sample qualitative research abstract

Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

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Home » Research Paper Abstract – Writing Guide and Examples

Research Paper Abstract – Writing Guide and Examples

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Research Paper Abstract

Research Paper Abstract is a brief summary of a research pape r that describes the study’s purpose, methods, findings, and conclusions . It is often the first section of the paper that readers encounter, and its purpose is to provide a concise and accurate overview of the paper’s content. The typical length of an abstract is usually around 150-250 words, and it should be written in a concise and clear manner.

Research Paper Abstract Structure

The structure of a research paper abstract usually includes the following elements:

• Background or Introduction: Briefly describe the problem or research question that the study addresses.
• Methods : Explain the methodology used to conduct the study, including the participants, materials, and procedures.
• Results : Summarize the main findings of the study, including statistical analyses and key outcomes.
• Conclusions : Discuss the implications of the study’s findings and their significance for the field, as well as any limitations or future directions for research.
• Keywords : List a few keywords that describe the main topics or themes of the research.

How to Write Research Paper Abstract

Here are the steps to follow when writing a research paper abstract:

• Start by reading your paper: Before you write an abstract, you should have a complete understanding of your paper. Read through the paper carefully, making sure you understand the purpose, methods, results, and conclusions.
• Identify the key components : Identify the key components of your paper, such as the research question, methods used, results obtained, and conclusion reached.
• Write a draft: Write a draft of your abstract, using concise and clear language. Make sure to include all the important information, but keep it short and to the point. A good rule of thumb is to keep your abstract between 150-250 words.
• Use clear and concise language : Use clear and concise language to explain the purpose of your study, the methods used, the results obtained, and the conclusions drawn.
• Emphasize your findings: Emphasize your findings in the abstract, highlighting the key results and the significance of your study.
• Revise and edit: Once you have a draft, revise and edit it to ensure that it is clear, concise, and free from errors.
• Check the formatting: Finally, check the formatting of your abstract to make sure it meets the requirements of the journal or conference where you plan to submit it.

Research Paper Abstract Examples

Research Paper Abstract Examples could be following:

Title : “The Effectiveness of Cognitive-Behavioral Therapy for Treating Anxiety Disorders: A Meta-Analysis”

Abstract : This meta-analysis examines the effectiveness of cognitive-behavioral therapy (CBT) in treating anxiety disorders. Through the analysis of 20 randomized controlled trials, we found that CBT is a highly effective treatment for anxiety disorders, with large effect sizes across a range of anxiety disorders, including generalized anxiety disorder, panic disorder, and social anxiety disorder. Our findings support the use of CBT as a first-line treatment for anxiety disorders and highlight the importance of further research to identify the mechanisms underlying its effectiveness.

Title : “Exploring the Role of Parental Involvement in Children’s Education: A Qualitative Study”

Abstract : This qualitative study explores the role of parental involvement in children’s education. Through in-depth interviews with 20 parents of children in elementary school, we found that parental involvement takes many forms, including volunteering in the classroom, helping with homework, and communicating with teachers. We also found that parental involvement is influenced by a range of factors, including parent and child characteristics, school culture, and socio-economic status. Our findings suggest that schools and educators should prioritize building strong partnerships with parents to support children’s academic success.

Title : “The Impact of Exercise on Cognitive Function in Older Adults: A Systematic Review and Meta-Analysis”

Abstract : This paper presents a systematic review and meta-analysis of the existing literature on the impact of exercise on cognitive function in older adults. Through the analysis of 25 randomized controlled trials, we found that exercise is associated with significant improvements in cognitive function, particularly in the domains of executive function and attention. Our findings highlight the potential of exercise as a non-pharmacological intervention to support cognitive health in older adults.

When to Write Research Paper Abstract

The abstract of a research paper should typically be written after you have completed the main body of the paper. This is because the abstract is intended to provide a brief summary of the key points and findings of the research, and you can’t do that until you have completed the research and written about it in detail.

Once you have completed your research paper, you can begin writing your abstract. It is important to remember that the abstract should be a concise summary of your research paper, and should be written in a way that is easy to understand for readers who may not have expertise in your specific area of research.

Purpose of Research Paper Abstract

The purpose of a research paper abstract is to provide a concise summary of the key points and findings of a research paper. It is typically a brief paragraph or two that appears at the beginning of the paper, before the introduction, and is intended to give readers a quick overview of the paper’s content.

The abstract should include a brief statement of the research problem, the methods used to investigate the problem, the key results and findings, and the main conclusions and implications of the research. It should be written in a clear and concise manner, avoiding jargon and technical language, and should be understandable to a broad audience.

The abstract serves as a way to quickly and easily communicate the main points of a research paper to potential readers, such as academics, researchers, and students, who may be looking for information on a particular topic. It can also help researchers determine whether a paper is relevant to their own research interests and whether they should read the full paper.

About the author

Muhammad Hassan

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GUIDANCE ON SUBMISSION OF QUALITATIVE RESEARCH ABSTRACTS

General guidance

Authors should refer to the general information and guidelines contained in the Society’s “Guidance for Submission of Abstracts”. The general guidance therein applies to qualitative research abstracts. This includes the maximum permitted limit of 250 words, and the instruction that abstracts should be structured. In keeping with all submissions to the Society, subsequent presentation must reflect and elaborate on the abstract. Research studies or findings not referred to in the abstract should not be presented.

This document contains specific guidance on the content of qualitative research abstracts.

How guidance on content is to be applied by authors and Council.

Council recognises that the nature of qualitative research makes its comprehensive communication within short abstracts a challenge.  Therefore, whilst the key areas to be included within abstracts are set out below, it is recognised that emphasis on each area will vary in different cases, and that not every listed sub-area will be covered.  Certain elements are likely to receive greater attention at the time of presentation than within the abstract.  In particular, presentation of the paper should include sufficient empirical data to allow judgement of the conclusions drawn.

Content of abstracts

• Research question/objective and design: clear statement of the research question/objective and its relevance. Methodological or theoretical perspectives should be clearly outlined.
• Population and sampling: who the subjects were and what sampling strategies were used .
• Methods of data collection: clear exposition of data collection: access, selection, method of collection, type of data, relationship of researcher to subjects/setting (what data were collected, from where/whom, by whom)
• Quality of data and analysis: strategies to enhance quality of data analysis e.g. triangulation, respondent validation; and to enhance validity e.g. attention to negative cases, consideration of alternative explanations, team analysis, peer review panels
• Application of critical thinking to analysis: attention to the influence of the researcher on data collected and on analysis. Critical approach to the status of data collected
• Theoretical and empirical context: evidence that design and analysis take into account and add to previous knowledge
• Conclusions: justified in relation to data collected, sufficient original data presented to substantiate interpretations, reasoned consideration of transferability  to groups/settings beyond those studied

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Qualitative psychology ®.

• Recommendations for Designing and Reviewing Qualitative Research in Psychology: Promoting Methodological Integrity (PDF, 166KB) February 2017 by Heidi M. Levitt, Sue L. Motulsky, Fredrick J. Wertz, Susan L. Morrow, and Joseph G. Ponterotto
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Abstract Writing: A Step-by-Step Guide With Tips & Examples

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Introduction

Abstracts of research papers have always played an essential role in describing your research concisely and clearly to researchers and editors of journals, enticing them to continue reading. However, with the widespread availability of scientific databases, the need to write a convincing abstract is more crucial now than during the time of paper-bound manuscripts.

Abstracts serve to "sell" your research and can be compared with your "executive outline" of a resume or, rather, a formal summary of the critical aspects of your work. Also, it can be the "gist" of your study. Since most educational research is done online, it's a sign that you have a shorter time for impressing your readers, and have more competition from other abstracts that are available to be read.

The APCI (Academic Publishing and Conferences International) articulates 12 issues or points considered during the final approval process for conferences & journals and emphasises the importance of writing an abstract that checks all these boxes (12 points). Since it's the only opportunity you have to captivate your readers, you must invest time and effort in creating an abstract that accurately reflects the critical points of your research.

With that in mind, let’s head over to understand and discover the core concept and guidelines to create a substantial abstract. Also, learn how to organise the ideas or plots into an effective abstract that will be awe-inspiring to the readers you want to reach.

What is Abstract? Definition and Overview

The word "Abstract' is derived from Latin abstractus meaning "drawn off." This etymological meaning also applies to art movements as well as music, like abstract expressionism. In this context, it refers to the revealing of the artist's intention.

