research study 12.1

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12.1 Creating a Rough Draft for a Research Paper

Learning objectives.

• Apply strategies for drafting an effective introduction and conclusion.
• Identify when and how to summarize, paraphrase, and directly quote information from research sources.
• Apply guidelines for citing sources within the body of the paper and the bibliography.
• Use primary and secondary research to support ideas.
• Identify the purposes for which writers use each type of research.

At last, you are ready to begin writing the rough draft of your research paper. Putting your thinking and research into words is exciting. It can also be challenging. In this section, you will learn strategies for handling the more challenging aspects of writing a research paper, such as integrating material from your sources, citing information correctly, and avoiding any misuse of your sources.

The Structure of a Research Paper

Research papers generally follow the same basic structure: an introduction that presents the writer’s thesis, a body section that develops the thesis with supporting points and evidence, and a conclusion that revisits the thesis and provides additional insights or suggestions for further research.

Your writing voice will come across most strongly in your introduction and conclusion, as you work to attract your readers’ interest and establish your thesis. These sections usually do not cite sources at length. They focus on the big picture, not specific details. In contrast, the body of your paper will cite sources extensively. As you present your ideas, you will support your points with details from your research.

Writing Your Introduction

There are several approaches to writing an introduction, each of which fulfills the same goals. The introduction should get readers’ attention, provide background information, and present the writer’s thesis. Many writers like to begin with one of the following catchy openers:

• A surprising fact
• A thought-provoking question
• An attention-getting quote
• A brief anecdote that illustrates a larger concept
• A connection between your topic and your readers’ experiences

The next few sentences place the opening in context by presenting background information. From there, the writer builds toward a thesis, which is traditionally placed at the end of the introduction. Think of your thesis as a signpost that lets readers know in what direction the paper is headed.

Jorge decided to begin his research paper by connecting his topic to readers’ daily experiences. Read the first draft of his introduction. The thesis is underlined. Note how Jorge progresses from the opening sentences to background information to his thesis.

Beyond the Hype: Evaluating Low-Carb Diets

I. Introduction

Over the past decade, increasing numbers of Americans have jumped on the low-carb bandwagon. Some studies estimate that approximately 40 million Americans, or about 20 percent of the population, are attempting to restrict their intake of food high in carbohydrates (Sanders and Katz, 2004; Hirsch, 2004). Proponents of low-carb diets say they are not only the most effective way to lose weight, but they also yield health benefits such as lower blood pressure and improved cholesterol levels. Meanwhile, some doctors claim that low-carb diets are overrated and caution that their long-term effects are unknown. Although following a low-carbohydrate diet can benefit some people, these diets are not necessarily the best option for everyone who wants to lose weight or improve their health.

Write the introductory paragraph of your research paper. Try using one of the techniques listed in this section to write an engaging introduction. Be sure to include background information about the topic that leads to your thesis.

Writers often work out of sequence when writing a research paper. If you find yourself struggling to write an engaging introduction, you may wish to write the body of your paper first. Writing the body sections first will help you clarify your main points. Writing the introduction should then be easier. You may have a better sense of how to introduce the paper after you have drafted some or all of the body.

Writing Your Conclusion

In your introduction, you tell readers where they are headed. In your conclusion, you recap where they have been. For this reason, some writers prefer to write their conclusions soon after they have written their introduction. However, this method may not work for all writers. Other writers prefer to write their conclusion at the end of the paper, after writing the body paragraphs. No process is absolutely right or absolutely wrong; find the one that best suits you.

No matter when you compose the conclusion, it should sum up your main ideas and revisit your thesis. The conclusion should not simply echo the introduction or rely on bland summary statements, such as “In this paper, I have demonstrated that.…” In fact, avoid repeating your thesis verbatim from the introduction. Restate it in different words that reflect the new perspective gained through your research. That helps keep your ideas fresh for your readers. An effective writer might conclude a paper by asking a new question the research inspired, revisiting an anecdote presented earlier, or reminding readers of how the topic relates to their lives.

Writing at Work

If your job involves writing or reading scientific papers, it helps to understand how professional researchers use the structure described in this section. A scientific paper begins with an abstract that briefly summarizes the entire paper. The introduction explains the purpose of the research, briefly summarizes previous research, and presents the researchers’ hypothesis. The body provides details about the study, such as who participated in it, what the researchers measured, and what results they recorded. The conclusion presents the researchers’ interpretation of the data, or what they learned.

Using Source Material in Your Paper

One of the challenges of writing a research paper is successfully integrating your ideas with material from your sources. Your paper must explain what you think, or it will read like a disconnected string of facts and quotations. However, you also need to support your ideas with research, or they will seem insubstantial. How do you strike the right balance?

You have already taken a step in the right direction by writing your introduction. The introduction and conclusion function like the frame around a picture. They define and limit your topic and place your research in context.

In the body paragraphs of your paper, you will need to integrate ideas carefully at the paragraph level and at the sentence level. You will use topic sentences in your paragraphs to make sure readers understand the significance of any facts, details, or quotations you cite. You will also include sentences that transition between ideas from your research, either within a paragraph or between paragraphs. At the sentence level, you will need to think carefully about how you introduce paraphrased and quoted material.

Earlier you learned about summarizing, paraphrasing, and quoting when taking notes. In the next few sections, you will learn how to use these techniques in the body of your paper to weave in source material to support your ideas.

Summarizing Sources

When you summarize material from a source, you zero in on the main points and restate them concisely in your own words. This technique is appropriate when only the major ideas are relevant to your paper or when you need to simplify complex information into a few key points for your readers.

Be sure to review the source material as you summarize it. Identify the main idea and restate it as concisely as you can—preferably in one sentence. Depending on your purpose, you may also add another sentence or two condensing any important details or examples. Check your summary to make sure it is accurate and complete.

In his draft, Jorge summarized research materials that presented scientists’ findings about low-carbohydrate diets. Read the following passage from a trade magazine article and Jorge’s summary of the article.

Assessing the Efficacy of Low-Carbohydrate Diets

Adrienne Howell, Ph.D.

Over the past few years, a number of clinical studies have explored whether high-protein, low-carbohydrate diets are more effective for weight loss than other frequently recommended diet plans, such as diets that drastically curtail fat intake (Pritikin) or that emphasize consuming lean meats, grains, vegetables, and a moderate amount of unsaturated fats (the Mediterranean diet). A 2009 study found that obese teenagers who followed a low-carbohydrate diet lost an average of 15.6 kilograms over a six-month period, whereas teenagers following a low-fat diet or a Mediterranean diet lost an average of 11.1 kilograms and 9.3 kilograms respectively. Two 2010 studies that measured weight loss for obese adults following these same three diet plans found similar results. Over three months, subjects on the low-carbohydrate diet plan lost anywhere from four to six kilograms more than subjects who followed other diet plans.

In three recent studies, researchers compared outcomes for obese subjects who followed either a low-carbohydrate diet, a low-fat diet, or a Mediterranean diet and found that subjects following a low-carbohydrate diet lost more weight in the same time (Howell, 2010).

A summary restates ideas in your own words—but for specialized or clinical terms, you may need to use terms that appear in the original source. For instance, Jorge used the term obese in his summary because related words such as heavy or overweight have a different clinical meaning.

On a separate sheet of paper, practice summarizing by writing a one-sentence summary of the same passage that Jorge already summarized.

Paraphrasing Sources

When you paraphrase material from a source, restate the information from an entire sentence or passage in your own words, using your own original sentence structure. A paraphrased source differs from a summarized source in that you focus on restating the ideas, not condensing them.

Again, it is important to check your paraphrase against the source material to make sure it is both accurate and original. Inexperienced writers sometimes use the thesaurus method of paraphrasing—that is, they simply rewrite the source material, replacing most of the words with synonyms. This constitutes a misuse of sources. A true paraphrase restates ideas using the writer’s own language and style.

In his draft, Jorge frequently paraphrased details from sources. At times, he needed to rewrite a sentence more than once to ensure he was paraphrasing ideas correctly. Read the passage from a website. Then read Jorge’s initial attempt at paraphrasing it, followed by the final version of his paraphrase.

Dieters nearly always get great results soon after they begin following a low-carbohydrate diet, but these results tend to taper off after the first few months, particularly because many dieters find it difficult to follow a low-carbohydrate diet plan consistently.

People usually see encouraging outcomes shortly after they go on a low-carbohydrate diet, but their progress slows down after a short while, especially because most discover that it is a challenge to adhere to the diet strictly (Heinz, 2009).

After reviewing the paraphrased sentence, Jorge realized he was following the original source too closely. He did not want to quote the full passage verbatim, so he again attempted to restate the idea in his own style.

Because it is hard for dieters to stick to a low-carbohydrate eating plan, the initial success of these diets is short-lived (Heinz, 2009).

On a separate sheet of paper, follow these steps to practice paraphrasing.

• Choose an important idea or detail from your notes.
• Without looking at the original source, restate the idea in your own words.
• Check your paraphrase against the original text in the source. Make sure both your language and your sentence structure are original.
• Revise your paraphrase if necessary.

Quoting Sources Directly

Most of the time, you will summarize or paraphrase source material instead of quoting directly. Doing so shows that you understand your research well enough to write about it confidently in your own words. However, direct quotes can be powerful when used sparingly and with purpose.

Quoting directly can sometimes help you make a point in a colorful way. If an author’s words are especially vivid, memorable, or well phrased, quoting them may help hold your reader’s interest. Direct quotations from an interviewee or an eyewitness may help you personalize an issue for readers. And when you analyze primary sources, such as a historical speech or a work of literature, quoting extensively is often necessary to illustrate your points. These are valid reasons to use quotations.

Less experienced writers, however, sometimes overuse direct quotations in a research paper because it seems easier than paraphrasing. At best, this reduces the effectiveness of the quotations. At worst, it results in a paper that seems haphazardly pasted together from outside sources. Use quotations sparingly for greater impact.

When you do choose to quote directly from a source, follow these guidelines:

• Make sure you have transcribed the original statement accurately.
• Represent the author’s ideas honestly. Quote enough of the original text to reflect the author’s point accurately.
• Never use a stand-alone quotation. Always integrate the quoted material into your own sentence.
• Use ellipses (…) if you need to omit a word or phrase. Use brackets [ ] if you need to replace a word or phrase.
• Make sure any omissions or changed words do not alter the meaning of the original text. Omit or replace words only when absolutely necessary to shorten the text or to make it grammatically correct within your sentence.
• Remember to include correctly formatted citations that follow the assigned style guide.

Jorge interviewed a dietician as part of his research, and he decided to quote her words in his paper. Read an excerpt from the interview and Jorge’s use of it, which follows.

Personally, I don’t really buy into all of the hype about low-carbohydrate miracle diets like Atkins and so on. Sure, for some people, they are great, but for most, any sensible eating and exercise plan would work just as well.

Registered dietician Dana Kwon (2010) admits, “Personally, I don’t really buy into all of the hype.…Sure, for some people, [low-carbohydrate diets] are great, but for most, any sensible eating and exercise plan would work just as well.”

Notice how Jorge smoothly integrated the quoted material by starting the sentence with an introductory phrase. His use of ellipses and brackets did not change the source’s meaning.

Documenting Source Material

Throughout the writing process, be scrupulous about documenting information taken from sources. The purpose of doing so is twofold:

• To give credit to other writers or researchers for their ideas
• To allow your reader to follow up and learn more about the topic if desired

You will cite sources within the body of your paper and at the end of the paper in your bibliography. For this assignment, you will use the citation format used by the American Psychological Association (also known as APA style). For information on the format used by the Modern Language Association (MLA style), see Chapter 13 “APA and MLA Documentation and Formatting” .

Citing Sources in the Body of Your Paper

In-text citations document your sources within the body of your paper. These include two vital pieces of information: the author’s name and the year the source material was published. When quoting a print source, also include in the citation the page number where the quoted material originally appears. The page number will follow the year in the in-text citation. Page numbers are necessary only when content has been directly quoted, not when it has been summarized or paraphrased.

Within a paragraph, this information may appear as part of your introduction to the material or as a parenthetical citation at the end of a sentence. Read the examples that follow. For more information about in-text citations for other source types, see Chapter 13 “APA and MLA Documentation and Formatting” .

Leibowitz (2008) found that low-carbohydrate diets often helped subjects with Type II diabetes maintain a healthy weight and control blood-sugar levels.

The introduction to the source material includes the author’s name followed by the year of publication in parentheses.

Low-carbohydrate diets often help subjects with Type II diabetes maintain a healthy weight and control blood-sugar levels (Leibowitz, 2008).

The parenthetical citation at the end of the sentence includes the author’s name, a comma, and the year the source was published. The period at the end of the sentence comes after the parentheses.

Creating a List of References

Each of the sources you cite in the body text will appear in a references list at the end of your paper. While in-text citations provide the most basic information about the source, your references section will include additional publication details. In general, you will include the following information:

• The author’s last name followed by his or her first (and sometimes middle) initial
• The year the source was published
• The source title
• For articles in periodicals, the full name of the periodical, along with the volume and issue number and the pages where the article appeared

Additional information may be included for different types of sources, such as online sources. For a detailed guide to APA or MLA citations, see Chapter 13 “APA and MLA Documentation and Formatting” . A sample reference list is provided with the final draft of Jorge’s paper later in this chapter.

Using Primary and Secondary Research

As you write your draft, be mindful of how you are using primary and secondary source material to support your points. Recall that primary sources present firsthand information. Secondary sources are one step removed from primary sources. They present a writer’s analysis or interpretation of primary source materials. How you balance primary and secondary source material in your paper will depend on the topic and assignment.

Using Primary Sources Effectively

Some types of research papers must use primary sources extensively to achieve their purpose. Any paper that analyzes a primary text or presents the writer’s own experimental research falls in this category. Here are a few examples:

• A paper for a literature course analyzing several poems by Emily Dickinson
• A paper for a political science course comparing televised speeches delivered by two presidential candidates
• A paper for a communications course discussing gender biases in television commercials
• A paper for a business administration course that discusses the results of a survey the writer conducted with local businesses to gather information about their work-from-home and flextime policies
• A paper for an elementary education course that discusses the results of an experiment the writer conducted to compare the effectiveness of two different methods of mathematics instruction

For these types of papers, primary research is the main focus. If you are writing about a work (including nonprint works, such as a movie or a painting), it is crucial to gather information and ideas from the original work, rather than relying solely on others’ interpretations. And, of course, if you take the time to design and conduct your own field research, such as a survey, a series of interviews, or an experiment, you will want to discuss it in detail. For example, the interviews may provide interesting responses that you want to share with your reader.

Using Secondary Sources Effectively

For some assignments, it makes sense to rely more on secondary sources than primary sources. If you are not analyzing a text or conducting your own field research, you will need to use secondary sources extensively.

As much as possible, use secondary sources that are closely linked to primary research, such as a journal article presenting the results of the authors’ scientific study or a book that cites interviews and case studies. These sources are more reliable and add more value to your paper than sources that are further removed from primary research. For instance, a popular magazine article on junk-food addiction might be several steps removed from the original scientific study on which it is loosely based. As a result, the article may distort, sensationalize, or misinterpret the scientists’ findings.

Even if your paper is largely based on primary sources, you may use secondary sources to develop your ideas. For instance, an analysis of Alfred Hitchcock’s films would focus on the films themselves as a primary source, but might also cite commentary from critics. A paper that presents an original experiment would include some discussion of similar prior research in the field.

Jorge knew he did not have the time, resources, or experience needed to conduct original experimental research for his paper. Because he was relying on secondary sources to support his ideas, he made a point of citing sources that were not far removed from primary research.

Some sources could be considered primary or secondary sources, depending on the writer’s purpose for using them. For instance, if a writer’s purpose is to inform readers about how the No Child Left Behind legislation has affected elementary education, a Time magazine article on the subject would be a secondary source. However, suppose the writer’s purpose is to analyze how the news media has portrayed the effects of the No Child Left Behind legislation. In that case, articles about the legislation in news magazines like Time , Newsweek , and US News & World Report would be primary sources. They provide firsthand examples of the media coverage the writer is analyzing.

Avoiding Plagiarism

Your research paper presents your thinking about a topic, supported and developed by other people’s ideas and information. It is crucial to always distinguish between the two—as you conduct research, as you plan your paper, and as you write. Failure to do so can lead to plagiarism.

Intentional and Accidental Plagiarism

Plagiarism is the act of misrepresenting someone else’s work as your own. Sometimes a writer plagiarizes work on purpose—for instance, by purchasing an essay from a website and submitting it as original course work. In other cases, a writer may commit accidental plagiarism due to carelessness, haste, or misunderstanding. To avoid unintentional plagiarism, follow these guidelines:

• Understand what types of information must be cited.
• Understand what constitutes fair use of a source.
• Keep source materials and notes carefully organized.
• Follow guidelines for summarizing, paraphrasing, and quoting sources.

When to Cite

Any idea or fact taken from an outside source must be cited, in both the body of your paper and the references list. The only exceptions are facts or general statements that are common knowledge. Common-knowledge facts or general statements are commonly supported by and found in multiple sources. For example, a writer would not need to cite the statement that most breads, pastas, and cereals are high in carbohydrates; this is well known and well documented. However, if a writer explained in detail the differences among the chemical structures of carbohydrates, proteins, and fats, a citation would be necessary. When in doubt, cite.

In recent years, issues related to the fair use of sources have been prevalent in popular culture. Recording artists, for example, may disagree about the extent to which one has the right to sample another’s music. For academic purposes, however, the guidelines for fair use are reasonably straightforward.

Writers may quote from or paraphrase material from previously published works without formally obtaining the copyright holder’s permission. Fair use means that the writer legitimately uses brief excerpts from source material to support and develop his or her own ideas. For instance, a columnist may excerpt a few sentences from a novel when writing a book review. However, quoting or paraphrasing another’s work at excessive length, to the extent that large sections of the writing are unoriginal, is not fair use.

As he worked on his draft, Jorge was careful to cite his sources correctly and not to rely excessively on any one source. Occasionally, however, he caught himself quoting a source at great length. In those instances, he highlighted the paragraph in question so that he could go back to it later and revise. Read the example, along with Jorge’s revision.

Heinz (2009) found that “subjects in the low-carbohydrate group (30% carbohydrates; 40% protein, 30% fat) had a mean weight loss of 10 kg (22 lbs) over a 4-month period.” These results were “noticeably better than results for subjects on a low-fat diet (45% carbohydrates, 35% protein, 20% fat)” whose average weight loss was only “7 kg (15.4 lbs) in the same period.” From this, it can be concluded that “low-carbohydrate diets obtain more rapid results.” Other researchers agree that “at least in the short term, patients following low-carbohydrate diets enjoy greater success” than those who follow alternative plans (Johnson & Crowe, 2010).

