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Example of a Quantitative Research Paper
Posted by Rene Tetzner | Sep 4, 2021 | How To Get Published | 0 |
Example of a Quantitative Research Paper for Students & Researchers This example of a quantitative research paper is designed to help students and other researchers who are learning how to write about their work. The reported research observes the behaviour of restaurant customers, and example paragraphs are combined with instructions for logical argumentation. Authors are encouraged to observe a traditional structure for organising quantitative research papers, to formulate research questions, working hypotheses and investigative tools, to report results accurately and thoroughly, and to present thoughtful interpretation and logical discussion of evidence.
The structure of the example and the nature of its contents follow the recommendations of the Publication Manual of the American Psychological Association . This APA style calls for parenthetical author–date citations in the paper’s main text (with page numbers when material is quoted) and a final list of complete references for all sources cited, so I have given a few sample references here. Content has been kept as simple as possible to focus attention on the way in which the paper presents the research process and its results. As is the case in many research projects, the more the author learns and thinks about the topic, the more complex the issues become, and here the researcher discusses a hypothesis that proved incorrect. An APA research paper would normally include additional elements such as an abstract, keywords and perhaps tables, figures and appendices similar to those referred to in the example. These elements have been eliminated for brevity here, so do be sure to check the APA Manual (or any other guidelines you are following) for the necessary instructions.
Surprises at a Local “Family” Restaurant: Example Quantitative Research Paper
A quantitative research paper with that title might start with a paragraph like this:
Quaintville, located just off the main highway only five miles from the university campus, may normally be a sleepy community, but recent plans to close the only fast-food restaurant ever to grace its main street have been met with something of a public outcry. Regular clients argue that Pudgy’s Burgers fills a vital function and will be sorely missed. As the editor of the Quaintville Times would have it, “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p. A3). On the other hand, the most outspoken of Quaintville residents in favour of the planned closure look forward to the eradication of a local eyesore and tend to consider the restaurant more of “a hazard than a benefit to the health of some of our poorest families” (“Local dive,” 2017, p. 1).
Following this opening a brief introduction to published scholarship and other issues associated with the problem would be appropriate, so here the researcher might add a paragraph or two discussing:
• A selection of recently published studies that investigate the effect of inexpensive fast-food restaurants on the health of low-income families, especially their children (Shunts, 2013; Whinner, 2015). • Fast-food restaurants that have responded to criticism about the quality of their food by offering healthy menu items. This could be enhanced with evidence that when such choices are available, they are rare purchases for many families (Parkson, 2016), particularly in small towns and rural areas (Shemble, 2017). • The interesting trend in several independent studies suggesting that families form a much smaller portion of the clientele of fast-food restaurants than anticipated.
Explaining how the current research is related to the published scholarship as well as the specific problem is vital. Here, for instance, the author might be thinking that Pudgy’s, which has healthy menu items as well as the support of so many long-term residents, will prove an exception to the trends revealed by other studies. Research questions and hypotheses should be constructed to articulate and explore that idea. Research questions, for instance, could be developed from that claim in the Quaintville Times as well as from the published scholarship:
• Do families constitute the majority of Pudgy’s regular clientele? • Does the restaurant offer a decent family meal for a fair price? • Do families linger in the restaurant’s comfort and warmth?> • Do children use the indoor play area provided by the restaurant?
Working hypotheses can be constructed by anticipating answers to these questions. The example paper assumes a simple hypothesis something along the lines of “Families do indeed constitute the majority of Pudgy’s clientele.” The exact opposite supposition would work as well – “Families do not constitute the majority of Pudgy’s clientele” – and so would hypotheses exploring and combining other aspects of the situation, such as “Pudgy’s healthy menu options and indoor play area are positive and appealing considerations for families” or “The comfortable atmosphere of Pudgy’s with its play area makes it much more than a restaurant for local families.”
The exact wording of your questions and hypotheses will ultimately depend on your focus and aims, but certain terms, concepts and categories may require definition to ensure precision in communicating your ideas to readers. Here, for instance, exactly what is meant by ‘a family,’ ‘a decent meal,’ ‘a fair price’ and even ‘comfortable’ could be briefly but carefully defined. A general statement about your understanding of how the current research will explore the problem, answer your questions and test your hypotheses is usually required as well, setting the stage for the more detailed Method section that follows. This statement might be something as simple as “I intend to observe the restaurant’s customers over a two-month period with the objective of learning about Pudgy’s clientele and measuring the use and value of the establishment for local families.” On the other hand, outlining your research might require a paragraph or two of introductory discussion.
Method Whether a brief general statement or a longer explanation of how the research will proceed appears among your introductory material, it is in the Method section that you should report exactly what you did to conduct your investigation, explain the conditions and controls you applied to increase the reliability and value of your research, and reveal any difficulties you encountered. For example:
My observations took place at Pudgy’s Burgers in January and February of 2018. Each session was approximately four hours long, and I aimed to obtain an equivalent number of observations for all opening hours of the week (the restaurant’s hours are listed in Table 1), but course requirements made this difficult. Tuesday and Thursday afternoons are therefore underrepresented, and observations from 1:00 pm to 5:00 pm on two consecutive Tuesdays (6 and 13 February) are the work of my classmate, Jake Jenkins. Without his assistance, I could not have met my objective of gathering observations for every opening hour of the week at least twice (Table 2 outlines the overall pattern of observation sessions). Serving staff at the restaurant assure me that I have now “seen ‘em all,” so I believe my observations have resulted in a representative sampling of local customers over two months when that “winter wind” has been especially busy about its work.
To avoid detection by the customers I was observing and the possibility of altering their behaviour, I obtained permission from Pudgy’s manager, Mr Jobson, to sit at the staff table in a dark and quiet corner of the restaurant where clients never go. This table is labelled in the plan of Pudgy’s Burgers and its grounds that I have included as Figure 1. From there I could see the customers both at the service counter and at their tables, but they could not see me, at least not clearly, and if they did, they paid me no more attention than they did the restaurant employees. From the staff table I could also see the row of indoor park-style children’s toys running down the north wall of windows, as well as the take out lane and the people waiting in their cars.
A Method section often features subheadings to separate and present particularly important aspects of the research methodology, such as the Customer Fact Sheet developed and used by the author of this study.
The Customer Fact Sheet Recording thorough and equivalent information about every Pudgy’s customer I observed was crucial for quantifying and analysing the results of my study. I therefore prepared a Customer Fact Sheet (included as Appendix I at the end of this paper) for gathering key pieces of information and recording observations about each individual, couple or group who purchased food or beverages. This sheet ensured that vital details such as date, weather conditions, time of arrival, eat in or take out order, number in party, approximate age of individuals, food purchased, food consumed, healthy choices, amount spent, who paid, dessert or extra beverage, children playing, interaction with other children and families, time of departure and other important details were recorded in every case. The Customer Fact Sheet proved particularly helpful when my classmate performed observations for me and was invaluable for evaluating the data I collected. I initially hoped to complete at least 500 of these Customer Fact Sheets and was pleased to increase that number by 100 for a total of 600 or an average of just over 10 per day over the 59 days of the study.
Notice in the three example paragraphs for the Method section that clear references to Tables 1 & 2, Figure 1 and Appendix I are provided to let readers know when and why these extra elements are relevant and helpful. Be sure also to include in your description of methods any additional approaches or sources of information that should be considered part of your research procedures, such as:
• Receipt information about customer purchases provided by the restaurant manager. • Conversations with restaurant servers who might confirm family relationships and estimated ages or tell you what was eaten and what was not by particular customer groups. • The analysis you performed to make sense of your results, such as counting customers, meals and behaviours and working out percentages and averages overall as well as for certain categories in order to answer the research questions.
Results The Results section is where you report what you discovered during your research, including the findings that do not support your hypothesis (or hypotheses) as well as those that do. Returning to your research questions to indicate exactly how the data you gathered answers them is an excellent way to stay focused and enable the selectivity that may be necessary to meet length requirements or maintain a clear line of argumentation. A Results section for the Pudgy’s research project might start like this:
The results of my investigation were both surprising and more complex than I had anticipated. I asked whether families constituted the majority of Pudgy’s clientele and assumed they did, but my research shows that they do not (see Figure 2 for information on customer categories). Even when the loosest definition of family as explained in my introduction is applied, only slightly over 25% (152) of the 600 Customer Fact Sheets record family visits to the restaurant. Among them fathers alone with their children are the most frequent patrons (68 Customer Fact Sheets or nearly 45% of the family category). The only day of the week on which families approach 50% of the restaurant’s customers is Sunday, particularly in the afternoon, when family groups account for 48% of the total customers averaged over the eight Sundays of observation. On all other days of the week, individual customers are the most frequent patrons, with their numbers hovering around 50% on most days. Single men visit the restaurant more often than any other customers and constitute as much as 61% of the clientele on a few weekday evenings.