Based on this, you can determine the meaning of an abstract: A condensed research summary. It must be self-contained and independent of the body of the research. However, it should outline the subject, the strategies used to study the problem, and the methods implemented to attain the outcomes. The specific elements of the study differ based on the area of study; however, together, it must be a succinct summary of the entire research paper.

Abstracts are typically written at the end of the paper, even though it serves as a prologue. In general, the abstract must be in a position to:

• Describe the paper.
• Identify the problem or the issue at hand.
• Explain to the reader the research process, the results you came up with, and what conclusion you've reached using these results.
• Include keywords to guide your strategy and the content.

Furthermore, the abstract you submit should not reflect upon any of  the following elements:

• Examine, analyse or defend the paper or your opinion.
• What you want to study, achieve or discover.
• Be redundant or irrelevant.

After reading an abstract, your audience should understand the reason - what the research was about in the first place, what the study has revealed and how it can be utilised or can be used to benefit others. You can understand the importance of abstract by knowing the fact that the abstract is the most frequently read portion of any research paper. In simpler terms, it should contain all the main points of the research paper.

What is the Purpose of an Abstract?

Abstracts are typically an essential requirement for research papers; however, it's not an obligation to preserve traditional reasons without any purpose. Abstracts allow readers to scan the text to determine whether it is relevant to their research or studies. The abstract allows other researchers to decide if your research paper can provide them with some additional information. A good abstract paves the interest of the audience to pore through your entire paper to find the content or context they're searching for.

Abstract writing is essential for indexing, as well. The Digital Repository of academic papers makes use of abstracts to index the entire content of academic research papers. Like meta descriptions in the regular Google outcomes, abstracts must include keywords that help researchers locate what they seek.

Types of Abstract

Informative and Descriptive are two kinds of abstracts often used in scientific writing.

A descriptive abstract gives readers an outline of the author's main points in their study. The reader can determine if they want to stick to the research work, based on their interest in the topic. An abstract that is descriptive is similar to the contents table of books, however, the format of an abstract depicts complete sentences encapsulated in one paragraph. It is unfortunate that the abstract can't be used as a substitute for reading a piece of writing because it's just an overview, which omits readers from getting an entire view. Also, it cannot be a way to fill in the gaps the reader may have after reading this kind of abstract since it does not contain crucial information needed to evaluate the article.

To conclude, a descriptive abstract is:

• A simple summary of the task, just summarises the work, but some researchers think it is much more of an outline
• Typically, the length is approximately 100 words. It is too short when compared to an informative abstract.
• A brief explanation but doesn't provide the reader with the complete information they need;
• An overview that omits conclusions and results

An informative abstract is a comprehensive outline of the research. There are times when people rely on the abstract as an information source. And the reason is why it is crucial to provide entire data of particular research. A well-written, informative abstract could be a good substitute for the remainder of the paper on its own.

A well-written abstract typically follows a particular style. The author begins by providing the identifying information, backed by citations and other identifiers of the papers. Then, the major elements are summarised to make the reader aware of the study. It is followed by the methodology and all-important findings from the study. The conclusion then presents study results and ends the abstract with a comprehensive summary.

In a nutshell, an informative abstract:

• Has a length that can vary, based on the subject, but is not longer than 300 words.
• Contains all the content-like methods and intentions
• Offers evidence and possible recommendations.

Informative Abstracts are more frequent than descriptive abstracts because of their extensive content and linkage to the topic specifically. You should select different types of abstracts to papers based on their length: informative abstracts for extended and more complex abstracts and descriptive ones for simpler and shorter research papers.

What are the Characteristics of a Good Abstract?

• A good abstract clearly defines the goals and purposes of the study.
• It should clearly describe the research methodology with a primary focus on data gathering, processing, and subsequent analysis.
• A good abstract should provide specific research findings.
• It presents the principal conclusions of the systematic study.
• It should be concise, clear, and relevant to the field of study.
• A well-designed abstract should be unifying and coherent.
• It is easy to grasp and free of technical jargon.
• It is written impartially and objectively.

What are the various sections of an ideal Abstract?

By now, you must have gained some concrete idea of the essential elements that your abstract needs to convey . Accordingly, the information is broken down into six key sections of the abstract, which include:

An Introduction or Background

Research methodology, objectives and goals, limitations.

Let's go over them in detail.

The introduction, also known as background, is the most concise part of your abstract. Ideally, it comprises a couple of sentences. Some researchers only write one sentence to introduce their abstract. The idea behind this is to guide readers through the key factors that led to your study.

It's understandable that this information might seem difficult to explain in a couple of sentences. For example, think about the following two questions like the background of your study:

• What is currently available about the subject with respect to the paper being discussed?
• What isn't understood about this issue? (This is the subject of your research)

While writing the abstract’s introduction, make sure that it is not lengthy. Because if it crosses the word limit, it may eat up the words meant to be used for providing other key information.

Research methodology is where you describe the theories and techniques you used in your research. It is recommended that you describe what you have done and the method you used to get your thorough investigation results. Certainly, it is the second-longest paragraph in the abstract.

In the research methodology section, it is essential to mention the kind of research you conducted; for instance, qualitative research or quantitative research (this will guide your research methodology too) . If you've conducted quantitative research, your abstract should contain information like the sample size, data collection method, sampling techniques, and duration of the study. Likewise, your abstract should reflect observational data, opinions, questionnaires (especially the non-numerical data) if you work on qualitative research.

The research objectives and goals speak about what you intend to accomplish with your research. The majority of research projects focus on the long-term effects of a project, and the goals focus on the immediate, short-term outcomes of the research. It is possible to summarise both in just multiple sentences.

In stating your objectives and goals, you give readers a picture of the scope of the study, its depth and the direction your research ultimately follows. Your readers can evaluate the results of your research against the goals and stated objectives to determine if you have achieved the goal of your research.

In the end, your readers are more attracted by the results you've obtained through your study. Therefore, you must take the time to explain each relevant result and explain how they impact your research. The results section exists as the longest in your abstract, and nothing should diminish its reach or quality.

One of the most important things you should adhere to is to spell out details and figures on the results of your research.

Instead of making a vague assertion such as, "We noticed that response rates varied greatly between respondents with high incomes and those with low incomes", Try these: "The response rate was higher for high-income respondents than those with lower incomes (59 30 percent vs. 30 percent in both cases; P<0.01)."

You're likely to encounter certain obstacles during your research. It could have been during data collection or even during conducting the sample . Whatever the issue, it's essential to inform your readers about them and their effects on the research.

Research limitations offer an opportunity to suggest further and deep research. If, for instance, you were forced to change for convenient sampling and snowball samples because of difficulties in reaching well-suited research participants, then you should mention this reason when you write your research abstract. In addition, a lack of prior studies on the subject could hinder your research.

Your conclusion should include the same number of sentences to wrap the abstract as the introduction. The majority of researchers offer an idea of the consequences of their research in this case.

Your conclusion should include three essential components:

• A significant take-home message.
• Corresponding important findings.
• The Interpretation.

Even though the conclusion of your abstract needs to be brief, it can have an enormous influence on the way that readers view your research. Therefore, make use of this section to reinforce the central message from your research. Be sure that your statements reflect the actual results and the methods you used to conduct your research.

Good Abstract Examples

Abstract example #1.

Children’s consumption behavior in response to food product placements in movies.

The abstract:

"Almost all research into the effects of brand placements on children has focused on the brand's attitudes or behavior intentions. Based on the significant differences between attitudes and behavioral intentions on one hand and actual behavior on the other hand, this study examines the impact of placements by brands on children's eating habits. Children aged 6-14 years old were shown an excerpt from the popular film Alvin and the Chipmunks and were shown places for the item Cheese Balls. Three different versions were developed with no placements, one with moderately frequent placements and the third with the highest frequency of placement. The results revealed that exposure to high-frequency places had a profound effect on snack consumption, however, there was no impact on consumer attitudes towards brands or products. The effects were not dependent on the age of the children. These findings are of major importance to researchers studying consumer behavior as well as nutrition experts as well as policy regulators."

Abstract Example #2

Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. The abstract:

"The research conducted in this study investigated the effects of Facebook use on women's moods and body image if the effects are different from an internet-based fashion journal and if the appearance comparison tendencies moderate one or more of these effects. Participants who were female ( N = 112) were randomly allocated to spend 10 minutes exploring their Facebook account or a magazine's website or an appearance neutral control website prior to completing state assessments of body dissatisfaction, mood, and differences in appearance (weight-related and facial hair, face, and skin). Participants also completed a test of the tendency to compare appearances. The participants who used Facebook were reported to be more depressed than those who stayed on the control site. In addition, women who have the tendency to compare appearances reported more facial, hair and skin-related issues following Facebook exposure than when they were exposed to the control site. Due to its popularity it is imperative to conduct more research to understand the effect that Facebook affects the way people view themselves."