After reviewing the paragraph, Jorge realized that he had drifted into unoriginal writing. Most of the paragraph was taken verbatim from a single article. Although Jorge had enclosed the material in quotation marks, he knew it was not an appropriate way to use the research in his paper.

Low-carbohydrate diets may indeed be superior to other diet plans for short-term weight loss. In a study comparing low-carbohydrate diets and low-fat diets, Heinz (2009) found that subjects who followed a low-carbohydrate plan (30% of total calories) for 4 months lost, on average, about 3 kilograms more than subjects who followed a low-fat diet for the same time. Heinz concluded that these plans yield quick results, an idea supported by a similar study conducted by Johnson and Crowe (2010). What remains to be seen, however, is whether this initial success can be sustained for longer periods.

As Jorge revised the paragraph, he realized he did not need to quote these sources directly. Instead, he paraphrased their most important findings. He also made sure to include a topic sentence stating the main idea of the paragraph and a concluding sentence that transitioned to the next major topic in his essay.

Working with Sources Carefully

Disorganization and carelessness sometimes lead to plagiarism. For instance, a writer may be unable to provide a complete, accurate citation if he didn’t record bibliographical information. A writer may cut and paste a passage from a website into her paper and later forget where the material came from. A writer who procrastinates may rush through a draft, which easily leads to sloppy paraphrasing and inaccurate quotations. Any of these actions can create the appearance of plagiarism and lead to negative consequences.

Carefully organizing your time and notes is the best guard against these forms of plagiarism. Maintain a detailed working bibliography and thorough notes throughout the research process. Check original sources again to clear up any uncertainties. Allow plenty of time for writing your draft so there is no temptation to cut corners.

Citing other people’s work appropriately is just as important in the workplace as it is in school. If you need to consult outside sources to research a document you are creating, follow the general guidelines already discussed, as well as any industry-specific citation guidelines. For more extensive use of others’ work—for instance, requesting permission to link to another company’s website on your own corporate website—always follow your employer’s established procedures.

Academic Integrity

The concepts and strategies discussed in this section of Chapter 12 “Writing a Research Paper” connect to a larger issue—academic integrity. You maintain your integrity as a member of an academic community by representing your work and others’ work honestly and by using other people’s work only in legitimately accepted ways. It is a point of honor taken seriously in every academic discipline and career field.

Academic integrity violations have serious educational and professional consequences. Even when cheating and plagiarism go undetected, they still result in a student’s failure to learn necessary research and writing skills. Students who are found guilty of academic integrity violations face consequences ranging from a failing grade to expulsion from the university. Employees may be fired for plagiarism and do irreparable damage to their professional reputation. In short, it is never worth the risk.

Key Takeaways

• An effective research paper focuses on the writer’s ideas. The introduction and conclusion present and revisit the writer’s thesis. The body of the paper develops the thesis and related points with information from research.
• Ideas and information taken from outside sources must be cited in the body of the paper and in the references section.
• Material taken from sources should be used to develop the writer’s ideas. Summarizing and paraphrasing are usually most effective for this purpose.
• A summary concisely restates the main ideas of a source in the writer’s own words.
• A paraphrase restates ideas from a source using the writer’s own words and sentence structures.
• Direct quotations should be used sparingly. Ellipses and brackets must be used to indicate words that were omitted or changed for conciseness or grammatical correctness.
• Always represent material from outside sources accurately.
• Plagiarism has serious academic and professional consequences. To avoid accidental plagiarism, keep research materials organized, understand guidelines for fair use and appropriate citation of sources, and review the paper to make sure these guidelines are followed.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cochrane Training

Chapter 12: synthesizing and presenting findings using other methods.

Joanne E McKenzie, Sue E Brennan

Key Points:

• Meta-analysis of effect estimates has many advantages, but other synthesis methods may need to be considered in the circumstance where there is incompletely reported data in the primary studies.
• Alternative synthesis methods differ in the completeness of the data they require, the hypotheses they address, and the conclusions and recommendations that can be drawn from their findings.
• These methods provide more limited information for healthcare decision making than meta-analysis, but may be superior to a narrative description where some results are privileged above others without appropriate justification.
• Tabulation and visual display of the results should always be presented alongside any synthesis, and are especially important for transparent reporting in reviews without meta-analysis.
• Alternative synthesis and visual display methods should be planned and specified in the protocol. When writing the review, details of the synthesis methods should be described.
• Synthesis methods that involve vote counting based on statistical significance have serious limitations and are unacceptable.

Cite this chapter as: McKenzie JE, Brennan SE. Chapter 12: Synthesizing and presenting findings using other methods. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

12.1 Why a meta-analysis of effect estimates may not be possible

Meta-analysis of effect estimates has many potential advantages (see Chapter 10 and Chapter 11 ). However, there are circumstances where it may not be possible to undertake a meta-analysis and other statistical synthesis methods may be considered (McKenzie and Brennan 2014).

Some common reasons why it may not be possible to undertake a meta-analysis are outlined in Table 12.1.a . Legitimate reasons include limited evidence; incompletely reported outcome/effect estimates, or different effect measures used across studies; and bias in the evidence. Other commonly cited reasons for not using meta-analysis are because of too much clinical or methodological diversity, or statistical heterogeneity (Achana et al 2014). However, meta-analysis methods should be considered in these circumstances, as they may provide important insights if undertaken and interpreted appropriately.

Table 12.1.a Scenarios that may preclude meta-analysis, with possible solutions

12.2 Statistical synthesis when meta-analysis of effect estimates is not possible

A range of statistical synthesis methods are available, and these may be divided into three categories based on their preferability ( Table 12.2.a ). Preferable methods are the meta-analysis methods outlined in Chapter 10 and Chapter 11 , and are not discussed in detail here. This chapter focuses on methods that might be considered when a meta-analysis of effect estimates is not possible due to incompletely reported data in the primary studies. These methods divide into those that are ‘acceptable’ and ‘unacceptable’. The ‘acceptable’ methods differ in the data they require, the hypotheses they address, limitations around their use, and the conclusions and recommendations that can be drawn (see Section 12.2.1 ). The ‘unacceptable’ methods in common use are described (see Section 12.2.2 ), along with the reasons for why they are problematic.

Compared with meta-analysis methods, the ‘acceptable’ synthesis methods provide more limited information for healthcare decision making. However, these ‘acceptable’ methods may be superior to a narrative that describes results study by study, which comes with the risk that some studies or findings are privileged above others without appropriate justification. Further, in reviews with little or no synthesis, readers are left to make sense of the research themselves, which may result in the use of seemingly simple yet problematic synthesis methods such as vote counting based on statistical significance (see Section 12.2.2.1 ).

All methods first involve calculation of a ‘standardized metric’, followed by application of a synthesis method. In applying any of the following synthesis methods, it is important that only one outcome per study (or other independent unit, for example one comparison from a trial with multiple intervention groups) contributes to the synthesis. Chapter 9 outlines approaches for selecting an outcome when multiple have been measured. Similar to meta-analysis, sensitivity analyses can be undertaken to examine if the findings of the synthesis are robust to potentially influential decisions (see Chapter 10, Section 10.14 and Section 12.4 for examples).

Authors should report the specific methods used in lieu of meta-analysis (including approaches used for presentation and visual display), rather than stating that they have conducted a ‘narrative synthesis’ or ‘narrative summary’ without elaboration. The limitations of the chosen methods must be described, and conclusions worded with appropriate caution. The aim of reporting this detail is to make the synthesis process more transparent and reproducible, and help ensure use of appropriate methods and interpretation.

Table 12.2.a Summary of preferable and acceptable synthesis methods

12.2.1 Acceptable synthesis methods

12.2.1.1 summarizing effect estimates.

Description of method Summarizing effect estimates might be considered in the circumstance where estimates of intervention effect are available (or can be calculated), but the variances of the effects are not reported or are incorrect (and cannot be calculated from other statistics, or reasonably imputed) (Grimshaw et al 2003). Incorrect calculation of variances arises more commonly in non-standard study designs that involve clustering or matching ( Chapter 23 ). While missing variances may limit the possibility of meta-analysis, the (standardized) effects can be summarized using descriptive statistics such as the median, interquartile range, and the range. Calculating these statistics addresses the question ‘What is the range and distribution of observed effects?’

Reporting of methods and results The statistics that will be used to summarize the effects (e.g. median, interquartile range) should be reported. Box-and-whisker or bubble plots will complement reporting of the summary statistics by providing a visual display of the distribution of observed effects (Section 12.3.3 ). Tabulation of the available effect estimates will provide transparency for readers by linking the effects to the studies (Section 12.3.1 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.2 Combining P values

Description of method Combining P values can be considered in the circumstance where there is no, or minimal, information reported beyond P values and the direction of effect; the types of outcomes and statistical tests differ across the studies; or results from non-parametric tests are reported (Borenstein et al 2009). Combining P values addresses the question ‘Is there evidence that there is an effect in at least one study?’ There are several methods available (Loughin 2004), with the method proposed by Fisher outlined here (Becker 1994).

Fisher’s method combines the P values from statistical tests across k studies using the formula:

One-sided P values are used, since these contain information about the direction of effect. However, these P values must reflect the same directional hypothesis (e.g. all testing if intervention A is more effective than intervention B). This is analogous to standardizing the direction of effects before undertaking a meta-analysis. Two-sided P values, which do not contain information about the direction, must first be converted to one-sided P values. If the effect is consistent with the directional hypothesis (e.g. intervention A is beneficial compared with B), then the one-sided P value is calculated as

In studies that do not report an exact P value but report a conventional level of significance (e.g. P<0.05), a conservative option is to use the threshold (e.g. 0.05). The P values must have been computed from statistical tests that appropriately account for the features of the design, such as clustering or matching, otherwise they will likely be incorrect.

Reporting of methods and results There are several methods for combining P values (Loughin 2004), so the chosen method should be reported, along with details of sensitivity analyses that examine if the results are sensitive to the choice of method. The results from the test should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The albatross plot is likely to complement the analysis (Section 12.3.4 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.1.3 Vote counting based on the direction of effect

Description of method Vote counting based on the direction of effect might be considered in the circumstance where the direction of effect is reported (with no further information), or there is no consistent effect measure or data reported across studies. The essence of vote counting is to compare the number of effects showing benefit to the number of effects showing harm for a particular outcome. However, there is wide variation in the implementation of the method due to differences in how ‘benefit’ and ‘harm’ are defined. Rules based on subjective decisions or statistical significance are problematic and should be avoided (see Section 12.2.2 ).

To undertake vote counting properly, each effect estimate is first categorized as showing benefit or harm based on the observed direction of effect alone, thereby creating a standardized binary metric. A count of the number of effects showing benefit is then compared with the number showing harm. Neither statistical significance nor the size of the effect are considered in the categorization. A sign test can be used to answer the question ‘is there any evidence of an effect?’ If there is no effect, the study effects will be distributed evenly around the null hypothesis of no difference. This is equivalent to testing if the true proportion of effects favouring the intervention (or comparator) is equal to 0.5 (Bushman and Wang 2009) (see Section 12.4.2.3 for guidance on implementing the sign test). An estimate of the proportion of effects favouring the intervention can be calculated ( p = u / n , where u = number of effects favouring the intervention, and n = number of studies) along with a confidence interval (e.g. using the Wilson or Jeffreys interval methods (Brown et al 2001)). Unless there are many studies contributing effects to the analysis, there will be large uncertainty in this estimated proportion.

Reporting of methods and results The vote counting method should be reported in the ‘Data synthesis’ section of the review. Failure to recognize vote counting as a synthesis method has led to it being applied informally (and perhaps unintentionally) to summarize results (e.g. through the use of wording such as ‘3 of 10 studies showed improvement in the outcome with intervention compared to control’; ‘most studies found’; ‘the majority of studies’; ‘few studies’ etc). In such instances, the method is rarely reported, and it may not be possible to determine whether an unacceptable (invalid) rule has been used to define benefit and harm (Section 12.2.2 ). The results from vote counting should be reported alongside any available effect estimates (either individual results or meta-analysis results of a subset of studies) using text, tabulation and appropriate visual displays (Section 12.3 ). The number of studies contributing to a synthesis based on vote counting may be larger than a meta-analysis, because only minimal statistical information (i.e. direction of effect) is required from each study to vote count. Vote counting results are used to derive the harvest and effect direction plots, although often using unacceptable methods of vote counting (see Section 12.3.5 ). Limitations of the method should be acknowledged ( Table 12.2.a ).

12.2.2 Unacceptable synthesis methods

12.2.2.1 vote counting based on statistical significance.

Conventional forms of vote counting use rules based on statistical significance and direction to categorize effects. For example, effects may be categorized into three groups: those that favour the intervention and are statistically significant (based on some predefined P value), those that favour the comparator and are statistically significant, and those that are statistically non-significant (Hedges and Vevea 1998). In a simpler formulation, effects may be categorized into two groups: those that favour the intervention and are statistically significant, and all others (Friedman 2001). Regardless of the specific formulation, when based on statistical significance, all have serious limitations and can lead to the wrong conclusion.

The conventional vote counting method fails because underpowered studies that do not rule out clinically important effects are counted as not showing benefit. Suppose, for example, the effect sizes estimated in two studies were identical. However, only one of the studies was adequately powered, and the effect in this study was statistically significant. Only this one effect (of the two identical effects) would be counted as showing ‘benefit’. Paradoxically, Hedges and Vevea showed that as the number of studies increases, the power of conventional vote counting tends to zero, except with large studies and at least moderate intervention effects (Hedges and Vevea 1998). Further, conventional vote counting suffers the same disadvantages as vote counting based on direction of effect, namely, that it does not provide information on the magnitude of effects and does not account for differences in the relative sizes of the studies.

12.2.2.2 Vote counting based on subjective rules

Subjective rules, involving a combination of direction, statistical significance and magnitude of effect, are sometimes used to categorize effects. For example, in a review examining the effectiveness of interventions for teaching quality improvement to clinicians, the authors categorized results as ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’ (Boonyasai et al 2007). Categorization was based on direction of effect and statistical significance (using a predefined P value of 0.05) when available. If statistical significance was not reported, effects greater than 10% were categorized as ‘beneficial’ or ‘detrimental’, depending on their direction. These subjective rules often vary in the elements, cut-offs and algorithms used to categorize effects, and while detailed descriptions of the rules may provide a veneer of legitimacy, such rules have poor performance validity (Ioannidis et al 2008).

A further problem occurs when the rules are not described in sufficient detail for the results to be reproduced (e.g. ter Wee et al 2012, Thornicroft et al 2016). This lack of transparency does not allow determination of whether an acceptable or unacceptable vote counting method has been used (Valentine et al 2010).

12.3 Visual display and presentation of the data

Visual display and presentation of data is especially important for transparent reporting in reviews without meta-analysis, and should be considered irrespective of whether synthesis is undertaken (see Table 12.2.a for a summary of plots associated with each synthesis method). Tables and plots structure information to show patterns in the data and convey detailed information more efficiently than text. This aids interpretation and helps readers assess the veracity of the review findings.

12.3.1 Structured tabulation of results across studies

Ordering studies alphabetically by study ID is the simplest approach to tabulation; however, more information can be conveyed when studies are grouped in subpanels or ordered by a characteristic important for interpreting findings. The grouping of studies in tables should generally follow the structure of the synthesis presented in the text, which should closely reflect the review questions. This grouping should help readers identify the data on which findings are based and verify the review authors’ interpretation.

If the purpose of the table is comparative, grouping studies by any of following characteristics might be informative:

• comparisons considered in the review, or outcome domains (according to the structure of the synthesis);
• study characteristics that may reveal patterns in the data, for example potential effect modifiers including population subgroups, settings or intervention components.

If the purpose of the table is complete and transparent reporting of data, then ordering the studies to increase the prominence of the most relevant and trustworthy evidence should be considered. Possibilities include:

• certainty of the evidence (synthesized result or individual studies if no synthesis);
• risk of bias, study size or study design characteristics; and
• characteristics that determine how directly a study addresses the review question, for example relevance and validity of the outcome measures.

One disadvantage of grouping by study characteristics is that it can be harder to locate specific studies than when tables are ordered by study ID alone, for example when cross-referencing between the text and tables. Ordering by study ID within categories may partly address this.

The value of standardizing intervention and outcome labels is discussed in Chapter 3, Section 3.2.2 and Section 3.2.4 ), while the importance and methods for standardizing effect estimates is described in Chapter 6 . These practices can aid readers’ interpretation of tabulated data, especially when the purpose of a table is comparative.

12.3.2 Forest plots

Forest plots and methods for preparing them are described elsewhere ( Chapter 10, Section 10.2 ). Some mention is warranted here of their importance for displaying study results when meta-analysis is not undertaken (i.e. without the summary diamond). Forest plots can aid interpretation of individual study results and convey overall patterns in the data, especially when studies are ordered by a characteristic important for interpreting results (e.g. dose and effect size, sample size). Similarly, grouping studies in subpanels based on characteristics thought to modify effects, such as population subgroups, variants of an intervention, or risk of bias, may help explore and explain differences across studies (Schriger et al 2010). These approaches to ordering provide important techniques for informally exploring heterogeneity in reviews without meta-analysis, and should be considered in preference to alphabetical ordering by study ID alone (Schriger et al 2010).

12.3.3 Box-and-whisker plots and bubble plots

Box-and-whisker plots (see Figure 12.4.a , Panel A) provide a visual display of the distribution of effect estimates (Section 12.2.1.1 ). The plot conventionally depicts five values. The upper and lower limits (or ‘hinges’) of the box, represent the 75th and 25th percentiles, respectively. The line within the box represents the 50th percentile (median), and the whiskers represent the extreme values (McGill et al 1978). Multiple box plots can be juxtaposed, providing a visual comparison of the distributions of effect estimates (Schriger et al 2006). For example, in a review examining the effects of audit and feedback on professional practice, the format of the feedback (verbal, written, both verbal and written) was hypothesized to be an effect modifier (Ivers et al 2012). Box-and-whisker plots of the risk differences were presented separately by the format of feedback, to allow visual comparison of the impact of format on the distribution of effects. When presenting multiple box-and-whisker plots, the width of the box can be varied to indicate the number of studies contributing to each. The plot’s common usage facilitates rapid and correct interpretation by readers (Schriger et al 2010). The individual studies contributing to the plot are not identified (as in a forest plot), however, and the plot is not appropriate when there are few studies (Schriger et al 2006).

