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Research Methods Knowledge Base

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This paper should be used only as an example of a research paper write-up. Horizontal rules signify the top and bottom edges of pages. For sample references which are not included with this paper, you should consult the Publication Manual of the American Psychological Association, 4th Edition .

This paper is provided only to give you an idea of what a research paper might look like. You are not allowed to copy any of the text of this paper in writing your own report.

Because word processor copies of papers don’t translate well into web pages, you should note that an actual paper should be formatted according to the formatting rules for your context. Note especially that there are three formatting rules you will see in this sample paper which you should NOT follow. First, except for the title page, the running header should appear in the upper right corner of every page with the page number below it. Second, paragraphs and text should be double spaced and the start of each paragraph should be indented. Third, horizontal lines are used to indicate a mandatory page break and should not be used in your paper.

The Effects of a Supported Employment Program on Psychosocial Indicators for Persons with Severe Mental Illness William M.K. Trochim Cornell University

Running Head: SUPPORTED EMPLOYMENT

This paper describes the psychosocial effects of a program of supported employment (SE) for persons with severe mental illness. The SE program involves extended individualized supported employment for clients through a Mobile Job Support Worker (MJSW) who maintains contact with the client after job placement and supports the client in a variety of ways. A 50% simple random sample was taken of all persons who entered the Thresholds Agency between 3/1/93 and 2/28/95 and who met study criteria. The resulting 484 cases were randomly assigned to either the SE condition (treatment group) or the usual protocol (control group) which consisted of life skills training and employment in an in-house sheltered workshop setting. All participants were measured at intake and at 3 months after beginning employment, on two measures of psychological functioning (the BPRS and GAS) and two measures of self esteem (RSE and ESE). Significant treatment effects were found on all four measures, but they were in the opposite direction from what was hypothesized. Instead of functioning better and having more self esteem, persons in SE had lower functioning levels and lower self esteem. The most likely explanation is that people who work in low-paying service jobs in real world settings generally do not like them and experience significant job stress, whether they have severe mental illness or not. The implications for theory in psychosocial rehabilitation are considered.

The Effects of a Supported Employment Program on Psychosocial Indicators for Persons with Severe Mental Illness

Over the past quarter century a shift has occurred from traditional institution-based models of care for persons with severe mental illness (SMI) to more individualized community-based treatments. Along with this, there has been a significant shift in thought about the potential for persons with SMI to be “rehabilitated” toward lifestyles that more closely approximate those of persons without such illness. A central issue is the ability of a person to hold a regular full-time job for a sustained period of time. There have been several attempts to develop novel and radical models for program interventions designed to assist persons with SMI to sustain full-time employment while living in the community. The most promising of these have emerged from the tradition of psychiatric rehabilitation with its emphases on individual consumer goal setting, skills training, job preparation and employment support (Cook, Jonikas and Solomon, 1992). These are relatively new and field evaluations are rare or have only recently been initiated (Cook and Razzano, 1992; Cook, 1992). Most of the early attempts to evaluate such programs have naturally focused almost exclusively on employment outcomes. However, theory suggests that sustained employment and living in the community may have important therapeutic benefits in addition to the obvious economic ones. To date, there have been no formal studies of the effects of psychiatric rehabilitation programs on key illness-related outcomes. To address this issue, this study seeks to examine the effects of a new program of supported employment on psychosocial outcomes for persons with SMI.

Over the past several decades, the theory of vocational rehabilitation has experienced two major stages of evolution. Original models of vocational rehabilitation were based on the idea of sheltered workshop employment. Clients were paid a piece rate and worked only with other individuals who were disabled. Sheltered workshops tended to be “end points” for persons with severe and profound mental retardation since few ever moved from sheltered to competitive employment (Woest, Klein & Atkins, 1986). Controlled studies of sheltered workshop performance of persons with mental illness suggested only minimal success (Griffiths, 1974) and other research indicated that persons with mental illness earned lower wages, presented more behavior problems, and showed poorer workshop attendance than workers with other disabilities (Whitehead, 1977; Ciardiello, 1981).

In the 1980s, a new model of services called Supported Employment (SE) was proposed as less expensive and more normalizing for persons undergoing rehabilitation (Wehman, 1985). The SE model emphasizes first locating a job in an integrated setting for minimum wage or above, and then placing the person on the job and providing the training and support services needed to remain employed (Wehman, 1985). Services such as individualized job development, one-on-one job coaching, advocacy with co-workers and employers, and “fading” support were found to be effective in maintaining employment for individuals with severe and profound mental retardation (Revell, Wehman & Arnold, 1984). The idea that this model could be generalized to persons with all types of severe disabilities, including severe mental illness, became commonly accepted (Chadsey-Rusch & Rusch, 1986).

One of the more notable SE programs was developed at Thresholds, the site for the present study, which created a new staff position called the mobile job support worker (MJSW) and removed the common six month time limit for many placements. MJSWs provide ongoing, mobile support and intervention at or near the work site, even for jobs with high degrees of independence (Cook & Hoffschmidt, 1993). Time limits for many placements were removed so that clients could stay on as permanent employees if they and their employers wished. The suspension of time limits on job placements, along with MJSW support, became the basis of SE services delivered at Thresholds.

There are two key psychosocial outcome constructs of interest in this study. The first is the overall psychological functioning of the person with SMI. This would include the specification of severity of cognitive and affective symptomotology as well as the overall level of psychological functioning. The second is the level of self-reported self esteem of the person. This was measured both generally and with specific reference to employment.

The key hypothesis of this study is:

HO: A program of supported employment will result in either no change or negative effects on psychological functioning and self esteem.

which will be tested against the alternative:

HA: A program of supported employment will lead to positive effects on psychological functioning and self esteem.

The population of interest for this study is all adults with SMI residing in the U.S. in the early 1990s. The population that is accessible to this study consists of all persons who were clients of the Thresholds Agency in Chicago, Illinois between the dates of March 1, 1993 and February 28, 1995 who met the following criteria: 1) a history of severe mental illness (e.g., either schizophrenia, severe depression or manic-depression); 2) a willingness to achieve paid employment; 3) their primary diagnosis must not include chronic alcoholism or hard drug use; and 4) they must be 18 years of age or older. The sampling frame was obtained from records of the agency. Because of the large number of clients who pass through the agency each year (e.g., approximately 500 who meet the criteria) a simple random sample of 50% was chosen for inclusion in the study. This resulted in a sample size of 484 persons over the two-year course of the study.

On average, study participants were 30 years old and high school graduates (average education level = 13 years). The majority of participants (70%) were male. Most had never married (85%), few (2%) were currently married, and the remainder had been formerly married (13%). Just over half (51%) are African American, with the remainder Caucasian (43%) or other minority groups (6%). In terms of illness history, the members in the sample averaged 4 prior psychiatric hospitalizations and spent a lifetime average of 9 months as patients in psychiatric hospitals. The primary diagnoses were schizophrenia (42%) and severe chronic depression (37%). Participants had spent an average of almost two and one-half years (29 months) at the longest job they ever held.

While the study sample cannot be considered representative of the original population of interest, generalizability was not a primary goal – the major purpose of this study was to determine whether a specific SE program could work in an accessible context. Any effects of SE evident in this study can be generalized to urban psychiatric agencies that are similar to Thresholds, have a similar clientele, and implement a similar program.

All but one of the measures used in this study are well-known instruments in the research literature on psychosocial functioning. All of the instruments were administered as part of a structured interview that an evaluation social worker had with study participants at regular intervals.

Two measures of psychological functioning were used. The Brief Psychiatric Rating Scale (BPRS)(Overall and Gorham, 1962) is an 18-item scale that measures perceived severity of symptoms ranging from “somatic concern” and “anxiety” to “depressive mood” and “disorientation.” Ratings are given on a 0-to-6 Likert-type response scale where 0=“not present” and 6=“extremely severe” and the scale score is simply the sum of the 18 items. The Global Assessment Scale (GAS)(Endicott et al, 1976) is a single 1-to-100 rating on a scale where each ten-point increment has a detailed description of functioning (higher scores indicate better functioning). For instance, one would give a rating between 91-100 if the person showed “no symptoms, superior functioning…” and a value between 1-10 if the person “needs constant supervision…”

Two measures of self esteem were used. The first is the Rosenberg Self Esteem (RSE) Scale (Rosenberg, 1965), a 10-item scale rated on a 6-point response format where 1=“strongly disagree” and 6=“strongly agree” and there is no neutral point. The total score is simply the sum across the ten items, with five of the items being reversals. The second measure was developed explicitly for this study and was designed to measure the Employment Self Esteem (ESE) of a person with SMI. This is a 10-item scale that uses a 4-point response format where 1=“strongly disagree” and 4=“strongly agree” and there is no neutral point. The final ten items were selected from a pool of 97 original candidate items, based upon high item-total score correlations and a judgment of face validity by a panel of three psychologists. This instrument was deliberately kept simple – a shorter response scale and no reversal items – because of the difficulties associated with measuring a population with SMI. The entire instrument is provided in Appendix A.

