social media use among college students based on the purpose of use, and to quantify how these profiles were associated with beneficial and harmful outcomes. We used LPA, which serves as a person-centered statistical tool. LPA was conducted with Mplus version 8.2 using estimation of robust standard errors to account for non-normality. Akaike (AIC) and Bayesian Information Criterion (BIC) were assessed to select the best-fitting class solution. Lower values of AIC and BIC by at least 10 units suggest an empirically significant better fit. In conjunction, entropy values and significance tests of the Lo-Mendell-Rubin adjusted Likelihood Ratio Test (aLRT) were examined to support empirical identification of a best fitting class solution. Higher entropy values indicate a greater distinction of classes within a solution and a significant LRT indicates that an n number of classes is significantly better than an n-1 model. Interpretability of the fit was also based on significant class membership probabilities. Solutions with class membership probabilities less than 5% were not considered.

Based on the 16 items indicating the purposes of social media use, one to five-class solutions were considered. Model fit comparisons, parsimony, and class membership probabilities were all considered in selecting the best-fitting class solution. Table  1 presents the sequential class solutions. The three-class solution was identified as the best-fitting class solution, primarily using the aLRT tests. Both the AIC and BIC sequentially decreased from one to four-class solutions, and increased in the five-class model, but the aLRT test was not significant for the four-class model. This indicated that the four-class solution did not significantly fit the data better than the three-class model, and that a fourth cluster did not necessarily represent a meaningful class beyond a three-class solution. The entropy for the three-class solution was 0.79, indicating that there was a meaningful distinction between the classes. All class membership probabilities were greater than 5%, with the smallest class representing 25.3% of the sample.

Sequential class solutions for latent profile analysis

LL Log Likelihood, AIC Akaike Information Criteria, BIC Bayesian

Information Criteria, aBIC Adjusted Bayesian Information Criteria, aLRT

Lo-Mendell-Rubin adjusted Likelihood Ratio Test

We examined the following results to develop appropriate labels for each class: (a) summary statistics across each profile for each of the 16 purposes of the social media use variable, (b) the number of platforms the respondents used, and (c) the frequency of social media use. The three profiles varied in the level of engagement in each of the social media activities. Figure  1 displays the overall mean responses as well as the means by class across the 16 social media use purposes. Table  2 includes the frequencies of checking social media in general, as well as active and passive use, among the full sample and three groups.

Means and standard deviations of the types of social media use

*Items were rated on a scale of 1 ( never ) to 10 ( all the time )

Uses of Social Media

Overall, the participants primarily (i.e., “ sometimes ” to “ very ofte n” on a scale of 1 = “ never ” to 5 = “ very often” ) used social media for nine purposes: entertainment, passing time without a particular purpose, browsing, staying connected with friends and family members, sharing good or interesting things with friends, casually looking at what other people are doing or posting about, seeking information, keeping up with trends, and following sports/fitness (see Fig. ​ Fig.1). 1 ). On average, the participants used three to four social media platforms (μ = 3.73, SD = 1.24). On a scale of 1 (“ never” ) to 10 (“ all the time” ), on average, participants checked their social media accounts between several times a day and once an hour (μ = 7.50, SD = 1.64), actively updated their social media accounts between once a week and several times a week (μ = 4.92, SD = 2.44), and passively used social media between several times a day and once an hour (μ = 7.26, SD = 1.89).

Types of Users

When looking at the characteristics of the three-class profiles, an “Active User” group emerged, which represented 32.4% of the sample. Students in this group primarily (at least “sometimes”) used social media platforms for all but three (i.e., seeking advice, venting, and getting income) of the 16 purposes and used them at a higher frequency for these reasons, compared to Average and Passive Users (see Fig. ​ Fig.1). 1 ). On average, students in this group used more than four different social media platforms—the only group to do so. The “Active Users” checked social media more frequently than the other two groups – between once an hour and several times an hour (μ = 8.32, SD = 1.34) and actively engaged with social media about once a day (μ = 6.00, SD = 2.40), while also passively using social media between once an hour and several times an hour (μ = 8.18, SD = 1.64). In sum, Active Users used more social media outlets for more varying purposes and visited these platforms more frequently than Average and Passive Users (see Table ​ Table1 1 ).

The second group that emerged was the “Passive Users” group. This group represented 25.3% of the study sample, and primarily used social media for five purposes: to be entertained, pass time, browse, stay connected with friends/family, and casually looking at what other people are doing or posting about. It is important to note that Passive Users used social media for these purposes at frequency levels that were much lower than the overall study sample. They used around 3 different social media platforms (μ = 3.08, SD = 1.26). Students in this group checked social media less frequently than the other two groups – between once a day and several times a day (μ = 6.53, SD = 1.92), actively used social media less frequently – between several times a month to once a week (μ = 3.50, SD = 2.16), and passively used social media less frequently – between once a day to several times a day (μ = 6.29, SD = 2.17).