The report of results might then continue by providing information about other categories of customer, what different types of customers ate and did, and any additional results that help answer the other research questions posed in the introductory paragraphs. Major trends revealed by the data should be reported, and both content and writing style should be clear and factual. Interpretation and discussion are best saved for the Discussion section except in those rare instances when guidelines indicate that research results and discussion should be combined in a single section. Although you will need to inform readers about any mathematical or statistical analysis of your raw data if you have not already done so in the Method section, the raw data itself is usually not appropriate for a short research paper. Selecting the most convincing and relevant evidence as the focus is, however, and the raw data can usually be made available via a university’s website or a journal’s online archives for expert readers and future researchers.
Discussion The Discussion section of a quantitative paper is where you interpret your research results and discuss their implications. Here the hypotheses as well as the research questions established in the introductory material are important. Were your primary suppositions confirmed by your results or not? Be precise and concise as you discuss your findings, but keep in mind that matters need not be quite as black and white or as strictly factual as they were in the Results section. Your ideas and argument should be soundly based on the data you collected, of course, but the Discussion is the place for describing complexities and expressing uncertainties as well as offering interpretations and explanations. The following opening briefly restates primary findings, picks up other important threads from the Results section and sets the stage for discussing the complexities involved in assessing the true value of Pudgy’s to the Quaintville community:
Although I had anticipated that families constitute the majority of Pudgy’s clientele, the evidence gathered over two months of observation does not support this supposition. In fact, individuals are the most frequent customers, with groups of teenagers running a close second. These teenagers are often in the restaurant when families are and they sometimes sit on the indoor toys instead of at the plastic tables and chairs, which I can confirm as extremely uncomfortable. On a few occasions the presence of teenagers appeared to intimidate the children and prevent them from playing on the facilities intended for them. In accordance with Parkson (2016) and Shemble (2017), my research also showed that most families who eat at Pudgy’s do not choose the healthier low-fat menu items, with the limited number and extremely high prices of these items offering little incentive. The few parents who make healthy choices for themselves and their children often do not insist upon the children eating those items, adding waste (of both food and money) to the problem. Furthermore, although Pudgy’s prices for their more traditional fast-food items are the lowest in town, at least two of the restaurants in Quaintville offer equivalent meals for similar prices and far healthier ones for just a little more.
The claim, then, in the Quaintville Times that “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p.A3) is revealed as more sentiment than fact. It would be equally erroneous, however, to insist that Pudgy’s Burgers has no value for the local community or to call it more of “a hazard…to the health of some of our poorest families” (“Local dive,” 2017, p.1) than any other restaurants serving burgers and chips in Quaintville. Indeed, I suspect those “poorest families” very rarely visit local restaurants at all, but my observations have revealed a great deal about who does eat at Pudgy’s, what they do when they are there and what kind of value the establishment actually has for Quaintville residents.
The discussion could then continue with information about the customers, behaviours and other issues that render the findings more complex and the restaurant more valuable to the community than the primary results noted above may indicate:
• Perhaps the restaurant serves a vital function as a social gathering place for all those single customers. Do they usually remain alone or do they meet up with others to linger and talk over coffee or lunch? • Do the teenagers who gather at Pudgy’s have an alternative place to meet out of the cold? In towns without recreation centres or other facilities for teens, restaurants with informal, open-door policies can be vital. Where might those teenagers go or what might they be doing were Pudgy’s not there? • Even though the evidence showed that families are not the most frequent customers, you may want to consider the value the restaurant has for the families who do use it. Those single fathers are certainly worthy of some attention, for instance, and perhaps family groups occasionally met up with other families, ate together and then lingered for dessert and talk as their children enjoyed the toys. This would be worth discussing too. • Less measurable considerations viewed through a qualitative research lens may be helpful as well, but the data collected through observations should support such discussions. Remember as you analyse your data, reflect on your findings, determine their meaning and develop your argument that it is important to keep the limitations of your methodology and thus of your results and their implications clearly in mind.
Offering recommendations is also standard in the Discussion section of a quantitative research paper, and here recommendations might be particularly useful if the franchise had not yet finalised its decision about closing Pudgy’s and was actively seeking community feedback. The researcher might suggest that Pudgy’s could better serve families by increasing the number of healthy food items on the menu, offering these for more affordable prices and making an effort to keep the teenagers off the children’s toys. Finally, the last part of a Discussion usually provides concluding comments, so summarising your key points and clearly articulating the main messages you want your readers to take away with them are essential. In some organisational templates, Conclusions are offered in a separate final section of the paper instead of at the end of the Discussion, so always check the guidelines.
References These references follow APA style, but since special fonts may not display properly in all online situations, please note that the titles of books and the names and volume numbers of journals are (and should be) in italic font. The list represents a sample only; a paper the length of the one posited in this example would almost certainly mention, discuss and list more than half a dozen studies and sources.
Chapton, D. (2017, September 29). Will Quaintville lose its favourite family restaurant? Quaintville Times , pp. A1, A3. Local dive sees last days. (2017, Autumn). Quaintville Community Newsletter , pp. 1–2. Shemble, M. (2017). Is anyone really eating healthy fast food in rural towns? Country Food & Families , 14 , 12–23. Shunts, P. (2013). The true cost of high-fat fast food for low-income families. Journal of Family Health & Diet , 37 , 3–19. Parkson, L. (2016). Family diets, fast foods and unhealthy choices. In S. Smith & J. Jones (eds.), Modern diets and family health (pp. 277–294). Philadelphia, PA: The Family Press. Whinner, N. (2015). Healthy families take time: The impact of fatty fast foods on child health. Journal of Family Health & Diet , 39 , 31–43.
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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.
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Characteristics of Quantitative Research
Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.
Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].
Its main characteristics are :
- The data is usually gathered using structured research instruments.
- The results are based on larger sample sizes that are representative of the population.
- The research study can usually be replicated or repeated, given its high reliability.
- Researcher has a clearly defined research question to which objective answers are sought.
- All aspects of the study are carefully designed before data is collected.
- Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
- Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
- Researcher uses tools, such as questionnaires or computer software, to collect numerical data.
The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.
Things to keep in mind when reporting the results of a study using quantitative methods :
- Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
- Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
- Explain the techniques you used to "clean" your data set.
- Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
- Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
- When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
- Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
- Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
- Always tell the reader what to look for in tables and figures .
NOTE: When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. [email protected] Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.
Basic Research Design for Quantitative Studies
Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:
- Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
- Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
- Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].
Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.
- Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
- Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
- Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.
Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .
- Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.
Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.
- Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
- Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
- Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
- Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.
Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.
- Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
- Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
- Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.
Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.
Strengths of Using Quantitative Methods
Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.
Among the specific strengths of using quantitative methods to study social science research problems:
- Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
- Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
- Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
- You can summarize vast sources of information and make comparisons across categories and over time; and,
- Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.
Limitations of Using Quantitative Methods
Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.
Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:
- Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
- Uses a static and rigid approach and so employs an inflexible process of discovery;
- The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
- Results provide less detail on behavior, attitudes, and motivation;
- Researcher may collect a much narrower and sometimes superficial dataset;
- Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
- The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
- Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.
Finding Examples of How to Apply Different Types of Research Methods
SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.
SAGE Research Methods Online and Cases
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- URL: https://libguides.usc.edu/writingguide
The Quantitative Methods Paper is made up of five sections:
Introduction Lit Review Methods Analysis Discussion and Conclusion
These sections are discussed below.
Introduction (Point Value - 10)
Sure the introduction to any paper introduces your paper to the reader, but the introduction section is more important than that to an academic paper (yes, that's what you are writing). There are many papers and journals out there in the world for social scientists to read. Your introduction needs to convince the sociologist that he or she needs to spend precious time reading YOUR paper. If you can't show why studying your dependent variable is important in a couple of paragraphs, then you need to get a new dependent variable. Why are things interesting or important? Perhaps it is because the topic is controversial (Some people believe/feel/act one way, and some another.). In any event that is the point of the intro.
Lit Review (Point Value - 10)
In this section, the main question that needs to be answered is what has been written before on your topic? In particular, you are interested in what has been written concerning any relationship between your dependent variable and your independent variables. In a normal academic paper, you need to demonstrate that you know every detail of the material important to your hypotheses. However, in this class I am only asking you to produce a minimal literature review.
What do I do if nothing has been written before on the topic?
This is an extremely unlikely occurrence. I would begin by looking for articles using alternate terms which have the same meaning as your concept. I would also talk to the professor. He is wise in the ways of science and can probably help.