A bubble plot (see Figure 12.4.a , Panel B) can also be used to provide a visual display of the distribution of effects, and is more suited than the box-and-whisker plot when there are few studies (Schriger et al 2006). The plot is a scatter plot that can display multiple dimensions through the location, size and colour of the bubbles. In a review examining the effects of educational outreach visits on professional practice, a bubble plot was used to examine visually whether the distribution of effects was modified by the targeted behaviour (O’Brien et al 2007). Each bubble represented the effect size (y-axis) and whether the study targeted a prescribing or other behaviour (x-axis). The size of the bubbles reflected the number of study participants. However, different formulations of the bubble plot can display other characteristics of the data (e.g. precision, risk-of-bias assessments).

12.3.4 Albatross plot

The albatross plot (see Figure 12.4.a , Panel C) allows approximate examination of the underlying intervention effect sizes where there is minimal reporting of results within studies (Harrison et al 2017). The plot only requires a two-sided P value, sample size and direction of effect (or equivalently, a one-sided P value and a sample size) for each result. The plot is a scatter plot of the study sample sizes against two-sided P values, where the results are separated by the direction of effect. Superimposed on the plot are ‘effect size contours’ (inspiring the plot’s name). These contours are specific to the type of data (e.g. continuous, binary) and statistical methods used to calculate the P values. The contours allow interpretation of the approximate effect sizes of the studies, which would otherwise not be possible due to the limited reporting of the results. Characteristics of studies (e.g. type of study design) can be identified using different colours or symbols, allowing informal comparison of subgroups.

The plot is likely to be more inclusive of the available studies than meta-analysis, because of its minimal data requirements. However, the plot should complement the results from a statistical synthesis, ideally a meta-analysis of available effects.

12.3.5 Harvest and effect direction plots

Harvest plots (see Figure 12.4.a , Panel D) provide a visual extension of vote counting results (Ogilvie et al 2008). In the plot, studies based on the categorization of their effects (e.g. ‘beneficial effects’, ‘no effects’ or ‘detrimental effects’) are grouped together. Each study is represented by a bar positioned according to its categorization. The bars can be ‘visually weighted’ (by height or width) and annotated to highlight study and outcome characteristics (e.g. risk-of-bias domains, proximal or distal outcomes, study design, sample size) (Ogilvie et al 2008, Crowther et al 2011). Annotation can also be used to identify the studies. A series of plots may be combined in a matrix that displays, for example, the vote counting results from different interventions or outcome domains.

The methods papers describing harvest plots have employed vote counting based on statistical significance (Ogilvie et al 2008, Crowther et al 2011). For the reasons outlined in Section 12.2.2.1 , this can be misleading. However, an acceptable approach would be to display the results based on direction of effect.

The effect direction plot is similar in concept to the harvest plot in the sense that both display information on the direction of effects (Thomson and Thomas 2013). In the first version of the effect direction plot, the direction of effects for each outcome within a single study are displayed, while the second version displays the direction of the effects for outcome domains across studies . In this second version, an algorithm is first applied to ‘synthesize’ the directions of effect for all outcomes within a domain (e.g. outcomes ‘sleep disturbed by wheeze’, ‘wheeze limits speech’, ‘wheeze during exercise’ in the outcome domain ‘respiratory’). This algorithm is based on the proportion of effects that are in a consistent direction and statistical significance. Arrows are used to indicate the reported direction of effect (for either outcomes or outcome domains). Features such as statistical significance, study design and sample size are denoted using size and colour. While this version of the plot conveys a large amount of information, it requires further development before its use can be recommended since the algorithm underlying the plot is likely to have poor performance validity.

12.4 Worked example

The example that follows uses four scenarios to illustrate methods for presentation and synthesis when meta-analysis is not possible. The first scenario contrasts a common approach to tabulation with alternative presentations that may enhance the transparency of reporting and interpretation of findings. Subsequent scenarios show the application of the synthesis approaches outlined in preceding sections of the chapter. Box 12.4.a summarizes the review comparisons and outcomes, and decisions taken by the review authors in planning their synthesis. While the example is loosely based on an actual review, the review description, scenarios and data are fabricated for illustration.

Box 12.4.a The review

12.4.1 Scenario 1: structured reporting of effects

We first address a scenario in which review authors have decided that the tools used to measure satisfaction measured concepts that were too dissimilar across studies for synthesis to be appropriate. Setting aside three of the 15 studies that reported on the birth partner’s satisfaction with care, a structured summary of effects is sought of the remaining 12 studies. To keep the example table short, only one outcome is shown per study for each of the measurement periods (antenatal, intrapartum or postpartum).

Table 12.4.a depicts a common yet suboptimal approach to presenting results. Note two features.

• Studies are ordered by study ID, rather than grouped by characteristics that might enhance interpretation (e.g. risk of bias, study size, validity of the measures, certainty of the evidence (GRADE)).
• Data reported are as extracted from each study; effect estimates were not calculated by the review authors and, where reported, were not standardized across studies (although data were available to do both).

Table 12.4.b shows an improved presentation of the same results. In line with best practice, here effect estimates have been calculated by the review authors for all outcomes, and a common metric computed to aid interpretation (in this case an odds ratio; see Chapter 6 for guidance on conversion of statistics to the desired format). Redundant information has been removed (‘statistical test’ and ‘P value’ columns). The studies have been re-ordered, first to group outcomes by period of care (intrapartum outcomes are shown here), and then by risk of bias. This re-ordering serves two purposes. Grouping by period of care aligns with the plan to consider outcomes for each period separately and ensures the table structure matches the order in which results are described in the text. Re-ordering by risk of bias increases the prominence of studies at lowest risk of bias, focusing attention on the results that should most influence conclusions. Had the review authors determined that a synthesis would be informative, then ordering to facilitate comparison across studies would be appropriate; for example, ordering by the type of satisfaction outcome (as pre-defined in the protocol, starting with global measures of satisfaction), or the comparisons made in the studies.

The results may also be presented in a forest plot, as shown in Figure 12.4.b . In both the table and figure, studies are grouped by risk of bias to focus attention on the most trustworthy evidence. The pattern of effects across studies is immediately apparent in Figure 12.4.b and can be described efficiently without having to interpret each estimate (e.g. difference between studies at low and high risk of bias emerge), although these results should be interpreted with caution in the absence of a formal test for subgroup differences (see Chapter 10, Section 10.11 ). Only outcomes measured during the intrapartum period are displayed, although outcomes from other periods could be added, maximizing the information conveyed.

An example description of the results from Scenario 1 is provided in Box 12.4.b . It shows that describing results study by study becomes unwieldy with more than a few studies, highlighting the importance of tables and plots. It also brings into focus the risk of presenting results without any synthesis, since it seems likely that the reader will try to make sense of the results by drawing inferences across studies. Since a synthesis was considered inappropriate, GRADE was applied to individual studies and then used to prioritize the reporting of results, focusing attention on the most relevant and trustworthy evidence. An alternative might be to report results at low risk of bias, an approach analogous to limiting a meta-analysis to studies at low risk of bias. Where possible, these and other approaches to prioritizing (or ordering) results from individual studies in text and tables should be pre-specified at the protocol stage.

Table 12.4.a Scenario 1: table ordered by study ID, data as reported by study authors

* All scales operate in the same direction; higher scores indicate greater satisfaction. CI = confidence interval; MD = mean difference; OR = odds ratio; POR = proportional odds ratio; RD = risk difference; RR = risk ratio.

Table 12.4.b Scenario 1: intrapartum outcome table ordered by risk of bias, standardized effect estimates calculated for all studies

* Outcomes operate in the same direction. A higher score, or an event, indicates greater satisfaction. ** Mean difference calculated for studies reporting continuous outcomes. † For binary outcomes, odds ratios were calculated from the reported summary statistics or were directly extracted from the study. For continuous outcomes, standardized mean differences were calculated and converted to odds ratios (see Chapter 6 ). CI = confidence interval; POR = proportional odds ratio.

Figure 12.4.b Forest plot depicting standardized effect estimates (odds ratios) for satisfaction

Box 12.4.b How to describe the results from this structured summary

12.4.2 Overview of scenarios 2–4: synthesis approaches

We now address three scenarios in which review authors have decided that the outcomes reported in the 15 studies all broadly reflect satisfaction with care. While the measures were quite diverse, a synthesis is sought to help decision makers understand whether women and their birth partners were generally more satisfied with the care received in midwife-led continuity models compared with other models. The three scenarios differ according to the data available (see Table 12.4.c ), with each reflecting progressively less complete reporting of the effect estimates. The data available determine the synthesis method that can be applied.

• Scenario 2: effect estimates available without measures of precision (illustrating synthesis of summary statistics).
• Scenario 3: P values available (illustrating synthesis of P values).
• Scenario 4: directions of effect available (illustrating synthesis using vote-counting based on direction of effect).

For studies that reported multiple satisfaction outcomes, one result is selected for synthesis using the decision rules in Box 12.4.a (point 2).

Table 12.4.c Scenarios 2, 3 and 4: available data for the selected outcome from each study

* All scales operate in the same direction. Higher scores indicate greater satisfaction. ** For a particular scenario, the ‘available data’ column indicates the data that were directly reported, or were calculated from the reported statistics, in terms of: effect estimate, direction of effect, confidence interval, precise P value, or statement regarding statistical significance (either statistically significant, or not). CI = confidence interval; direction = direction of effect reported or can be calculated; MD = mean difference; NS = not statistically significant; OR = odds ratio; RD = risk difference; RoB = risk of bias; RR = risk ratio; sig. = statistically significant; SMD = standardized mean difference; Stand. = standardized.

12.4.2.1 Scenario 2: summarizing effect estimates

In Scenario 2, effect estimates are available for all outcomes. However, for most studies, a measure of variance is not reported, or cannot be calculated from the available data. We illustrate how the effect estimates may be summarized using descriptive statistics. In this scenario, it is possible to calculate odds ratios for all studies. For the continuous outcomes, this involves first calculating a standardized mean difference, and then converting this to an odds ratio ( Chapter 10, Section 10.6 ). The median odds ratio is 1.32 with an interquartile range of 1.02 to 1.53 (15 studies). Box-and-whisker plots may be used to display these results and examine informally whether the distribution of effects differs by the overall risk-of-bias assessment ( Figure 12.4.a , Panel A). However, because there are relatively few effects, a reasonable alternative would be to present bubble plots ( Figure 12.4.a , Panel B).

An example description of the results from the synthesis is provided in Box 12.4.c .

Box 12.4.c How to describe the results from this synthesis

12.4.2.2 Scenario 3: combining P values

In Scenario 3, there is minimal reporting of the data, and the type of data and statistical methods and tests vary. However, 11 of the 15 studies provide a precise P value and direction of effect, and a further two report a P value less than a threshold (<0.001) and direction. We use this scenario to illustrate a synthesis of P values. Since the reported P values are two-sided ( Table 12.4.c , column 6), they must first be converted to one-sided P values, which incorporate the direction of effect ( Table 12.4.c , column 7).

Fisher’s method for combining P values involved calculating the following statistic:

The combination of P values suggests there is strong evidence of benefit of midwife-led models of care in at least one study (P < 0.001 from a Chi 2 test, 13 studies). Restricting this analysis to those studies judged to be at an overall low risk of bias (sensitivity analysis), there is no longer evidence to reject the null hypothesis of no benefit of midwife-led model of care in any studies (P = 0.314, 3 studies). For the five studies reporting continuous satisfaction outcomes, sufficient data (precise P value, direction, total sample size) are reported to construct an albatross plot ( Figure 12.4.a , Panel C). The location of the points relative to the standardized mean difference contours indicate that the likely effects of the intervention in these studies are small.

An example description of the results from the synthesis is provided in Box 12.4.d .

Box 12.4.d How to describe the results from this synthesis

12.4.2.3 Scenario 4: vote counting based on direction of effect

In Scenario 4, there is minimal reporting of the data, and the type of effect measure (when used) varies across the studies (e.g. mean difference, proportional odds ratio). Of the 15 results, only five report data suitable for meta-analysis (effect estimate and measure of precision; Table 12.4.c , column 8), and no studies reported precise P values. We use this scenario to illustrate vote counting based on direction of effect. For each study, the effect is categorized as beneficial or harmful based on the direction of effect (indicated as a binary metric; Table 12.4.c , column 9).

Of the 15 studies, we exclude three because they do not provide information on the direction of effect, leaving 12 studies to contribute to the synthesis. Of these 12, 10 effects favour midwife-led models of care (83%). The probability of observing this result if midwife-led models of care are truly ineffective is 0.039 (from a binomial probability test, or equivalently, the sign test). The 95% confidence interval for the percentage of effects favouring midwife-led care is wide (55% to 95%).

The binomial test can be implemented using standard computer spreadsheet or statistical packages. For example, the two-sided P value from the binomial probability test presented can be obtained from Microsoft Excel by typing =2*BINOM.DIST(2, 12, 0.5, TRUE) into any cell in the spreadsheet. The syntax requires the smaller of the ‘number of effects favouring the intervention’ or ‘the number of effects favouring the control’ (here, the smaller of these counts is 2), the number of effects (here 12), and the null value (true proportion of effects favouring the intervention = 0.5). In Stata, the bitest command could be used (e.g. bitesti 12 10 0.5 ).

A harvest plot can be used to display the results ( Figure 12.4.a , Panel D), with characteristics of the studies represented using different heights and shading. A sensitivity analysis might be considered, restricting the analysis to those studies judged to be at an overall low risk of bias. However, only four studies were judged to be at a low risk of bias (of which, three favoured midwife-led models of care), precluding reasonable interpretation of the count.

An example description of the results from the synthesis is provided in Box 12.4.e .

Box 12.4.e How to describe the results from this synthesis

Figure 12.4.a Possible graphical displays of different types of data. (A) Box-and-whisker plots of odds ratios for all outcomes and separately by overall risk of bias. (B) Bubble plot of odds ratios for all outcomes and separately by the model of care. The colours of the bubbles represent the overall risk of bias judgement (green = low risk of bias; yellow = some concerns; red = high risk of bias). (C) Albatross plot of the study sample size against P values (for the five continuous outcomes in Table 12.4.c , column 6). The effect contours represent standardized mean differences. (D) Harvest plot (height depicts overall risk of bias judgement (tall = low risk of bias; medium = some concerns; short = high risk of bias), shading depicts model of care (light grey = caseload; dark grey = team), alphabet characters represent the studies)

12.5 Chapter information

Authors: Joanne E McKenzie, Sue E Brennan

Acknowledgements: Sections of this chapter build on chapter 9 of version 5.1 of the Handbook , with editors Jonathan J Deeks, Julian PT Higgins and Douglas G Altman.

We are grateful to the following for commenting helpfully on earlier drafts: Miranda Cumpston, Jamie Hartmann-Boyce, Tianjing Li, Rebecca Ryan and Hilary Thomson.

Funding: JEM is supported by an Australian National Health and Medical Research Council (NHMRC) Career Development Fellowship (1143429). SEB’s position is supported by the NHMRC Cochrane Collaboration Funding Program.

12.6 References

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McKenzie JE, Brennan SE. Complex reviews: methods and considerations for summarising and synthesising results in systematic reviews with complexity. Report to the Australian National Health and Medical Research Council. 2014.

O’Brien MA, Rogers S, Jamtvedt G, Oxman AD, Odgaard-Jensen J, Kristoffersen DT, Forsetlund L, Bainbridge D, Freemantle N, Davis DA, Haynes RB, Harvey EL. Educational outreach visits: effects on professional practice and health care outcomes. Cochrane Database of Systematic Reviews 2007; 4 : CD000409.

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For permission to re-use material from the Handbook (either academic or commercial), please see here for full details.

12.1 Describing Single Variables

Learning objectives.

• Use frequency tables and histograms to display and interpret the distribution of a variable.
• Compute and interpret the mean, median, and mode of a distribution and identify situations in which the mean, median, or mode is the most appropriate measure of central tendency.
• Compute and interpret the range and standard deviation of a distribution.
• Compute and interpret percentile ranks and  z  scores.

Descriptive statistics  refers to a set of techniques for summarizing and displaying data. Let us assume here that the data are quantitative and consist of scores on one or more variables for each of several study participants. Although in most cases the primary research question will be about one or more statistical relationships between variables, it is also important to describe each variable individually. For this reason, we begin by looking at some of the most common techniques for describing single variables.

The Distribution of a Variable

Every variable has a  distribution , which is the way the scores are distributed across the levels of that variable. For example, in a sample of 100 university students, the distribution of the variable “number of siblings” might be such that 10 of them have no siblings, 30 have one sibling, 40 have two siblings, and so on. In the same sample, the distribution of the variable “sex” might be such that 44 have a score of “male” and 56 have a score of “female.”

Frequency Tables

One way to display the distribution of a variable is in a  frequency table . Table 12.1, for example, is a frequency table showing a hypothetical distribution of scores on the Rosenberg Self-Esteem Scale for a sample of 40 college students. The first column lists the values of the variable—the possible scores on the Rosenberg scale—and the second column lists the frequency of each score. This table shows that there were three students who had self-esteem scores of 24, five who had self-esteem scores of 23, and so on. From a frequency table like this, one can quickly see several important aspects of a distribution, including the range of scores (from 15 to 24), the most and least common scores (22 and 17, respectively), and any extreme scores that stand out from the rest.

There are a few other points worth noting about frequency tables. First, the levels listed in the first column usually go from the highest at the top to the lowest at the bottom, and they usually do not extend beyond the highest and lowest scores in the data. For example, although scores on the Rosenberg scale can vary from a high of 30 to a low of 0, Table 12.1 only includes levels from 24 to 15 because that range includes all the scores in this particular data set. Second, when there are many different scores across a wide range of values, it is often better to create a grouped frequency table, in which the first column lists ranges of values and the second column lists the frequency of scores in each range. Table 12.2, for example, is a grouped frequency table showing a hypothetical distribution of simple reaction times for a sample of 20 participants. In a grouped frequency table, the ranges must all be of equal width, and there are usually between five and 15 of them. Finally, frequency tables can also be used for categorical variables, in which case the levels are category labels. The order of the category labels is somewhat arbitrary, but they are often listed from the most frequent at the top to the least frequent at the bottom.