All four of the measures evidenced strong reliability and validity. Internal consistency reliability estimates using Cronbach’s alpha ranged from .76 for ESE to .88 for SE. Test-retest reliabilities were nearly as high, ranging from .72 for ESE to .83 for the BPRS. Convergent validity was evidenced by the correlations within construct. For the two psychological functioning scales the correlation was .68 while for the self esteem measures it was somewhat lower at .57. Discriminant validity was examined by looking at the cross-construct correlations which ranged from .18 (BPRS-ESE) to .41 (GAS-SE).

A pretest-posttest two-group randomized experimental design was used in this study. In notational form, the design can be depicted as:

R = the groups were randomly assigned
O = the four measures (i.e., BPRS, GAS, RSE, and ESE)
X = supported employment

The comparison group received the standard Thresholds protocol which emphasized in-house training in life skills and employment in an in-house sheltered workshop. All participants were measured at intake (pretest) and at three months after intake (posttest).

This type of randomized experimental design is generally strong in internal validity. It rules out threats of history, maturation, testing, instrumentation, mortality and selection interactions. Its primary weaknesses are in the potential for treatment-related mortality (i.e., a type of selection-mortality) and for problems that result from the reactions of participants and administrators to knowledge of the varying experimental conditions. In this study, the drop-out rate was 4% (N=9) for the control group and 5% (N=13) in the treatment group. Because these rates are low and are approximately equal in each group, it is not plausible that there is differential mortality. There is a possibility that there were some deleterious effects due to participant knowledge of the other group’s existence (e.g., compensatory rivalry, resentful demoralization). Staff were debriefed at several points throughout the study and were explicitly asked about such issues. There were no reports of any apparent negative feelings from the participants in this regard. Nor is it plausible that staff might have equalized conditions between the two groups. Staff were given extensive training and were monitored throughout the course of the study. Overall, this study can be considered strong with respect to internal validity.

Between 3/1/93 and 2/28/95 each person admitted to Thresholds who met the study inclusion criteria was immediately assigned a random number that gave them a 50/50 chance of being selected into the study sample. For those selected, the purpose of the study was explained, including the nature of the two treatments, and the need for and use of random assignment. Participants were assured confidentiality and were given an opportunity to decline to participate in the study. Only 7 people (out of 491) refused to participate. At intake, each selected sample member was assigned a random number giving them a 50/50 chance of being assigned to either the Supported Employment condition or the standard in-agency sheltered workshop. In addition, all study participants were given the four measures at intake.

All participants spent the initial two weeks in the program in training and orientation. This consisted of life skill training (e.g., handling money, getting around, cooking and nutrition) and job preparation (employee roles, coping strategies). At the end of that period, each participant was assigned to a job site – at the agency sheltered workshop for those in the control condition, and to an outside employer if in the Supported Employment group. Control participants were expected to work full-time at the sheltered workshop for a three-month period, at which point they were posttested and given an opportunity to obtain outside employment (either Supported Employment or not). The Supported Employment participants were each assigned a case worker – called a Mobile Job Support Worker (MJSW) – who met with the person at the job site two times per week for an hour each time. The MJSW could provide any support or assistance deemed necessary to help the person cope with job stress, including counseling or working beside the person for short periods of time. In addition, the MJSW was always accessible by cellular telephone, and could be called by the participant or the employer at any time. At the end of three months, each participant was post-tested and given the option of staying with their current job (with or without Supported Employment) or moving to the sheltered workshop.

There were 484 participants in the final sample for this study, 242 in each treatment. There were 9 drop-outs from the control group and 13 from the treatment group, leaving a total of 233 and 229 in each group respectively from whom both pretest and posttest were obtained. Due to unexpected difficulties in coping with job stress, 19 Supported Employment participants had to be transferred into the sheltered workshop prior to the posttest. In all 19 cases, no one was transferred prior to week 6 of employment, and 15 were transferred after week 8. In all analyses, these cases were included with the Supported Employment group (intent-to-treat analysis) yielding treatment effect estimates that are likely to be conservative.

The major results for the four outcome measures are shown in Figure 1.

Insert Figure 1 about here

It is immediately apparent that in all four cases the null hypothesis has to be accepted – contrary to expectations, Supported Employment cases did significantly worse on all four outcomes than did control participants.

The mean gains, standard deviations, sample sizes and t-values (t-test for differences in average gain) are shown for the four outcome measures in Table 1.

Insert Table 1 about here

The results in the table confirm the impressions in the figures. Note that all t-values are negative except for the BPRS where high scores indicate greater severity of illness. For all four outcomes, the t-values were statistically significant (p<.05).

Conclusions

The results of this study were clearly contrary to initial expectations. The alternative hypothesis suggested that SE participants would show improved psychological functioning and self esteem after three months of employment. Exactly the reverse happened – SE participants showed significantly worse psychological functioning and self esteem.

There are two major possible explanations for this outcome pattern. First, it seems reasonable that there might be a delayed positive or “boomerang” effect of employment outside of a sheltered setting. SE cases may have to go through an initial difficult period of adjustment (longer than three months) before positive effects become apparent. This “you have to get worse before you get better” theory is commonly held in other treatment-contexts like drug addiction and alcoholism. But a second explanation seems more plausible – that people working full-time jobs in real-world settings are almost certainly going to be under greater stress and experience more negative outcomes than those who work in the relatively safe confines of an in-agency sheltered workshop. Put more succinctly, the lesson here might very well be that work is hard. Sheltered workshops are generally very nurturing work environments where virtually all employees share similar illness histories and where expectations about productivity are relatively low. In contrast, getting a job at a local hamburger shop or as a shipping clerk puts the person in contact with co-workers who may not be sympathetic to their histories or forgiving with respect to low productivity. This second explanation seems even more plausible in the wake of informal debriefing sessions held as focus groups with the staff and selected research participants. It was clear in the discussion that SE persons experienced significantly higher job stress levels and more negative consequences. However, most of them also felt that the experience was a good one overall and that even their “normal” co-workers “hated their jobs” most of the time.

One lesson we might take from this study is that much of our contemporary theory in psychiatric rehabilitation is naive at best and, in some cases, may be seriously misleading. Theory led us to believe that outside work was a “good” thing that would naturally lead to “good” outcomes like increased psychological functioning and self esteem. But for most people (SMI or not) work is at best tolerable, especially for the types of low-paying service jobs available to study participants. While people with SMI may not function as well or have high self esteem, we should balance this with the desire they may have to “be like other people” including struggling with the vagaries of life and work that others struggle with.

Future research in this are needs to address the theoretical assumptions about employment outcomes for persons with SMI. It is especially important that attempts to replicate this study also try to measure how SE participants feel about the decision to work, even if traditional outcome indicators suffer. It may very well be that negative outcomes on traditional indicators can be associated with a “positive” impact for the participants and for the society as a whole.

Chadsey-Rusch, J. and Rusch, F.R. (1986). The ecology of the workplace. In J. Chadsey-Rusch, C. Haney-Maxwell, L. A. Phelps and F. R. Rusch (Eds.), School-to-Work Transition Issues and Models. (pp. 59-94), Champaign IL: Transition Institute at Illinois.

Ciardiello, J.A. (1981). Job placement success of schizophrenic clients in sheltered workshop programs. Vocational Evaluation and Work Adjustment Bulletin, 14, 125-128, 140.

Cook, J.A. (1992). Job ending among youth and adults with severe mental illness. Journal of Mental Health Administration, 19(2), 158-169.

Cook, J.A. & Hoffschmidt, S. (1993). Psychosocial rehabilitation programming: A comprehensive model for the 1990’s. In R.W. Flexer and P. Solomon (Eds.), Social and Community Support for People with Severe Mental Disabilities: Service Integration in Rehabilitation and Mental Health. Andover, MA: Andover Publishing.

Cook, J.A., Jonikas, J., & Solomon, M. (1992). Models of vocational rehabilitation for youth and adults with severe mental illness. American Rehabilitation, 18, 3, 6-32.

Cook, J.A. & Razzano, L. (1992). Natural vocational supports for persons with severe mental illness: Thresholds Supported Competitive Employment Program, in L. Stein (ed.), New Directions for Mental Health Services, San Francisco: Jossey-Bass, 56, 23-41.

Endicott, J.R., Spitzer, J.L. Fleiss, J.L. and Cohen, J. (1976). The Global Assessment Scale: A procedure for measuring overall severity of psychiatric disturbance. Archives of General Psychiatry, 33, 766-771.

Griffiths, R.D. (1974). Rehabilitation of chronic psychotic patients. Psychological Medicine, 4, 316-325.

Overall, J. E. and Gorham, D. R. (1962). The Brief Psychiatric Rating Scale. Psychological Reports, 10, 799-812.