At the end of the lit review, you state your hypotheses.
Methods (Point Value - 10)
The method section has three parts:
- Describe the data set.
This analysis utilizes interview data collected by the National Opinion Research Center (NORC) in the 1994 General Social Survey (hereafter GS S). The GSS, a nationwide annual survey, offers the advantage of multi-stage probability sampling and can be considered representative of English-speaking, noninstitutionalized adults (18 years of age and older) living in U.S. households. (For more detailed information on the GSS, see Babbie and Halley .) This examination of the relationships between x, y, and z relies on a subset of 958 of the 2992 original respondents. The data extract includes only questions asked on both interview ballots B and C for Version 2 of the 1994 GSS. This provides the researcher with a continuous set of questions with a lower number of missing cases; however, the trade-off is the lower number of total cases. Following is a brief description of the variables considered and of the frequency distributions for these variables.
Describe the variables
- How was the question asked in the survey?
- What were the response categories?
- If you had to recode the response categories, what are the categories that will be used in your analysis?
- What is the distribution of the dependent variable?
- Answer the same four questions with each of your independent and control variables in your analysis.
What type of analysis are you going to do?
In this class we are going to concentrate on making sure you can calculate univariate frequency distributions, crosstabular analysis, including control variables, and regression analysis.
Analysis (Total Point value - 60--itemized below)
The analysis section starts off with you restating your hypotheses. Then you begin your examination of whether those hypotheses were supported by the data.
- 1st Crosstab (Point Value - 15)
Using the output from the 1st crosstab, tell the reader if it was supported. Then you show the reader how you know it was supported (Hint: Talk about the %s in the crosstab table) Then you tell the reader if the results you see are statistically significant. Display the Chi-Square value (for this course use the Pearson's Chi-Square) and the p-value (normally the Asymp Sig; however, if you have 2X2 table use the Exact Sig (1-sided) result). If your table has significant results talk about the strength of the relationship.
- 2nd Crosstab (Point Value - 15)
Do the same thing as crosstab 1.
- Controlled Crosstab (Point Value - 20)
The controlled crosstabular analysis is also referred to by the phrase "the elaboration method". While we will have gone over this in class, you may want to look that phrase up in a couple of methods texts for a more in depth discussion. The first thing you have to do is choose which of the two hypotheses you tested is your primary hypothesis (HINT: it is most likely the hypothesis tested in crosstab 1. You are then going to control the relationship between the variables in your primary hypothesis by looking at the relationship between your independent variable and your dependent variable at every level of your control variable. What this means is that the computer builds a crosstab table to examine the relationship between your IV and DB for each responce category of the control variable. For example, if I were interested in the relationship between political party (PARTYID) and frequency of sexual relations (SEXFREQ) and I controlled that relationship by sex. SPSS would build a table crossing PARTYID and SEXFREQ for males and another table crossing PARTYID and SEXFREQ for females. If I had controlled by AGE instead, SPSS would have built a table crossing PARTYID and SEXFREQ for each age category. Each of these separate tables will have its own chi-square statistics and its own lambda and/or gamma statistics (if you asked SPSS to calculate statistics). Now, for the write up there are just about 5 different variations for the controlled crosstab write-up. You will need to see which one fits your situation. One of the major factors in deciding which variation you use will be the relationship you originally observed between your IV and DV in your earlier crosstabular analysis. Here we go: The first two cases occur when your initial crosstabular analysis weren't significant. If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and they are still not significant , you can then say: " My original relationship was not significant and when controlled by my control variable, Z, the relationship remained non-significant." . If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and one or more of the tables IS SIGNIFICANT, then you can say: " My original relationship was not significant; however, controlling by Z revealed a suppressed relationship between X and Y ". The next three cases occur when your initial crosstabular relationship was significant. If the original crosstabular analysis relating your independent variable to your dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the tables STILL SHOW A SIGNIFICANT RELATIONSHIP, then you can say: " My original relationship was significant and when controlled by Z remains significant. The relationship between X and Y is not caused by the influence of Z ". If the original crosstabular analysis relating your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the crosstab tables ARE NOT SIGNIFICANT, then you can say: " My original relationship was significant, but controlling for Z, the relationship now appears to be spurious. Z appears to be responsible for the observed relationships between X and Y. " Lastly, we have the tricky one--the mixed case. This case is, of course, what most of you are likely to see when you look at your controlled crosstabular analysis. IF the original crosstab comparing your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and see that SOME of the tables ARE SIGNIFICANT and SOME ARE NOT SIGNIFICANT, then you will need to make a judgment call. Here's the judgment: Were there enough respondents in each of the controlled crosstab tables? WHY IS THIS THE IMPORTANT JUDGMENT CALL? We know that as your N in a crosstab table increases that smaller differences are more likely to be considered statistically significant. It is possible that your data still exhibits the same patterns (in the percentages) that you saw in your earlier crosstab , but since your sample is divided across several tables it won't be statistically significant. IF you believe that the table does show the same pattern, but fails to be significant due to a small number of respondents. You may argue that. If you can argue that for all the controlled crosstab tables that aren't significant (if there aren't too many), then you could state that " It appears that the relationship between X and Y persists when one looks at the patterns in the column percentages; however, some of the controlled crosstab tables are not statistically significant. Still, I would argue against calling this a spurious relationship. My reading is that the relationship between X and Y is not truly caused by Z. " OTHERWISE, you will need to argue that the control variable mediates the relationship. That is, the control variable really helps delineate in which situations the relationship holds. For instance, you might find that your relationship between X and Y holds for whites but not for blacks or holds for males but not for females. This can be very important information. In this case you will need to report the significant relationships like you did in Crosstab 1.
- Regression (Point Value - 10)
We didn't get to regression this year, however, I would like to point out a few things that you will have to interpret. We look at the F statistic (and its significance) to determine if the model is significant. We look at the r-square to determine the amount of variation in the dependent variable that can be explained by the variables in the model. We look at the t-statistic (and its significance) for each independent variable. These tell us whether each IV is significantly related to the DV, controlling for the other variables in the model. We look at the b line to figure out the slope of the line. We look at the Betas to determine which variable has the most strongest relationship with the dependent variable.
- Conclusion (Point Value - 10)
As opposed to the rest of the paper which tends to be heavily formatted, the conclusion section is yours to say what you want. HOWEVER, you must say something. Traditionally, the conclusion section begins one more time with a statement of your hypotheses. This is followed by a summary of your findings. Were your hypotheses supported or not? The conclusion is more than just a summary, however, because you also get to speculate on how to do things better. For instance, it could be the case that your hypotheses weren't supported, but you really believe that the relationship exists. You could then bring up issues of validity and reliability. You could state that future research should ask more or different questions. You could state that future research should use more variables or add different variables. You could argue that the sample was poor. It's your opportunity to brainstorm on how future research should be done. Why do this? Well, the idea is that we, as social scientists, stand on the shoulders of the others that have come before. We owe future researchers who are reading your article to glean some knowledge about how to approach your concept at least some guideposts as to what we think worked, what didn't work, why, and what we would do if we were going to continue to do research on your topic.
Sample 1: Paper without tables attached.
- Research article
- Open Access
- Published: 22 July 2004
A quantitative analysis of qualitative studies in clinical journals for the 2000 publishing year
- Kathleen Ann McKibbon 1 , 2 &
- Cynthia S Gadd 1
BMC Medical Informatics and Decision Making volume 4 , Article number: 11 ( 2004 ) Cite this article
Quantitative studies are becoming more recognized as important to understanding health care with all of its richness and complexities. The purpose of this descriptive survey was to provide a quantitative evaluation of the qualitative studies published in 170 core clinical journals for 2000.
All identified studies that used qualitative methods were reviewed to ascertain which clinical journals publish qualitative studies and to extract research methods, content (persons and health care issues studied), and whether mixed methods (quantitative and qualitative methods) were used.
60 330 articles were reviewed. 355 reports of original qualitative studies and 12 systematic review articles were identified in 48 journals. Most of the journals were in the discipline of nursing. Only 4 of the most highly cited health care journals, based on ISI Science Citation Index (SCI) Impact Factors, published qualitative studies. 37 of the 355 original reports used both qualitative and quantitative (mixed) methods. Patients and non-health care settings were the most common groups of people studied. Diseases and conditions were cancer, mental health, pregnancy and childbirth, and cerebrovascular disease with many other diseases and conditions represented. Phenomenology and grounded theory were commonly used; substantial ethnography was also present. No substantial differences were noted for content or methods when articles published in all disciplines were compared with articles published in nursing titles or when studies with mixed methods were compared with studies that included only qualitative methods.