A  histogram  is a graphical display of a distribution. It presents the same information as a frequency table but in a way that is even quicker and easier to grasp. The histogram in Figure 12.1 presents the distribution of self-esteem scores in Table 12.1. The  x- axis of the histogram represents the variable and the  y- axis represents frequency. Above each level of the variable on the  x- axis is a vertical bar that represents the number of individuals with that score. When the variable is quantitative, as in this example, there is usually no gap between the bars. When the variable is categorical, however, there is usually a small gap between them. (The gap at 17 in this histogram reflects the fact that there were no scores of 17 in this data set.)

Figure 12.1 Histogram Showing the Distribution of Self-Esteem Scores Presented in Table 12.1

Distribution Shapes

When the distribution of a quantitative variable is displayed in a histogram, it has a shape. The shape of the distribution of self-esteem scores in Figure 12.1 is typical. There is a peak somewhere near the middle of the distribution and “tails” that taper in either direction from the peak. The distribution of Figure 12.1 is unimodal, meaning it has one distinct peak, but distributions can also be bimodal, meaning they have two distinct peaks. Figure 12.2, for example, shows a hypothetical bimodal distribution of scores on the Beck Depression Inventory. Distributions can also have more than two distinct peaks, but these are relatively rare in psychological research.

Figure 12.2 Histogram Showing a Hypothetical Bimodal Distribution of Scores on the Beck Depression Inventory

Another characteristic of the shape of a distribution is whether it is symmetrical or skewed. The distribution in the center of Figure 12.3 is symmetrical . Its left and right halves are mirror images of each other. The distribution on the left is negatively  skewed , with its peak shifted toward the upper end of its range and a relatively long negative tail. The distribution on the right is positively skewed, with its peak toward the lower end of its range and a relatively long positive tail.

Figure 12.3 Histograms Showing Negatively Skewed, Symmetrical, and Positively Skewed Distributions

An  outlier  is an extreme score that is much higher or lower than the rest of the scores in the distribution. Sometimes outliers represent truly extreme scores on the variable of interest. For example, on the Beck Depression Inventory, a single clinically depressed person might be an outlier in a sample of otherwise happy and high-functioning peers. However, outliers can also represent errors or misunderstandings on the part of the researcher or participant, equipment malfunctions, or similar problems. We will say more about how to interpret outliers and what to do about them later in this chapter.

Measures of Central Tendency and Variability

It is also useful to be able to describe the characteristics of a distribution more precisely. Here we look at how to do this in terms of two important characteristics: their central tendency and their variability.

Central Tendency

The  central tendency  of a distribution is its middle—the point around which the scores in the distribution tend to cluster. (Another term for central tendency is  average .) Looking back at Figure 12.1, for example, we can see that the self-esteem scores tend to cluster around the values of 20 to 22. Here we will consider the three most common measures of central tendency: the mean, the median, and the mode.

The  mean  of a distribution (symbolized  M ) is the sum of the scores divided by the number of scores. It is an average. As a formula, it looks like this:

In this formula, the symbol Σ (the Greek letter sigma) is the summation sign and means to sum across the values of the variable  X .  N  represents the number of scores. The mean is by far the most common measure of central tendency, and there are some good reasons for this. It usually provides a good indication of the central tendency of a distribution, and it is easily understood by most people. In addition, the mean has statistical properties that make it especially useful in doing inferential statistics.

An alternative to the mean is the median . The median is the middle score in the sense that half the scores in the distribution are less than it and half are greater than it. The simplest way to find the median is to organize the scores from lowest to highest and locate the score in the middle. Consider, for example, the following set of seven scores:

8 4 12 14 3 2 3

To find the median, simply rearrange the scores from lowest to highest and locate the one in the middle.

2 3 3  4  8 12 14

In this case, the median is 4 because there are three scores lower than 4 and three scores higher than 4. When there is an even number of scores, there are two scores in the middle of the distribution, in which case the median is the value halfway between them. For example, if we were to add a score of 15 to the preceding data set, there would be two scores (both 4 and 8) in the middle of the distribution, and the median would be halfway between them (6).

One final measure of central tendency is the mode. The  mode  is the most frequent score in a distribution. In the self-esteem distribution presented in Table 12.1 and Figure 12.1, for example, the mode is 22. More students had that score than any other. The mode is the only measure of central tendency that can also be used for categorical variables.

In a distribution that is both unimodal and symmetrical, the mean, median, and mode will be very close to each other at the peak of the distribution. In a bimodal or asymmetrical distribution, the mean, median, and mode can be quite different. In a bimodal distribution, the mean and median will tend to be between the peaks, while the mode will be at the tallest peak. In a skewed distribution, the mean will differ from the median in the direction of the skew (i.e., the direction of the longer tail). For highly skewed distributions, the mean can be pulled so far in the direction of the skew that it is no longer a good measure of the central tendency of that distribution. Imagine, for example, a set of four simple reaction times of 200, 250, 280, and 250 milliseconds (ms). The mean is 245 ms. But the addition of one more score of 5,000 ms—perhaps because the participant was not paying attention—would raise the mean to 1,445 ms. Not only is this measure of central tendency greater than 80% of the scores in the distribution, but it also does not seem to represent the behavior of anyone in the distribution very well. This is why researchers often prefer the median for highly skewed distributions (such as distributions of reaction times).

Keep in mind, though, that you are not required to choose a single measure of central tendency in analyzing your data. Each one provides slightly different information, and all of them can be useful.

Measures of Variability

The  variability  of a distribution is the extent to which the scores vary around their central tendency. Consider the two distributions in Figure 12.4, both of which have the same central tendency. The mean, median, and mode of each distribution are 10. Notice, however, that the two distributions differ in terms of their variability. The top one has relatively low variability, with all the scores relatively close to the center. The bottom one has relatively high variability, with the scores are spread across a much greater range.

Figure 12.4 Histograms Showing Hypothetical Distributions With the Same Mean, Median, and Mode (10) but With Low Variability (Top) and High Variability (Bottom)

One simple measure of variability is the  range , which is simply the difference between the highest and lowest scores in the distribution. The range of the self-esteem scores in Table 12.1, for example, is the difference between the highest score (24) and the lowest score (15). That is, the range is 24 − 15 = 9. Although the range is easy to compute and understand, it can be misleading when there are outliers. Imagine, for example, an exam on which all the students scored between 90 and 100. It has a range of 10. But if there was a single student who scored 20, the range would increase to 80—giving the impression that the scores were quite variable when in fact only one student differed substantially from the rest.

By far the most common measure of variability is the standard deviation. The standard deviation  of a distribution is the average distance between the scores and the mean. For example, the standard deviations of the distributions in Figure 12.4 are 1.69 for the top distribution and 4.30 for the bottom one. That is, while the scores in the top distribution differ from the mean by about 1.69 units on average, the scores in the bottom distribution differ from the mean by about 4.30 units on average.

Computing the standard deviation involves a slight complication. Specifically, it involves finding the difference between each score and the mean, squaring each difference, finding the mean of these squared differences, and finally finding the square root of that mean. The formula looks like this:

The computations for the standard deviation are illustrated for a small set of data in Table 12.3. The first column is a set of eight scores that has a mean of 5. The second column is the difference between each score and the mean. The third column is the square of each of these differences. Notice that although the differences can be negative, the squared differences are always positive—meaning that the standard deviation is always positive. At the bottom of the third column is the mean of the squared differences, which is also called the  variance  (symbolized  SD 2 ). Although the variance is itself a measure of variability, it generally plays a larger role in inferential statistics than in descriptive statistics. Finally, below the variance is the square root of the variance, which is the standard deviation.

N   or   N   − 1

If you have already taken a statistics course, you may have learned to divide the sum of the squared differences by  N  − 1 rather than by  N  when you compute the variance and standard deviation. Why is this?

By definition, the standard deviation is the square root of the mean of the squared differences. This implies dividing the sum of squared differences by N , as in the formula just presented. Computing the standard deviation this way is appropriate when your goal is simply to describe the variability in a sample. And learning it this way emphasizes that the variance is in fact the mean  of the squared differences—and the standard deviation is the square root of this  mean .

However, most calculators and software packages divide the sum of squared differences by  N  − 1. This is because the standard deviation of a sample tends to be a bit lower than the standard deviation of the population the sample was selected from. Dividing the sum of squares by  N  − 1 corrects for this tendency and results in a better estimate of the population standard deviation. Because researchers generally think of their data as representing a sample selected from a larger population—and because they are generally interested in drawing conclusions about the population—it makes sense to routinely apply this correction.

Percentile Ranks and  z  Scores

In many situations, it is useful to have a way to describe the location of an individual score within its distribution. One approach is the percentile rank. The percentile rank  of a score is the percentage of scores in the distribution that are lower than that score. Consider, for example, the distribution in Table 12.1. For any score in the distribution, we can find its percentile rank by counting the number of scores in the distribution that are lower than that score and converting that number to a percentage of the total number of scores. Notice, for example, that five of the students represented by the data in Table 12.1 had self-esteem scores of 23. In this distribution, 32 of the 40 scores (80%) are lower than 23. Thus each of these students has a percentile rank of 80. (It can also be said that they scored “at the 80th percentile.”) Percentile ranks are often used to report the results of standardized tests of ability or achievement. If your percentile rank on a test of verbal ability were 40, for example, this would mean that you scored higher than 40% of the people who took the test.

Another approach is the  z  score. The  z score  for a particular individual is the difference between that individual’s score and the mean of the distribution, divided by the standard deviation of the distribution:

z = ( X − M ) / SD

A  z  score indicates how far above or below the mean a raw score is, but it expresses this in terms of the standard deviation. For example, in a distribution of intelligence quotient (IQ) scores with a mean of 100 and a standard deviation of 15, an IQ score of 110 would have a  z  score of (110 − 100) / 15 = +0.67. In other words, a score of 110 is 0.67 standard deviations (approximately two thirds of a standard deviation) above the mean. Similarly, a raw score of 85 would have a  z  score of (85 − 100) / 15 = −1.00. In other words, a score of 85 is one standard deviation below the mean.

There are several reasons that  z  scores are important. Again, they provide a way of describing where an individual’s score is located within a distribution and are sometimes used to report the results of standardized tests. They also provide one way of defining outliers. For example, outliers are sometimes defined as scores that have  z  scores less than −3.00 or greater than +3.00. In other words, they are defined as scores that are more than three standard deviations from the mean. Finally,  z  scores play an important role in understanding and computing other statistics, as we will see shortly.

Online Descriptive Statistics

Although many researchers use commercially available software such as SPSS and Excel to analyze their data, there are several free online analysis tools that can also be extremely useful. Many allow you to enter or upload your data and then make one click to conduct several descriptive statistical analyses. Among them are the following.

Rice Virtual Lab in Statistics

http://onlinestatbook.com/stat_analysis/index.html

VassarStats

http://faculty.vassar.edu/lowry/VassarStats.html

Bright Stat

http://www.brightstat.com

For a more complete list, see  http://statpages.org/index.html .

Key Takeaways

• Every variable has a distribution—a way that the scores are distributed across the levels. The distribution can be described using a frequency table and histogram. It can also be described in words in terms of its shape, including whether it is unimodal or bimodal, and whether it is symmetrical or skewed.
• The central tendency, or middle, of a distribution can be described precisely using three statistics—the mean, median, and mode. The mean is the sum of the scores divided by the number of scores, the median is the middle score, and the mode is the most common score.
• The variability, or spread, of a distribution can be described precisely using the range and standard deviation. The range is the difference between the highest and lowest scores, and the standard deviation is the average amount by which the scores differ from the mean.
• The location of a score within its distribution can be described using percentile ranks or  z  scores. The percentile rank of a score is the percentage of scores below that score, and the  z  score is the difference between the score and the mean divided by the standard deviation.
• 11, 8, 9, 12, 9, 10, 12, 13, 11, 13, 12, 6, 10, 17, 13, 11, 12, 12, 14, 14
• Practice: For the data in Exercise 1, compute the mean, median, mode, standard deviation, and range.
• the percentile ranks for scores of 9 and 14
• the  z  scores for scores of 8 and 12.

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12.1 What Is Social Psychology?

Learning objectives.

By the end of this section, you will be able to:

• Define social psychology
• Describe situational versus dispositional influences on behavior
• Describe the fundamental attribution error
• Explain actor-observer bias
• Describe self-serving bias
• Explain the just-world hypothesis

Social psychology examines how people affect one another, and it looks at the power of the situation. According to the American Psychological Association (n.d.), social psychologists "are interested in all aspects of personality and social interaction, exploring the influence of interpersonal and group relationships on human behavior." Throughout this chapter, we will examine how the presence of other individuals and groups of people impacts a person's behaviors, thoughts, and feelings. Essentially, people will change their behavior to align with the social situation at hand. If we are in a new situation or are unsure how to behave, we will take our cues from other individuals.

The field of social psychology studies topics at both the intra- and interpersonal levels. Intrapersonal topics (those that pertain to the individual) include emotions and attitudes, the self, and social cognition (the ways in which we think about ourselves and others). Interpersonal topics (those that pertain to dyads and groups) include helping behavior ( Figure 12.2 ), aggression, prejudice and discrimination, attraction and close relationships, and group processes and intergroup relationships.

Social psychologists focus on how people conceptualize and interpret situations and how these interpretations influence their thoughts, feelings, and behaviors (Ross & Nisbett, 1991). Thus, social psychology studies individuals in a social context and how situational variables interact to influence behavior. In this chapter, we discuss the intrapersonal processes of self-presentation, cognitive dissonance and attitude change, and the interpersonal processes of conformity and obedience, aggression and altruism, and, finally, love and attraction.

Situational and Dispositional Influences on Behavior

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958). An internal factor is an attribute of a person and includes personality traits and temperament. Social psychologists have tended to take the situationist perspective, whereas personality psychologists have promoted the dispositionist perspective. Modern approaches to social psychology, however, take both the situation and the individual into account when studying human behavior (Fiske, Gilbert, & Lindzey, 2010). In fact, the field of social-personality psychology has emerged to study the complex interaction of internal and situational factors that affect human behavior (Mischel, 1977; Richard, Bond, & Stokes-Zoota, 2003).

Fundamental Attribution Error

In the United States, the predominant culture tends to favor a dispositional approach in explaining human behavior. Why do you think this is? We tend to think that people are in control of their own behaviors, and, therefore, any behavior change must be due to something internal, such as their personality, habits, or temperament. According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state . This erroneous assumption is called the fundamental attribution error (Ross, 1977; Riggio & Garcia, 2009). To better understand, imagine this scenario: Jamie returns home from work, and opens the front door to a happy greeting from spouse Morgan who inquires how the day has been. Instead of returning the spouse’s kind greeting, Jamie yells, “Leave me alone!” Why did Jamie yell? How would someone committing the fundamental attribution error explain Jamie’s behavior? The most common response is that Jamie is a mean, angry, or unfriendly person (traits). This is an internal or dispositional explanation. However, imagine that Jamie was just laid off from work due to company downsizing. Would your explanation for Jamie’s behavior change? Your revised explanation might be that Jamie was frustrated and disappointed about being laid off and was therefore in a bad mood (state). This is now an external or situational explanation for Jamie’s behavior.

The fundamental attribution error is so powerful that people often overlook obvious situational influences on behavior. A classic example was demonstrated in a series of experiments known as the quizmaster study (Ross, Amabile, & Steinmetz, 1977). Student participants were randomly assigned to play the role of a questioner (the quizmaster) or a contestant in a quiz game. Questioners developed difficult questions to which they knew the answers, and they presented these questions to the contestants. The contestants answered the questions correctly only 4 out of 10 times ( Figure 12.3 ). After the task, the questioners and contestants were asked to rate their own general knowledge compared to the average student. Questioners did not rate their general knowledge higher than the contestants, but the contestants rated the questioners’ intelligence higher than their own. In a second study, observers of the interaction also rated the questioner as having more general knowledge than the contestant. The obvious influence on performance is the situation. The questioners wrote the questions, so of course they had an advantage. Both the contestants and observers made an internal attribution for the performance. They concluded that the questioners must be more intelligent than the contestants.

The halo effect refers to the tendency to let the overall impression of an individual color the way in which we feel about their character. For instance, we might assume that people who are physically attractive are more likely to be good people than less attractive individuals. Another example of how the halo effect might manifest would involve assuming that someone whom we perceive to be outgoing or friendly has a better moral character than someone who is not.

As demonstrated in the examples above, the fundamental attribution error is considered a powerful influence in how we explain the behaviors of others. However, it should be noted that some researchers have suggested that the fundamental attribution error may not be as powerful as it is often portrayed. In fact, a recent review of more than 173 published studies suggests that several factors (e.g., high levels of idiosyncrasy of the character and how well hypothetical events are explained) play a role in determining just how influential the fundamental attribution error is (Malle, 2006).

Is the Fundamental Attribution Error a Universal Phenomenon?

You may be able to think of examples of the fundamental attribution error in your life. Do people in all cultures commit the fundamental attribution error? Research suggests that they do not. People from an individualistic culture , that is, a culture that focuses on individual achievement and autonomy, have the greatest tendency to commit the fundamental attribution error. Individualistic cultures, which tend to be found in western countries such as the United States, Canada, and the United Kingdom, promote a focus on the individual. Therefore, a person’s disposition is thought to be the primary explanation for her behavior. In contrast, people from a collectivistic culture , that is, a culture that focuses on communal relationships with others, such as family, friends, and community ( Figure 12.4 ), are less likely to commit the fundamental attribution error (Markus & Kitayama, 1991; Triandis, 2001).

Why do you think this is the case? Collectivistic cultures, which tend to be found in east Asian countries and in Latin American and African countries, focus on the group more than on the individual (Nisbett, Peng, Choi, & Norenzayan, 2001). This focus on others provides a broader perspective that takes into account both situational and cultural influences on behavior; thus, a more nuanced explanation of the causes of others’ behavior becomes more likely. Table 12.1 compares individualistic and collectivist cultures.

Masuda and Nisbett (2001) demonstrated that the kinds of information that people attend to when viewing visual stimuli (e.g., an aquarium scene) can differ significantly depending on whether the observer comes from a collectivistic versus an individualistic culture. Japanese participants were much more likely to recognize objects that were presented when they occurred in the same context in which they were originally viewed. Manipulating the context in which object recall occurred had no such impact on American participants. Other researchers have shown similar differences across cultures. For example, Zhang, Fung, Stanley, Isaacowitz, and Zhang (2014) demonstrated differences in the ways that holistic thinking might develop between Chinese and American participants, and Ramesh and Gelfand (2010) demonstrated that job turnover rates are more related to the fit between a person and the organization in which they work in an Indian sample, but the fit between the person and their specific job was more predictive of turnover in an American sample.