Rosenberg, M. (1965). Society and Adolescent Self Image. Princeton, NJ, Princeton University Press.

Wehman, P. (1985). Supported competitive employment for persons with severe disabilities. In P. McCarthy, J. Everson, S. Monn & M. Barcus (Eds.), School-to-Work Transition for Youth with Severe Disabilities, (pp. 167-182), Richmond VA: Virginia Commonwealth University.

Whitehead, C.W. (1977). Sheltered Workshop Study: A Nationwide Report on Sheltered Workshops and their Employment of Handicapped Individuals. (Workshop Survey, Volume 1), U.S. Department of Labor Service Publication. Washington, DC: U.S. Government Printing Office.

Woest, J., Klein, M. and Atkins, B.J. (1986). An overview of supported employment strategies. Journal of Rehabilitation Administration, 10(4), 130-135.

Figure 1. Pretest and posttest means for treatment (SE) and control groups for the four outcome measures.

The Employment Self Esteem Scale

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How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

Which details would you need to be reminded of?
Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
Which version of a questionnaire did you use in your survey?
Exactly which visual stimulus did you show participants, and is it publicly available?
What participants did you decide to exclude?
What process did you adjust, during your work?

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement .

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing on the reporting of reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .)

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

Keep in mind future replicability, alongside understanding and readability.
Follow checklists, and field- and journal-specific guidelines.
Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
How to Write a Great Title
How to Write an Abstract
How to Report Statistics
How to Write Discussions and Conclusions
How to Edit Your Work

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The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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Organizing Your Social Sciences Research Paper

6. The Methodology
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II. Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
Tools and methods used to identify and collect information, and how you identified relevant variables,
The ways in which you processed the data and the procedures you used to analyze that data, and
The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE : Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE : If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III. Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism, run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin

The methods section of an APA format psychology paper provides the methods and procedures used in a research study or experiment . This part of an APA paper is critical because it allows other researchers to see exactly how you conducted your research.

Method refers to the procedure that was used in a research study. It included a precise description of how the experiments were performed and why particular procedures were selected. While the APA technically refers to this section as the 'method section,' it is also often known as a 'methods section.'

The methods section ensures the experiment's reproducibility and the assessment of alternative methods that might produce different results. It also allows researchers to replicate the experiment and judge the study's validity.

This article discusses how to write a methods section for a psychology paper, including important elements to include and tips that can help.

What to Include in a Method Section

So what exactly do you need to include when writing your method section? You should provide detailed information on the following:

Research design
Participants
Participant behavior

The method section should provide enough information to allow other researchers to replicate your experiment or study.

Components of a Method Section

The method section should utilize subheadings to divide up different subsections. These subsections typically include participants, materials, design, and procedure.

Participants

In this part of the method section, you should describe the participants in your experiment, including who they were (and any unique features that set them apart from the general population), how many there were, and how they were selected. If you utilized random selection to choose your participants, it should be noted here.

For example: "We randomly selected 100 children from elementary schools near the University of Arizona."

At the very minimum, this part of your method section must convey:

Basic demographic characteristics of your participants (such as sex, age, ethnicity, or religion)
The population from which your participants were drawn
Any restrictions on your pool of participants
How many participants were assigned to each condition and how they were assigned to each group (i.e., randomly assignment , another selection method, etc.)
Why participants took part in your research (i.e., the study was advertised at a college or hospital, they received some type of incentive, etc.)

Information about participants helps other researchers understand how your study was performed, how generalizable the result might be, and allows other researchers to replicate the experiment with other populations to see if they might obtain the same results.

In this part of the method section, you should describe the materials, measures, equipment, or stimuli used in the experiment. This may include:

Testing instruments
Technical equipment
Any psychological assessments that were used
Any special equipment that was used

For example: "Two stories from Sullivan et al.'s (1994) second-order false belief attribution tasks were used to assess children's understanding of second-order beliefs."

For standard equipment such as computers, televisions, and videos, you can simply name the device and not provide further explanation.

Specialized equipment should be given greater detail, especially if it is complex or created for a niche purpose. In some instances, such as if you created a special material or apparatus for your study, you might need to include an illustration of the item in the appendix of your paper.

In this part of your method section, describe the type of design used in the experiment. Specify the variables as well as the levels of these variables. Identify:

The independent variables
Dependent variables
Control variables
Any extraneous variables that might influence your results.

Also, explain whether your experiment uses a within-groups or between-groups design.

For example: "The experiment used a 3x2 between-subjects design. The independent variables were age and understanding of second-order beliefs."

The next part of your method section should detail the procedures used in your experiment. Your procedures should explain:

What the participants did
How data was collected
The order in which steps occurred

For example: "An examiner interviewed children individually at their school in one session that lasted 20 minutes on average. The examiner explained to each child that he or she would be told two short stories and that some questions would be asked after each story. All sessions were videotaped so the data could later be coded."

Keep this subsection concise yet detailed. Explain what you did and how you did it, but do not overwhelm your readers with too much information.

Tips for How to Write a Methods Section

In addition to following the basic structure of an APA method section, there are also certain things you should remember when writing this section of your paper. Consider the following tips when writing this section:

Use the past tense : Always write the method section in the past tense.
Be descriptive : Provide enough detail that another researcher could replicate your experiment, but focus on brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
Use an academic tone : Use formal language and avoid slang or colloquial expressions. Word choice is also important. Refer to the people in your experiment or study as "participants" rather than "subjects."
Use APA format : Keep a style guide on hand as you write your method section. The Publication Manual of the American Psychological Association is the official source for APA style.
Make connections : Read through each section of your paper for agreement with other sections. If you mention procedures in the method section, these elements should be discussed in the results and discussion sections.
Proofread : Check your paper for grammar, spelling, and punctuation errors.. typos, grammar problems, and spelling errors. Although a spell checker is a handy tool, there are some errors only you can catch.

After writing a draft of your method section, be sure to get a second opinion. You can often become too close to your work to see errors or lack of clarity. Take a rough draft of your method section to your university's writing lab for additional assistance.

A Word From Verywell

The method section is one of the most important components of your APA format paper. The goal of your paper should be to clearly detail what you did in your experiment. Provide enough detail that another researcher could replicate your study if they wanted.

Finally, if you are writing your paper for a class or for a specific publication, be sure to keep in mind any specific instructions provided by your instructor or by the journal editor. Your instructor may have certain requirements that you need to follow while writing your method section.

Frequently Asked Questions

While the subsections can vary, the three components that should be included are sections on the participants, the materials, and the procedures.

Describe who the participants were in the study and how they were selected.
Define and describe the materials that were used including any equipment, tests, or assessments
Describe how the data was collected

To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded, left-aligned and in title case.

The purpose of the methods section is to describe what you did in your experiment. It should be brief, but include enough detail that someone could replicate your experiment based on this information. Your methods section should detail what you did to answer your research question. Describe how the study was conducted, the study design that was used and why it was chosen, and how you collected the data and analyzed the results.

Erdemir F. How to write a materials and methods section of a scientific article ? Turk J Urol . 2013;39(Suppl 1):10-5. doi:10.5152/tud.2013.047

Kallet RH. How to write the methods section of a research paper . Respir Care . 2004;49(10):1229-32. PMID: 15447808.

American Psychological Association. Publication Manual of the American Psychological Association (7th ed.). Washington DC: The American Psychological Association; 2019.

American Psychological Association. APA Style Journal Article Reporting Standards . Published 2020.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Organizing Academic Research Papers: 6. The Methodology

Purpose of Guide
Design Flaws to Avoid
Glossary of Research Terms
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
Executive Summary
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tertiary Sources
What Is Scholarly vs. Popular?
Qualitative Methods
Quantitative Methods
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Annotated Bibliography
Dealing with Nervousness
Using Visual Aids
Grading Someone Else's Paper
How to Manage Group Projects
Multiple Book Review Essay
Reviewing Collected Essays
About Informed Consent
Writing Field Notes
Writing a Policy Memo
Writing a Research Proposal
Acknowledgements

The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

Readers need to know how the data was obtained because the method you choose affects the results and, by extension, how you likely interpreted those results.
Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
The research method must be appropriate to the objectives of the study . For example, be sure you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring . For any problems that did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.

Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation .
The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.

II. Content

An effectively written methodology section should:

Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
Indicate how the approach fits the overall research design . Your methods should have a clear connection with your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is unsuited to achieving the stated objective of your paper.
Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom.
Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors?
Provide background and rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
Provide a rationale for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
Address potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

III. Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose. Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. How to Write a Scientific Paper: Writing the Methods Section. Revista Portuguesa de Pneumologia 17 (2011): 232-238; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Writing the Experimental Report: Methods, Results, and Discussion . The Writing Lab and The OWL. Purdue University; Methods and Materials . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Another Writing Tip

Knowing the Relationship Between Theories and Methods

Introspectively engage in an ongoing dialectic between theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics. Part 1, Chapter 3. Boise State University; The Theory-Method Relationship . S-Cool Revision. United Kingdom.