The clinical literature includes many qualitative studies although they are often published in nursing journals or journals with low SCI Impact Factor journals. Many qualitative studies incorporate both qualitative and quantitative methods.
Peer Review reports
Quantitative studies provide answers or insights for many important questions or issues in health care and clinical research. Other important questions dealing with why, how, contexts, and experiences of individuals or groups, can be best addressed using qualitative methods. Other issues benefit from interleaving or integration of both research traditions. Miller and Crabtree [ 1 ], describe their experiences working in family medicine, a clinical domain where balancing qualitative and quantitative research styles benefits both patients and their families and health care professionals. They embrace holding "quantitative objectivism in one hand and qualitative revelations in another" and encourage others to use findings from both paradigms in understanding and practicing effective health care. Creswell and colleagues expand on this theme by stating that "When used in combination, both quantitative and qualitative data yield a more complete analysis, and they complement each other" [ 2 ].
Most studies in the major clinical journals have been quantitative studies. Very few qualitative studies and even fewer that combine both qualitative and quantitative approaches are published. An example of the breadth of qualitative studies and how findings and results can be combined across paradigms is a study by Jolly and Wiles [ 3 , 4 ] who used mixed methods to study a nurse-led intervention for 422 adults after myocardial infarction and 175 adults with new-onset angina in 67 general practices in the United Kingdom. Their study showed statistically insignificant results at 1 month for eating healthy food, participating in exercise programs, and successful smoking cessation. Although patients in the nurse-led group were more likely to attend a rehabilitation program (37% vs. 22%, P = 0.001) attendance was disappointingly low. The researchers interviewed a group of patients using qualitative methods and found that people felt survival after a myocardial infarction indicated that the event had not been all that serious. Health care professionals often communicated simplified data about recurrence and being "back to normal" in 6 weeks. Because of these two issues, patients felt that their cardiac problems had probably been mild and therefore were not sufficiently motivated to implement major lifestyle changes.
Another example of the use of mixed methods was research done by Willms and Wilson and their colleagues [ 5 – 7 ] on smoking cessation. They found the meanings that patients who smoked attributed to their cigarettes (peer acceptance, coping during a time of stress and feeling out of control, feeling more like an adult, and smoking as more glamorous, tough, and rebellious) had more influence on cessation than did such external conditions as nicotine gum or counseling. Until the complex issues of why individuals smoke were dealt with, few were motivated to change their attitudes towards smoking and thus stop smoking.
Another effective example of integrated qualitative (ethnography) and quantitative (epidemiology) methods was a study done by Borkan and colleagues [ 8 ] to determine predictors of recovery after hip fracture in elderly patients. Traditional predictors such as age, type of break, and comorbidity, were collected by using standard questionnaires. In-depth interviews were used to collect injury narratives focusing on internal explanations of the fracture, sense of disability, and view of the future after hip fracture. None of the epidemiology factors predicted successful outcomes but those who perceived their fracture as more external or mechanical as opposed to an internal or organic problem (e.g., related to chronic disease) were more likely to have good recovery. Persons who perceived their disability in the context of autonomy, independence, and connection with the outside world also showed better ambulation at 3 and 6 months than persons with a more narrow and confined view of the fracture and its resulting disability.
Donovan and colleagues [ 9 ] used mixed methods to study prostate cancer screening and treatment choices to determine why study recruitment was lower than expected. Rousseau and Eccles and their colleagues [ 10 , 11 ] used qualitative methods (case interviews) to explain the limited use of computerized guidelines for asthma and angina in a primary care study done in the United Kingdom. Many other examples exist; Creswell and colleagues describe 5 additional mixed methods studies in primary care as well as provide criteria for evaluating mixed methods studies [ 2 ].
We postulate that qualitative studies, either stand-alone reports or studies with mixed methods, are occurring more frequently in health care. This paper was done to describe the publishing of qualitative studies in 1 year of clinical literature, document and present the range of content and techniques in these studies, and establish a baseline for subsequent studies. We defined our sample to include all articles published in a set of major general medical, mental health, or nursing journals during 2000. We determined how many qualitative studies were published and in which journals, and extracted design methods and healthcare content, and how often studies used mixed methods and analyses. Because the nursing literature published a higher proportion of qualitative studies in our sample we also compared studies published in nursing journals with other journals to ascertain if quantitative differences exist across disciplines in the use of qualitative methods. Our analysis is a quantitative review of qualitative studies in health care in 2000.
The Health Information Research Unit of the Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences at McMaster University in Hamilton, Ontario, Canada was the editorial office for four evidence-based summary journals in 2000: ACP Journal Club (internal medicine content), Evidence-Based Medicine (family/general practice content), Evidence-Based Nursing (general care nursing content), and Evidence-Based Mental Health (mental health care content). Their purpose is to provide enhanced abstracts and commentaries on important high-quality original studies and review articles for their respective clinical audiences. To identify these studies and review articles, 6 research staff read major clinical journals to ascertain if articles were in 1 or more categories of therapy, diagnosis, prognosis, etiology, economics, clinical prediction guides, differential diagnosis, and qualitative studies and if so, did each meet predefined methodology criteria for study quality[ 12 ]. For 2000 we intensified our data collection to provide data to update and develop new clinical retrieval searching hedges for MEDLINE, PsycINFO, CINAHL, and EMBASE using methods described by Haynes and colleagues[ 13 ]. One hundred and seventy journals provided data for this article.
The staff of the Health Information Research Unit has established quality criteria for the 8 categories of clinical literature that must be met before articles are judged appropriate for clinical application and publication in an abstract journal. Qualitative studies have 3 criteria:
• content relates to how people feel or experience certain situations, specifically those that relate to health care
• data collection methods and analyses are appropriate (primary analytical mode is inductive rather than deductive)
• units of collection and analysis are ideas, thoughts, concepts, phrases, incidents, or stories that become categories or themes.
The reading methods have been developed during the past 13 years and inter-rater reliability kappa (chance adjusted agreement) for identifying categories and applying criteria is consistently > 80%.
For this paper, KAM, one of the readers, analyzed the qualitative studies. Qualitative systematic reviews were excluded leaving only reports of original studies. These were assessed to extract journal title, qualitative study type, data collection methods, research question, persons studied, setting, and disease or health condition considered. In addition, studies with mixed methods were further analyzed although we did not use stringent criteria for assessing the quality [ 2 ] of the combination of methods. We identified mixed methods articles using a loose criterion of "some numerical or statistical analysis of quantitative data or qualitative data that had been turned into quantitative data". (An example of quantifying qualitative data is the study done by Borkan and colleagues [ 8 ] on hip fracture.) The analysis had to be fairly substantial–for example, a simple descriptive analysis of baseline demographics of the participants was not sufficient to be included as a mixed methods article.
In addition, Giacomini and Cook [ 14 , 15 ], as part of the Evidence-Based Working Group in the Users' Guides to the Medical Literature, describe attributes that they have identified as belonging to high-quality qualitative studies: participant selection, data collection, and analysis methods. These aspects were also extracted for analysis in this report.
Data were taken from article abstracts and if needed, the full text was reviewed. Methodologies assessed were phenomenology, grounded theory, ethnography, case studies, narrative analysis, participant action, critical incident techniques, and discourse analysis. Author descriptions were used and if an additional methodology was found it was added to the list of types using definitions and descriptions from the Handbook of Qualitative Analysis, 2 nd edition by Denzin and Lincoln [ 16 ]. Data collection and sampling procedures were also extracted. Multiple designations were allowed. To assess the reproducibility a random 10% (n = 35) sample of citations was reviewed using predefined decision rules by another researcher trained in research methods.
The 170 journals included 60 330 articles of which 31 496 (52%) contained original data or were review articles. 3830 of these (6%) passed criteria for being high-quality and clinically relevant in 1 of the 8 categories. 367 articles met quality criteria for original studies or reviews of qualitative studies. Table 1 lists the journals that published at least 1 qualitative study. Twelve systematic reviews were excluded leaving 355 qualitative studies for assessment. Approximately 0.6% of all articles in the 170 journals and 9% of all high-quality, clinically relevant studies were qualitative studies.
The reproducibility of the categorization was measured by kappa (chance adjusted agreement): 0.92 for disease/condition, 0.83 for groups studied, 0.81 for setting, 0.73 for data collection, and 0.63 for data analysis type. The agreement for data analysis type was disappointing but not surprising in that 20% of the studies did not label their analyses necessitating assignment of analysis type by the data extractors. Agreement was low for participant selection methods (kappa 0.5) and therefore data on participant selection methods are not reported.