Abstract Example #3

The Relationship Between Cell Phone Use and Academic Performance in a Sample of U.S. College Students

"The cellphone is always present on campuses of colleges and is often utilised in situations in which learning takes place. The study examined the connection between the use of cell phones and the actual grades point average (GPA) after adjusting for predictors that are known to be a factor. In the end 536 students in the undergraduate program from 82 self-reported majors of an enormous, public institution were studied. Hierarchical analysis ( R 2 = .449) showed that use of mobile phones is significantly ( p < .001) and negative (b equal to -.164) connected to the actual college GPA, after taking into account factors such as demographics, self-efficacy in self-regulated learning, self-efficacy to improve academic performance, and the actual high school GPA that were all important predictors ( p < .05). Therefore, after adjusting for other known predictors increasing cell phone usage was associated with lower academic performance. While more research is required to determine the mechanisms behind these results, they suggest the need to educate teachers and students to the possible academic risks that are associated with high-frequency mobile phone usage."

Quick tips on writing a good abstract

There exists a common dilemma among early age researchers whether to write the abstract at first or last? However, it's recommended to compose your abstract when you've completed the research since you'll have all the information to give to your readers. You can, however, write a draft at the beginning of your research and add in any gaps later.

If you find abstract writing a herculean task, here are the few tips to help you with it:

1. Always develop a framework to support your abstract

Before writing, ensure you create a clear outline for your abstract. Divide it into sections and draw the primary and supporting elements in each one. You can include keywords and a few sentences that convey the essence of your message.

2. Review Other Abstracts

Abstracts are among the most frequently used research documents, and thousands of them were written in the past. Therefore, prior to writing yours, take a look at some examples from other abstracts. There are plenty of examples of abstracts for dissertations in the dissertation and thesis databases.

3. Avoid Jargon To the Maximum

When you write your abstract, focus on simplicity over formality. You should  write in simple language, and avoid excessive filler words or ambiguous sentences. Keep in mind that your abstract must be readable to those who aren't acquainted with your subject.

4. Focus on Your Research

It's a given fact that the abstract you write should be about your research and the findings you've made. It is not the right time to mention secondary and primary data sources unless it's absolutely required.

Conclusion: How to Structure an Interesting Abstract?

Abstracts are a short outline of your essay. However, it's among the most important, if not the most important. The process of writing an abstract is not straightforward. A few early-age researchers tend to begin by writing it, thinking they are doing it to "tease" the next step (the document itself). However, it is better to treat it as a spoiler.

The simple, concise style of the abstract lends itself to a well-written and well-investigated study. If your research paper doesn't provide definitive results, or the goal of your research is questioned, so will the abstract. Thus, only write your abstract after witnessing your findings and put your findings in the context of a larger scenario.

The process of writing an abstract can be daunting, but with these guidelines, you will succeed. The most efficient method of writing an excellent abstract is to centre the primary points of your abstract, including the research question and goals methods, as well as key results.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• How to Write an Abstract

Expedite peer review, increase search-ability, and set the tone for your study

The abstract is your chance to let your readers know what they can expect from your article. Learn how to write a clear, and concise abstract that will keep your audience reading.

How your abstract impacts editorial evaluation and future readership

After the title , the abstract is the second-most-read part of your article. A good abstract can help to expedite peer review and, if your article is accepted for publication, it’s an important tool for readers to find and evaluate your work. Editors use your abstract when they first assess your article. Prospective reviewers see it when they decide whether to accept an invitation to review. Once published, the abstract gets indexed in PubMed and Google Scholar , as well as library systems and other popular databases. Like the title, your abstract influences keyword search results. Readers will use it to decide whether to read the rest of your article. Other researchers will use it to evaluate your work for inclusion in systematic reviews and meta-analysis. It should be a concise standalone piece that accurately represents your research. 

What to include in an abstract

The main challenge you’ll face when writing your abstract is keeping it concise AND fitting in all the information you need. Depending on your subject area the journal may require a structured abstract following specific headings. A structured abstract helps your readers understand your study more easily. If your journal doesn’t require a structured abstract it’s still a good idea to follow a similar format, just present the abstract as one paragraph without headings. 

Background or Introduction – What is currently known? Start with a brief, 2 or 3 sentence, introduction to the research area. 

Objectives or Aims – What is the study and why did you do it? Clearly state the research question you’re trying to answer.

Methods – What did you do? Explain what you did and how you did it. Include important information about your methods, but avoid the low-level specifics. Some disciplines have specific requirements for abstract methods. 

• CONSORT for randomized trials.
• STROBE for observational studies
• PRISMA for systematic reviews and meta-analyses

Results – What did you find? Briefly give the key findings of your study. Include key numeric data (including confidence intervals or p values), where possible.

Conclusions – What did you conclude? Tell the reader why your findings matter, and what this could mean for the ‘bigger picture’ of this area of research. 

Writing tips

The main challenge you may find when writing your abstract is keeping it concise AND convering all the information you need to.

• Keep it concise and to the point. Most journals have a maximum word count, so check guidelines before you write the abstract to save time editing it later.
• Write for your audience. Are they specialists in your specific field? Are they cross-disciplinary? Are they non-specialists? If you’re writing for a general audience, or your research could be of interest to the public keep your language as straightforward as possible. If you’re writing in English, do remember that not all of your readers will necessarily be native English speakers.
• Focus on key results, conclusions and take home messages.
• Write your paper first, then create the abstract as a summary.
• Check the journal requirements before you write your abstract, eg. required subheadings.
• Include keywords or phrases to help readers search for your work in indexing databases like PubMed or Google Scholar.
• Double and triple check your abstract for spelling and grammar errors. These kind of errors can give potential reviewers the impression that your research isn’t sound, and can make it easier to find reviewers who accept the invitation to review your manuscript. Your abstract should be a taste of what is to come in the rest of your article.

Don’t

• Sensationalize your research.
• Speculate about where this research might lead in the future.
• Use abbreviations or acronyms (unless absolutely necessary or unless they’re widely known, eg. DNA).
• Repeat yourself unnecessarily, eg. “Methods: We used X technique. Results: Using X technique, we found…”
• Contradict anything in the rest of your manuscript.
• Include content that isn’t also covered in the main manuscript.
• Include citations or references.

Tip: How to edit your work

Editing is challenging, especially if you are acting as both a writer and an editor. Read our guidelines for advice on how to refine your work, including useful tips for setting your intentions, re-review, and consultation with colleagues.

• How to Write a Great Title
• How to Write Your Methods
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

Publisher’s Note

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

Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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• How to Write An Abstract For Research Papers: Tips & Examples

In many ways, an abstract is like a trailer of a movie or the synopsis of your favorite book. Its job is to whet the reader’s appetite by sharing important information about your work. After reading a well-written abstract, one should have enough interest to explore the full research thesis. 

So how do you write an interesting abstract that captures the core of your study? First, you need to understand your research objectives and match them with the key results of your study. In this article, we will share some tips for writing an effective abstract, plus samples you can learn from. 

What is an Abstract in Research Writing?

In simple terms, an abstract is a concise write-up that gives an overview of your systematic investigation. According to Grammarly, it is a self-contained summary of a larger work, and it serves as a preview of the bigger document. 

It usually appears at the beginning of your thesis or research paper and helps the reader to have an overview of your work without going into great detail. This means that when someone reads your abstract, it should give them a clear idea of the purpose of your systematic investigation, your problem statement, key results, and any gaps requiring further investigation. 

So how long should your abstract be to capture all of these details? The reality is you don’t need a lot of words to capture key pieces of information in your abstract. Typically, 6–7 sentences made up of 150–250 words should be just right. 

Read: Writing Research Proposals: Tips, Examples & Mistakes

What are the Characteristics of a Good Abstract? 

• A good abstract clearly states the aims and objectives of the research.
• It outlines the research methodology for data gathering , processing and analysis. 
• A good abstract summarizes specific research results.
• It states the key conclusions of the systematic investigation.
• It is brief yet straight to the point. 
• A good abstract is unified and coherent. 
• It is easy to understand and devoid of technical jargon. 
• It is written in an unbiased and objective manner. 

What is the Purpose of an Abstract? 