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J Korean Med Sci
v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLE 1. Descriptive research question (quantitative research)
- Presents research variables to be assessed (distinct phenotypes and subphenotypes)
“BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
EXAMPLE 2. Relationship research question (quantitative research)
- Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
“Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
EXAMPLE 3. Comparative research question (quantitative research)
- Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
“BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
EXAMPLE 4. Exploratory research question (qualitative research)
- Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
“Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
EXAMPLE 5. Relationship research question (quantitative research)
- Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
“Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

EXAMPLE 1. Working hypothesis (quantitative research)
- A hypothesis that is initially accepted for further research to produce a feasible theory
“As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
“In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
EXAMPLE 2. Exploratory hypothesis (qualitative research)
- Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
“We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
“Conclusion
Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
“We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
EXAMPLE 4. Statistical hypothesis (quantitative research)
- An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
“Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
“Statistical Analysis
( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

EXAMPLE 1. Background, hypotheses, and aims are provided
“Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
“ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
“This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
EXAMPLE 2. Background, hypotheses, and aims are provided
“We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
“ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
EXAMPLE 3. Background, aim, and hypothesis are provided
“In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
“This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
“ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Barroga E, Matanguihan GJ.
Methodology: Barroga E, Matanguihan GJ.
Writing - original draft: Barroga E, Matanguihan GJ.
Writing - review & editing: Barroga E, Matanguihan GJ.

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Research Paper Example Outline

Before you plan on writing a well-researched paper, make a rough draft. An outline can be a great help when it comes to organizing vast amounts of research material for your paper.

Here is an outline of a research paper example:

Here is a downloadable sample of a standard research paper outline:

Research Paper Outline

Want to create the perfect outline for your paper? Check out this in-depth guide on creating a research paper outline for a structured paper!

Good Research Paper Examples for Students

Here are some more samples of research paper for students to learn from:

Fiscal Research Center - Action Plan

Qualitative Research Paper Example

Research Paper Example Introduction

How to Write a Research Paper Example

Research Paper Example for High School

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Home » Research Paper Format – Types, Examples and Templates

Research Paper Format – Types, Examples and Templates

Table of Contents

Research paper format is an essential aspect of academic writing that plays a crucial role in the communication of research findings . The format of a research paper depends on various factors such as the discipline, style guide, and purpose of the research. It includes guidelines for the structure, citation style, referencing , and other elements of the paper that contribute to its overall presentation and coherence. Adhering to the appropriate research paper format is vital for ensuring that the research is accurately and effectively communicated to the intended audience. In this era of information, it is essential to understand the different research paper formats and their guidelines to communicate research effectively, accurately, and with the required level of detail. This post aims to provide an overview of some of the common research paper formats used in academic writing.

Research Paper Formats

Research Paper Formats are as follows:

APA (American Psychological Association) format
MLA (Modern Language Association) format
Chicago/Turabian style
IEEE (Institute of Electrical and Electronics Engineers) format
AMA (American Medical Association) style
Harvard style
Vancouver style
ACS (American Chemical Society) style
ASA (American Sociological Association) style
APSA (American Political Science Association) style

APA (American Psychological Association) Format

Here is a general APA format for a research paper:

Title Page: The title page should include the title of your paper, your name, and your institutional affiliation. It should also include a running head, which is a shortened version of the title, and a page number in the upper right-hand corner.
Abstract : The abstract is a brief summary of your paper, typically 150-250 words. It should include the purpose of your research, the main findings, and any implications or conclusions that can be drawn.
Introduction: The introduction should provide background information on your topic, state the purpose of your research, and present your research question or hypothesis. It should also include a brief literature review that discusses previous research on your topic.
Methods: The methods section should describe the procedures you used to collect and analyze your data. It should include information on the participants, the materials and instruments used, and the statistical analyses performed.
Results: The results section should present the findings of your research in a clear and concise manner. Use tables and figures to help illustrate your results.
Discussion : The discussion section should interpret your results and relate them back to your research question or hypothesis. It should also discuss the implications of your findings and any limitations of your study.
References : The references section should include a list of all sources cited in your paper. Follow APA formatting guidelines for your citations and references.

Some additional tips for formatting your APA research paper:

Use 12-point Times New Roman font throughout the paper.
Double-space all text, including the references.
Use 1-inch margins on all sides of the page.
Indent the first line of each paragraph by 0.5 inches.
Use a hanging indent for the references (the first line should be flush with the left margin, and all subsequent lines should be indented).
Number all pages, including the title page and references page, in the upper right-hand corner.

APA Research Paper Format Template

APA Research Paper Format Template is as follows:

Title Page:

Title of the paper
Author’s name
Institutional affiliation
A brief summary of the main points of the paper, including the research question, methods, findings, and conclusions. The abstract should be no more than 250 words.

Introduction:

Background information on the topic of the research paper
Research question or hypothesis
Significance of the study
Overview of the research methods and design
Brief summary of the main findings
Participants: description of the sample population, including the number of participants and their characteristics (age, gender, ethnicity, etc.)
Materials: description of any materials used in the study (e.g., survey questions, experimental apparatus)
Procedure: detailed description of the steps taken to conduct the study
Presentation of the findings of the study, including statistical analyses if applicable
Tables and figures may be included to illustrate the results

Discussion:

Interpretation of the results in light of the research question and hypothesis
Implications of the study for the field
Limitations of the study
Suggestions for future research

References:

A list of all sources cited in the paper, in APA format

Formatting guidelines:

Double-spaced
12-point font (Times New Roman or Arial)
1-inch margins on all sides
Page numbers in the top right corner
Headings and subheadings should be used to organize the paper
The first line of each paragraph should be indented
Quotations of 40 or more words should be set off in a block quote with no quotation marks
In-text citations should include the author’s last name and year of publication (e.g., Smith, 2019)

APA Research Paper Format Example

APA Research Paper Format Example is as follows:

The Effects of Social Media on Mental Health

University of XYZ

This study examines the relationship between social media use and mental health among college students. Data was collected through a survey of 500 students at the University of XYZ. Results suggest that social media use is significantly related to symptoms of depression and anxiety, and that the negative effects of social media are greater among frequent users.

Social media has become an increasingly important aspect of modern life, especially among young adults. While social media can have many positive effects, such as connecting people across distances and sharing information, there is growing concern about its impact on mental health. This study aims to examine the relationship between social media use and mental health among college students.

Participants: Participants were 500 college students at the University of XYZ, recruited through online advertisements and flyers posted on campus. Participants ranged in age from 18 to 25, with a mean age of 20.5 years. The sample was 60% female, 40% male, and 5% identified as non-binary or gender non-conforming.

Data was collected through an online survey administered through Qualtrics. The survey consisted of several measures, including the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and questions about social media use.

Procedure :

Participants were asked to complete the online survey at their convenience. The survey took approximately 20-30 minutes to complete. Data was analyzed using descriptive statistics, correlations, and multiple regression analysis.

Results indicated that social media use was significantly related to symptoms of depression (r = .32, p < .001) and anxiety (r = .29, p < .001). Regression analysis indicated that frequency of social media use was a significant predictor of both depression symptoms (β = .24, p < .001) and anxiety symptoms (β = .20, p < .001), even when controlling for age, gender, and other relevant factors.

The results of this study suggest that social media use is associated with symptoms of depression and anxiety among college students. The negative effects of social media are greater among frequent users. These findings have important implications for mental health professionals and educators, who should consider addressing the potential negative effects of social media use in their work with young adults.

References :

References should be listed in alphabetical order according to the author’s last name. For example:

Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3-17.

Note: This is just a sample Example do not use this in your assignment.

MLA (Modern Language Association) Format

MLA (Modern Language Association) Format is as follows:

Page Layout : Use 8.5 x 11-inch white paper, with 1-inch margins on all sides. The font should be 12-point Times New Roman or a similar serif font.
Heading and Title : The first page of your research paper should include a heading and a title. The heading should include your name, your instructor’s name, the course title, and the date. The title should be centered and in title case (capitalizing the first letter of each important word).
In-Text Citations : Use parenthetical citations to indicate the source of your information. The citation should include the author’s last name and the page number(s) of the source. For example: (Smith 23).
Works Cited Page : At the end of your paper, include a Works Cited page that lists all the sources you used in your research. Each entry should include the author’s name, the title of the work, the publication information, and the medium of publication.
Formatting Quotations : Use double quotation marks for short quotations and block quotations for longer quotations. Indent the entire quotation five spaces from the left margin.
Formatting the Body : Use a clear and readable font and double-space your text throughout. The first line of each paragraph should be indented one-half inch from the left margin.