The 355 qualitative studies appeared in 48 journals (mean 7.4 articles per journal, range 1 to 86). These 48 journals were only 28% of the 170 clinical journals being read. Most of the qualitative studies were published in nursing journals: The 17 nursing titles included 214 qualitative studies (61% of all of the qualitative studies).
Few qualitative studies were published in the high-circulation, general healthcare journals. Using SCI Impact Factor ranking for 2000, only 4 of the top 20 journals (Table 2 ) published qualitative studies. These 4 journals published 15 qualitative studies with BMJ publishing 12. The highest-ranking journal with qualitative studies was Annals of Internal Medicine , ranked number 6. JAMA , ranked number 2, published articles about qualitative studies in 2000 [ 14 , 15 ] but did not publish any qualitative studies.
Mixed qualitative and quantitative studies
37 qualitative studies (11%) included qualitative and quantitative methods and analyses. These were published in 17 journals with only 1 article in BMJ from the top 20 journal titles in Table 1 . Social Science and Medicine published 10 of these mixed methodology studies–the most of any title studied.
Content of the studies is shown in Table 3 . Many studies dealt with a range of participants and settings. Patients (56%), family (22%), and other non-health care professionals (14%) were studied more often than health professionals (nurses (21%), physicians (11%), and others (5%)). Non health care settings occurred more often with home or similar settings being studied in 44% of studies and other community settings in 16%. Health care settings were the hospital (25%), clinic (17%), nursing home (5%), and the emergency department (2%).
Disease/condition breakdowns represented common health care situations: cancer (11%), mental health (10%), pregnancy and childbirth (9%), cerebrovascular disease (10%), general issues such as vaccinations or Internet use, and nonspecific spectrum of diseases (e.g., all patients in a clinic) (12%). Many uncommon issues were also assessed. For example, Tongprateep [ 17 ] reports a phenomenology study designed to help nurses better understand essential elements of spirituality and health among rural Thai elders.
Analysis of the 37 articles with mixed methods showed similar patterns for settings, persons studied, and disease/condition evaluated except that more physicians were studied (P < 0.025) and more situations dealing with injury (P < 0.001) were evaluated. For the 211 articles in Nursing journals, very little difference was also seen except that fewer physicians were studied (P < 0.001) and more studies were done outside clinical settings (P < 0.001).
Phenomenology (37%), grounded theory (35%), and ethnography (18%) were used most often with some case studies (7%), narrative analysis (6%), participant action (3%) research, critical incident techniques (1%), and discourse analysis (1%) (Table 4 ). More than one qualitative method was used in 8% of studies. This pattern of methodology choice was similar for the 37 mixed methods studies and the 211 Nursing articles except that mixed studies methods used relatively more case studies and the Nursing studies used fewer of them (P < 0.025). The mixed methods studies did not included participatory action research, critical incident technique, or discourse analysis studies, methods that could be difficult to combine with quantitative studies.
Semi-structured interviews were used (77%) with some focus groups (18%) and observation (14%). These methods are major data gathering techniques in qualitative studies. Questionnaires (7%), document analysis (6%), and structured (4%) and unstructured interviews (1%) were used less often. For mixed methods studies, patterns were similar although questionnaires were used more frequently (24% vs. 7%, P < 0.01). Nursing studies did not differ for data gathering techniques.
Sampling is important in all studies–often no single right way exists for a study question. Purposive, snowball, and theoretical sampling are often used in qualitative studies and random and consecutive sampling for quantitative studies. All methods were represented in this analysis but the breakdowns are not reported because of low inter-rater agreements for categorization and missing author information.
In 2000 the major clinical journals published many qualitative studies–approximately 9% of all high-quality, clinically relevant articles. Most of the qualitative studies were reports of original research although 12 (3%) were systematic reviews. Most of the qualitative studies were in nursing journals although some medical journals such as BMJ and Annals of Internal Medicine also published several. Three of the high circulation medical journals ( New England Journal of Medicine, Lancet, and JAMA ) and 16 of the top 20 clinical journals, based on SCI Impact Factors, did not publish any qualitative studies. This is likely a reflection on the emphasis on a positivist, numerical approach that many of these journals embrace. The difference in proportion of qualitative studiers published in nursing journals is probably because of two historical, but linked factors. Qualitative studies have roots in women's studies and the nursing profession has always dealt with the patient as much more of a whole person rather than basic sciences facts and numbers. Both of these factors lead to more emphasis on understanding and embracing qualitative methods for research and practice. This view is substantiated by the fact that MEDLINE indexes most of the qualitative studies under the term Nursing Methodology Research until 2003.
A substantial proportion of the qualitative studies (11%) included both qualitative and quantitative (mixed) data. In general, these mixed methods studies were similar to the single methodology studies except they did more assessments of physicians and relied more on questionnaires to gather data for analysis. The presence of these mixed methods or multipardigmatic studies as described by Miller and Crabtree [ 1 ] and Creswell [ 18 ] is encouraging for those who espouse harnessing methodologies appropriate for exploring, explaining, and interpreting the complexities and ranges of issues in health care practice and research.
It is also interesting comparing qualitative studies in Nursing and non-Nursing journals. Regardless of the differences in proportion of qualitative studies published, from a content point of view few differences exist between the Nursing and non-Nursing journals except that more physicians were studied in the non-Nursing journals and fewer studies were done in clinical settings–not unsurprising findings. This indicates that the content and methods of qualitative studies seem to be similar across disciplines or if the methods are combined with quantitative methods.
This review of the publication of qualitative studies is limited in several ways. The proportion of journals studied was very low in relation to the total number of journals published. MEDLINE indexes over 4000 journals and this number is still a relatively small proportion of all journals that deal with health care. In addition, all of the journals searched were published in English so we do not know about qualitative studies in other languages. Although our criteria were relatively strict for including qualitative studies, our criteria for mixed methods studies could certainly have been stronger. We did not count the number of high-quality quantitative studies that could have included some qualitative analyses. We studied only 1 year of publishing; much could have changed since 2002.
Qualitative studies provide insight into social, emotional, and experiential aspects of health and health care and as such, they have an important place in understanding health and health care. Hopefully more studies will be published and more will be published in the high impact (high circulation) journals. This paper provides a basis for measuring increases.
Qualitative studies are being done and are published in a wide range of healthcare journals. These journals however are not the highest impact journals. It is encouraging to see that the number of qualitative studies that were published in 2000 and also the number of studies that combined qualitative and quantitative methods. More can be done however to complete and publish qualitative studies, and where appropriate, integrate the best of both methodologies. Both qualitative and quantitative researchers and clinicians need to work together to make this happen. Journal editors can also encourage submission of qualitative and mixed methods studies and facilitate publication of those they do receive.
This work was done in partial fulfillment of PhD requirements for KAM. Both authors have supplied intellectual input in designing and implanting the survey. KAM has collected and analyzed the data and both authors have contributed to writing the paper and agree on its content.
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McKibbon, K.A., Gadd, C.S. A quantitative analysis of qualitative studies in clinical journals for the 2000 publishing year. BMC Med Inform Decis Mak 4 , 11 (2004). https://doi.org/10.1186/1472-6947-4-11
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DOI : https://doi.org/10.1186/1472-6947-4-11
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Published on 20.8.2020 in Vol 22 , No 8 (2020) :August
Digital Inequality During a Pandemic: Quantitative Study of Differences in COVID-19–Related Internet Uses and Outcomes Among the General Population
Authors of this article:
- Alexander JAM van Deursen, Prof Dr
University of Twente, Enschede, Netherlands
Alexander JAM van Deursen, Prof Dr
University of Twente
Phone: 31 622942142
Email: [email protected]
Background: The World Health Organization considers coronavirus disease (COVID-19) to be a public emergency threatening global health. During the crisis, the public’s need for web-based information and communication is a subject of focus. Digital inequality research has shown that internet access is not evenly distributed among the general population.
Objective: The aim of this study was to provide a timely understanding of how different people use the internet to meet their information and communication needs and the outcomes they gain from their internet use in relation to the COVID-19 pandemic. We also sought to reveal the extent to which gender, age, personality, health, literacy, education, economic and social resources, internet attitude, material access, internet access, and internet skills remain important factors in obtaining internet outcomes after people engage in the corresponding uses.
Methods: We used a web-based survey to draw upon a sample collected in the Netherlands. We obtained a dataset with 1733 respondents older than 18 years.