Every abstract has two major purposes. First, it communicates the relevance of your systematic investigation to readers. After reading your abstract, people can determine how relevant your study is to their primary or secondary research purpose. 

The second purpose of an abstract is to communicate your key findings to those who don’t have time to read the whole paper. Research papers typically run into tens of pages so it takes time to read and digest them. To help readers grasp the core ideas in a systematic investigation, it pays to have a well-written abstract that outlines important information concerning your study. 

In all, your abstract should accurately outline the most important information in your research. Many times, it determines whether people would go ahead to read your dissertation. Abstracts are often indexed along with keywords on academic databases, so they make your thesis easily findable.

Learn About: How to Write a Problem Statement for your Research

What are the Sections of an Abstract?

You already know the key pieces of information that your abstract should communicate. These details are broken into six important sections of the abstract which are: 

• The Introduction or Background
• Research Methodology
• Aims and Objectives 
• Limitations

Let’s discuss them in detail. 

• The Introduction or Background 

The introduction or background is the shortest part of your abstract and usually consists of 2–3 sentences. In fact, some researchers write a single sentence as the introduction of their abstract. The whole idea here is to take the reader through the important events leading to your research. 

Understandably, this information may appear difficult to convey in a few sentences. To help out, consider answering these two questions in the background to your study : 

• What is already known about the subject, related to the paper in question? 
• What is not known about the subject (this is the focus of your study)? 

As much as possible, ensure that your abstract’s introduction doesn’t eat into the word count for the other key information. 

• Research Methodology 

This is the section where you spell out any theories and methods adopted for your study. Ideally, you should cover what has been done and how you went about it to achieve the results of your systematic investigation. It is usually the second-longest section in the abstract. 

In the research methodology section, you should also state the type of research you embarked on; that is, qualitative research or quantitative research —this will inform your research methods too. If you’ve conducted quantitative research, your abstract should contain information like the sample size, data collection methods , sampling technique, and duration of your experiment. 

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In the end, readers are most interested in the results you’ve achieved with your study. This means you should take time to outline every relevant outcome and show how they affect your research population . Typically, the results section should be the longest one in your abstract and nothing should compromise its range and quality. 

An important thing you should do here is spelled out facts and figures about research outcomes. Instead of a vague statement like, “we noticed that response rates differed greatly between high-income and low-income respondents”, try this: “The response rate was higher in high-income respondents than in their low-income counterparts (59% vs 30%, respectively; P

• Conclusion 

Like the introduction, your conclusion should contain a few sentences that wrap up your abstract. Most researchers express a theoretical opinion about the implications of their study, here. 

Your conclusion should contain three important elements: 

• The primary take-home message
• The additional findings of importance
• The perspective 

Although the conclusion of your abstract should be short, it has a great impact on how readers perceive your study. So, take advantage of this section to reiterate the core message in your systematic investigation. Also, make sure any statements here reflect the true outcomes and methods of your research. 

• Limitations 

Chances are you must have faced certain challenges in the course of your research—it could be at the data collection phase or during sampling . Whatever these challenges are, it pays to let your readers know about them, and the impact they had on your study. 

For example, if you had to switch to convenience sampling or snowball sampling due to difficulties in contacting well-suited research participants, you should include this in your abstract. Also, a lack of previous studies in the research area could pose a limitation on your study. Research limitations provide an opportunity to make suggestions for further research. 

Research aims and objectives speak to what you want to achieve with your study. Typically, research aims focus on a project’s long-term outcomes while the objectives focus on the immediate, short-term outcome of the investigation. You may summarize both using a single paragraph comprising a few sentences.

Stating your aims and objectives will give readers a clear idea of the scope, depth, and direction that your research will ultimately take. Readers would measure your research outcomes against stated aims and objectives to know if you achieved the purpose of your study. 

Use For Free: Research Form Templates

Abstract Writing Styles and General Guidelines 

Now that you know the different sections plus information that your abstract should contain, let’s look at how to write an abstract for your research paper.

A common question that comes up is, should I write my abstract first or last? It’s best to write your abstract after you’ve finished working on the research because you have full information to present to your readers. However, you can always create a draft at the beginning of your systematic investigation and fill in the gaps later.  

Does writing an abstract seem like a herculean task? Here are a few tips to help out. 

1. Always create a framework for your abstract 

Before you start writing, take time to develop a detailed outline for your abstract. Break it into sections and sketch the main and supporting points for each section. You can list keywords plus 1–2 sentences that capture your core messaging. 

2. Read Other Abstracts 

Abstracts are one of the most common research documents, and thousands of them have been written in time past. So, before writing yours, try to study a couple of samples from others. You can find lots of dissertation abstract examples in thesis and dissertation databases.

3. Steer Clear of Jargon As Much As Possible 

While writing your abstract, emphasize clarity over style. This means you should communicate in simple terms and avoid unnecessary filler words and ambiguous sentences. Remember, your abstract should be understandable to readers who are not familiar with your topic. 

4. Focus on Your Research

It goes without saying that your abstract should be solely focused on your research and what you’ve discovered. It’s not the time to cite primary and secondary data sources unless this is absolutely necessary. 

This doesn’t mean you should ignore the scholarly background of your work. You might include a sentence or two summarizing the scholarly background to show the relevance of your work to a broader debate, but there’s no need to mention specific publications. 

Going further, here are some abstract writing guidelines from the University of Bergen: 

• An abstract briefly explains the salient aspects of the content. 
• Abstracts should be accurate and succinct, self-contained, and readable.  
• The abstract should paraphrase and summarise rather than quote from the paper.
• Abstracts should relate only to the paper to be presented/assessed.

Types of Abstracts with Examples 

According to the University of Adelaide, there are two major types of abstracts written for research purposes. First, we have informative abstracts and descriptive abstracts. 

1. Informative Abstract  

An informative abstract is the more common type of abstract written for academic research. It highlights the most important aspects of your systematic investigation without going into unnecessary or irrelevant details that the reader might not find useful. 

The length varies according to discipline, but an informative abstract is rarely more than 10% of the length of the entire work. In the case of longer work, it may be much less.

In any informative abstract, you’d touch on information like the purpose, method, scope, results, and conclusion of your study. By now, you’re thinking, “this is the type of abstract we’ve been discussing all along”, and you wouldn’t be far from the truth. 

Advantages of Informative Abstracts

• These abstracts save time for both the researcher and the readers. 
• It’s easy to refer to these abstracts as secondary research sources. 

Disadvantages of Informative Abstracts

• These types of abstracts lack personality.

Example of an Informative Abstract

• Sample Informative Abstract Based on Experimental Work From Colorado State University
• Sample Informative Abstract Based on Non-experimental Work From Colorado State University

2. Descriptive Abstract 

A descriptive abstract reads like a synopsis and focuses on enticing the reader with interesting information. They don’t care as much for data and details, and instead read more like overviews that don’t give too much away. 

You’d find descriptive abstracts in artistic criticism pieces and entertainment research as opposed to scientific investigations. This type of abstract makes no judgments about the work, nor does it provide results or conclusions of the research. They are usually written in 100 words or less. 

Advantages of Descriptive Abstracts

• It gives a very brief overview of the research paper. 
• It is easier to write descriptive abstracts compared to informational abstracts. 

Disadvantages of Descriptive Abstracts

• They are suitable for scientific research. 
• Descriptive abstracts might omit relevant information that deepens your knowledge of the systematic investigation.

Example of Descriptive Abstracts 

• Sample Descriptive Abstract From Colorado State University

FAQs About Writing Abstracts in Research Papers

1. How Long Should an Abstract Be?

A typical abstract should be about six sentences long or less than 150 words. Most universities have specific word count requirements that fall within 150–300 words. 

2. How Do You Start an Abstract Sentence?

There are several ways to start your abstract. Consider the following methods: 

• State a problem or uncertainty
• Make a general statement with the present research action.
• State the purpose or objective of your research
• State a real-world phenomena or a standard practice.

3. Should you cite in an abstract?

While you can refer to information from specific research papers, there’s no need to cite sources in your abstract. Your abstract should focus on your original research, not on the work of others. 

4. What should not be included in an abstract?

An abstract shouldn’t have numeric references, bibliographies, sections, or even footnotes. 

5. Which tense is used in writing an abstract?

An abstract should be written in the third-person present tense. Use the simple past tense when describing your methodology and specific findings from your study. 

Writing an abstract might appear challenging but with these steps, you should get it right. The easiest approach to writing a good abstract is centering it on key information including your research problem and objectives, methodology, and key results.