MLA Research Paper Template

MLA Research Paper Format Template is as follows:

Use 8.5 x 11 inch white paper.
Use a 12-point font, such as Times New Roman.
Use double-spacing throughout the entire paper, including the title page and works cited page.
Set the margins to 1 inch on all sides.
Use page numbers in the upper right corner, beginning with the first page of text.
Include a centered title for the research paper, using title case (capitalizing the first letter of each important word).
Include your name, instructor’s name, course name, and date in the upper left corner, double-spaced.

In-Text Citations

When quoting or paraphrasing information from sources, include an in-text citation within the text of your paper.
Use the author’s last name and the page number in parentheses at the end of the sentence, before the punctuation mark.
If the author’s name is mentioned in the sentence, only include the page number in parentheses.

Works Cited Page

List all sources cited in alphabetical order by the author’s last name.
Each entry should include the author’s name, title of the work, publication information, and medium of publication.
Use italics for book and journal titles, and quotation marks for article and chapter titles.
For online sources, include the date of access and the URL.

Here is an example of how the first page of a research paper in MLA format should look:

Headings and Subheadings

Use headings and subheadings to organize your paper and make it easier to read.
Use numerals to number your headings and subheadings (e.g. 1, 2, 3), and capitalize the first letter of each word.
The main heading should be centered and in boldface type, while subheadings should be left-aligned and in italics.
Use only one space after each period or punctuation mark.
Use quotation marks to indicate direct quotes from a source.
If the quote is more than four lines, format it as a block quote, indented one inch from the left margin and without quotation marks.
Use ellipses (…) to indicate omitted words from a quote, and brackets ([…]) to indicate added words.

Works Cited Examples

Book: Last Name, First Name. Title of Book. Publisher, Publication Year.
Journal Article: Last Name, First Name. “Title of Article.” Title of Journal, volume number, issue number, publication date, page numbers.
Website: Last Name, First Name. “Title of Webpage.” Title of Website, publication date, URL. Accessed date.

Here is an example of how a works cited entry for a book should look:

Smith, John. The Art of Writing Research Papers. Penguin, 2021.

MLA Research Paper Example

MLA Research Paper Format Example is as follows:

Your Professor’s Name

Course Name and Number

Date (in Day Month Year format)

Word Count (not including title page or Works Cited)

Title: The Impact of Video Games on Aggression Levels

Video games have become a popular form of entertainment among people of all ages. However, the impact of video games on aggression levels has been a subject of debate among scholars and researchers. While some argue that video games promote aggression and violent behavior, others argue that there is no clear link between video games and aggression levels. This research paper aims to explore the impact of video games on aggression levels among young adults.

Background:

The debate on the impact of video games on aggression levels has been ongoing for several years. According to the American Psychological Association, exposure to violent media, including video games, can increase aggression levels in children and adolescents. However, some researchers argue that there is no clear evidence to support this claim. Several studies have been conducted to examine the impact of video games on aggression levels, but the results have been mixed.

Methodology:

This research paper used a quantitative research approach to examine the impact of video games on aggression levels among young adults. A sample of 100 young adults between the ages of 18 and 25 was selected for the study. The participants were asked to complete a questionnaire that measured their aggression levels and their video game habits.

The results of the study showed that there was a significant correlation between video game habits and aggression levels among young adults. The participants who reported playing violent video games for more than 5 hours per week had higher aggression levels than those who played less than 5 hours per week. The study also found that male participants were more likely to play violent video games and had higher aggression levels than female participants.

The findings of this study support the claim that video games can increase aggression levels among young adults. However, it is important to note that the study only examined the impact of video games on aggression levels and did not take into account other factors that may contribute to aggressive behavior. It is also important to note that not all video games promote violence and aggression, and some games may have a positive impact on cognitive and social skills.

Conclusion :

In conclusion, this research paper provides evidence to support the claim that video games can increase aggression levels among young adults. However, it is important to conduct further research to examine the impact of video games on other aspects of behavior and to explore the potential benefits of video games. Parents and educators should be aware of the potential impact of video games on aggression levels and should encourage young adults to engage in a variety of activities that promote cognitive and social skills.

Works Cited:

American Psychological Association. (2017). Violent Video Games: Myths, Facts, and Unanswered Questions. Retrieved from https://www.apa.org/news/press/releases/2017/08/violent-video-games
Ferguson, C. J. (2015). Do Angry Birds make for angry children? A meta-analysis of video game influences on children’s and adolescents’ aggression, mental health, prosocial behavior, and academic performance. Perspectives on Psychological Science, 10(5), 646-666.
Gentile, D. A., Swing, E. L., Lim, C. G., & Khoo, A. (2012). Video game playing, attention problems, and impulsiveness: Evidence of bidirectional causality. Psychology of Popular Media Culture, 1(1), 62-70.
Greitemeyer, T. (2014). Effects of prosocial video games on prosocial behavior. Journal of Personality and Social Psychology, 106(4), 530-548.

Chicago/Turabian Style

Chicago/Turabian Formate is as follows:

Margins : Use 1-inch margins on all sides of the paper.
Font : Use a readable font such as Times New Roman or Arial, and use a 12-point font size.
Page numbering : Number all pages in the upper right-hand corner, beginning with the first page of text. Use Arabic numerals.
Title page: Include a title page with the title of the paper, your name, course title and number, instructor’s name, and the date. The title should be centered on the page and in title case (capitalize the first letter of each word).
Headings: Use headings to organize your paper. The first level of headings should be centered and in boldface or italics. The second level of headings should be left-aligned and in boldface or italics. Use as many levels of headings as necessary to organize your paper.
In-text citations : Use footnotes or endnotes to cite sources within the text of your paper. The first citation for each source should be a full citation, and subsequent citations can be shortened. Use superscript numbers to indicate footnotes or endnotes.
Bibliography : Include a bibliography at the end of your paper, listing all sources cited in your paper. The bibliography should be in alphabetical order by the author’s last name, and each entry should include the author’s name, title of the work, publication information, and date of publication.
Formatting of quotations: Use block quotations for quotations that are longer than four lines. Indent the entire quotation one inch from the left margin, and do not use quotation marks. Single-space the quotation, and double-space between paragraphs.
Tables and figures: Use tables and figures to present data and illustrations. Number each table and figure sequentially, and provide a brief title for each. Place tables and figures as close as possible to the text that refers to them.
Spelling and grammar : Use correct spelling and grammar throughout your paper. Proofread carefully for errors.

Chicago/Turabian Research Paper Template

Chicago/Turabian Research Paper Template is as folows:

Title of Paper

Name of Student

Professor’s Name

I. Introduction

A. Background Information

B. Research Question

C. Thesis Statement

II. Literature Review

A. Overview of Existing Literature

B. Analysis of Key Literature

C. Identification of Gaps in Literature

III. Methodology

A. Research Design

B. Data Collection

C. Data Analysis

IV. Results

A. Presentation of Findings

B. Analysis of Findings

C. Discussion of Implications

V. Conclusion

A. Summary of Findings

B. Implications for Future Research

C. Conclusion

VI. References

A. Bibliography

B. In-Text Citations

VII. Appendices (if necessary)

A. Data Tables

C. Additional Supporting Materials

Chicago/Turabian Research Paper Example

Title: The Impact of Social Media on Political Engagement

Name: John Smith

Class: POLS 101

Professor: Dr. Jane Doe

Date: April 8, 2023

I. Introduction:

Social media has become an integral part of our daily lives. People use social media platforms like Facebook, Twitter, and Instagram to connect with friends and family, share their opinions, and stay informed about current events. With the rise of social media, there has been a growing interest in understanding its impact on various aspects of society, including political engagement. In this paper, I will examine the relationship between social media use and political engagement, specifically focusing on how social media influences political participation and political attitudes.

II. Literature Review:

There is a growing body of literature on the impact of social media on political engagement. Some scholars argue that social media has a positive effect on political participation by providing new channels for political communication and mobilization (Delli Carpini & Keeter, 1996; Putnam, 2000). Others, however, suggest that social media can have a negative impact on political engagement by creating filter bubbles that reinforce existing beliefs and discourage political dialogue (Pariser, 2011; Sunstein, 2001).

III. Methodology:

To examine the relationship between social media use and political engagement, I conducted a survey of 500 college students. The survey included questions about social media use, political participation, and political attitudes. The data was analyzed using descriptive statistics and regression analysis.

Iv. Results:

The results of the survey indicate that social media use is positively associated with political participation. Specifically, respondents who reported using social media to discuss politics were more likely to have participated in a political campaign, attended a political rally, or contacted a political representative. Additionally, social media use was found to be associated with more positive attitudes towards political engagement, such as increased trust in government and belief in the effectiveness of political action.

V. Conclusion:

The findings of this study suggest that social media has a positive impact on political engagement, by providing new opportunities for political communication and mobilization. However, there is also a need for caution, as social media can also create filter bubbles that reinforce existing beliefs and discourage political dialogue. Future research should continue to explore the complex relationship between social media and political engagement, and develop strategies to harness the potential benefits of social media while mitigating its potential negative effects.