Actor-Observer Bias

Returning to our earlier example, Jamie was laid off, but an observer would not know. So a naïve observer would tend to attribute Jamie’s hostile behavior to Jamie’s disposition rather than to the true, situational cause. Why do you think we underestimate the influence of the situation on the behaviors of others? One reason is that we often don’t have all the information we need to make a situational explanation for another person’s behavior. The only information we might have is what is observable. Due to this lack of information we have a tendency to assume the behavior is due to a dispositional, or internal, factor. When it comes to explaining our own behaviors, however, we have much more information available to us. If you came home from school or work angry and yelled at your dog or a loved one, what would your explanation be? You might say you were very tired or feeling unwell and needed quiet time—a situational explanation. The actor-observer bias is the phenomenon of attributing other people’s behavior to internal factors (fundamental attribution error) while attributing our own behavior to situational forces (Jones & Nisbett, 1971; Nisbett, Caputo, Legant, & Marecek, 1973; Choi & Nisbett, 1998). As actors of behavior, we have more information available to explain our own behavior. However as observers, we have less information available; therefore, we tend to default to a dispositionist perspective.

One study on the actor-observer bias investigated reasons male participants gave for why they liked their girlfriend (Nisbett et al., 1973). When asked why participants liked their own girlfriend, participants focused on internal, dispositional qualities of their girlfriends (for example, her pleasant personality). The participants’ explanations rarely included causes internal to themselves, such as dispositional traits (for example, “I need companionship.”). In contrast, when speculating why a male friend likes his girlfriend, participants were equally likely to give dispositional and external explanations. This supports the idea that actors tend to provide few internal explanations but many situational explanations for their own behavior. In contrast, observers tend to provide more dispositional explanations for a friend’s behavior ( Figure 12.5 ).

Self-Serving Bias

We can understand self-serving bias by digging more deeply into attribution , a belief about the cause of a result. One model of attribution proposes three main dimensions: locus of control (internal versus external), stability (stable versus unstable), and controllability (controllable versus uncontrollable). In this context, stability refers to the extent in which the circumstances that result in a given outcome are changeable. The circumstances are considered stable if they are unlikely to change. Controllability refers to the extent to which the circumstances that are associated with a given outcome can be controlled. Obviously, those things that we have the power to control would be labeled controllable (Weiner, 1979).

Following an outcome, self-serving biases are those attributions that enable us to see ourselves in a favorable light (for example, making internal attributions for success and external attributions for failures). When you do well at a task, for example acing an exam, it is in your best interest to make a dispositional attribution for your behavior (“I’m smart,”) instead of a situational one (“The exam was easy,”). Self-serving bias is the tendency to explain our successes as due to dispositional (internal) characteristics, but to explain our failures as due to situational (external) factors. Again, this is culture dependent. This bias serves to protect self-esteem. You can imagine that if people always made situational attributions for their behavior, they would never be able to take credit and feel good about their accomplishments.

Consider the example of how we explain our favorite sports team’s wins. Research shows that we make internal, stable, and controllable attributions for our team’s victory ( Figure 12.6 ) (Grove, Hanrahan, & McInman, 1991). For example, we might tell ourselves that our team is talented (internal), consistently works hard (stable), and uses effective strategies (controllable). In contrast, we are more likely to make external, unstable, and uncontrollable attributions when our favorite team loses. For example, we might tell ourselves that the other team has more experienced players or that the referees were unfair (external), the other team played at home (unstable), and the cold weather affected our team’s performance (uncontrollable).

Just-World Hypothesis

One consequence of westerners’ tendency to provide dispositional explanations for behavior is victim blame (Jost & Major, 2001). When people experience bad fortune, others tend to assume that they somehow are responsible for their own fate. A common ideology, or worldview, in the United States is the just-world hypothesis. The just-world hypothesis is the belief that people get the outcomes they deserve (Lerner & Miller, 1978). In order to maintain the belief that the world is a fair place, people tend to think that good people experience positive outcomes, and bad people experience negative outcomes (Jost, Banaji, & Nosek, 2004; Jost & Major, 2001). The ability to think of the world as a fair place, where people get what they deserve, allows us to feel that the world is predictable and that we have some control over our life outcomes (Jost et al., 2004; Jost & Major, 2001). For example, if you want to experience positive outcomes, you just need to work hard to get ahead in life.

Can you think of a negative consequence of the just-world hypothesis? One negative consequence is people’s tendency to blame poor individuals for their plight. What common explanations are given for why people live in poverty? Have you heard statements such as, “The poor are lazy and just don’t want to work” or “Poor people just want to live off the government”? What types of explanations are these, dispositional or situational? These dispositional explanations are clear examples of the fundamental attribution error. Blaming poor people for their poverty ignores situational factors that impact them, such as high unemployment rates, recession, poor educational opportunities, and the familial cycle of poverty ( Figure 12.7 ). Other research shows that people who hold just-world beliefs have negative attitudes toward people who are unemployed and people living with AIDS (Sutton & Douglas, 2005). In the United States and other countries, victims of sexual assault may find themselves blamed for their abuse. Victim advocacy groups, such as Domestic Violence Ended (DOVE), attend court in support of victims to ensure that blame is directed at the perpetrators of sexual violence, not the victims.

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• Authors: Rose M. Spielman, William J. Jenkins, Marilyn D. Lovett
• Publisher/website: OpenStax
• Book title: Psychology 2e
• Publication date: Apr 22, 2020
• Location: Houston, Texas
• Book URL: https://openstax.org/books/psychology-2e/pages/1-introduction
• Section URL: https://openstax.org/books/psychology-2e/pages/12-1-what-is-social-psychology

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Social Media, Thin-Ideal, Body Dissatisfaction and Disordered Eating Attitudes: An Exploratory Analysis

Pilar aparicio-martinez.

1 Departamento de Enfermería, Universidad de Córdoba, Campus de Menéndez Pidal, 1470 Córdoba, Spain

2 Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK

3 Grupo Investigación epidemiológica en Atención primaria (GC-12) del Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Universitario Reina Sofía, 14071 Córdoba, Spain; se.ocu@oreuqavm

Alberto-Jesus Perea-Moreno

4 Departamento de Física Aplicada, Universidad de Córdoba, ceiA3, Campus de Rabanales, 14071 Córdoba, Spain; se.ocu@aerepa (A.-J.P.-M.); se.ocu@pijam1af (M.P.M.-J.)

María Pilar Martinez-Jimenez

María dolores redel-macías.

5 Departamento Ingeniería Rural, Ed Leonardo da Vinci, Campus de Rabanales, Universidad de Córdoba, Campus de Excelencia Internacional Agroalimentario, ceiA3, 1470 Cordoba, Spain; se.ocu@lederdm

Claudia Pagliari

6 eHealth Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK; [email protected]

Manuel Vaquero-Abellan

Disordered eating attitudes are rapidly increasing, especially among young women in their twenties. These disordered behaviours result from the interaction of several factors, including beauty ideals. A significant factor is social media, by which the unrealistic beauty ideals are popularized and may lead to these behaviours. The objectives of this study were, first, to determine the relationship between disordered eating behaviours among female university students and sociocultural factors, such as the use of social network sites, beauty ideals, body satisfaction, body image and the body image desired to achieve and, second, to determine whether there is a sensitive relationship between disordered eating attitudes, addiction to social networks, and testosterone levels as a biological factor. The data ( N = 168) was obtained using validated surveys (EAT-26, BSQ, CIPE-a, SNSA) and indirect measures of prenatal testosterone. The data was analysed using chi-square, Student’s t-test, correlation tests and logistic regression tests. The results showed that disordered eating attitudes were linked to self-esteem ( p < 0.001), body image ( p < 0.001), body desired to achieve ( p < 0.001), the use of social media ( p < 0.001) and prenatal testosterone ( p < 0.01). The findings presented in this study suggest a relationship between body image, body concerns, body dissatisfaction, and disordered eating attitudes among college women.

1. Introduction

Mental health problems have increased, especially among young people, over the last decade [ 1 ]. The most common mental problems are behavioural, emotional, and hyperkinetic disorders. Among these illnesses, disordered eating behaviours are rapidly increasing in a short time, especially among young women [ 2 , 3 ]. These disordered attitudes are defined as afflictions in which people suffer severe disruption in their eating behaviours, thoughts and emotions. The people who suffer from these complaints are usually preoccupied with food and weight. In this sense, disordered eating is used to describe a range of irregular eating behaviours that may or may not warrant a diagnosis of a specific disordered eating attitude [ 4 ].

These disorders usually occur in women in their twenties or during adolescence [ 3 ]. People who suffer these disorders usually present altered attitudes, behaviours, weight perception and physical appearance [ 5 ]. Moreover, disordered eating behaviours or attitudes are defined as unhealthy or maladaptive eating behaviours, such as restricting or binging and/or purging [ 6 ]. These behaviours are not categorized as an eating disorder, though they are considered a phase of diagnosed eating disorders [ 7 ].

The concern from health care systems is based on the fact that these severe mental disorders usually puts in danger the well-being and health of the people who suffer them [ 5 ]. One-third of the women in the world have suffered from these mental problems at some point in their life [ 6 ]. If they are inadequately treated, they may develop severe clinical disorders [ 8 ]. Moreover, around 1% of the people with these disordered eating attitudes struggle with unhealthy and emotional problems through all their lives [ 6 ].

Out of the population with disordered eating attitudes, 16% of them present overeating, 20% purged by vomiting, and 61% food restraining [ 9 ]. These frequencies changed as people aged, with food restriction being more common in older women and vomiting during adolescence [ 10 ]. Moreover, recent data have discussed the increase of how the minimum age of the people with disorders is around 12 years of age and decreasing. Meanwhile, the prevalence of disordered eating attitudes appears to increase as young adults or adolescents grow older [ 10 ].

Although these diseases have a crucial psychobiological component, social and cultural factors have a significant influence. Among these factors, advertising has been described as an internalizing or normalizing means to spread unrealistic beauty ideals. Therefore, a higher incidence of these diseases is presented in advanced and modern societies and people with the best living conditions, mostly caused by the popularization of thin and muscular ideals [ 11 , 12 , 13 ].

Several biological factors have been linked to disordered eating attitudes, with up to 50% of disordered eating being described as familiarly transmitted [ 5 , 14 ]. Researchers have also suggested that neurotransmitters in the brain are involved in disordered eating attitudes and, therefore, eating disorders [ 15 , 16 ]. Additionally, the hormones have been linked as factors to puberty, body perception and body concerns [ 17 , 18 ]. Testosterone is included among those hormones highly studied, with blood samples providing a more precise method of examination. Nevertheless, different researchers pointed out the possibility of using indirect markers to avoid taking biological samples and creating risks for the participants. In this sense, most studies have linked testosterone and estrogenic levels via the 2D:4D digital ratio as an indirect indicator [ 19 ], which heavily dictates attractiveness [ 17 ]. This ratio, which is based on the difference in length of the phalanges of the hands (2D:4D ratio) having a lower ratio as an indicator of the existence of a higher level of testosterone, is used for the determination of intrauterine testosterone levels during gestation [ 20 ]. This ratio has reflected the relationship with self-perception, body image, body dissatisfaction, and disordered eating behaviours [ 20 , 21 ]. Based on these studies, the hormone levels, and the indirect marker, might appear to have essential roles in disordered eating attitudes [ 22 ]. Nevertheless, other authors have described how biological or genetic factors are essential, but may not determine, these disordered eating attitudes [ 23 ].

Other factors, such as ethical or familiar factors, contribute to the development of this disordered eating behaviours [ 24 ]. In this sense, previous studies have established that the probability of developing a disordered eating attitude or a diagnosis of eating disorders is higher if the mother had a disordered eating or self-esteem problems [ 25 , 26 ]. Moreover, ethnicity has been linked to the perception of beauty ideals, self-esteem and body perception [ 27 , 28 ].

Another critical factor is the media by which beauty ideals have been promoted. The media plays a vital role in formulating what is attractive in society, increasing the thin beauty ideal among females being unattainable [ 29 , 30 ]. These ideals confirmed the way young people perceived themselves and, therefore, how they value themselves [ 10 , 31 ]. This contradiction between what society portrays as a role model and the real body that many young women have has resulted in body concerns. Body concerns usually maintain over time and increase body dissatisfaction. This body dissatisfaction emerges because of the distortion on the body image, its perception and, therefore, body concern [ 32 , 33 ]. This dissatisfaction also plays an essential role in disordered eating attitudes since it provokes emotional and psychological distress [ 34 ].

In this sense, the theory of social comparison and numerous studies have studied the relationship between body dissatisfaction and disordered eating attitudes to better understand the causes of these illnesses. These previous works showed that real comparisons with other people leads to a distortion of body image and may favour disorderly feeding [ 11 , 29 , 35 ]. Additionally, Fredrickson and Roberts (1997) suggested that sexualization and self-objectification promoted via media should be considered as a risk factor for disordered eating attitudes [ 36 , 37 , 38 ]. Based on previous and recent studies it seems that the role of the media in disordered eating attitudes is noteworthy [ 1 , 11 , 39 ].

This paper presents a research study in which these objectives have been pursued: first, to determine the relationship between disordered eating attitudes in female university students and sociocultural factors, such as the use of social network sites, beauty ideals, body satisfaction, the body image and the body image desired to achieve. Second, to determine whether there is a sensitive relationship between disordered eating attitudes, addiction to social networks, and other biological factors, such as testosterone levels.

2. Background

College-aged women may be at particular risk for body dissatisfaction and disordered eating practices due to the unhealthy weight gain that often occurs during this life stage [ 3 , 31 ]. The promotion of beauty ideals in the media disseminates disordered eating [ 40 , 41 ], drive for thinness and body dissatisfaction among female college students [ 42 ]. Furthermore, the growth of social networking sites (SNS), such as Facebook or Instagram, has also increased the exposure to thin and fit ideals [ 2 , 43 , 44 ]. The social media are more used than any other media as a mean of communication. These internet-based sites pulled the users to create personal profiles and share, view, comment and ‘like’ peer-generated content [ 20 ].

Importantly, young people, almost 90% of them (ages 18–29), reported being active users and being continuously exposed to different content and images in this medium [ 14 , 45 ]. Among the most active users of these media stands out the influencers. These new media role models have a significant impact in the last tendencies, the news and the trends that young people are following [ 46 ]. In this sense, researchers have also pointed out how social media and influencers may have the key to decrease body dissatisfaction and body concerns. Nevertheless, substantial studies have shown that economic interests are linked with the promotion of dieting in social media, or even surgery [ 47 ].

The last publications concluded that the most dangerous social media was Instagram, followed by Facebook and Twitter. These conclusions were based on the instant satisfaction of reviewing and having peer views in the images posted by the users [ 48 ]. Especially on Instagram, the message is accommodated according to the image uploaded [ 47 ].

These studies concluded that the influence of the advertising and the promotion of the thin and muscular ideals might more be connected with the perception that young people has regarding body, dieting and social media [ 49 ]. Additionally, the objectification suggests that the media’s sexual objectification of women modifies their body appearance. Due to this, it could be concluded that self-perception slowly shapes attractiveness resulting in a modification in the body-image, body dissatisfaction and disordered eating attitude. That being said, the proposed hypotheses are as follows:

Among young women, self-image will be linked to body dissatisfaction, the thin-ideal and the desire to change one’s body shape.

The level of body dissatisfaction among female college students will be high and be linked to self-esteem.

The young women’s eating behaviours will be linked to the degree of body dissatisfaction and the frequency of using social media.

The young women’s body image and body description will be slightly connected to prenatal testosterone levels.

3. Methodology

3.1. design and sample.

In the first phase, a cross-sectional study was carried out focused on female college students, aged from 18 to 25 years. The sample was recruited to participate in an in-person survey from April to May 2018 from the University of Cordoba. The selection of the sample was based on non-probability convenience sampling. This method of sampling was selected based on the accessibility of the students and previous scheduling with the professors.

The final sample was constituted by 168 subjects, from biological, education, informatics and nursing degrees who agreed to participate in the study voluntarily. The initial sample was 224, though the final sample was 168 after applying the exclusion terms. The mean age of the sample was 20 ± 0.76.

3.2. Measures

All the surveys used in the study are validated in different languages, including Spanish. Moreover, these surveys are used globally among health professionals and researchers in the health field [ 50 ].

The demographic and anthropometric data were not included in this study since the objective focused on the socio-cultural and individual factors. In this sense, the perception of young people was focused on social media, self-appearance, specific social network sites and distorted eating behaviours.

The EAT-26 with the reduced version of 26 items, was used to assess the frequency of disordered eating attitudes [ 51 , 52 ]. This test measures the low, medium and high risk of having a disordered eating attitude. Moreover, three different disordered eating behaviours can be reflected depending on the answers to each item. In this sense, these three subscales are dieting (focused on questions 1, 6, 7, 10, 11, 12, 14, 16, 17, 22, 23, 24, 26), bulimia and food preoccupation (focused on questions 3, 4, 9, 18, 21, 25) and food oral control (2, 5, 8, 13, 15, 8, 20). Total scores were calculated by taking the sum of the 26 items, based on the value from 0 to 3, where higher scores, over 20 points, indicated higher levels of disordered eating behaviours. This validated survey based on screening disorder eating attitudes when the score is over 20 points [ 52 ]. Nevertheless, this survey does not provide a definite diagnosis of eating disorders; therefore, a clinical evaluation is needed. This evaluation can be carried out via individual interviews.

The body satisfaction questionnaire (BSQ) [ 53 ], whose Spanish adaptation was completed by Raich [ 54 ], was used. The stereotypes perception survey from the University of Granada was also used [ 55 ].

The questions referring to body image included illustrations of women’s bodies. These illustrations comprise seven body images that vary from underweight to obese, numbered from 1 to 7. Additionally, a specific section focused on body satisfaction, examining their satisfaction on a scale from 1 to 7, with lower scores relating to higher levels of body dissatisfaction. In this section, one of the questions examined the steps each young person would take to attain a body type that corresponded to the ideal.

The body image concerns were observed by using the BSQ, a self-report instrument evaluating weight and shape preoccupations [ 54 ]. Sample items include: “Have you been so worried about your shape that you have felt you ought to diet?”; “Have you noticed the shape of others and felt that your shape compared unfavourably?” The questions were answered on a six-point Likert scale (1 = never, five = always).