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Survey indicates addressing workplace environment, work-life balance, and flexibility are key to attracting and retaining veterinarians in academia

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Referencing growing concerns over the recruitment and retention of faculty in academic veterinary medicine, the authors hypothesized that among surveyed veterinary residents and early-career faculty, work-life balance and workplace climate and culture are stronger motivators than financial considerations, regardless of demographic factors such as gender, race/ethnicity, and area of specialization.

541 participants were included in data analysis.

A mixed methods approach was utilized, incorporating both quantitative data and qualitative, free-text responses to better understand veterinary career choices by contextualizing factors associated with academic medicine.

Factors underpinning career-related decision-making were ranked by level of importance as (1) workplace environment/culture, (2) personal well-being/work-life balance, (3) salary and bonuses, (4) geographic location, (5) facilities and resources, (6) benefits, and (7) schedule flexibility. Desires for workload balance, schedule flexibility, support from leadership, and mentorship and collaboration were among the top themes of qualitative responses for both residents and early career faculty respondents. Factors influencing career decision-making for resident and early-career faculty are varied. Workplace environment, work-life balance, and schedule flexibility are areas that academic institutions can address and continue to improve and that are likely to positively impact entry into academia and the desire to stay.

CLINICAL RELEVANCE

This study sought to understand factors related to career decision-making and interest in academic veterinary medicine among residents and early-career faculty. Understanding these factors can support efforts to recruit and retain faculty in academic veterinary medicine.

T he changing landscape of veterinary medicine has prompted a national dialogue in areas like stress and burnout, 1 – 4 market growth, 5 – 8 and retention. 7 , 9 A report 7 commissioned by the Mars Veterinary Group indicates 40,959 veterinarians will need to enter companion animal practice by 2030 to account for both market growth (22,909 positions) and retirements (18,050 positions). 9 This is slightly higher than the Bureau of Labor Statistics' anticipated job growth rate of 19% (16,800 positions) between 2021 and 2031. 6 The estimate of new veterinarians in this same period is 26,000, indicating a potential 16% shortage based on demand. Further, turnover rates have increased since the COVID-19 pandemic, with an annual turnover rate of 16% in 2021, 5 due in part to an increasingly competitive labor market.

Academic veterinary medicine is under pressure, attempting to educate future veterinarians in the context of high market demand and serving interrelated missions including research, outreach, and patient care. Clinical faculty shortages have been an ongoing challenge due to turnover and low numbers of applicants for open positions. Few studies have addressed what factors motivate veterinary clinical residents and early-career faculty to seek or remain in academic positions; studies 10 – 16 in several academic career fields, including veterinary medicine, highlight the importance of lifestyle and work-life balance, mentorship, research opportunities, desire to teach, and professional development. Salary has been a strong driver of career-related decisions for human medical residents with high educational debt. 12 , 17 – 21 Additionally, 10 – 12 issues of departmental culture, work-life balance, administrative support of clinical medicine, compensation, and excessive research expectations were cited as reasons for leaving academic veterinary medicine.

One limitation of veterinary studies focused on recruitment and retention of clinical faculty is the overreliance on quantitative data, which allows for larger sample sizes and easy comparisons among factors but also reduces the depth of information that respondents provide. We use a mixed methods approach incorporating both quantitative data and qualitative, free-text responses to better understand veterinary career choices by contextualizing factors associated with academic medicine. For example, when someone indicates that geographic location matters quantitatively, their free-text response qualitatively contextualizes what specifically they are considering.

Here we describe a large-scale, survey-based study assessing factors associated with academic career choice among clinical veterinary residents and early-career faculty. We hypothesize that for both groups, work-life balance and workplace climate and culture are stronger motivators than financial considerations, regardless of demographic factors such as gender, race/ethnicity, and area of specialization. This hypothesis is supported by the Self-Determination Theory 22 and Employee Engagement Theory. 23 The Employee Engagement Theory posits that meaningfulness, safety, and availability within the work role predict employee engagement, with meaning derived from the work role as the primary motivator. The Self-Determination Theory posits that people engage in activities (including work) for a variety of reasons and will be more fulfilled and mentally healthy when environmental conditions support self-determination (ie, experienced as originating by the actor) of activities.

Survey development

The American Association of Veterinary Clinicians (AAVC) approved the development of a survey to assess factors associated with academic career selection among residents and early-stage faculty ( Table 1 ) . The survey was created by individuals (KF, MEB, and JML) ( Supplementary Material S1 ) with expertise in the areas of academic veterinary medicine, psychology, education, and organizational change and received Texas A&M University IRB exemption. Engaging individuals with expertise in multiple fields established face validity, and feedback from others in the field of academic veterinary medicine ensured content validity. Survey questions were developed around categories of concern referenced by leaders across academic veterinary medicine, and clarification was sought to ensure that the survey reflected the areas referenced. Qualtrics software was utilized as a delivery instrument. This study utilized a convergent mixed methods design with a questionnaire variant, with the variant due to whether the respondent was a resident or an early-stage faculty member. Closed-ended (quantitative) survey questions promoted data collection from a larger representative sample (n = 541), while open-ended (qualitative) survey questions expanded on these responses to develop a deeper understanding of the quantitative questions. Respondents were asked to provide demographic information and characteristics of their specialty area and classification at the time of the survey. The remaining survey questions sought feedback regarding factors contributing to career-related decisions, academic career interest, and factors affecting decisions to pursue veterinary private practice or academic careers. Residents were asked to rate their interest in pursuing an academic career on a 1 to 5 scale (not at all interested to very interested), and all respondents were asked to rate seven items for making career-related decisions on a 1 to 5 Likert scale (not at all important to extremely important). Open-ended questions allowed respondents to expand on their responses and to provide feedback on perceived strengths and challenges of veterinary academic careers.

Results for demographic characteristics for early career faculty respondents, including number of responses and percentage of total responses by category.

Eligibility criteria and distribution

Individuals eligible to complete the survey included clinical faculty within the first 5 years of their academic appointment (early career) at a US or Canadian School or College of Veterinary Medicine and veterinary residents in private or academic practice. The survey was distributed in April 2023 by email through an AAVC-generated list of clinical faculty and a separate list of candidates who were selected through the Veterinary Internship and Residency Matching Program in 2020. A second recruitment email was sent in late April 2023 to the AAVC list of current US and Canadian veterinary teaching hospital directors and clinical department heads with the intent that these individuals would distribute the survey to eligible faculty and residents at their home institutions.

Data collection and analysis

The survey was designed so qualitative and quantitative questions were paralleled to facilitate data integration. This allowed for the comparison of data points for emergent themes and an understanding of variation among responses. Using one survey consisting of both quantitative and qualitative questions resulted in the same sample for each method. Differences in sample size can be explained by a lower response rate to open-ended survey questions among respondents. 23 , 24

Early-stage data analysis was conducted individually for both data collection methods, followed by data integration for interpretation. The Qualtrics survey system (Qualtrics XM; Qualtrics International Inc) was used for statistical computations (eg, mean, median, range, SD), which were confirmed by manual data analysis. ANOVA tests were used to determine if there were statistically significant differences in responses by demographic based on the ability of ANOVA to compare variations between group means to the variation within the groups. For all comparisons, a P value of .05 was accepted for statistical significance.

Qualitative data were organized using ATLAS.ti (ATLAS.ti Scientific Software Development), a qualitative analysis software to aid in developing codes and creating a codebook to organize emergent themes. Open (ie, identifying important ideas and phrases) and axial (ie, identifying connections among these ideas) coding 22 allowed for code group organization for comparison of qualitative findings with quantitative data responses. This comparison informed a deeper understanding of the quantitative survey data.

Of the approximately 2,000 individuals contacted, 563 individuals participated in this study. After review, 541 were included in the data analysis. The remaining 22 responses were excluded based on qualitative responses indicating that respondents did not identify either as a resident or early-career clinical faculty member. Of the 541 respondents, 54% (n = 294) were early-career faculty and 46% (247) were residents. Resident responses included 227 academic residents and 20 private practice residents.

Quantitative responses

Among faculty respondents, the median age was 42 (range, 27 to 69), and most respondents were White (92%) and female (74%). Detailed demographic data are outlined ( Table 2 ) . Forty-six percent of respondents were small animal exclusive, 29% were predominately small animal, 9% were food animal/mixed practice, and 13% were predominately equine. The top 3 disciplines represented were small animal surgery (10.2%; n = 30), small animal internal medicine (8.8%; 26), and small animal primary care (7.5%; 22).