Vii. References:

Delli Carpini, M. X., & Keeter, S. (1996). What Americans know about politics and why it matters. Yale University Press.
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.
Sunstein, C. R. (2001). Republic.com. Princeton University Press.

IEEE (Institute of Electrical and Electronics Engineers) Format

IEEE (Institute of Electrical and Electronics Engineers) Research Paper Format is as follows:

Title : A concise and informative title that accurately reflects the content of the paper.
Abstract : A brief summary of the paper, typically no more than 250 words, that includes the purpose of the study, the methods used, the key findings, and the main conclusions.
Introduction : An overview of the background, context, and motivation for the research, including a clear statement of the problem being addressed and the objectives of the study.
Literature review: A critical analysis of the relevant research and scholarship on the topic, including a discussion of any gaps or limitations in the existing literature.
Methodology : A detailed description of the methods used to collect and analyze data, including any experiments or simulations, data collection instruments or procedures, and statistical analyses.
Results : A clear and concise presentation of the findings, including any relevant tables, graphs, or figures.
Discussion : A detailed interpretation of the results, including a comparison of the findings with previous research, a discussion of the implications of the results, and any recommendations for future research.
Conclusion : A summary of the key findings and main conclusions of the study.
References : A list of all sources cited in the paper, formatted according to IEEE guidelines.

In addition to these elements, an IEEE research paper should also follow certain formatting guidelines, including using 12-point font, double-spaced text, and numbered headings and subheadings. Additionally, any tables, figures, or equations should be clearly labeled and referenced in the text.

AMA (American Medical Association) Style

AMA (American Medical Association) Style Research Paper Format:

Title Page: This page includes the title of the paper, the author’s name, institutional affiliation, and any acknowledgments or disclaimers.
Abstract: The abstract is a brief summary of the paper that outlines the purpose, methods, results, and conclusions of the study. It is typically limited to 250 words or less.
Introduction: The introduction provides a background of the research problem, defines the research question, and outlines the objectives and hypotheses of the study.
Methods: The methods section describes the research design, participants, procedures, and instruments used to collect and analyze data.
Results: The results section presents the findings of the study in a clear and concise manner, using graphs, tables, and charts where appropriate.
Discussion: The discussion section interprets the results, explains their significance, and relates them to previous research in the field.
Conclusion: The conclusion summarizes the main points of the paper, discusses the implications of the findings, and suggests future research directions.
References: The reference list includes all sources cited in the paper, listed in alphabetical order by author’s last name.

In addition to these sections, the AMA format requires that authors follow specific guidelines for citing sources in the text and formatting their references. The AMA style uses a superscript number system for in-text citations and provides specific formats for different types of sources, such as books, journal articles, and websites.

Harvard Style

Harvard Style Research Paper format is as follows:

Title page: This should include the title of your paper, your name, the name of your institution, and the date of submission.
Abstract : This is a brief summary of your paper, usually no more than 250 words. It should outline the main points of your research and highlight your findings.
Introduction : This section should introduce your research topic, provide background information, and outline your research question or thesis statement.
Literature review: This section should review the relevant literature on your topic, including previous research studies, academic articles, and other sources.
Methodology : This section should describe the methods you used to conduct your research, including any data collection methods, research instruments, and sampling techniques.
Results : This section should present your findings in a clear and concise manner, using tables, graphs, and other visual aids if necessary.
Discussion : This section should interpret your findings and relate them to the broader research question or thesis statement. You should also discuss the implications of your research and suggest areas for future study.
Conclusion : This section should summarize your main findings and provide a final statement on the significance of your research.
References : This is a list of all the sources you cited in your paper, presented in alphabetical order by author name. Each citation should include the author’s name, the title of the source, the publication date, and other relevant information.

In addition to these sections, a Harvard Style research paper may also include a table of contents, appendices, and other supplementary materials as needed. It is important to follow the specific formatting guidelines provided by your instructor or academic institution when preparing your research paper in Harvard Style.

Vancouver Style

Vancouver Style Research Paper format is as follows:

The Vancouver citation style is commonly used in the biomedical sciences and is known for its use of numbered references. Here is a basic format for a research paper using the Vancouver citation style:

Title page: Include the title of your paper, your name, the name of your institution, and the date.
Abstract : This is a brief summary of your research paper, usually no more than 250 words.
Introduction : Provide some background information on your topic and state the purpose of your research.
Methods : Describe the methods you used to conduct your research, including the study design, data collection, and statistical analysis.
Results : Present your findings in a clear and concise manner, using tables and figures as needed.
Discussion : Interpret your results and explain their significance. Also, discuss any limitations of your study and suggest directions for future research.
References : List all of the sources you cited in your paper in numerical order. Each reference should include the author’s name, the title of the article or book, the name of the journal or publisher, the year of publication, and the page numbers.

ACS (American Chemical Society) Style

ACS (American Chemical Society) Style Research Paper format is as follows:

The American Chemical Society (ACS) Style is a citation style commonly used in chemistry and related fields. When formatting a research paper in ACS Style, here are some guidelines to follow:

Paper Size and Margins : Use standard 8.5″ x 11″ paper with 1-inch margins on all sides.
Font: Use a 12-point serif font (such as Times New Roman) for the main text. The title should be in bold and a larger font size.
Title Page : The title page should include the title of the paper, the authors’ names and affiliations, and the date of submission. The title should be centered on the page and written in bold font. The authors’ names should be centered below the title, followed by their affiliations and the date.
Abstract : The abstract should be a brief summary of the paper, no more than 250 words. It should be on a separate page and include the title of the paper, the authors’ names and affiliations, and the text of the abstract.
Main Text : The main text should be organized into sections with headings that clearly indicate the content of each section. The introduction should provide background information and state the research question or hypothesis. The methods section should describe the procedures used in the study. The results section should present the findings of the study, and the discussion section should interpret the results and provide conclusions.
References: Use the ACS Style guide to format the references cited in the paper. In-text citations should be numbered sequentially throughout the text and listed in numerical order at the end of the paper.
Figures and Tables: Figures and tables should be numbered sequentially and referenced in the text. Each should have a descriptive caption that explains its content. Figures should be submitted in a high-quality electronic format.
Supporting Information: Additional information such as data, graphs, and videos may be included as supporting information. This should be included in a separate file and referenced in the main text.
Acknowledgments : Acknowledge any funding sources or individuals who contributed to the research.

ASA (American Sociological Association) Style

ASA (American Sociological Association) Style Research Paper format is as follows:

Title Page: The title page of an ASA style research paper should include the title of the paper, the author’s name, and the institutional affiliation. The title should be centered and should be in title case (the first letter of each major word should be capitalized).
Abstract: An abstract is a brief summary of the paper that should appear on a separate page immediately following the title page. The abstract should be no more than 200 words in length and should summarize the main points of the paper.
Main Body: The main body of the paper should begin on a new page following the abstract page. The paper should be double-spaced, with 1-inch margins on all sides, and should be written in 12-point Times New Roman font. The main body of the paper should include an introduction, a literature review, a methodology section, results, and a discussion.
References : The reference section should appear on a separate page at the end of the paper. All sources cited in the paper should be listed in alphabetical order by the author’s last name. Each reference should include the author’s name, the title of the work, the publication information, and the date of publication.
Appendices : Appendices are optional and should only be included if they contain information that is relevant to the study but too lengthy to be included in the main body of the paper. If you include appendices, each one should be labeled with a letter (e.g., Appendix A, Appendix B, etc.) and should be referenced in the main body of the paper.

APSA (American Political Science Association) Style

APSA (American Political Science Association) Style Research Paper format is as follows:

Title Page: The title page should include the title of the paper, the author’s name, the name of the course or instructor, and the date.
Abstract : An abstract is typically not required in APSA style papers, but if one is included, it should be brief and summarize the main points of the paper.
Introduction : The introduction should provide an overview of the research topic, the research question, and the main argument or thesis of the paper.
Literature Review : The literature review should summarize the existing research on the topic and provide a context for the research question.
Methods : The methods section should describe the research methods used in the paper, including data collection and analysis.
Results : The results section should present the findings of the research.
Discussion : The discussion section should interpret the results and connect them back to the research question and argument.
Conclusion : The conclusion should summarize the main findings and implications of the research.
References : The reference list should include all sources cited in the paper, formatted according to APSA style guidelines.

In-text citations in APSA style use parenthetical citation, which includes the author’s last name, publication year, and page number(s) if applicable. For example, (Smith 2010, 25).