Lastly, an “Average User” group emerged between the Active Users and Passive Users. This group represented 42.4% of the sample, making it the largest group. This group primarily used social media for nine purposes (all purposes, except for networking, meeting new people, expressing ideas, venting, seeking advice, getting income, and help with school). They reported using between three and four social media platforms (μ = 3.79, SD = 1.15). Overall, Average Users’ patterns of use are less frequent than Active Users’ and more frequent than Passive Users’. They checked social media several times a day to once an hour (μ = 7.44, SD = 1.33), actively used social media close to several times a week (μ = 4.94, SD = 2.19), and passively used social media between several times a day to once an hour (μ = 7.14, SD = 1.53).

Social Media Use Profile Membership and Psychosocial Outcomes

Once we identified the most appropriate number of class profiles, we used categorical regressions to build models to investigate the relationship between class (the independent variable) and each of the “benefits” and “harms” outcome variables, using the Average Users as the reference group. This allows examination of whether the Active and Passive Users generally scored significantly higher or lower in the outcome variables in relation to Average Users. The “benefits” outcome variables were: perceived social media benefits, life satisfaction, and social connectedness, as well as experience, reflection, and search for the meaning of life. The “harms” outcome variables were: perceived social media stress, distress, anxiety, anger, depression, loneliness, problematic social media use, and social comfort on social media. We used RStudio version 1.1.463 to conduct the categorical regression analyses. To avoid the risk of Type I error in interpreting the regression results, we adopted a conservative alpha of 0.01 to determine significance.

Compared to the Average Users, on average, Active Users had significantly higher levels of all beneficial outcomes, except for satisfaction with life, and higher levels of all the harmful outcomes listed, except for distress and loneliness. Relative to the Average Users, on average, Passive Users reported significantly lower levels of beneficial outcomes on two measures (perceived social media benefits and social connectedness), and lower levels of harmful outcomes on three measures (lower levels of social media stress, problematic social media use, social comfort on social media). Tables  3 and ​ and4 4 show the complete results of the categorical regression models.

Categorical regression coefficients of “Beneficial” psychosocial outcomes

Referent group is Average Users

Categorical regression coefficients of “Harmful” psychosocial outcomes

The current study examined the social media use patterns among emerging adults (18-25) based on their frequency of social media use for different purposes. As with previous studies that found multiple groups of distinct use (e.g., Bányai et al., 2017 ; Ilakkuvan et al., 2019 ; Kurek et al., 2017 ), we found three unique profiles of individuals who used social media for different purposes: Active, Passive, and Average Users. We also found associations between these profiles and beneficial/harmful psychosocial outcomes. Notably, our study appears to be the first to examine differences in perceived benefits and harms associated with social media use profiles among emerging adults. These associations provide important nuances to understanding how individuals with varying purposes and levels of social media use may reap benefits, while also incurring some psychological costs.

One major trend observed in our findings is that regardless of which profile of social media use individuals fit within, it appeared that students who participated in our study experienced a combination of both beneficial and harmful outcomes associated with social media use. There was no one particular profile that experienced only beneficial or only harmful outcomes. As expected, the Active Users reported significantly higher benefits (except for satisfaction with life) and more harmful psychosocial outcomes (except for distress and loneliness) than the Average Users. The results explain the “double-edged sword” effect of social media, in which the benefits reaped by the Active Users came with psychological costs. In line with previous literature (e.g., Bányai et al., 2017 ), the Active profile resembled an “at-risk” group for experiencing harmful outcomes. However, our findings on the perceived benefits provided additional insight: perhaps the motive for individuals in this group to continue to actively engage in social media may be from the high level of benefits and sense of connectedness they gained from it, although they also suffered and faced stress from social media use.

Whereas most previous studies on social media use patterns found that passive users were usually associated with harmful outcomes (Frost & Rickwood, 2017 ; Verduyn et al., 2017 ), we found nuanced context to understand their experiences by exploring both beneficial and harmful outcomes among emerging adults. As with the Active Users, the Passive Users also reported beneficial and harmful outcomes associated with social media use, but this group appeared to experience significantly lower levels of both sets of outcomes than the Average Users. These individuals reported significantly lower social media stress, problematic social media use, and social comfort on social media. Thus, one might speculate that this group resembles a “low-risk” group. However, our findings also suggest that they experienced fewer benefits associated with social media use—such as lower social connectedness. It appears that Passive Users may experience fewer harmful psychological outcomes, but they may be “missing out” on potential benefits that may be helpful in building relationships and networks.

Of note, meaning in life was a potential outcome of social media use in our study. Despite social media’s potential for facilitating a search for meaning in life (Thomas & Briggs, 2016 ), no studies have directly examined this process. Given the plethora of information available and the different types of social connections that can be made on social media, it is reasonable to anticipate that individuals may develop greater insight into their meaning in life or use social media to search for meaning. In fact, we found that the Active Users scored significantly higher on the presence of, reflection on, and search for meaning when compared to Average Users. No previous research appeared to connect meaning in life with social media use, but we can speculate as to why this pattern of results emerged. In line with the Uses and Gratifications Theory (Ruggiero, 2000 ), it is possible that Active Users were more likely to frequently seek information or to form social connections that helped with the development of their own meaning in life. Indeed, Active Users reported the most social connectedness among the three groups. Active Users may depend heavily on online relationships within which they formed their insights about their meaning in life in relation to others in the world. Collectively, our results provide evidence of nuanced processes of social media use that may contribute to emerging adults’ meaning in life and well-being.