Results for demographic characteristics for resident respondents, including number of responses and percentage of total responses by category.

Factors underpinning career-related decision-making were ranked by level of importance as (1) workplace environment/culture ( x ¯ = 4.42, SD 0.6); (2) personal well-being/work-life balance ( x ¯ = 4.11, SD 0.88); (3) benefits ( x ¯ = 3.91, SD 0.93); (4) facilities and resources ( x ¯ = 3.8, SD 0.88); (5) schedule flexibility ( x ¯ = 3.76, SD 0.93); (6) geographic location ( x ¯ = 3.71, SD 1.09); and (7) salary and bonuses ( x ¯ = 3.57, SD 0.85). The means for both early career faculty and residents for ease of comparison are provided ( Figure 1 ) .

Ratings of factors related to career decision-making by quantitative category, separated by early career faculty and resident.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.03.0082

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Among resident respondents, the median age was 31 (range, 23 to 51), and most respondents were White (85%) and female (75%). Detailed demographic data are outlined ( Table 2 ). Forty-five percent of respondents were small animal exclusive, 24% were predominately small animal, 12% were food animal/mixed practice, and 18% were predominately equine. The top 3 service areas represented were emergency and critical care (11%; n = 27; inclusive of large animal and small animal), small animal surgery (9.3%; 23), and small animal internal medicine (8.5%; 21).

Factors underpinning career-related decision-making were ranked by level of importance as (1) workplace environment/culture ( x ¯ = 4.43, SD 0.68); (2) personal well-being/work-life balance ( x ¯ = 4.3, SD 0.77); (3) salary and bonuses ( x ¯ = 3.86, SD 0.87); (4) geographic location ( x ¯ = 3.85, SD 1.10); (5) facilities and resources ( x ¯ = 3.84, SD 0.75); (6) benefits ( x ¯ = 3.62, SD 0.91); and (7) schedule flexibility ( x ¯ = 3.51, SD 0.97).

Residents were asked to rate their interest in pursuing an academic career from 1 (not at all) to 5 (very interested). When asked if they have ever considered pursuing an academic career, 213 residents responded with 57 (27%) indicating they have not considered it and 156 (73%) indicated that they have. However, responses to a Likert scale question regarding current interest in pursuing an academic career (1, not at all interested; 3, neutral; and 5, very interested) received a mean score of 2.8 (SD, 1.36), indicating a low level of interest in pursuing an academic career among the respondent group. Female respondents were slightly more interested in pursuing an academic career ( x ¯ = 2.86, SD 1.36) compared to male ( x ¯ = 2.61, SD 1.34), while Asian respondents were least likely to indicate interest in pursuing an academic career ( x ¯ = 2.25, SD 1.36; compared to Black or African American, x ¯ = 2.84, SD 1.46; White, x ¯ = 2.85, SD 1.37; prefer to self-describe, x ¯ = 2.6, SD 0.83). Small group sizes may account for some variation in responses.

Qualitative response data, factors impacting career choices

Salary and benefits.

Faculty noted that although they expected academia to offer lower salaries than practice, the gap has recently increased, and academic institutions need to be more salary competitive. Many faculty referenced cost-of-living increases, more consistent merit raises, and production bonuses (including for emergency duty) as steps academic institutions should consider to address salary disparity between academic and private practice. In contrast, residents indicated that academic institutions need to significantly raise base salaries for faculty without a loss of perceived benefits associated with an academic position. Many residents viewed the higher salary seen in private practice as a reflection of the value of their education and contribution to the institution. Several residents referenced the opportunity for production in addition to salary as a draw to private practice.

In addition to salary, benefits were seen as a primary contributor to decision-making regarding a career. These included student loan forgiveness or assistance, paid time off, retirement, and insurance (medical, dental, etc). Several respondents noted that some difference in salary between private practice and academia is buffered by more robust benefits packages at academic institutions.

Organizational culture

Both resident and faculty respondents provided similar responses referencing organizational culture. These responses indicated a need for a positive organizational culture characterized by a noncompetitive, collegial environment. Concerns about hierarchies and the politicized nature of academic institutions were major themes in respondent comments. Several respondents indicated a concern over entrenched behaviors in academic institutions that perpetuated a toxic environment and bullying behaviors.

Faculty and resident respondents indicated that leadership and administration within both private practice and academia were considerations in making career decisions. This included the reputation of leadership, honest and transparent communication, and advocacy for employees. Leaders were seen as critical to establishing a collaborative environment and support network that promoted team cohesion, respect, and accountability. This was of particular concern for academia owing to its complex organizational structure that potentially limits effective communication and reporting channels.

Some resident respondents indicated they decided not to pursue an academic career based on their experience in an academic training program or during their degree program. Reasons given included burnout, decreased well-being and mental health, lack of research or teaching interest, and a perception that institutions do not value faculty.

Meaningfulness and fulfillment

Both respondent groups also provided similar responses highlighting the importance of meaningfulness and fulfillment. Both respondent groups referenced a desire to impact the next generation of veterinarians and the profession through teaching and mentoring, as well as consistent intellectual stimulation and continued learning. It was also noted that mission and vision alignment with personal and professional goals and values was important in making career decisions. Contributing to outreach efforts, decision-making, and positive change was seen as an antecedent to this goal.

Caseload and task variety fall within this category; many respondents noted their interest in an academic career was driven by a desire to see a variety of cases, including complex or challenging cases. Academic medicine was perceived as promoting fulfillment through access to resources and colleagues across multiple specialties which encouraged these types of advanced cases. These respondents referenced this as synonymous with the chance to practice leading-edge, evidence-based, quality patient care, which was a priority for them in selecting a career path.

Mentorship and collaboration

Both faculty and residents indicated a desire to have consistent mentorship and collaboration with others in the institution. Mentoring was most often referenced in the context of learning how to navigate academia, while collaboration was highlighted as the opportunity to work with colleagues from within the respondent's specialty as well as other specialties within the same hospital environment. This factor also did not appear as an option in the quantitative ratings, and thus, the qualitative questions provided additional insight regarding academic considerations made by faculty and residents. This theme is an example of the power of mixed methods research, as it was not 1 of the 7 factors listed in the quantitative ratings.

Professional growth and development

An additional emergent theme focused on institutional investment in professional development opportunities and resources as a positive factor for academic veterinary medicine compared to private practice, which was a consistent theme across respondent comments. Some respondents referenced research as a driving force, encouraging clinicians to contribute to advances and discoveries through research projects while, in turn, participating in knowledge and skill-building through their own and colleagues' research. Many respondents also noted additional opportunities for continuing education and training that can be less accessible in private practice.

Personal considerations

Both residents and early-career faculty noted similar personal considerations as factors in making career choices. Geographic location was frequently referenced, specifically a desire for access to urban/metropolitan areas. For many, this was seen as a deterrent to entering academic medicine, as many institutions are based in smaller towns or more rural areas. Aligned with this were references to available opportunities within the community, strong educational opportunities for children, and a political and cultural climate that paralleled individuals' values. Several respondents also indicated a need to accommodate partners' career needs and proximity to extended family or other support networks.

Recognition and respect

A desire for respect and recognition was voiced by both residents and faculty; again, this theme emerged and did not have a parallel in the quantitative data. Faculty and resident respondents indicated a desire to feel supported by institutional leadership and their colleagues and to be recognized for their efforts in teaching, research, and patient care. Faculty also voiced a desire for greater understanding and acknowledgment of veterinary academic medicine by campus leadership and administration, particularly given the unique nature of practicing veterinary medicine within academic institutions.

Support and team environment

Respondents noted the importance of access to libraries, research facilities, equipment, travel opportunities, training, and professional development. Those who referenced these factors most often associated these resources with academic veterinary medicine, viewing this as a positive contributor to considering a career in academia. These examples provide further depth to our understanding of the facilities and resources factor in the quantitative ratings.

One factor noted by early-career faculty was the desire for adequate administrative support to complete clerical tasks and requirements (eg, scheduling meetings, reminder emails, evaluations, and promotion documents), as well as support for administrative research tasks (eg, grant writing, IACUC protocols, and consent documentation).

Respondents consistently addressed the need to maintain an adequate number of well-trained support staff, limit attrition, and plan to address staffing shortages. Several respondents noted the need for an adequate number of faculty, while the majority noted the need for strong technical, administrative, and research staff support. These staff were seen as critical to success in a faculty role, and respondents referenced the need to address well-being, burnout, and salary rates for these groups.