About the author

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Researcher, Academic Writer, Web developer

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SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation

Open access
Published: 16 April 2024
Volume 56 , article number 149 , ( 2024 )

Cite this article

You have full access to this open access article

Congcong Ma 1 , 2 ,
Jiaqi Mi 1 ,
Wanlin Gao 1 , 2 &
Sha Tao 1 , 2

Image sample augmentation refers to strategies for increasing sample size by modifying current data or synthesizing new data based on existing data. This technique is of vital significance in enhancing the performance of downstream learning tasks in widespread small-sample scenarios. In recent years, GAN-based image augmentation methods have gained significant attention and research focus. They have achieved remarkable generation results on large-scale datasets. However, their performance tends to be unsatisfactory when applied to datasets with limited samples. Therefore, this paper proposes a semantic similarity-based small-sample image augmentation method named SSGAN. Firstly, a relatively shallow pyramid-structured GAN-based backbone network was designed, aiming to enhance the model’s feature extraction capabilities to adapt to small sample sizes. Secondly, a feature selection module based on high-dimensional semantics was designed to optimize the loss function, thereby improving the model’s learning capacity. Lastly, extensive comparative experiments and comprehensive ablation experiments were carried out on the “Flower” and “Animal” datasets. The results indicate that the proposed method outperforms other classical GANs methods in well-established evaluation metrics such as FID and IS, with improvements of 18.6 and 1.4, respectively. The dataset augmented by SSGAN significantly enhances the performance of the classifier, achieving a 2.2% accuracy improvement compared to the best-known method. Furthermore, SSGAN demonstrates excellent generalization and robustness.

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1 Introduction

Small-sample image datasets often lead to overfitting and limited generalization capabilities in deep learning tasks. Due to factors such as high data annotation costs and sample imbalance, the issue of small samples is prevalent [ 10 , 23 , 25 ]. For example, this problem is commonly encountered in datasets of plant phenotype images for disease and pest identification [ 17 ], images for the diagnosis of severe diseases [ 13 ], and images of equipment failures [ 3 ], among others. Image sample augmentation is a direct and effective approach to address the issue of small samples. Existing image sample augmentation methods include augmentation methods based on geometric transformations and color transformations, traditional sample augmentation methods, and GAN-based augmentation methods. The first type of augmentation methods, such as flipping, rotation, and random noise, often lack diversity in generating augmented samples [ 5 ]. The second type of methods, such as SMOTE [ 14 ] and Mixup [ 4 ], are based on existing samples and have shown promising augmentation results. In recent years, with the remarkable achievements of deep learning in solving practical problems, GAN-based augmentation methods have been extensively researched and proven to have the ability to generate high-quality and diverse images. Indeed, WGAN introduced a generative adversarial network model based on the Wasserstein distance [ 2 ]. It focuses on measuring the distance between the generated data distribution and the real data distribution, addressing issues such as unstable training in traditional GANs. By minimizing the Wasserstein distance, WGAN ensures the diversity of generated samples. While many GANs [ 1 , 18 , 19 ] have shown excellent enhancement results on large-scale datasets, their performance significantly deteriorates when applied to datasets with limited samples. This can be attributed to two main factors. Firstly, existing network architectures may not adequately extract features from the training images. Secondly, the slow "learning speed" of GANs makes them less suitable for small-sample scenarios, where a limited number of samples are available for training. More detailed review of related studies will be summarized in Sect. 2 . To address these challenges, we propose a semantic similarity-based GAN for small-sample image augmentation. We optimize the network architecture to enhance its feature extraction capability specifically for small-sample datasets. Additionally, a high-dimensional semantic-based feature filtering module is designed that is able to influence the model’s learning process and enhance its learning ability. Ultimately, our proposed method aims to improve the enhancement performance on small-sample image datasets. We conducted extensive comparative experiments and comprehensive ablation experiments on the “Flower” and “Animal” datasets. SSGAN stands out among various GANs, with improvements of 18.6 and 1.4 in terms of FID and IS metrics, respectively, indicating that the generated images exhibit good clarity and diversity. The dataset augmented by SSGAN assists the classifier in achieving a 9% accuracy improvement, surpassing other classical methods by 2.2%. The results demonstrate the effectiveness of the proposed method for small-sample augmentation.

The main contributions and innovations of this paper are as follows:

We designed a pyramid structure for the backbone network to effectively extract features from small sample images. The introduction of pyramid connections enables the fusion of features at different scales, allowing the model to capture multi-dimensional perspectives and enhance its feature extraction capability.

Integrating a high-dimensional semantic-based feature filtering module into GAN, enhancing the model's learning ability and generating samples that closely resemble real samples, thus obtaining high-quality augmented data and improving the accuracy of classification tasks.

Validating the effectiveness and generalization of the proposed method on different small-sample datasets.

The remaining sections of this paper are organized as follows: Sect. 2 provides a comprehensive review of related works. Section 3 presents a detailed description of the proposed method. Section 4 introduces the experimental setup. Section 5 presents the experimental results. Section 6 includes the discussion and conclusion.

2 Related Works

There are many research studies focusing on image sample augmentation, primarily classified into three categories: basic augmentation methods, traditional augmentation methods, and GAN-based augmentation methods.

2.1 Basic Augmentation Methods

Basic image augmentation methods primarily include geometric transformation-based methods and color transformation-based methods. Among them, geometric transformation-based methods involve operations such as flipping, rotation, cropping, and zooming. These methods do not alter the content of the image itself, making them the simplest way to enhance the image dataset. However, excessive use of these methods may result in a dataset with limited diversity, generating “low-value” data. On the other hand, color transformation-based methods enhance the image by modifying its content. These methods include random noise, smooth blurring, color transformations based on HSV or RGB [ 24 ], and random erasing. Such augmentation methods can increase the diversity and variability of the dataset to a certain extent.

2.2 Traditional Augmentation Methods

Traditional augmentation methods mainly include SMOTE, SamplePairing [ 9 ], and Mixup. SMOTE (Synthetic Minority Over-sampling Technique) is a technique for synthesizing minority class samples by utilizing the k-nearest neighbor approach. It generates new samples by synthesizing samples from the same class based on their features, commonly used for generating minority class samples in imbalanced datasets. SamplePairing is another method for synthesizing new samples by combining samples with different labels, but it has limited interpretability. Mixup is a data augmentation method based on the principle of minimizing neighborhood risk. It generates new samples by linearly interpolating between pairs of samples, and it has shown good enhancement performance. Traditional augmentation methods are based on existing samples for sample augmentation, but their augmentation effectiveness is limited.

2.3 GAN-Based Augmentation Methods

Generative Adversarial Networks (GANs) are unsupervised data augmentation methods that utilize a generative network and a discriminative network to learn the data distribution and generate high-quality and diverse new samples. GANs have been extensively researched in the field of data augmentation, such as WGAN [ 2 ], SAGAN [ 22 ], ACGAN [ 15 ], ReACGAN [ 12 ], DCGAN [ 16 ], WGAN-GP [ 8 , 21 ], among others. WGAN, for instance, introduces the Wasserstein distance to alleviate the instability and mode collapse issues in GAN training, ensuring the diversity of generated samples. SAGAN incorporates self-attention mechanisms to enhance the focus on detailed image features, thus improving the quality of generated images. ACGAN introduces additional structure to the latent space of the GAN by incorporating a specialized cost function. This modification leads to the generation of higher quality samples. ACGAN not only generates realistic samples but also enables the discriminator to predict the class labels of the generated samples. ReACGAN introduces the concept of inter-data cross-entropy loss and employs auxiliary measures to address the issue of gradient explosion. This approach alleviates the problem of limited diversity in generated samples within GAN models. However, existing methods still require a significant amount of training samples to achieve satisfactory generation performance, making them less effective for small sample enhancement. To address this limitation, we propose a novel image small sample enhancement approach based on semantic filtering. Specifically, the challenge of limited training sample quantity is addressed by designing a shallow pyramid structure for the generator network, which allows effective feature extraction from small sample images. Additionally, we incorporate a semantic filtering module based on high-dimensional semantic features into the existing GAN structure to optimize the semantic similarity between generated and real images. For further details, please refer to Chapter 3.

3.1 Overall Structure of SSGAN

The overall structure of SSGAN is illustrated in Fig. 1 . The SSGAN model consists of three main components: the generative network, the discriminative network, and the perceptual network. The generative network is responsible for transforming an input random noise vector \(z\) into an image \(G\left(z\right)\) with the expectation of deceiving the discriminative network. The discriminative network serves as a binary classifier to distinguish between the generated images \(G\left(z\right)\) and the real images \({P}_{r}\) . These two components engage in a game to drive the GAN towards achieving Nash equilibrium. Our novel contribution is the inclusion of a perception network, which is responsible for extracting high-dimensional semantic features from the input images and comparing their semantic similarity with the distribution of real images. This facilitates the generator network in producing superior outputs.