Limitations

We acknowledged that there are several limitations to our study. First, our results were based on a college sample from a university in the Mid-Atlantic region and thus we are not able to generalize our findings to college students in other geographical regions and to non-college populations. Second, although we developed new items that reflect purposes of social media use across multiple domains of use (e.g., social network, information seeking, entertainment), participants may have used social media for other purposes that were not captured in the current set of items. Furthermore, future research would need to examine the psychometric validity of these measures and test to see if our findings may be replicated. Third, given the self-report nature of these items (as well as our outcome measures), we may have captured more subjective, rather than objective, experiences of social media use and its relation to harms and benefits. Therefore, responses may have been subject to self-perception and recall bias. Lastly, causal relations between the variables of interest may not be inferred from our cross-sectional research design.

Implications for Practice

Despite these limitations, we believe our approach and results have important implications for future practice and research. First, the social media use items we developed and the profile types we identified showed potential as a tool for clinicians and other mental health professionals. Because the current study drew data from a single cross-sectional university student sample, future studies can further validate the social media use items with diverse populations so that it can be used as a tool to assess different dimensions of maladaptive social media use. The three profiles may be used to conceptualize people’s patterns of social media use and identify related psychological risks and protective factors. Second, to date, most studies on social media use patterns have examined primarily harmful outcomes. The most salient contribution of our study is that we examined the benefits as well as the costs associated with social media use. This enabled us to identify a “double-edged sword” effect of experiencing both harms and benefits, which has important implications. Our results suggest that while Active Users experienced “costs” from their social media use, they also perceived that they were benefiting from their social media use, which may have motivated them to actively use social media more. It is important for clinicians, educators, and other professionals to understand these perceived benefits, while being wary of the harmful effects of problematic or frequent social media use.

Implications for Future Research

For future research, we believe it is important to examine the mechanisms for social media harms and benefits, and to test potential interventions to minimize the harms, while maintaining or enhancing the benefits. Additionally, it would be important to conduct research on approaches that can promote the benefits more effectively, especially among Passive Users that seem to be “missing out” on the benefits of social media use, while also helping Active Users to understand the concurring costs of social media. Moreover, future studies should consider predictors of the different patterns of social media use. Although we have identified three descriptive clusters of social media use, we are limited in what we can say about the factors that lead to such patterns. Many factors may differentiate adults’ social media use, including individual differences (e.g., personality differences), group factors (e.g., collective self-esteem, group social identity), and offline social contextual variables such as relationship satisfaction and sense of belongingness. For example, it would be important for future researchers to assess how the benefits and harms fof social media use may be contextualized among underrepresented and marginalized youth and emerging adults (e.g., racial minority individuals, LGBTQ individuals). As Dari et al. ( 2021 ) suggested, community-based participatory research could be used to explore ecologically valid and relevant lived experiences of social media use that can inform how the harms may involve experiences such as online racism (Keum & Miller, 2017 , 2018 ) and benefits can include culturally-relevant social support networks (Keum, 2017 ). Another opportunity for future research would be to examine differences in psychosocial outcomes based on social media platforms. There is evidence from social media marketing research that social media users may engage with social media platforms differently, which could lead to different psychosocial outcomes (Goodrich & de Mooij, 2013 ). In addition to quantitative methods, researchers can consider innovative qualitative approaches such as the Online Photovoice method which gives participants opportunities to express their own lived experiences online with as little bias and influence from the researchers (Tanhan & Strack, 2020 ). Finally, our data were collected prior to the start of the COVID-19 pandemic. It is unclear how the reliance on internet connections during the pandemic may have influenced young adults’ social media use and its relationship with psychosocial well-being. The interaction between COVID-19-related psychosocial implications and social media use would need to be studied.

In conclusion, with the ever-growing presence and influence of social media on the day-to-day lives of emerging adults, who are going through a critical developmental stage, it is crucial that the full extent of the effects on users’ psychological and overall well-being are thoroughly investigated and understood. The knowledge base that our study has added can inform educational and other interventions aiming to optimize the benefits and minimize the harms associated with social media, and help emerging adults navigate the right balance in this digital world.

Acknowledgements

Portions of the research findings were presented at the 2020 American Psychological Association Annual Meeting.

Authors’ Contribution

Brian TaeHyuk Keum: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project Administration.

Yu-Wei Wang: Conceptualization, Methodology, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.

Julia Callaway: Software, Validation, Formal analyses, Writing – review & editing, Visualization.

Israel Abebe: Validation, Formal analyses, Writing – review & editing.

Tiana Cruz: Investigation, Data Curation, Writing – review & editing.

Seini O’Connor: Writing – review & editing, Visualization.

Data Availability

Code availability, declarations.

The study received Institutional Review Board approval (#316599-15).

All participants were provided informed consent and consented to participated in the study.

All participants were provided informed consent that data will be used for publication.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Publisher’s note

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

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