Politics and bureaucracy

Respondents noted that politics and bureaucracy were concerns when deciding between private practice and academia. Factors cited in academia included a lack of accountability, inefficiency, a slow rate of change, institutional politics, and barriers or restrictions to accomplishing goals. Factors reported in private practice included increased corporatization, money-making strategies, contract terms and noncompetes, and expectations for productivity and revenue generation.

Both early-career faculty and residents noted bureaucracy within academic institutions as a potential barrier to individual autonomy. Respondents referenced a desire to contribute to decision-making in program curriculum and unit operations. On an individual level, there was a desire for flexible work options, participation in external consulting, autonomy in research, and setting career goals.

Workload and work-life balance

The workload and work-life balance affiliated with roles in private practice or academic medicine were consistently referenced by respondents throughout the survey. Referenced topics included balanced effort allocations, novelty and variety in the workday, caseload expectations, and requirements outside of the primary role.

Many early-career faculty desired protected time to complete nonclinical tasks (teaching, research, training, service, and outreach), while residents were more likely to reference optional research responsibilities or a clinical-only appointment with no research expectations. Respondents also noted a perceived overload in responsibilities in academic medicine, specifically referencing committee service, meetings, nonclinical paperwork, and mentoring.

Both respondent groups referenced research as a factor in making career decisions. Respondents recognized research as a core component of a career in academia but differed on whether this was an incentive or disincentive. Many faculty respondents saw the ability to conduct research as a positive factor for academic veterinary medicine when there are resources and protected time for engaging in research. However, some resident respondents noted that requirements to publish, workload expectations, and the potential for salary to be grant dependent were detractors from interest in an academic career.

Respondents noted on-call and emergency duty as potential stressors and indicated a desire for additional compensation for emergency duty or a consistent schedule with less on-call or emergency duty. Several respondents referenced the desire for a 4-day work week, no after-hours work, and no weekend work. The majority of respondents desired support from leadership in setting boundaries and expectations around work hours. There was some variation in how this differed between private practice and academic medicine. Some respondents noted an interest in academic medicine because it allowed greater work-life balance with fewer demands outside of work hours; others noted more schedule flexibility and short work weeks in private practice, perceiving an increased workload in academia.

In the survey described here, residents noted the following considerations in descending order of importance: (1) workplace environment and culture, (2) personal well-being and work-life balance, (3) salary and bonuses, (4) geographic location, (5) facilities and resources, (6) benefits, and (7) schedule flexibility. Early-career faculty noted considerations in descending order of importance: (1) workplace environment and culture, (2) personal well-being and work-life balance, (3) benefits, (4) facilities and resources, (5) schedule flexibility, (6) geographic location, and (7) salary and bonuses. These findings support the hypothesis that work-life balance and workplace climate and culture are stronger motivators than financial considerations among respondents. Ascertaining the emphasis placed on each factor is a necessary step in understanding decision-making surrounding an academic veterinary medicine career. The findings of this study support Furr and Raczkoski 11 in the weight placed on schedule flexibility, work-life balance, and the desire to influence veterinarians and veterinary medicine. However, the findings differ in the priority placed on participation in translational research. Further, understanding the similarities and differences in responses between residents and early-career faculty can contribute to goal-setting in recruiting and retaining academic veterinary clinicians. It is important to note that different methodologies in these studies limit the opportunity for direct comparison.

Workplace environment and culture were the highest-ranked factor for career consideration among both residents and faculty. These findings align with previous studies 4 , 10 , 11 , 13 , 25 noting that workplace environment is a necessary factor in addressing clinician attrition within academic medicine, ranking higher than salary among both faculty and house officers in human healthcare. This study's findings support existing research indicating that workplace culture is a key priority in academic career selection and retention, 1 , 4 , 11 , 17 , 26 with mentorship, 10 , 13 , 17 – 21 , 25 , 27 professional development, 14 , 28 – 30 leadership support, 10 , 14 , 31 , 32 team collegiality, 15 , 33 , 34 and recognition of efforts all contributing to a favorable workplace culture. 25

Bureaucracy in academic institutions was a detractor to academic career interest for many respondents. Barriers to goal accomplishment, inefficiency, and the hierarchical nature of universities have been shown to decrease participation in academia. 10 , 34 – 37 A desire to participate in decision-making and enact meaningful change contributes to interest in pursuing an academic career. Studies indicate concerns about shared governance among faculty and the need for academic institutions to create pathways for meaningful participation among faculty resulting in actionable outcomes. 15 , 31 , 37 , 38 As noted by several respondents, creating communication channels with campus leadership and administration could increase understanding and acknowledgement of the vital roles played by veterinary faculty. However, academic institutions have potential advantages over corporatized private practice regarding contracts, noncompetes, and stringent production expectations.

Personal well-being and work-life balance were seen as strong contributing factors to making career decisions, particularly when considering an academic career. Work-life balance was viewed both as hours spent on work-related tasks as well as the distribution of duties within that time. Respondents noted a desire for balance between clinical, teaching, and research duties that supported achievable outcomes in all areas. This is supported by existing studies that reference concerns about balancing time commitments and priorities in academic medicine. 29 , 39 – 43 Academic institutions should work to outline responsibilities clearly and make efforts to limit conflict between key mission areas.

Many respondents noted the desire to teach and influence the next generation of veterinarians as a strong driver for a career in academia, aligning with findings in other academic medical disciplines. 11 , 16 , 28 , 32 , 44 Those interested in this area derived a sense of meaning and purpose in this work and alignment of their personal goals and values with the mission of the institution. 11 , 25 , 28 , 32

Many resident respondents noted that requirements and expectations for research lowered their interest in an academic career and that the ability to have a clinical appointment without research expectations, or with optional research expectations, could impact their decision-making. Both faculty and resident respondents noted that additional responsibilities in academia such as committee service, meetings, and clerical tasks add to the overall workload and decrease satisfaction. One strategy for academic institutions is to establish expectations and resources during clinical-track faculty recruitment, including flexibility in requiring research as part of faculty appointments. 10 , 11 Institutions could benefit from providing residents and faculty enhanced research support, as well as opportunities for flexibility in academic appointment requirements. Additionally, there is strong evidence to support structured academic mentoring programs for faculty and residents. 11 , 13 , 20 , 28 , 33

Study respondents desired access to modern, specialized clinical equipment and novel procedures. These respondents felt that academic medicine allowed for more opportunity in this area than private practice. Building a robust infrastructure to support student training, clinical trials, benchtop research, specialty care, and clinical skills training will be necessary to attract and retain faculty in academic medicine. 17 , 30 , 34 , 35

Schedule flexibility, referenced as 4-day work weeks and limited or no on-call expectations, was also referenced, with many respondents noting this as a strength of private practice. However, many faculty respondents indicated a belief that academic medicine allowed for greater work-life balance with fewer demands outside of work hours. Interestingly, residents ranked schedule flexibility last overall, while also noting a desire for shorter work weeks perceived as available in academia. The desire for a shorter workweek and greater schedule flexibility in academic medicine has been supported by existing studies 10 , 17 , 32 , 34 , 45 across multiple fields. Further research is needed to ascertain average differences in work weeks and schedules in academia versus private practice.

Geographic location and political climate also informed personal well-being and supported existing studies 10 , 11 , 35 , 45 indicating that most residents seek careers within metropolitan or suburban areas, with a small percentage looking to work within rural communities. This is a potential detractor for academic veterinary medicine specifically, given that many teaching hospitals are based in rural areas. Several respondents noted that local and state politics impacted their decision to live in certain areas. Future research could focus on understanding individual differences and the role of academia in supporting the needs of employees within the larger geographic and political environment.

Concerns over the increasing pay gap between private practice and academia 10 , 11 , 17 were supported by the study's findings. The findings also support other studies 7 , 10 , 11 , 14 , 36 noting salary as a contextual factor behind workplace environment and clinician well-being. Recommendations have been made to consider additional pay for emergency duty or a percentage of productivity like that in private practice. 2 , 34 However, studies 35 , 46 have also acknowledged limitations to university budgets and financial models that potentially constrain options for increasing faculty salaries.

Both faculty and resident respondents referenced student loan forgiveness as a positive contributor to an academic career. This aligns with existing studies 37 , 41 noting that student loan forgiveness or tuition reimbursement can help to offset salary differences between academic and private practice careers. Providing information and encouraging both faculty and residents to utilize this resource are opportunities to address concerns related to compensation.