Results: Men are more likely to engage in COVID-19–related communication uses. Age is positively related to COVID-19–related information uses and negatively related to information and communication outcomes. Agreeableness is negatively related to both outcomes and to information uses. Neuroticism is positively related to both uses and to communication outcomes. Conscientiousness is not related to any of the uses or outcomes. Introversion is negatively related to communication outcomes. Finally, openness relates positively to all information uses and to both outcomes. Physical health has negative relationships with both outcomes. Health perception contributes positively to information uses and both outcomes. Traditional literacy has a positive relationship with information uses and both outcomes. Education has a positive relationship with information and communication uses. Economic and social resources played no roles. Internet attitude is positively related to information uses and outcomes but negatively related to communication uses and outcomes. Material access and internet access contributed to all uses and outcomes. Finally, several of the indicators and outcomes became insignificant after accounting for engagement in internet uses.
Conclusions: Digital inequality is a major concern among national and international scholars and policy makers. This contribution aimed to provide a broader understanding in the case of a major health pandemic by using the ongoing COVID-19 crisis as a context for empirical work. Several groups of people were identified as vulnerable, such as older people, less educated people, and people with physical health problems, low literacy levels, or low levels of internet skills. Generally, people who are already relatively advantaged are more likely to use the information and communication opportunities provided by the internet to their benefit in a health pandemic, while less advantaged individuals are less likely to benefit. Therefore, the COVID-19 crisis is also enforcing existing inequalities.
The World Health Organization considers coronavirus disease (COVID-19) to be a public emergency threatening global health [ 1 ]. Governments worldwide have taken stringent action, including requiring social distancing, closing public services, schools and universities, and canceling cultural events [ 2 , 3 ]. People are being advised or ordered to stay at home and socially isolate themselves to avoid being infected [ 4 ]. The ongoing pandemic represents an outbreak of an unparalleled scale, and it has induced widespread fear and uncertainty.
In this paper, we focus on the role of the internet during the crisis. The internet has become a crucial source for the general public, as it provides access to general information, the latest national and international developments, and guidelines on behavioral norms during the crisis. In this respect, the internet plays an important role in the great challenges facing governments regarding the transfer of knowledge and guidelines to the population at large. When individuals understand the need and rationale behind government-enforced measures, they are more motivated to comply and even adopt measures voluntarily [ 5 , 6 ]. In addition to informational purposes, the internet enables individuals to share news and experiences with people they cannot meet face-to-face, remain in contact with friends and family, seek support, and ask questions of official agencies, including health agencies. Further, the internet enables people to take initiatives such as raising money or preparing packaged meals for people in need, such as health workers or people who have lost their jobs. In sum, the internet plays a vital role for people of all social strata and backgrounds during a time of worldwide crisis. All people should thus be able to use the internet as a source of information and communication.
However, digital inequality research has shown that internet access is not evenly distributed among the general population [ 7 , 8 ]. The basic idea of digital inequality stems from a comparative perspective of social and information inequality, as there are benefits associated with internet access and negative consequences of lack of access [ 9 ]. Calamities are often a story of inequality [ 10 ]; therefore, in this paper, we aimed to gain a deeper and broader understanding of the differences in how people use the internet to cope during the COVID-19 crisis. Van Dijk’s resources and appropriation theory [ 8 ] explains differences or inequalities of internet access by considering personal and positional categories of individuals and the individuals’ resources. Internet access itself is considered to be a process of appropriation involving attitudinal access, material access, skills access, and in the final stage, usage access. The latter entails differences in the type of activities that people perform on the internet. The consequences of the process are the outcomes of internet use. These outcomes in turn reinforce personal and positional inequalities and an unequal distribution of resources [ 8 ] ( Figure 1 ). The first goal of this paper is to provide a timely understanding of how different people use the internet and the outcomes they gain from it in relation to the COVID-19 pandemic.
Internet use and outcome differences between groups of people are likely to have profound consequences on how people manage a crisis. For example, older people are most in danger of being infected with the virus and most likely to die from the infection [ 11 ], and they also use the internet less and have the fewest internet outcomes [ 12 ]. The latter may further endanger their peculiar situation, as limited internet use and outcomes may result in a lack of critical information or necessary support.
COVID-19–Related Internet Uses and Outcomes
To study differences in internet uses and outcomes during the COVID-19 pandemic, it is necessary to understand the types of uses and outcomes that are at play. Typically, uses and outcomes are studied by following conceptual classifications that distinguish different domains, such as economic, social, cultural, or personal domains [ 13 ]. Here, we take the COVID-19 pandemic as the domain of interest. Within this domain, we consider two main and conceptually different types of uses and outcomes: information and communication [ 14 , 15 ]. Information internet uses involve searching for information on all aspects of COVID-19. Potential information outcomes include becoming better informed about the disease, understanding why certain measures are necessary, and limiting the risk of becoming infected by developing greater awareness of one’s own behavior. Communication internet uses include talking to friends about the crisis, asking questions on social media or online fora, giving advice, or offering support to others. Communication outcomes include finding people on the internet who can offer support or share concern, being less lonely, and protecting others from potential COVID-19 risks. Studying both types of uses and outcomes is important, as prior research has shown that communication uses can compensate for information uses to attain beneficial internet outcomes [ 16 ].
Determinants of COVID-19–Related Internet Uses and Outcomes
Digital inequality research suggests that the vast amount of web-based information and communication possibilities around the COVID-19 pandemic are likely to be difficult to grasp and conceptualize for sections of the general population [ 7 ]. Some frequently observed personal categorical inequalities are gender, age, personality, and health [ 7 ]. Earlier research revealed that men and women differ in their internet activities; women are more likely to use email and social media, whereas men are more likely to use the internet to obtain information [ 17 , 18 ]. Age in general has a negative impact on all types of internet uses and outcomes [ 7 ]. In the COVID-19 crisis, older people are especially vulnerable; therefore, it is very important for them to know how to behave and be safe. We hypothesize that (H1) men are more likely to be involved in information-related uses and outcomes while women are more likely to be involved in communication-related internet uses and outcomes regarding COVID-19-related internet uses and outcomes. We also hypothesize that (H2) age contributes negatively to COVID-19–related internet uses and outcomes.
An individual’s personality may hinder or stimulate their engagement in certain COVID-19–related activities. Cognitive appraisal theory suggests that individuals complete two types of cognitive appraisal processes in a crisis [ 19 ]. The process starts with an evaluation of the crisis as a potential source of danger or life disruption. If the crisis is not determined to be dangerous, it is not considered a stressor and does not require intervention. If the crisis is determined to be relevant, it is considered a stressor and must be evaluated further by balancing the demands of the crisis and the person’s resources [ 20 ]. At this point, personality enters the equation [ 20 ]. There is a general consensus regarding the Big Five model when personality traits are studied. This model proposes five personality traits of agreeableness, neuroticism, conscientiousness, introversion, and openness [ 21 ]. However, there is no agreement as to whether these traits contribute to or detract from resisting disturbance [ 20 ]. There is also no consensus on how the Big Five personality traits relate to internet use [ 7 , 22 ]. For example, conscientiousness relates to people who abide by rules. On one hand, one might argue that this would result in a greater need for information on how to behave. On the other hand, the internet is unstructured, and rules and policies are absent to a large extent. When linking personality traits to internet use for psychological adjustments to the COVID-19 crisis, it is not evident whether these traits will support or hinder COVID-19–related internet uses and outcomes. We hypothesize that (H3a) agreeableness, (H3b) neuroticism, (H3c) contentiousness, (H3d) introversion, and (H3e) openness are related to COVID-19–related internet uses and outcomes.
An individual’s health may play an important role in how they approach COVID-19. To gain an elaborate understanding of how health relates to COVID-19–related internet uses and outcomes, we followed earlier research that distinguishes between different health aspects [ 23 ]: A person’s physical functioning or the degree to which their health currently interferes with activities such as sports, carrying groceries, climbing stairs, and walking, their mental health or psychological distress and well-being, and their health perception concerning their own health rating in general. During a crisis, we expect that people with health issues are more likely to turn to the internet for comfort and reassurance. We hypothesize that (H4a) physical functioning, (H4b) mental health, and (H4c) health perception contribute negatively to COVID-19–related internet uses and outcomes.
The final type of personal inequality considered in this study is traditional literacy, which is known to have a substantial impact on how the internet is used [ 24 , 25 ]. We consider literacy to be the ability to read, write, and understand text, which is also framed under the umbrella terms functional literacy or fundamental literacy [ 24 ]. Functional or traditional literacy can be considered as the basic dimension of all literacy concepts [ 26 ]. Considering the crucial role the internet is playing in the COVID-19 crisis, a low level of literacy is a potentially large inhibitor of understanding information and being involved in web-based communication. We hypothesize that (H5) traditional literacy contributes positively to COVID-19–related internet uses and outcomes.