The Appearance Evaluation (AE) subscale of the Multidimensional Body-Self Relations Questionnaire-Appearance Scales (MBSRQ) was used to measure self-perception and stereotypes [ 56 ]. Participants rate the extent to which they agree with seven statements (e.g., “Most people would consider me good-looking”) on a five-point scale (1 = disagree, 5 = agree) with lower scores indicating lower self-perception and stereotypes.

Finally, self-esteem was evaluated by the Rosenberg survey (CIPE-a) composed of ten questions, which provided us with high, medium or low levels of self-esteem. The questions were given a scale on a four-point scale (1 = disagree, 4 = agree), with lower scores indicating lower self-esteem [ 57 ].

On the other hand, the survey that focused on social networks had preliminary yes/no items about having social network accounts on Twitter, Facebook, Instagram, YouTube or Snapchat. Participants indicated how often they access/check their respective accounts daily on a five-point scale: hardly ever, sometimes, usually, all most all the time and always. Additionally, the participants’ daily use (hours per day in social networks and highly visual social media, i.e., Instagram, Snapchat), number of accounts and importance given to these was rated on a 1 (strongly disagree) to 5 (strongly agree) scale.

Meanwhile, addiction to social networks was evaluated by a validated survey called the Social Networks Addiction Questionnaire (SNSA) [ 50 ]. The survey is based on the DSM-IV-TR [ 27 ], a diagnostic instrument that does not recognize psychological addictions as disorders but as a prior stage that can lead to addiction. The survey is formed by 24 items applying a five-point rating system (from 0 to 4), taking into account the frequency from “never” to “always” [ 56 ].

The study has focused on the indirect determination of intrauterine testosterone levels during the gestation, determined experimentally from the difference in length of the phalanges of the hands (2D:4D ratio). This measure was selected to determine the possible relation with sociocultural factors indirectly. The selection of this method was based on reducing the risks, vulnerability and protecting biological or genetic material from the participants. When the ratio is higher, i.e., the difference between the second and fourth finger, lower levels of testosterone are implied [ 21 ]. 2D:4D is an indicator of testosterone and oestrogen levels [ 58 ], which heavily dictate attractiveness [ 17 ]. Therefore, this digit ratio may be related to self-perception, body image, body dissatisfaction and disordered eating attitudes.

3.3. Instruments

The instruments used to obtain the image of the hands were a Canon Camera EOS700D (produced by Canon Inc., which is a Japanese company founded in Ota, Tokyo) and a Manfrotto Compact Advance tripod (produced by Manfrotto, which is an Italian company founded in, produced and distributed form the USA). Additionally, free access software GeoGebra ( https://www.geogebra.org ), which is a free access software founded in Austria and later updated and mass produced in USA, was used to analyse the indirect marker of testosterone levels (2D:4D ratio).

3.4. Procedure

Participants approved a participant information statement, consent form and questionnaires, followed by the approval of the Research Ethics Committee of Public Health System in Cordoba (Ethical Approval number 273, reference 3773).

The participants were undergraduate students with health, education, life and engineering studies. The recruitment took place in different classrooms of the University, the objective of the study, ethical indications, risks for the participants and voluntary participation in the study being previously explained. During the recruitment a teacher and a researcher were present in the classroom the entire time.

The inclusion of the participants was based on an initial survey, which was provided previously in the same classroom. In this survey, the students were asked about the previous diagnosis of conduct or emotional disorders, addiction to technologies, abuse of substances and having a social network account. Those students that had a previous diagnosis of conduct, emotional disorders, or addiction were eliminated from the sample and were not given the survey of the study. Those students that did not have an account on any social network were also excluded from the study ( Figure 1 ).

Flow chart of the recruitment and selection of the sample.

3.5. Statistical Analysis

Mean and standard deviation (SD) were calculated for the quantitative variables and frequencies in the case of qualitative variables. Firstly, we studied the normalization of the data using the Kolmogorov-Smirnov test ( p < 0.05). Moreover, Cronbach’s alpha test was used for determining the consistency among the scales and subscales and, especially, the SNS test showed acceptable value (0.77) and the EAT-26 (0.83) was excellent. In order to assess the first objective, the χ 2 test was used for the qualitative variables, such as gender and body image, and the Student’s t -test was applied to compare quantitative variables, such as the EAT-26 score and age. Additionally, correlational analyses were used to examine relations between all variables.

Moreover, the second set of analyses examined the impact of the relationship between disordered eating attitudes and the rest the factors measured. For this purpose, the crude and adjusted odds ratio (OR) values were calculated for the logistic regression. In the end, the ROC (receiver operating characteristic) curves and the validity indices were used for the diagnostic accuracy of disordered eating attitudes having body dissatisfaction and social networks addiction.

First Phase

The initial analysis of the data showed that women ( N = 168) had a range of age between 21 and 22, 96.7% of them being Caucasian ethnicity. Moreover, the body image that they had was in range between 3 and 4, which may imply a normal weight. The perception that they had of themselves was fatter (3.56 ± 1.2) when compared to the desired body image (2.99 ± 0.83) ( Table 1 ). Additionally, the most common description of body satisfaction showed low and medium-high levels of body satisfaction (48.7%). In this sense, the difference among the group with lower and higher levels of body satisfaction was related to the body image given by the women (χ 2 = 113.64, p < 0.001).

Mean, standard deviation and confidence intervals.

Moreover, the results from the data showed that almost 93% of the women desired to change at least three zones of their body using at least two different methods (1.98 ± 0.82). The methods most used were physical activity (92%), diet (48%), surgery (24%) and beauty or alimentary products (23%). Among the zones to be modified by a surgical procedure 68% of the women indicated breast implants.

The analysis of the results from the EAT-26 test showed that most of the women had a medium probability of having disordered eating attitudes (18.34 ± 10.7). Figure 2 reflects the frequency of the scores from the EAT-26 related to body satisfaction.

Results from the EAT-26 related to body satisfaction.

The figure displays a higher frequency of scores over 20 points in disordered eating behaviours in the lower points of the body satisfaction scale. This figure implies that there were more values over 20 points when women suffered higher levels of body dissatisfaction. Additionally, the analysis between the score in the disordered eating behaviour test and level of body satisfaction showed significant differences among individuals with low and high levels of body satisfaction and scores over 20 points in the EAT-26 (χ 2 = 375.34, p < 0.001). Moreover, a more in-depth analysis of the data, based on women with more than 20 points in the EAT-26, 48 out of 168 women showed that 40.81% had food oral control, 38.77% presented bulimia and food preoccupation and 20.5% dieting.

Further study of the data was carried out in order to address the possible correlations between the body image that women perceived of themselves and the other variables analysed. In Table 2 , the correlations between the body image and the different variables have shown significant value with numerous factors, including disordered eating attitudes, self-esteem, desired body image or number of methods. These correlations were positive for a fatter body image in higher scores in the EAT-26 and more methods used to modify the body image and the current body image. Moreover, negative correlations were found for a curvier description that the women gave about their body and higher desires for a thinner body image, higher body dissatisfaction and lower levels of self-esteem.

Correlations with body image that women perceived of themselves.

Another variable that determines a “fatter” body image is the level of prenatal testosterone, measured by the 2D:4D ratio. This result displayed a positive relationship implying that a higher 2D:4D ratio, lower levels of intrauterine testosterone, may lead to a fatter body image.

On the other hand, Table 3 exposed the analysis of correlations between the score obtained in EAT-26 for disordered eating attitudes and the other factors analysed. This test displayed a negative correlation between having a higher score in the test and having lower levels of body satisfaction, self-esteem, the desired of having a thinner body image and worse perception of their own body.

Correlations with having higher scores in the disordered eating attitudes test.

Moreover, the positive correlations were obtained for numerous factors studied. The most highlighting positive correlations were reflected for a higher score in the SNS addiction test, a fatter body image and a higher difference in the 2D:4D ratio. These results implied that a higher 2D:4D ratio or fatter body image may lead to a higher score in the EAT-26.

The logistic regression model was used to define a disordered eating behaviour related to having lower levels of body satisfaction, the desired to achieve a thinner body image, lower levels of self-esteem, higher score in the SNS addiction test, higher duration of connection to this media and higher difference between the second and fourth finger ( Table 4 ).

Logistic regression for disordered eating attitudes.

From the analysis based on levels of self-esteem and social networks, the results showed that most women have high levels of self-esteem (31.1 ± 4.7) and low levels of addictive behaviour to social network sites (14.69 ± 10.37). Furthermore, the results of the social network sites presented a high dispersion of the results. In this sense, the confidence intervals (95%) were focused on medium levels regarding addictive behaviour to SNS (13.11–16.26).

Based on this, the correlations for the score in the SNS addition test were studied. The results indicated positive significance for the number of methods used to change their body image (<0.001), higher desired of a thinner body ( p < 0.001), lower levels of self-esteem ( p < 0.001), greater number of social media accounts ( p < 0.001), longer duration of the connections ( p < 0.001) and the importance given to the social networks ( p < 0.001). Nevertheless, the difference between the second and fourth phalange (2D:4D ratio) showed no significance with scores in the social network addiction test.

Finally, based on the results from the logistic regression, a probabilistic model was obtained. This model could diagnose 42.9% of the population with disordered eating attitudes (R 2 Cox and Snell 0.429) by knowing if the person had scored high in the SNS addiction test, body image, body dissatisfaction and high desire of having a thinner body. The specificity (90.3), sensibility (68.9) and valid index (84.6) results were optimal. Finally, the curve of the model was analysed ( Figure 3 ) obtaining an acceptable probabilistic high risk of a disordered eating attitudes (area = 0.94, p < 0.001, CI 0.88–0.97).

ROC curve from the logistic model for disordered eating.

5. Discussion

This study has reflected how different factors, such as the level of self-esteem ( Table 1 ), might play a significant role in disordered eating behaviours. Among these factors the body image that women perceived over themselves stood out as a significant element. In this sense, according to previous researchers, body image is multidimensional, being made of perceptual, behavioural and cognitive-affective domains created by the individual [ 46 ]. This perception is dependent on a variety of elements, including social media and beauty ideals. In the case of social media, the results from this study showed a relationship between the body image, body ideals and the use of social media ( Table 2 and Table 3 ). Furthermore, previous publications explained that the desire to achieve the beauty ideal emerges as the internalization of the portrayed image exposed by the media [ 59 , 60 ]. Homan (2010) discussed how, among female college students, two principal beauty ideals coexist: the athletic-ideal and thin-ideal [ 61 ]. The internalization of the athletic-ideal predicts compulsive exercise [ 61 , 62 , 63 ]. Meanwhile, the thin-ideal internalization predicts food restriction and body dissatisfaction, both leading to disordered eating attitudes and possible origins for eating disorders [ 64 , 65 , 66 ]. These results confirm the association obtained between the desire of having a thinner body image and the use of the media since this media is the primary source to promote such ideals ( Table 3 ).

The issue resides on the fact that the thin-ideal produces a worse body image with a tendency toward frustration based on a fatter body image than desired. This concern among young women results in making different choices to obtain the desired image, such as surgery [ 67 , 68 ]. In this sense, the results from this paper also showed a high frequency of women determined to undergo plastic surgery to improve their image, being focused on breast surgery.

Notwithstanding, internalization of the fit-ideal has been studied as a predictor of the use of social media content related to health and fitness [ 69 , 70 ]. In this case, the fit ideal or athletic ideal may become a replacement for the other ideals, leading to healthier behaviour [ 71 ].

The results ( Table 2 ) have established that body dissatisfaction might be a potential agent in body image and desire to change this body image. These publications also accord with our earlier observations, which showed that levels of body dissatisfaction were associated with the desire of changing the body image in order to achieve a thinner body, especially using dieting [ 72 ]. Based on this, the results appear to match with previous works about how body dissatisfaction and body concerns in young women and teenagers may be related to disordered eating attitudes [ 27 , 73 ].

Another significant outcome was the link between body concerns, body dissatisfaction and levels of self-esteem ( Table 2 ). These data are in accord with recent investigations which connected body dissatisfaction and self-esteem to mental illness and the role of emotional distress in behavioural disorders [ 48 ].

Another study found that body dissatisfaction and disordered eating attitudes could be related to a high level of intrauterine testosterone, measured by the 2D:4D ratio. The prenatal masculinization has been established as a potential intermediate phenotype for the development of these disorders in their offspring [ 74 ]. Following these studies, the results obtained in this paper seem to initially match such conclusions ( Table 3 ) [ 75 ]. These results are partially consistent with the existing literature relating to dieting, alimentary products, such as supplements, negative affect, body dissatisfaction and the tendency to thinness [ 71 ]. Nevertheless, the results obtained regarding the hormonal levels may be related to the environmental conditions during the pregnancy more than the individual level of hormones [ 76 ].

The results of the study ( Table 4 ) have shown how social network sites might play an important role in disordered eating attitudes. In the study carried out by Cohen et al. (2018), the influence of the social networks was determined by the content and the selfies that the users upload to them more than by the assiduity of the connections [ 20 ]. This is partially contradictory to the present results in which the addiction to SNS and the duration of the connections were linked to weight loss and unhealthy dieting. These results match with previous studies in the sociocultural factors, not included among biological measures [ 77 , 78 ]. Withstanding, it is important to note that the regression model obtained in this study have shown the probable role of factors, such as the degree of body satisfaction, self-esteem, use of SNS and other measures, such as the 2D:4D ratio, related to disordered eating behaviours.

Additionally, SNS addiction, which has been related to other mental disorders [ 79 ], has shown correlation with stereotypes, self-esteem, method of change, thinner body image and the desired part of the body to change. In this sense, prior investigations proved the addiction to social media as cause–effect of disordered behaviours [ 80 , 81 ].

The present study raises the possibility that disordered eating attitudes in women might be conditioned by the influence of the ideals of beauty imposed by the social environment and to a lesser extent by the exposure to intrauterine levels of testosterone extracted from the 2D:4D ratio of the phalanges. It is possible, therefore, that disordered eating attitudes are multidimensional disorders produced by the media, hormones, and factors related to body concerns. Although this study has focused on Spanish college students, the results ( Table 2 and Figure 2 ) seem to match with previous works conducted in Caucasian women [ 82 , 83 ]. These studies seem to distant themselves from publications focused on Latina or African American young women or adolescents [ 84 , 85 ]. Nevertheless, it is possible, therefore, that because the study was carried out in Spanish college students, the results might not match university women from other countries.

Nevertheless, as with all research, the current findings need to be considered in light of possible limitations of the study. Therefore, biases and possibly incorrect data may have been included, and causal inferences cannot be drawn. Additionally, as with the majority of the body image literature, the current participants were university students, based on the sample and size of the sample caution is recommended in not generalizing these results to other samples or different samples. Nevertheless, these results seem to provide essential data regarding social media, disordered eating and the perception of the young people about themselves. Another limitation present in this study is the lack of inclusion of further cultural factors, such as the mother–child relationship, and anthropometric data, such as BMI.

All being said, the results from this manuscript and the comparison with previous works suggest how the initial hypothesis has been entirely or partially confirmed, showing how disordered eating behaviours are complex eating attitudes.

6. Conclusions

This paper has argued the relationship between body image, body concerns, body dissatisfaction, and disordered eating behaviours present in college women from the south of Spain. This study has identified that women reported moderate levels of body dissatisfaction and body concerns, which were consistently and strongly associated with disordered eating attitudes. In this sense, this work has established high levels of body dissatisfaction, and the link with the desire to achieve a thinner body image. Additionally, the study has shown how body dissatisfaction and desire to achieve the thin-ideal appear to be universal among college women.

Additionally, one of the more significant findings to emerge from this study was that the thin-ideal seems to be widespread in social media. This ideal can promote unhealthy measures, such as dieting, increase body dissatisfaction and disordered eating attitudes. In this sense, the desire to change the body image and taking unhealthy measures was common, given the proliferation of the use of the social network sites where images and content encourage women to aspire to unrealistic and unattainable body ideals. In this sense, the study associated body dissatisfaction, body concerns, and general mental well-being, demonstrating that interventions to improve body perception and satisfaction are essential. Additionally, this research found that higher levels of prenatal testosterone might decrease the probability of having a disordered eating attitude among women. That said, the current study suggests a connection between disordered eating attitudes, negative impacts of exposure to thin-ideal content, addiction to social media and intrauterine testosterone levels.

Concerning practical implications, researchers have asserted that increasing body appreciation may be easier than attempting to decrease body dissatisfaction and for those disordered eating attitudes. Furthermore, the findings regarding the negative impact of exposure to social media related to women’s body satisfaction and body appreciation are notable. Despite the limitations present in this manuscript, the findings may help us to understand body concerns focused on the impact of exposure to social media.

In the end, future investigations should continue exploring differences in the levels of body dissatisfaction and disordered eating, including the differences between various ethnic groups. Given the findings regarding differences between those with higher and lower score in EAT-26, the role of social media may be essential in levels of body dissatisfaction and disordered eating attitudes within specific gender/age groups. Longitudinal research is needed to determine the direction of the association between the frequency of connections to social media and body dissatisfaction/disordered eating behaviours. Researchers may also consider culturally-relevant factors that may differentially influence such behaviours.

Acknowledgments

We would also like to thank of UCO Social Innova Project Galileo IV from the institution of OTRI of the University of Cordoba, Spain and the funding provided from “IDEP/Escuela de Doctorado” of the University of Cordoba to one of the authors. The content is the responsibility of the authors and does not necessarily represent the official views of the OTRI.

Author Contributions

Conceptualization: P.A.-M. and M.V.A.; methodology: P.A.-M. and M.P.M.-J.; validation: A.-J.P.-M.; formal analysis: P.A.-M. and A.-J.P.-M.; investigation: P.A.-M. and A.-J.P.-M.; resources: M.P.M.-J. and A.-J.P.-M.; data curation: M.D.R.-M.; writing—original draft preparation: P.A.-M., M.P.M.-J. and A.-J.P.-M.; writing—review and editing: M.V.A. and C.P.; visualization: C.P.; supervision: M.D.R.-M., C.P. and M.V.A.; project administration: M.V.A. and M.P.M.-J.; funding acquisition: M.V.A.

UCO Social Innova Project Galileo IV from the institution of OTRI of the University of Cordoba, Spain and the funding provided from “IDEP/Escuela de Doctorado” of the University of Cordoba.

Conflicts of Interest

The authors declare no conflict of interest.