Faculty respondents noted insurance (medical, dental, vision), paid leave time, and retirement plans as positive benefits to working in academia. Some resident respondents referenced retirement benefits, but most did not address insurance or paid leave time as considerations. This aligns with the fact that faculty rated benefits more highly on the list of important considerations than residents; however, responses do not clearly highlight what drives this variation. Like student loan forgiveness, benefits are seen as a possible way to decrease the gap in compensation between academia and private practice, with academia seen as being more robust. 32 However, with the growth in private practice corporatization, academic institutions may have to contend with practices that can offer competitive benefits packages as part of the recruitment process.

In conclusion, factors influencing career decision-making for resident and early-career faculty are varied. While academic institutions are somewhat limited in areas such as salary and geographic location, there are several key areas of focus that could impact a desire for an academic career. Benefits, facilities, and resources are all areas where academic medicine can continue to thrive and expand to attract and retain strong faculty clinicians. Workplace environment, work-life balance, and schedule flexibility are areas that academic institutions can address and continue to improve and that are likely to positively impact entry into academia and the desire to stay.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org

Acknowledgments

None reported.

Disclosures

The authors have nothing to disclosed. No AI-assisted technologies were used in the generation of this manuscript.

The research was funded by the authors' departments.

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Research Terms

Ai generator.

Research terms are specific words or phrases used in academic writing to describe the research process, methodologies, and findings. These include concepts like hypothesis , variables, sample size, literature review, and data analysis. Understanding these terms is crucial for interpreting research studies and effectively communicating ideas. Mastery of research terms enhances clarity in academic discourse, whether in a research project proposal , a qualitative research report , or the description of research methodology.

What are terms in research?

Terms in research refer to the specific words, phrases, and concepts used within a study to define its scope, methodology , and focus. These terms ensure clarity and precision, allowing researchers to communicate ideas and findings effectively. Clear definitions facilitate a shared understanding and maintain the integrity and replicability of research.

Examples of Research Terms

• Hypothesis : A proposed explanation for a phenomenon, to be tested through research.
• Variable : Any factor or element that can be changed and measured in research.
• Literature Review : A comprehensive survey of existing research and publications on a specific topic.
• Methodology : The systematic plan and approach used to conduct research.
• Data Collection : The process of gathering information for analysis in research.
• Sample : A subset of a population selected for observation and analysis.
• Control Group : A group in an experiment that does not receive the treatment, used for comparison.
• Validity : The extent to which a research study measures what it intends to measure.
• Reliability : The consistency of a research study or measuring test.
• Abstract : A brief summary of a research study’s aims, methods, results, and conclusions.
• Population : The entire group of individuals or instances about whom the research is concerned.
• Ethics : Moral principles that govern a researcher’s conduct and the conduct of the research.
• Bias : A systematic error introduced into sampling or testing by selecting or encouraging one outcome over others.
• Pilot Study : A small-scale preliminary study conducted to evaluate feasibility, time, cost, risk, and adverse events.
• Peer Review : A process by which a research study is evaluated by experts in the same field before publication.
• Quantitative Research : Research that relies on numerical data and statistical methods.
• Qualitative Research : Research that relies on non-numerical data, such as interviews, observations, and textual analysis.
• Case Study : An in-depth study of a particular case, individual, group, or event.
• Longitudinal Study : Research that follows subjects over a long period to observe changes and developments.
• Cross-sectional Study : Research that analyzes data from a population at a specific point in time.
• Independent Variable : The variable that is manipulated to observe its effect on the dependent variable.
• Dependent Variable : The variable being tested and measured in an experiment.
• Confounding Variable : An outside influence that affects the dependent and independent variables, causing a spurious association.
• Operational Definition : A clear, precise, and measurable definition of a variable for the purposes of a study.
• Statistical Significance : The likelihood that a result or relationship is caused by something other than mere chance.
• Random Sample : A sample that fairly represents a population because each member has an equal chance of inclusion.
• Correlation : A measure of the relationship between two variables.
• Experimental Group : The group in an experiment that receives the treatment.
• Theoretical Framework : A structure that guides research by providing a clear perspective and basis for the study.
• Meta-analysis : A statistical technique that combines the results of multiple studies to determine overall trends.

Research Terms List

5 Common Research Terminologies

• Hypothesis : A testable prediction about the relationship between two or more variables.
• Variable : An element, feature, or factor that can be changed and measured in research.

Confusing Terms in Research

• Reliability : The consistency of a research study or measuring test over time.
• Independent Variable : The variable that is manipulated to observe its effect.
• Dependent Variable : The variable being tested and measured, which is affected by the independent variable.
• Random Assignment : Assigning participants to experimental and control groups by chance to minimize pre-existing differences.
• Descriptive Research : Research that aims to describe characteristics of a population or phenomenon.
• Explanatory Research : Research that seeks to explain the reasons behind a phenomenon or relationship.

Key Research Terms

1. abstract.

• A brief summary of the research paper, outlining the main points, purpose, methods, results, and conclusions.

2. Hypothesis

• A testable statement or prediction about the relationship between two or more variables.

3. Variable

• An element, feature, or factor that can be changed and measured in research. Includes independent, dependent, and control variables.

4. Literature Review

• A comprehensive survey of existing research and publications relevant to the research topic.

5. Methodology

• The systematic plan for conducting research, including the methods, techniques, and procedures used to collect and analyze data.

6. Qualitative Research

• Research that focuses on understanding phenomena through non-numerical data such as interviews, observations, and texts.

7. Quantitative Research

• Research that focuses on quantifying data and analyzing it statistically to draw conclusions.

8. Sampling

• The process of selecting a subset of individuals from a population to represent the whole group in research.

9. Data Collection

• The process of gathering information from various sources to answer research questions.

10. Data Analysis

• The process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

Terms Synonymous to Research

FAQ’s

What is a variable in research.

A variable is any characteristic, number, or quantity that can be measured or quantified in research.

What is the difference between qualitative and quantitative research?

Qualitative research explores concepts and experiences in-depth, while quantitative research involves measuring and analyzing numerical data.

What is a literature review?

A literature review summarizes existing research on a topic, identifying trends, gaps, and key findings.

What is a sample in research?

A sample is a subset of a population selected for study to represent the entire group.

What is a hypothesis?

A hypothesis is a testable prediction or educated guess about the relationship between two or more variables in a study.

What is data collection?

Data collection involves gathering information from various sources to address a research question or hypothesis.

What is an independent variable?

An independent variable is the variable that is manipulated or changed in an experiment to observe its effect.

What is a dependent variable?

A dependent variable is the variable being tested and measured in an experiment, affected by the independent variable.

What is a control group?

A control group is a group in an experiment that does not receive the treatment, used for comparison against the experimental group.

What is a research methodology?

Research methodology is the systematic plan for conducting research, including methods for data collection and analysis.

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New directions for Indigenous and local knowledge research and application in fisheries science: Lessons from a systematic review

• Jones, Benjamin L. H.
• Santos, Rolando O.
• James, W. Ryan
• Costa, Sophia V.
• Adams, Aaron J.
• Boucek, Ross E.
• Coals, Lucy
• Cullen-Unsworth, Leanne C.
• Shephard, Samuel
• Rehage, Jennifer S.

Social‑ecological systems like fisheries provide food, livelihoods and recreation. However, lack of data and its integration into governance hinders their conservation and management. Stakeholders possess site‑specific knowledge crucial for confronting these challenges. There is increasing recognition that Indigenous and local knowledge (ILK) is valuable, but structural differences between ILK and quantitative archetypes have stalled the assimilation of ILK into fisheries management, despite acknowledged bias and uncertainty in scientific methods. Conducting a systematic review of fisheries‑associated ILK research (n = 397 articles), we examined how ILK is accessed, applied, distributed across space and species, and has evolved. We show that ILK has generated qualitative, semi‑quantitative and quantitative information for diverse taxa across 98 countries. Fisheries‑associated ILK research mostly targets small‑scale and artisanal fishers (70% of studies) and typically uses semi‑structured interviews (60%). We revealed large variability in sample size (n = 4–7638), predicted by the approach employed and the data generated (i.e. qualitative studies target smaller groups). Using thematic categorisation, we show that scientists are still exploring techniques, or 'validating' ILK through comparisons with quantitative scientific data (20%), and recording qualitative information of what fishers understand (40%). A few researchers are applying quantitative social science methods to derive trends in abundance, catch and effort. Such approaches facilitate recognition of local insight in fisheries management but fall short of accepting ILK as a valid complementary way of knowing about fisheries systems. This synthesis reveals that development and increased opportunities are needed to bridge ILK and quantitative scientific data.