Education is the most observed positional categorial inequality in digital divide research, and it is likely to play a role in the current context. People with higher levels of education are better equipped to comprehend web-based information and benefit from internet use [ 7 ]. We hypothesize that (H6) education contributes positively to COVID-19–related internet uses and outcomes.
When studying differences in internet uses and outcomes, the resources people can access are often derived from Pierre Bourdieu’s capital theory [ 27 ], which stresses the importance of including not only economic but also social and cultural resources to determine one’s status and position in society. In the COVID-19 pandemic, economic and social resources are likely to be important, as earlier research has shown that people with greater economic resources—mostly operationalized as income in digital inequality research—are known to use the internet more efficaciously and productively [ 7 , 28 ]. People with more social resources are more likely to have access to family, friends, or other contacts on the internet [ 29 ]. We hypothesize that (H7a) economic and (H7b) social resources contribute positively to COVID-19–related internet uses and outcomes.
The Internet Appropriation Process
The core of the resources and appropriation theory is access to technology, which is considered as a process of appropriation involving attitudinal, material, skills, and usage access. Attitudinal access concerns a person’s attitude towards the internet; according to theories of technology adoption, this type of access is crucial for using the internet [ 30 ]. Material access can be defined in terms of the different devices that people use to access the internet and all other web-based resources, including desktop computers, laptop computers, tablets, smartphones, game consoles, and interactive televisions [ 31 ]. Skills access concerns the skills necessary to use the internet, ranging from operational and information skills to social and content creation skills [ 32 ]. Prior research has revealed that all three types of internet access directly affect internet uses and outcomes [ 16 ]. We hypothesize that (H8a) attitudinal internet access, (H8b) material internet access, and (H8c) skills internet access contribute positively to COVID-19–related internet uses and outcomes.
The Effects of COVID-19–Related Internet Uses on Their Corresponding Outcomes
A recent multifaceted consideration of digital inequality revealed a strong effect of internet uses on outcomes [ 12 ]. Further, people’s internet activities appeared to be more important than their personal characteristics with regard to inequalities in outcomes of internet use. This suggests that the variables discussed in the prior sections will become less important for obtaining information outcomes when people are involved in COVID-19–related internet information uses. This is also true for COVID-19–related communication uses and outcomes. The second goal of this paper is to reveal the extent to which the indicators discussed remain important for obtaining internet outcomes after people are involved in the corresponding uses.
This study used a web-based survey and drew upon a sample collected in the Netherlands. To obtain a representative sample of the population, we used PanelClix, a professional organization for market research, to provide a panel of approximately 110,000 people. Members of the panel received a small incentive for every survey they completed. In the Netherlands, 98% of the population uses the internet; therefore, the internet user population is very closely representative of the general population in terms of its sociodemographic makeup. The panel included novice and advanced internet users. In total, we aimed to obtain a dataset with approximately 1700 respondents over the age of 18. Eventually, this resulted in the collection of 1733 responses over a 1-week period in April 2020. During the data collection period, three amendments to the sampling frame were made to ensure the representativity of the Dutch population. Accordingly, the analyses revealed that the gender, age, and formal education of our respondents largely matched official census data. As a result, only very small post hoc corrections were needed.
The web-based survey used software that checked for missing responses and prompted users to respond. The survey was pilot-tested with 10 internet users over two rounds. Amendments were made based on the feedback provided. No major comments were provided in the second round. The average time required to complete the survey was 20 minutes.
We initially developed 11 survey items pertaining to COVID-19–related internet use. Respondents were asked to indicate the extent to which they used the internet for various activities in the past month using a 5-point scale (“not” to “multiple times a day”) as an ordinal-level measure. Principal component analysis with varimax rotation was used to determine two underlying usage clusters, one related to information and one to communication. Factor loadings were employed at 0.4 and above for each item [ 33 ]. In total, 8 items (3 for information and 5 for communication) were retained in a two-factor structure with eigenvalues over 1.0, together accounting for 76% of the total variance.
For COVID-19-related information and communication internet outcomes, we developed 14 items mapped onto the use items. A 5-point agreement scale as an ordinal level measure was used. Principal component analysis with varimax rotation resulted in a structure that matched the conceptual definition of information outcomes (4 items) and communication outcomes (4 items). The two factors showed eigenvalues over 1.0 and explained 65% of the variance.
Gender was included as a dichotomous variable, and age was directly asked (mean 50.2, SD 17.0).
Personality was measured with the Quick Big Five personality questionnaire [ 34 ], which consists of 30 adjectives reflecting a valid and reliable measure of the Big Five traits. Participants were asked to rate the extent to which a particular adjective applied to them on a 7-point scale, ranging from completely untrue to completely true. The Cronbach α values for the five traits were .89 for agreeableness, .88 for neuroticism, .88 for conscientiousness, .87 for introversion, and .81 for openness.
Physical health, mental health, and health perception were measured with the Dutch version of the Medical Outcomes Study (MOS) Short-Form General Health Survey (SF-20) [ 35 ]. This instrument enables respondents to assess their general health and generates composite summary scores representing different types of health. We normalized the scales, with higher scores representing better functioning. Physical health was measured with 5 items (2-point scale; α=.89; mean 1.75, SD 0.34), mental health with 5 items (5-point scale; α=.85; mean 3.65, SD 0.77), and health perception with 5 items (5-point scale; α=.86; mean 3.39, SD 0.85).
To measure traditional literacy, we used the validated 11-item Diagnostic Illiteracy Scale [ 36 ]. Sample items included “I have difficulties with reading and understanding information from my municipality” and “I find it difficult to read and understand my telephone bill.” A 5-point agreement scale was used. Scores on the scale exhibited high internal consistency. Items were recoded so that higher scores corresponded with higher levels of literacy (α=.94; mean 4.33, SD 0.71).
To assess education, data regarding degrees earned were collected and used to create three groups: low (primary), middle (secondary), and high (tertiary) educational achievement.
Economic resources were objectively measured by seeking the annual family income in the last 12 months. Twelve categories were recoded into three categories of low for <€30,000 (US $35,503.50), middle for €30,000 to €70,000 (US $35,503.50 to $82841.50), and high for >€70,000 (>US $82841.50). For social resources, we used the MOS Social Support Survey [ 37 ]. Respondents completed 18 items covering emotional support (eg, “Someone you can count on to listen when you need to talk”), informational support (eg, “Someone to give you good advice about a crisis”), and tangible support (eg, “Someone to help you if you were confined to bed”). All items were rated on a 5-point Likert scale with anchors of none of the time (1) and most of the time (5). We computed an aggregate measure of support availability (α=.96; mean 3.83, SD 0.85).
Attitudinal internet access was measured by three items adapted from the Digital Motivation Scale [ 38 ]. A 5-point agreement scale was used, and all items were balanced for the direction of response (α=.74; mean 4.10, SD 0.70). An example statement is “Technologies such as the internet and mobile phones make life easier.” To measure material internet access, we considered 7 devices used to connect to the internet (mean 3.43, SD 1.53). Included were desktop computer, laptop computer, tablet, smartphone, smart TV, game console, and smart device (eg, activity tracker). Finally, skills internet access was adapted from the conceptual idea behind the Internet Skills Scale [ 32 ]. We proposed 30 items reflecting operational, information navigation, social, and creative internet skills. A 20-item single skills construct resulted from the principal component analysis. All items were scored on a 5-point scale that ranged from “not at all true of me” to “very true of me” and exhibited high internal consistency (α=.96; mean 3.67, SD 0.97). Example items are “I know how to open downloaded files,” “I find it hard to decide what the best keywords are to use for online searches,” and “I know which information I should and shouldn’t share online.”
To test the hypotheses and account for the sequentiality between COVID-19–related internet uses and outcomes, hierarchical regression analyses were used. In the first model, we tested our hypotheses by analyzing the significant determinants for the two types of COVID-19–related internet uses and the two corresponding outcomes. In the second model, we sought to determine the changes in the significance of the determinants after the internet uses were added to the models.
Table 1 provides an overview of the sample of people surveyed in the study.
Table 2 shows the mean scores of the survey questions related to internet uses and internet outcomes.
The first goal of this paper was addressed in the first model, as presented in Table 3 , where several significant determinants for COVID-19 uses and outcomes are revealed.
a Low: primary; middle: secondary; high: tertiary.
a COVID-19: coronavirus disease.
b RIVM: Rijksinstituut voor Volksgezondheid en Milieu.
Table 3 shows that men are more likely to be involved in COVID-19–related communication uses. Age is positively related to COVID-19–related information uses and negatively related to COVID-19 communication uses and outcomes. Concerning personality traits, agreeableness is negatively related to COVID-19–related information and communication uses and to communication outcomes. Neuroticism is positively related to both uses and to communication outcomes.