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12.2: Overview of Single-Subject Research

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• Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton
• Kwantlen Polytechnic U., Washington State U., & Texas A&M U.—Texarkana
• Explain what single-subject research is, including how it differs from other types of psychological research.
• Explain who uses single-subject research and why.

What Is Single-Subject Research?

Single-subject research is a type of quantitative research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small- n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this textbook is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some more applied areas of psychology.

Before continuing, it is important to distinguish single-subject research from case studies and other more qualitative approaches that involve studying in detail a small number of participants. As described in Chapter 5, case studies involve an in-depth analysis and description of an individual, which is typically primarily qualitative in nature. More broadly speaking, qualitative research focuses on understanding people’s subjective experience by observing behavior and collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are several important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

A second assumption of single-subject research is that it is important to discover causal relationships through the manipulation of an independent variable, the careful measurement of a dependent variable, and the control of extraneous variables. For this reason, single-subject research is often considered a type of experimental research with good internal validity. Recall, for example, that Hall and his colleagues measured their dependent variable (studying) many times—first under a no-treatment control condition, then under a treatment condition (positive teacher attention), and then again under the control condition. Because there was a clear increase in studying when the treatment was introduced, a decrease when it was removed, and an increase when it was reintroduced, there is little doubt that the treatment was the cause of the improvement.

A third assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity (Wolf, 1976) [1] . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques (Skinner, 1938) [2] . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For excellent examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis (Baer, Wolf, & Risley, 1968) [3] . Applied behavior analysis plays an especially important role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Excellent examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis .

Although most contemporary single-subject research is conducted from the behavioral perspective, it can in principle be used to address questions framed in terms of any theoretical perspective. For example, a studying technique based on cognitive principles of learning and memory could be evaluated by testing it on individual high school students using the single-subject approach. The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement (Kazdin, 1982) [4] .

• Wolf, M. (1976). Social validity: The case for subjective measurement or how applied behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11 , 203–214. ↵
• Skinner, B. F. (1938). T he behavior of organisms: An experimental analysis . New York, NY: Appleton-Century-Crofts. ↵
• Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1 , 91–97. ↵
• Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings . New York, NY: Oxford University Press. ↵

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Chapter 12: Field Research: A Qualitative Research Technique

12.4 Getting In and Choosing a Site

When embarking on a field research project, there are two major aspects to consider. The first is where to observe and the second is what role you will take in your field site. Your decision about each of these will be shaped by a number of factors, over some of which you will have control and others you will not. Your decision about where to observe and what role to play will also have consequences for the data you are able to gather and how you analyze and share those data with others. We will examine each of these contingencies in the following subsections.

Your research question might determine where you observe, by, but because field research often works inductively, you may not have a totally focused question before you begin your observations. In some cases, field researchers choose their final research question once they embark on data collection. Other times, they begin with a research question but remain open to the possibility that their focus may shift as they gather data. In either case, when you choose a site, there are a number of factors to consider. These questions include:

• What do you hope to accomplish with your field research?
• What is your topical/substantive interest?
• Where are you likely to observe behaviour that has something to do with that topic?
• How likely is it that you will actually have access to the locations that are of interest to you?
• How much time do you have to conduct your participant observations?
• Will your participant observations be limited to a single location, or will you observe in multiple locations?

Perhaps the best place to start, as you work to identify a site or sites for your field research, is to think about your limitations . One limitation that could shape where you conduct participant observation is time. Field researchers typically immerse themselves in their research sites for many months, sometimes even years. As demonstrated in Table 12.1 “Field Research Examples”, other field researchers have spent as much or even more time in the field. Do you have several years available to conduct research, or are you seeking a smaller-scale field research experience? How much time do you have to participate and observe per day? Per week? Identifying how available you’ll be in terms of time will help you determine where and what sort of research sites to choose. Also think about where you live and whether travel is an option for you. Some field researchers move to live with or near their population of interest. Is this something you might consider? How you answer these questions will shape how you identify your research site. Where might your field research questions take you?

In choosing a site, also consider how your social location might limit what or where you can study. The ascribed aspects of our locations are those that are involuntary, such as our age or race or mobility. For example, how might your ascribed status as an adult shape your ability to conduct complete participation in a study of children’s birthday parties? The achieved aspects of our locations, on the other hand, are those about which we have some choice. In field research, we may also have some choice about whether, or the extent to which, we reveal the achieved aspects of our identities.

Finally, in choosing a research site, consider whether your research will be a collaborative project or whether you are on your own. Collaborating with others has many benefits; you can cover more ground, and therefore collect more data, than you can on your own. Having collaborators in any research project, but especially field research, means having others with whom to share your trials and tribulations in the field. However, collaborative research comes with its own set of challenges, such as possible personality conflicts among researchers, competing commitments in terms of time and contributions to the project, and differences in methodological or theoretical perspectives (Shaffir, Marshall, & Haas, 1979). When considering something that is of interest to you, consider also whether you have possible collaborators. How might having collaborators shape the decisions you make about where to conduct participant observation?

This section began by asking you to think about limitations that might shape your field site decisions. But it makes sense to also think about the opportunities —social, geographic, and otherwise—that your location affords. Perhaps you are already a member of an organization where you would like to conduct research. Maybe you know someone who knows someone else who might be able to help you access a site. Perhaps you have a friend you could stay with, enabling you to conduct participant observations away from home. Choosing a site for participation is shaped by all these factors—your research question and area of interest, a few limitations, some opportunities, and sometimes a bit of being in the right place at the right time.

Choosing a role

As with choosing a research site, some limitations and opportunities beyond your control might shape the role you take once you begin your participant observation. You will also need to make some deliberate decisions about how you enter the field and who you will be once you are in.

In terms of entering the field, one of the earliest decisions you will need to make is whether to be overt or covert. As an overt researcher, you enter the field with your research participants having some awareness about the fact that they are the subjects of social scientific research. Covert researchers, on the other hand, enter the field as though they are full participants, opting not to reveal that they are also researchers or that the group they’ve joined is being studied. As you might imagine, there are pros and cons to both approaches. A critical point to keep in mind is that whatever decision you make about how you enter the field will affect many of your subsequent experiences in the field.

As an overt researcher, you may experience some trouble establishing rapport at first. Having an insider at the site who can vouch for you will certainly help, but the knowledge that subjects are being watched will inevitably (and understandably) make some people uncomfortable and possibly cause them to behave differently than they would, were they not aware of being research subjects. Because field research is typically a sustained activity that occurs over several months or years, it is likely that participants will become more comfortable with your presence over time. Overt researchers also avoid a variety of moral and ethical dilemmas that they might otherwise face.

As a covert researcher, “getting in” your site might be quite easy; however, once you are in, you may face other issues. Some questions to consider are:

• How long would you plan to conceal your identity?
• How might participants respond once they discover you’ve been studying them?
• How will you respond if asked to engage in activities you find unsettling or unsafe?

Researcher, Jun Li (2008) struggled with the ethical challenges of “getting in” to interview female gamblers as a covert researcher. Her research was part of a post-doctoral fellowship from the Ontario Problem Gambling Research Centre to study female gambling culture. In response to these ethical aspects, she changed her research role to overt; however, in her overt role female gamblers were reluctant to “speak their minds” to her (p. 100). As such, she once again adjusted her level of involvement in the study to one who participated in female gambling culture as an insider and observed as an outsider. You can read her interesting story at the following link: https://nsuworks.nova.edu/tqr/vol13/iss1/8 .

Beyond your own personal level of comfort with deceiving participants and willingness to take risks, it is possible that the decision about whether or not to enter the field covertly will be made for you. If you are conducting research while associated with any federally funded agency (and even many private entities), your institutional review board (IRB) probably will have something to say about any planned deception of research subjects. Some IRBs approve deception, but others look warily upon a field researcher engaging in covert participation. The extent to which your research site is a public location, where people may not have an expectation of privacy, might also play a role in helping you decide whether covert research is a reasonable approach.

Having an insider at your site who can vouch for you is helpful. Such insiders, with whom a researcher may have some prior connection or a closer relationship than with other site participants, are called key informants. A key informant can provide a framework for your observations, help translate what you observe, and give you important insight into a group’s culture. If possible, having more than one key informant at a site is ideal, as one informant’s perspective may vary from another’s.

Once you have made a decision about how to enter your field site, you will need to think about the role you will adopt while there. Aside from being overt or covert, how close will you be to participants? In the words of Fred Davis (1973), [12] who coined these terms in reference to researchers’ roles, “will you be a Martian, a Convert, or a bit of both”? Davis describes the Martian role as one in which a field researcher stands back a bit, not fully immersed in the lives of his subjects, in order to better problematize, categorize, and see with the eyes of a newcomer what’s being observed. From the Martian perspective, a researcher should remain disentangled from too much engagement with participants. The Convert, on the other hand, intentionally dives right into life as a participant. From this perspective, it is through total immersion that understanding is gained. Which approach do you feel best suits you?

In the preceding section we examined how ascribed and achieved statuses might shape how or which sites are chosen for field research. They also shape the role the researcher adopts in the field site. The fact that the authors of this textbook are professors, for example, is an achieved status. We can choose the extent to which we share this aspect of our identities with field study participants. In some situations, sharing that we are professors may enhance our ability to establish rapport; in other field sites it might stifle conversation and rapport-building. As you have seen from the examples provided throughout this chapter, different field researchers have taken different approaches when it comes to using their social locations to help establish rapport and dealing with ascribed statuses that differ from those of their “subjects

Whatever role a researcher chooses, many of the points made in Chapter 11 “Quantitative Interview Techniques” regarding power and relationships with participants apply to field research as well. In fact, the researcher/researched relationship is even more complex in field studies, where interactions with participants last far longer than the hour or two it might take to interview someone. Moreover, the potential for exploitation on the part of the researcher is even greater in field studies, since relationships are usually closer and lines between research and personal or off-the-record interaction may be blurred. These precautions should be seriously considered before deciding to embark upon a field research project

Field notes

The aim with field notes is to record your observations as straightforwardly and, while in the field, as quickly as possible, in a way that makes sense to you . Field notes are the first—and a necessary—step toward developing quality analysis. They are also the record that affirms what you observed. In other words, field notes are not to be taken lightly or overlooked as unimportant; however, they are not usually intended for anything other than the researcher’s own purposes as they relate to recollections of people, places and things related to the research project.

Some say that there are two different kinds of field notes: descriptive and analytic. Though the lines between what counts as description and what counts as analysis can become blurred, the distinction is nevertheless useful when thinking about how to write and how to interpret field notes. In this section, we will focus on descriptive field notes. Descriptive field notes are notes that simply describe a field researcher’s observations as straightforwardly as possible. These notes typically do not contain explanations of, or comments about, those observations. Instead, the observations are presented on their own, as clearly as possible. In the following section, we will define and examine the uses and writing of analytic field notes more closely.

Analysis of field research data

Field notes are data. But moving from having pages of data to presenting findings from a field study in a way that will make sense to others requires that those data be analyzed. Analysis of field research data is the focus in this final section of the chapter.

From description to analysis

Writing and analyzing field notes involves moving from description to analysis. In Section 12.4 “Field Notes”, we considered field notes that are mostly descriptive in nature. In this section we will consider analytic field notes. Analytic field notes are notes that include the researcher’s impressions about his observations. Analyzing field note data is a process that occurs over time, beginning at the moment a field researcher enters the field and continuing as interactions happen in the field, as the researcher writes up descriptive notes, and as the researcher considers what those interactions and descriptive notes mean.

Often field notes will develop from a more descriptive state to an analytic state when the field researcher exits a given observation period, with messy jotted notes or recordings in hand (or in some cases, literally on hand), and sits at a computer to type up those notes into a more readable format. We have already noted that carefully paying attention while in the field is important; so is what goes on immediately upon exiting the field. Field researchers typically spend several hours typing up field notes after each observation has occurred. This is often where the analysis of field research data begins. Having time outside of the field to reflect upon your thoughts about what you have seen and the meaning of those observations is crucial to developing analysis in field research studies.

Once the analytic field notes have been written or typed up, the field researcher can begin to look for patterns across the notes by coding the data. This will involve the iterative process of open and focused coding that is outlined in Chapter 10, “Qualitative Data Collection & Analysis Methods.” As mentioned in Section 12.4 “Field Notes”, it is important to note as much as you possibly can while in the field and as much as you can recall after leaving the field because you never know what might become important. Things that seem decidedly unimportant at the time may later reveal themselves to have some relevance.

As mentioned in Chapter 10, analysis of qualitative data often works inductively. The analytic process of field researchers and others who conduct inductive analysis is referred to as grounded theory (Glaser & Strauss, 1967; Charmaz, 2006). The goal when employing a grounded theory approach is to generate theory. Its name not only implies that discoveries are made from the ground up but also that theoretical developments are grounded in a researcher’s empirical observations and a group’s tangible experiences. Grounded theory requires that one begin with an open-ended and open-minded desire to understand a social situation or setting and involves a systematic process whereby the researcher lets the data guide her rather than guiding the data by preset hypotheses.

As exciting as it might sound to generate theory from the ground up, the experience can also be quite intimidating and anxiety-producing, since the open nature of the process can sometimes feel a little out of control. Without hypotheses to guide their analysis, researchers engaged in grounded theory work may experience some feelings of frustration or angst. The good news is that the process of developing a coherent theory that is grounded in empirical observations can be quite rewarding, not only to researchers, but also to their peers, who can contribute to the further development of new theories through additional research, and to research participants who may appreciate getting a bird’s-eye view of their every day.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Study: Eating More Than 12 Eggs a Week Shown Not to Impact Cholesterol Levels

Francisco J. Rivera Rosario is a science communications editor experienced in developing all types of science content including, scientific journal articles, infographics, medical educational videos, medication FAQ documents, and more.

• Eggs may not impact cholesterol levels as much as once thought, new research suggests.
• Preliminary results from a new study show that people who ate 12 or more fortified eggs a week had cholesterol levels similar to those who didn’t eat eggs.
• Experts say that when it comes to diet and cholesterol, it is the entirety of one’s diet that ultimately determines heart health, not a single ingredient.

Having an egg-heavy diet may not impact cholesterol levels as much as once thought, new research shows.

Preliminary results from a new study show that people who ate 12 or more fortified eggs a week had similar cholesterol levels to those who didn’t eat eggs at all. The study will be presented at the American College of Cardiology’s Annual Scientific Session on April 6 in Atlanta, Georgia.

Eggs have notoriously received a bad rap due to concerns that they may raise cholesterol levels or worsen heart health. The new research, however, may provide some reassurance that eating eggs may be OK, even for a more high-risk group of people.

“There has been a lot of controversy around how eggs, a food rich in cholesterol, but also protein, can affect cardiovascular health,” Fatima Rodriguez, MD, MPH , associate professor of cardiovascular medicine at Stanford University, told Health . “The question on the health effects of eating large amounts of eggs remains unanswered and this small study gives some insight that can be further studied in a larger study with blinded controls.”

Here’s what you need to know about the newest research on eggs, how they may or may not impact cholesterol levels, and how they can be part of a healthy diet , even for those paying special attention to cardiovascular health.

Impact of an Egg-Heavy Diet on Cholesterol Levels

For the study, funded by Eggland's Best, one of the largest egg producers in the U.S., researchers assessed the effects of consuming a diet high in fortified eggs as compared to a non-egg diet on cardiovascular biomarkers like cholesterol , inflammatory biomarkers, micronutrient levels, and many other endpoints.

Fortified eggs are eggs that have added nutrients like vitamin D , selenium, vitamin B2, 5, and 12, and omega-3 fatty acids. This is a common practice that is done in order to increase a food item’s nutritional value.

140 participants enrolled in the study and were randomized into two groups—the fortified eggs group, which consumed 12 or more fortified eggs a week, and the non-egg diet group, which consumed 2 eggs or fewer per week. Participants were allowed to prepare the eggs in whatever manner they preferred.

All of the participants in the study were over 50 years old, and all had experienced one previous cardiovascular event or had at least two cardiovascular risk factors. Twenty-seven percent of the participants were Black and 24% had diabetes.

Participants had in-person appointments at one month and after four months to assess their vital signs and blood cholesterol levels. Researchers also performed phone check-ins throughout the study to monitor egg consumption.

Researchers looked at the levels of HDL-cholesterol (good cholesterol) and LDL-cholesterol (bad cholesterol), of participants divided into the two groups at the beginning of the study and again after four months.

Results after a four-month follow-up showed that levels of HDL- and LDL-cholesterol were similar between both study groups. Results showed a small reduction of HDL- and LDL-cholesterol in the fortified egg group versus the non-egg diet group, but these changes were not statistically significant.

These results suggest that eating 12 or more fortified eggs each week had no negative effects on blood cholesterol.

This is what is known as a neutral study, a study that shows there is no statistically significant difference between the study groups. This means that, while there is no evidence of harm, there is no evidence of benefit either as it relates to changes in HDL- and LDL-cholesterol levels.

Study results also showed that blood levels of high-sensitivity troponin (a marker of heart damage) decreased slightly in the fortified egg group, and levels of vitamin B increased slightly.

“In this small single-center study, eating more than 12 fortified eggs per week did not change blood cholesterol levels in a clinically meaningful way after four months,” said Rodriguez. “As physicians, our patients may ask us if it’s okay to eat eggs, and this study lends some evidence that this amount of egg consumption may be ok.”

Does This Mean for Eggs Are Actually Heart-Healthy?

While the data provides some evidence suggesting that the consumption of 12 or more eggs did not have negative effects on blood cholesterol, experts suggest results should be taken with some caution.

The small study was a single-center trial, meaning it was conducted according to a single protocol at a single site. The study was also small and relied on patients self-reporting their egg consumption and other dietary patterns. Additionally, patients knew which group they were in (the egg-eating or non-egg-eating group), which could have influenced their health behaviors.

All of these factors “make it difficult to draw strong conclusions from this study,” according to Matthew Tomey, MD , a cardiologist and assistant professor of medicine at the Icahn School of Medicine at Mount Sinai.

“While I agree that the data shared do not provide evidence of harm with eating more eggs, I might stop short of citing the present study as sufficient ‘reassurance’ of the absence of harm,” Tomey told Health .

Information provided about the study also does not go into details regarding the participants’ diets outside of their egg consumption, including whether they ate fewer overall calories or consumed less saturated fat or if these results apply to non-fortified eggs, according to Martha Gulati, MD , professor of cardiology and director of preventive cardiology at Cedars-Sinai.