Conscientiousness is not related to any of the uses or outcomes. Introversion is negatively related to COVID-19–related communication uses and outcomes, suggesting that this is performed more by extraverted people. Finally, openness relates positively to information uses and to both outcomes.
The results further show that concerning the three health indicators, physical health is negatively related to communication uses and outcomes. Mental health did not contribute to any uses or outcomes. Health perception contributes positively to information uses and to both outcomes.
Traditional literacy has a positive relationship with information-type uses and with both outcomes, and education has a positive relationship with COVID-19–related information and communication uses. Economic and social resources were not related to any COVID-19 uses or outcomes.
Attitudinal internet access is positively related to information uses and outcomes but is negatively related to communication uses and outcomes. Material internet access contributes positively to all uses and outcomes, and skills access has a positive relationship with all uses and outcomes. Table 4 provides an overview of the hypotheses.
a ns: no significant contribution.
b –: significant negative contribution.
c R: reject.
d +: significant positive contribution.
e PS: partial support.
f S: support.
Finally, to address the second goal of the study, we tested what would happen to the contribution of the outcome determinants when the corresponding uses were added to the analyses (Model 2: see Tables 5 and 6 ). Adding the uses significantly increased the explained variance; also, several of the relationships between personal and positional categories and between resources and outcomes became insignificant. The relationships that remained significant for information outcomes were age, health perception, and traditional literacy. Furthermore, attitudinal internet access remained significant. For communication outcomes, the relationships that remained significant were age, openness, and traditional literacy.
a N/A: not applicable.
This paper aims to provide a comprehensive examination of digital inequality in the case of an unprecedented health pandemic. The first goal of the study was to reveal how inequality manifests itself in COVID-19–related internet information and communication uses and outcomes. The findings revealed several relationships between the background variables and the two types of internet uses and outcomes.
Older people were found to be less equipped to use the internet for information and communication during a time of crisis. However, they were more likely to engage in information-type COVID-19–related internet uses, possibly because they are at greatest risk from the disease [ 11 ]. This did not result in more beneficial information outcomes. Internet skills play an important role in translating internet uses into beneficial internet outcomes [ 39 ], and prior research has shown that older people have lower internet skill levels in general [ 32 ]. The finding that older people are less likely to perform communication activities or obtain communication-related outcomes is in line with prior studies [ 15 ]; however, these outcomes are important, as older people are more at risk of having severe complications when diagnosed with COVID-19. Regarding gender, contrary to general internet use, men were found to be more likely to engage in communication-type COVID-19–related internet uses during the crisis than women. A possible explanation is that men and women may respond to crisis news in different ways [ 40 ].
The positive effect of neuroticism suggests that a tendency to experience negative emotions such as anger, anxiety, or depression fuels the need to turn to the internet for COVID-19–related information and communication. People who score higher on the neuroticism scale may be more in need of guidelines on how to mitigate risks or may need more support from others to be comforted. Also, the openness trait supports both information and communication internet use and outcomes. A possible explanation is that a major crisis triggers adventure, unconventional ideas, imagination, awareness of feelings, curiosity, or a variety of experiences, all of which are aspects linked to high openness [ 21 ]. The negative contribution of agreeableness raises questions. A possible explanation is that agreeable people are less frequently sought out for communication activities. However, the internet may also be a very inviting environment for less agreeable people. Conscientiousness did not appear to be a significant determinant. People who are more stubborn and focused or more flexible and spontaneous both appear to be involved in information- and communication-type COVID-19–related internet uses and outcomes. Extroversion emerged as a trait that supports using the internet for communication uses and outcomes; this can be expected, as extroversion is marked by pronounced engagement with the external world [ 21 ].
Although we expected that psychological distress would play a role in the current context, as there would be a relatively high need for information and support from others, mental health did not surface as a significant contributor. Furthermore, we did find that physical health problems appear to encourage web-based COVID-19–related communication uses and outcomes. The most likely explanation is that people with underlying health problems are more at risk (and thus more bound to their homes) and thus have higher needs for communication with friends and family. A possible reason for the positive effect of health perception is that people who believe their personal health to be good may feel better equipped to support others during the COVID-19 pandemic.
As expected, traditional literacy played an important role. A lack of general ability to read, write, and understand text further disadvantages individuals in the case of the COVID-19 pandemic, as they have less access to information and communication sources. COVID-19 is a new, unknown, and complicated disease with characteristics that are often described in difficult medical language that is not easy to read. Similar findings were found for educational attainment. Research has long shown that education is one of the most prominent positional variables in digital divide research [ 7 ]. However, our results suggest that when less educated individuals are involved in information and communication internet uses, they are as likely to achieve the corresponding outcomes as people with higher levels of education. This is an important finding for designing interventions for those of lower levels of education.
An effect of economic resources did not emerge in relation to COVID-19–related internet uses and outcomes. The participants’ income did not make a difference in obtaining information and communication COVID-19–related internet outcomes. Earlier research often showed that income is especially important to consumptive and work-related internet uses [ 17 ], topics that are not considered here. Unexpectedly, social resources were not found to be influential. Apparently, a person who has an offline support network will not necessarily turn more to web-based information and communication support during a crisis.
Concerning internet access, we can first conclude that a person’s internet attitude is important for engaging in information uses and gaining information outcomes. Unexpectedly, there was a negative contribution of internet attitude to communication uses and outcomes, suggesting that individuals who have a negative evaluation of the internet in general are more likely to engage in communication uses in the event of a major crisis. Both material and skills internet access played important roles in achieving all uses and outcomes. Using a higher diversity of devices was related to higher COVID-19–related internet use and to more outcomes. The opportunities devices offer are known to be related to inequalities in internet uses and outcomes. As each device offers its own specific characteristics and advantages, a higher diversity of devices supports a larger range of use activities and outcomes [ 31 ]. Furthermore, internet skills play a fundamental role in COVID-19–related uses and in obtaining beneficial outcomes [ 12 ].
In this paper, several indicators surfaced for people’s web-based COVID-19–related uses and outcomes. The variety of important indicators raises the question of whether general policies to address digital inequalities in a time of crisis will be effective. The complex relationships between the different indicators on one hand and internet uses and outcomes on the other hand demand more focused policies, such as those related to health indicators and the need for information to enhance health outcomes. This study reveals that the greater an individual’s existing advantages, the more they benefit from the internet at a time of crisis; the converse is true as well. Marginalized people are likely to have fewer types of access available to take actions, behave as requested, or be comforted by help, creating a vicious cycle where already marginalized groups are further marginalized in a time of crisis.
To end on a positive note, the situation may become slightly less complex when we address the second goal of this paper. When people engage in information and communication internet uses in a crisis situation, their personal characteristics become less important to achieving the corresponding outcomes. This suggests that to achieve information and communication outcomes, policy or research should especially focus on encouraging people to engage in the corresponding internet uses, as we can assume to some extent that engagement with information and communication-related COVID-19 uses is the best way to achieve beneficial outcomes at a time when they are most needed.
The current study was conducted in the Netherlands, a country whose citizens have very high household internet penetration and high levels of educational attainment. Although differences in educational background and income are present and were taken into consideration, the observed inequalities may be even stronger in countries with a less homogeneous population. Given that the greatest burden of deaths has been in countries with very diverse populations, race and associated factors are likely to play a major role.
The aim of this study was to provide a broader picture of inequality in relation to how the internet is used in the case of a major global health crisis. A broad range of determinants was considered, and the relative importance of these indicators was revealed. However, a deeper understanding and further investigation to reveal the exact underlying mechanisms that cause these indicators to play a role would provide additional explanations. This suggests that further qualitative research is needed not only to obtain in-depth understanding of the mechanisms but also to understand the consequences of the observed inequalities to complement the findings of the current quantitative approach.
Digital inequality is a major concern among national and international scholars and policy makers. In this paper, we aimed to provide a broader understanding in the case of a major health pandemic by using the ongoing COVID-19 crisis as a context for empirical work. Several groups of people were identified as vulnerable, such as older people and people with lower levels of education, physical health problems, higher levels of neuroticism, low literacy levels, and low levels of trust. The general conclusion is that people who are already relatively advantaged are more likely to use the information and communication opportunities provided by the internet to their benefit in a health pandemic, while more disadvantaged individuals are less likely to benefit. Therefore, the COVID-19 crisis is also an enforcer of existing inequalities.
Conflicts of Interest
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Edited by G Eysenbach; submitted 11.05.20; peer-reviewed by S LaValley, T Hale; comments to author 13.07.20; revised version received 16.07.20; accepted 03.08.20; published 20.08.20
©Alexander JAM van Deursen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.08.2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
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