Experts are also interested in knowing more about the long-term cardiovascular effects of fortified egg consumption. “Four months is a good follow-up period, but I would want a longer study. Hopefully, they have food diaries on participants that will be analyzed, and perhaps this study will have a long follow-up to assess for [cardiovascular] outcomes,” said Gulati.

How to Incorporate Eggs Into a Healthy Diet

Though the study’s results suggest that egg consumption does not impact cholesterol as much as we once thought, when it comes to diet and cholesterol, it is the entirety of one’s diet that ultimately determines heart health.

“Nutrition is complicated and we need to be careful about looking at any one food in isolation,” said Tomey. “The impact of our diet on our health is a product of the totality of our food choices. When we avoid one food, the question comes, how are we replacing it in our diet?”

“I think dietary guidance is always a bit difficult,” added Gulati. “It is never one food that causes heart disease, it is the entire diet and the total saturated fat.”

As for whether eggs are a safe addition to a daily diet, experts agree that the answer is yes—in moderation and as long as the diet is balanced overall.

“Eggs are so commonly part of the American diet, and people want to know if they can eat eggs. It is a common clinical question posed to me,” said Gulati. “My answer is always this: You can consume eggs in moderation, but I need to know more about your diet and if you consume other sources of saturated fats. Because ultimately it is the total saturated fat consumption that will affect your LDL and increase the risk for atherosclerosis.”

For people who are looking to make a change to their diet, Tomey said it’s more important to zoom out and look at the big picture rather than focusing on one ingredient. “I would encourage anyone considering a dietary change for health promotion,” he said, “to evaluate the diet holistically.”

American College of Cardiology. Eggs may not be bad for your heart after all .

Nouhravesh N, et al. Prospective evaluation of fortified eggs related to improvement in the biomarker profile for your health: Primary results from the PROSPERITY trial. Abstract presented at American College of Cardiology's Annual Scientific Session. April 6, 2024, Atlanta, GA.

World Health Organization. Food fortification .

Centers for Disease Control and Prevention. LDL and HDL cholesterol and triglycerides .

Bresee L. The importance of negative and neutral studies for advancing clinical practice . Can J Hosp Pharm . 2017;70(6):403-404. doi:10.4212/cjhp.v70i6.1706

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5.1 Assumptions underlying research

Learning objectives.

Learners will be able to…

• Ground your research project and working question in the philosophical assumptions of social science
• Define the terms ‘ ontology ‘ and ‘ epistemology ‘ and explain how they relate to quantitative and qualitative research methods

Pre-awareness check (Knowledge)

Thinking back on your practice experience, what types of things were dependent on a person’s own truth and more subjective? What types of things would you consider irrefutable truths and more objective?

Last chapter, we reviewed the ethical commitment that social work researchers have to protect the people and communities impacted by their research. Answering the practical questions of harm, conflicts of interest, and other ethical issues will provide clear foundation of what you can and cannot do as part of your research project. In this chapter, we will transition from the real world to the conceptual world. Together, we will discover and explore the theoretical and philosophical foundations of your project. You should complete this chapter with a better sense of how theoretical and philosophical concepts help you answer your working question, and in turn, how theory and philosophy will affect the research project you design.

Embrace philosophy

The single biggest barrier to engaging with philosophy of science, at least according to some of my students, is the word philosophy. I had one student who told me that as soon as that word came up, she tuned out because she thought it was above her head. As we discussed in Chapter 1, some students already feel like research methods is too complex of a topic, and asking them to engage with philosophical concepts within research is like asking them to tap dance while wearing ice skates.

For those students, I would first answer that this chapter is my favorite one to write because it was the most impactful for me to learn during my MSW program. Finding my theoretical and philosophical home was important for me to develop as a clinician and a researcher. Following our advice in Chapter 2, you’ve hopefully chosen a topic that is important to your interests as a social work practitioner, and consider this chapter an opportunity to find your personal roots in addition to revising your working question and designing your research study.

Exploring theoretical and philosophical questions will cause your working question and research project to become clearer. Consider this chapter as something similar to getting a nice outfit for a fancy occasion. You have to try on a lot of different theories and philosophies before you find the one that fits with what you’re going for. There’s no right way to try on clothes, and there’s no one right theory or philosophy for your project. You might find a good fit with the first one you’ve tried on, or it might take a few different outfits. You have to find ideas that make sense together because they fit with how you think about your topic and how you should study it.

As you read this section, try to think about which assumptions  feel right for your working question and research project. Which assumptions match what you think and believe about your topic? The goal is not to find the “right” answer, but to develop your conceptual understanding of your research topic by finding the right theoretical and philosophical fit.

Theoretical and philosophical fluency

In addition to self-discovery, theoretical and philosophical fluency is a skill that social workers must possess in order to engage in social justice work. That’s because theory and philosophy help sharpen your perceptions of the social world. Just as social workers use empirical data to support their work, they also use theoretical and philosophical foundations. More importantly, theory and philosophy help social workers build heuristics that can help identify the fundamental assumptions at the heart of social conflict and social problems. They alert you to the patterns in the underlying assumptions that different people make and how those assumptions shape their worldview, what they view as true, and what they hope to accomplish. In the next section, we will review feminist and other critical perspectives on research, and they should help inform you of how assumptions about research can reinforce existing oppression.

Understanding these deeper structures is a true gift of social work research. Because we acknowledge the usefulness and truth value of multiple philosophies and worldviews contained in this chapter, we can arrive at a deeper and more nuanced understanding of the social world. Methods can be closely associated with particular worldviews or ideologies. There are necessarily philosophical and theoretical aspects to this, and this can be intimidating at times, but it’s important to critically engage with these questions to improve the quality of research.

Building your ice float

Although it may not seem like it right now, your project will develop a from a strong connection to previous theoretical and philosophical ideas about your topic. It’s likely you already have some (perhaps unstated) philosophical or theoretical ideas that undergird your thinking on the topic. Moreover, the philosophical questions we review here should inform how you understand different theories and practice modalities in social work, as they deal with the bedrock questions about science and human knowledge.

Before we can dive into philosophy, we need to recall our conversation from Chapter 1 about objective truth and subjective truths. Let’s test your knowledge with a quick example. Is crime on the rise in the United States? A recent Five Thirty Eight article highlights the disparity between historical trends on crime that are at or near their lowest in the thirty years with broad perceptions by the public that crime is on the rise (Koerth & Thomson-DeVeaux, 2020). [1] Social workers skilled at research can marshal objective facts, much like the authors do, to demonstrate that people’s perceptions are not based on a rational interpretation of the world. Of course, that is not where our work ends. Subjective facts might seek to decenter this narrative of ever-increasing crime, deconstruct is racist and oppressive origins, or simply document how that narrative shapes how individuals and communities conceptualize their world.

Objective does not mean right, and subjective does not mean wrong. Researchers must understand what kind of truth they are searching for so they can choose a theory(ies), develop a theoretical framework (in quantitative research), select an appropriate methodology, and make sure the research question(s) matches them all. As we discussed in Chapter 1, researchers seeking objective truth (one of the philosophical foundations at the bottom of Figure 5.1) often employ quantitative methods (one of the methods at the top of Figure 5.1). Similarly, researchers seeking subjective truths (again, at the bottom of Figure 5.1) often employ qualitative methods (at the top of Figure 5.1). This chapter is about the connective tissue, and by the time you are done reading, you should have a first draft of a theoretical and philosophical (a.k.a. paradigmatic) framework for your study.

Ontology: Assumptions about what is real and true

In section 1.2, we reviewed the two types of truth that social work researchers seek— objective truth and subjective truths —and linked these with the methods—quantitative and qualitative—that researchers use to study the world. If those ideas aren’t fresh in your mind, you may want to navigate back to that section for an introduction.

These two types of truth rely on different assumptions about what is real in the social world—i.e., they have a different ontology . Ontology refers to the study of being (literally, it means “rational discourse about being”). In philosophy, basic questions about existence are typically posed as ontological, e.g.:

• What is there?
• What types of things are there?
• How can we describe existence?
• What kind of categories can things go into?
• Are the categories of existence hierarchical?

Objective vs. subjective ontologies

At first, it may seem silly to question whether the phenomena we encounter in the social world are real. Of course you exist, your thoughts exist, your computer exists, and your friends exist. You can see them with your eyes. This is the ontological framework of  realism , which simply means that the concepts we talk about in science exist independent of observation (Burrell & Morgan, 1979). [2] Obviously, when we close our eyes, the universe does not disappear. You may be familiar with the philosophical conundrum: “If a tree falls in a forest and no one is around to hear it, does it make a sound?”

The natural sciences, like physics and biology, also generally rely on the assumption of realism. Lone trees falling make a sound. We assume that gravity and the rest of physics are there, even when no one is there to observe them. Mitochondria are easy to spot with a powerful microscope, and we can observe and theorize about their function in a cell. The gravitational force is invisible, but clearly apparent from observable facts, such as watching an apple fall from a tree. Of course, our theories about gravity have changed over the years. Improvements were made when observations could not be correctly explained using existing theories and new theories emerged that provided a better explanation of the data.

As we discussed in section 1.2, culture-bound syndromes are an excellent example of where you might come to question realism. Of course, from a Western perspective as researchers in the United States, we think that the Diagnostic and Statistical Manual (DSM) classification of mental health disorders is real and that these culture-bound syndromes are aberrations from the norm. But what about if you were a person from Korea experiencing Hwabyeong? Wouldn’t you consider the Western diagnosis of somatization disorder to be incorrect or incomplete? This conflict raises the question–do either Hwabyeong   or DSM diagnoses like post-traumatic stress disorder (PTSD) really exist at all…or are they just social constructs that only exist in our minds?

If your answer is “no, they do not exist,” you are adopting the ontology of anti-realism ( or relativism ), or the idea that social concepts do not exist outside of human thought. Unlike the realists who seek a single, universal truth, the anti-realists perceive a sea of truths, created and shared within a social and cultural context. Unlike objective truth, which is true for all, subjective truths will vary based on who you are observing and the context in which you are observing them. The beliefs, opinions, and preferences of people are actually truths that social scientists measure and describe. Additionally, subjective truths do not exist independent of human observation because they are the product of the human mind. We negotiate what is true in the social world through language, arriving at a consensus and engaging in debate within our socio-cultural context.

These theoretical assumptions should sound familiar if you’ve studied social constructivism or symbolic interactionism in MSW courses, most likely in human behavior in the social environment (HBSE). [3] From an anti-realist perspective, what distinguishes the social sciences from natural sciences is human thought. When we try to conceptualize trauma from an anti-realist perspective, we must pay attention to the feelings, opinions, and stories in people’s minds. In their most radical formulations, anti-realists propose that these feelings and stories are all that truly exist.

What happens when a situation is incorrectly interpreted? Certainly, who is correct about what is a bit subjective. It depends on who you ask. Even if you can determine whether a person is actually incorrect, they think they are right. Thus, what may not be objectively true for everyone is nevertheless true to the individual interpreting the situation. Furthermore, they act on the assumption that they are right. We all do. Much of our behaviors and interactions are a manifestation of our personal subjective truth. In this sense, even incorrect interpretations are truths, even though they are true only to one person or a group of misinformed people. This leads us to question whether the social concepts we think about really exist. For researchers using subjective ontologies, they might only exist in our minds; whereas, researchers who use objective ontologies which assume these concepts exist independent of thought.

How do we resolve this dichotomy? As social workers, we know that often times what appears to be an either/or situation is actually a both/and situation. Let’s take the example of trauma. There is clearly an objective thing called trauma. We can draw out objective facts about trauma and how it interacts with other concepts in the social world such as family relationships and mental health. However, that understanding is always bound within a specific cultural and historical context. Moreover, each person’s individual experience and conceptualization of trauma is also true. Much like a client who tells you their truth through their stories and reflections, when a participant in a research study tells you what their trauma means to them, it is real even though only they experience and know it that way. By using both objective and subjective analytic lenses, we can explore different aspects of trauma—what it means to everyone, always, everywhere, and what is means to one person or group of people, in a specific place and time.

Epistemology: Assumptions about how we know things

Having discussed what is true, we can proceed to the next natural question—how can we come to know what is real and true? This is epistemology . Epistemology is derived from the Ancient Greek epistēmē which refers to systematic or reliable knowledge (as opposed to doxa, or “belief”). Basically, it means “rational discourse about knowledge,” and the focus is the study of knowledge and methods used to generate knowledge. Epistemology has a history as long as philosophy, and lies at the foundation of both scientific and philosophical knowledge.

Epistemological questions include:

• What is knowledge?
• How can we claim to know anything at all?
• What does it mean to know something?
• What makes a belief justified?
• What is the relationship between the knower and what can be known?

While these philosophical questions can seem far removed from real-world interaction, thinking about these kinds of questions in the context of research helps you target your inquiry by informing your methods and helping you revise your working question. Epistemology is closely connected to method as they are both concerned with how to create and validate knowledge. Research methods are essentially epistemologies – by following a certain process we support our claim to know about the things we have been researching. Inappropriate or poorly followed methods can undermine claims to have produced new knowledge or discovered a new truth. This can have implications for future studies that build on the data and/or conceptual framework used.

Research methods can be thought of as essentially stripped down, purpose-specific epistemologies. The knowledge claims that underlie the results of surveys, focus groups, and other common research designs ultimately rest on epistemological assumptions of their methods. Focus groups and other qualitative methods usually rely on subjective epistemological (and ontological) assumptions. Surveys and other quantitative methods usually rely on objective epistemological assumptions. These epistemological assumptions often entail congruent subjective or objective ontological assumptions about the ultimate questions about reality.

Objective vs. subjective epistemologies

One key consideration here is the status of ‘truth’ within a particular epistemology or research method. If, for instance, some approaches emphasize subjective knowledge and deny the possibility of an objective truth, what does this mean for choosing a research method?

We began to answer this question in Chapter 1 when we described the scientific method and objective and subjective truths. Epistemological subjectivism focuses on what people think and feel about a situation, while epistemological objectivism focuses on objective facts irrelevant to our interpretation of a situation (Lin, 2015). [4]

While there are many important questions about epistemology to ask (e.g., “How can I be sure of what I know?” or “What can I not know?” see Willis, 2007 [5] for more), from a pragmatic perspective most relevant epistemological question in the social sciences is whether truth is better accessed using numerical data or words and performances. Generally, scientists approaching research with an objective epistemology (and realist ontology) will use quantitative methods to arrive at scientific truth. Quantitative methods examine numerical data to precisely describe and predict elements of the social world. For example, while people can have different definitions for poverty, an objective measurement such as an annual income of “less than $25,100 for a family of four” provides a precise measurement that can be compared to incomes from all other people in any society from any time period, and refers to real quantities of money that exist in the world. Mathematical relationships are uniquely useful in that they allow comparisons across individuals as well as time and space. In this book, we will review the most common designs used in quantitative research: surveys and experiments. These types of studies usually rely on the epistemological assumption that mathematics can represent the phenomena and relationships we observe in the social world.

Although mathematical relationships are useful, they are limited in what they can tell you. While you can use quantitative methods to measure individuals’ experiences and thought processes, you will miss the story behind the numbers. To analyze stories scientifically, we need to examine their expression in interviews, journal entries, performances, and other cultural artifacts using qualitative methods . Because social science studies human interaction and the reality we all create and share in our heads, subjectivists focus on language and other ways we communicate our inner experience. Qualitative methods allow us to scientifically investigate language and other forms of expression—to pursue research questions that explore the words people write and speak. This is consistent with epistemological subjectivism’s focus on individual and shared experiences, interpretations, and stories.

It is important to note that qualitative methods are entirely compatible with seeking objective truth. Approaching qualitative analysis with a more objective perspective, we look simply at what was said and examine its surface-level meaning. If a person says they brought their kids to school that day, then that is what is true. A researcher seeking subjective truth may focus on how the person says the words—their tone of voice, facial expressions, metaphors, and so forth. By focusing on these things, the researcher can understand what it meant to the person to say they dropped their kids off at school. Perhaps in describing dropping their children off at school, the person thought of their parents doing the same thing. In this way, subjective truths are deeper, more personalized, and difficult to generalize.

Putting it all together

As you might guess by the structure of the next two parts of this textbook, the distinction between quantitative and qualitative is important. Because of the distinct philosophical assumptions of objectivity and subjectivity, it will inform how you define the concepts in your research question, how you measure them, and how you gather and interpret your raw data. You certainly do not need to have a final answer right now! But stop for a minute and think about which approach feels right so far. In the next section, we will consider another set of philosophical assumptions that relate to ethics and the role of research in achieving social justice.

Key Takeaways

• Philosophers of science disagree on the basic tenets of what is true and how we come to know what is true.
• Researchers searching for objective truth will likely have a different research design, and methods than researchers searching for subjective truths.
• These differences are due to different assumptions about what is real and true (ontology) and how we can come to understand what is real and true (epistemology).

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Does an objective or subjective epistemological/ontological framework make the most sense for your research project?

• Are you more concerned with how people think and feel about your topic, their subjective truths—more specific to the time and place of your project?
• Or are you more concerned with objective truth, so that your results might generalize to populations beyond the ones in your study?

Using your answer to the above question, describe how either quantitative or qualitative methods make the most sense for your project.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in researching bullying among school-aged children, and how this impacts students’ academic success.

• If you are using an objective epistemological/ontological framework, what types of research questions might you ask?
• If you are using a subjective epistemological/ontological framework, what types of research questions might you ask?
• Koerth, M. & Thomson-DeVeaux, A. (2020, August 3). Many Americans are convinced crime is rising in the U.S. They're wrong. FiveThirtyEight . Retrieved from: https://fivethirtyeight.com/features/many-americans-are-convinced-crime-is-rising-in-the-u-s-theyre-wrong ↵
• Burrell, G. & Morgan, G. (1979). Sociological paradigms and organizational analysis . Routledge. ↵
• Here are links to two HBSE open textbooks, if you are unfamiliar with social work theories. https://uark.pressbooks.pub/hbse1/ and https://uark.pressbooks.pub/humanbehaviorandthesocialenvironment2/ ↵
• Lin, C. T. (2016). A critique of epistemic subjectivity. Philosophia, 44 (3), 915-920. ↵
• Wills, J. W. (2007).  World views, paradigms and the practice of social science research. Thousand Oaks, CA: Sage. ↵

a single truth, observed without bias, that is universally applicable

one truth among many, bound within a social and cultural context

assumptions about what is real and true

assumptions about how we come to know what is real and true

quantitative methods examine numerical data to precisely describe and predict elements of the social world

qualitative methods interpret language and behavior to understand the world from the perspectives of other people

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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