Overall Structure of the Model

The generator primarily consists of stacked transpose convolutional layers and pyramid connections. Specifically, it includes one fully connected layer, four transpose convolutional layers, and two sets of pyramid connections. The generator takes a one-dimensional random noise vector \(z\) , following a Gaussian distribution, as input and generates images of size 64 × 64 × 3 as output. The discriminator is mainly composed of stacked convolutional layers and pyramid connections. It comprises four convolutional layers, two sets of pyramid connections, and one fully connected layer. Conventional techniques such as ReLU [ 7 ] and LN [ 6 ] are used to prevent overfitting and gradient disappearance. The discriminator network takes RGB images as input and outputs binary classification results. The perceptual network is a substructure of the VGG-19 [ 21 ] network pre-trained on the ImageNet dataset. We fix its parameters and select the first 16 layers as our feature extraction network model. In the overall architecture of the model, our innovation lies in the design of a pyramid structure for the backbone network to accommodate small sample sizes in image datasets. We have also introduced the perceptual network, which serves as an image semantic feature extraction module.

3.2 Pyramid Connection

The function of the pyramid connection is to fuse feature maps of different scales through upsampling and downsampling operations in different ways. As the connected feature maps have sizes resembling a “pyramid” structure, we named it the pyramid connection. Figure 2 illustrates the details of the pyramid connection. In the figure, the generation network utilizes bilinear interpolation for upsampling, which, together with transposed convolution (Deconv), performs feature extraction and fusion operations in the model. This is because: (1) Using transpose convolution for upsampling, although it can increase the size of the feature maps and refine coarse feature maps, it often leads to the “checkerboard artifacts” due to uneven overlap of the convolution kernels. To address this issue, we introduce the pyramid connection and utilize upsampling with bilinear interpolation, which helps alleviate the problem of pixel discontinuity and mitigate the checkerboard effect. (2) Due to the varying expressive power of feature maps at different levels, shallow-level features primarily reflect details such as brightness, edges, while deep-level features capture overall structures and semantic information. The introduction of pyramid connections allows the model to integrate features from different dimensions, enhancing the feature extraction capability of the model. (3) Additionally, the introduction of pyramid connections provides the model with receptive fields different from those obtained by transpose convolution, further enhancing the model's performance.

Pyramid Connection Details

3.3 Perceptual Loss

As shown in Fig. 1 , we innovatively incorporate a perceptual network to extract high-dimensional feature maps. This network is based on a pre-trained VGG network with 16 layers, which exhibits strong generalization capabilities due to the rich species diversity in the ImageNet dataset. Based on this, we introduce the perceptual loss, which ensures high-dimensional semantic similarity between the generated samples and the original samples. The perceptual loss is defined as the Euclidean distance between the feature representations of the reconstructed images and the real images, as shown in Eq. 1 .

Here \(W\) and \(H\) respectively represent the dimension of the output feature map within the VGG network, namely height and width. \(\phi \left(\widetilde{x}\right)\) represents the output characteristic matrix of the generated image in Perceptual Network, and \(\phi \left(x\right)\) represents the output characteristic matrix of the real image in Perceptual Network.

In particular, we combine the original critic loss calculated by Wasserstein distance with perceptual loss as our loss function to optimize the GAN model. Our new objective can be expressed as follows:

where \({p}_{noise}\) denotes normal distribution, \({p}_{r}\) represents real plant data distribution. \({P}_{g}\) represents the data distribution of the generated image. \({\mathbb{E}}\) represents mathematical expectation. \({p}_{\widehat{x}}\) is defined implicitly as sampling uniformly along straight lines between pairs of points sampled from \({p}_{r}\) and the generator distribution \(G\left({p}_{noise}\right)\) . Enforcing the unit gradient norm constraint everywhere along these straight lines is sufficient. We train the discriminator and the generator by alternatively minimizing \({L}_{G}\) and \({L}_{D}\) .

4 Experimental Setup

4.1 experimental environment.

Our experiments are conducted on the graphics processing units (GPUs) of NVIDA GeForce RTX 3060Ti with 8 GB graphics memory size, 14 GHz memory clock, bit width is 256bit. In addition, the processor model of the computer is i7-12700 K, the memory size is 32 GB, and the operating system is Window 10. The model implementation is based on TensorFlow 2.0 framework, Integrated Development environment (IDE) is PyCharm. The main toolkits used are numpy, random, glob, imageio, math, time, os, etc. The main programming language used is Python 3.7.

4.2 Dataset

We conducted extensive experiments on two image datasets, “Flower” and “Animal”.

The “Flower” dataset consists of images of five different types of flowers: dandelions, sunflowers, tulips, daisies, and roses. Each category contains approximately 1000 images. It is worth noting that dandelions, daisies, and sunflowers belong to the family Asteraceae and share highly similar phenotypic features, which poses a challenge for our classification task.

The “Animal” dataset includes images of three animal categories: cats, dogs, and tigers, with 500 images per category. To facilitate the experiments, all images were resized to a uniform size of 64 × 64 pixels.

All the real images from the original dataset were included in the training of the GAN network. To evaluate the augmented effect of SSGAN, we also trained and tested the classifier using the augmented dataset.

The augmented dataset consists of 500 real images per category and 400 generated images per category. From each category, 100 randomly selected real images were used as the test set for the classifier, while the remaining data was used for training the classifier.

4.3 Hyperparameters

In contrast to other deep learning models, the training of a GAN requires iterative updates of the generator and discriminator, aiming to reach a Nash equilibrium state where both components have minimized their individual losses. Training is halted once the model reaches this equilibrium. At this stage, a lower loss value indicates superior model performance.

4.3.1 Learning Rate

We conducted experiments with different learning rates, and observed that excessively large learning rates led to significant oscillations in the model's performance. As the learning rate decreased, the oscillations gradually diminished, but the convergence speed also slowed down. We present the results of three different learning rates 10 –3 , 10 –4 , and 10 –5 to observe the model's training process, as shown in Fig. 3 . Figure 3 illustrates that when the learning rate was set to 10 –3 , the model exhibited significant oscillations. In comparison, the model converged faster when the learning rate was set to 10– 4 compared to 10 –5 . Consequently, we ultimately chose a learning rate of 10 –4 for our model.

Model training process with different learning rates

4.3.2 \({\varvec{\mu}}\) and \({\varvec{\lambda}}\)

In the loss function, we varied the hyperparameter μ and evaluated the model's training process. The symbols μ and λ represent hyperparameters in Eqs. 2 and 3 , where μ is a hyperparameter that controls the influence of the perceptual loss \({L}_{P}\) on the generator loss, and λ is a hyperparameter that controls the influence of the gradient penalty regularization term on the discriminator loss. Figure 4 illustrates the training results for different values of μ. Specifically, we tested μ with values of 1, 0.1, and 0.01. From the figure, it can be observed that when μ is set to 1, the model exhibits faster convergence but yields higher loss values. In contrast, when μ is set to 0.01, the model achieves faster convergence to the Nash equilibrium and demonstrates the lowest loss values compared to the case with μ set to 0.1.

Model training process with different values of μ in the loss function

In our experiments, we varied the hyperparameter λ in the loss function and examined the model's training process. Figure 5 presents the training results for different values of λ. Specifically, we tested λ with values of 1, 5, 10, and 15. From the figure, it can be observed that when λ is set to 1, SSGAN achieves the minimum loss value at the Nash equilibrium. Consequently, based on this observation, we determined the optimal hyperparameter settings for our model as a learning rate of 10–4, μ value of 0.01, and λ value of 1.

Model training process with different values of λ in the loss function

4.4 Evaluation Metrics

4.4.1 visualization of generated results.

The real visual feedback of the generated image is important metrics to evaluate the ability of model generation. This evaluation method will generate images for visual output, and compare them to observe the clarity of texture details, image diversity and whether pattern collapse occurs.

4.4.2 t-SNE

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a non-linear dimensionality reduction algorithm that is particularly suitable for reducing high-dimensional data to 2D or 3D while preserving the similarity in the joint probability distribution between the low-dimensional and original data. Let \({x}_{i}\) and \({x}_{j}\) represent points in the original space, and \({y}_{i}\) and \({y}_{j}\) represent their corresponding points in the low-dimensional space. The objective function \(Obj\) of t-SNE can be expressed as follows:

Here, \({p}_{ij}\) represents the Gaussian joint probability distribution between data points in the original data space, while \({q}_{ij}\) represents the corresponding joint probability distribution between points in the target space after dimensionality reduction. Specifically, \({q}_{ij}\) is computed using the Student’s t-distribution. The objective function, as defined in t-SNE, aims to minimize the Kullback–Leibler divergence between these two probability distributions, indicating the similarity between the distributions.

4.4.3 Objective Evaluation Metrics

Meanwhile, inception score (IS) and Fréchet inception distance (FID) are two other important indicators to measure the quality and diversity of the pictures generated by the GAN. IS evaluates the quality of the model from both image clarity and image diversity perspectives. But FID considers more the connection between the generated images and the real images. The larger the IS value, the smaller the FID value, and the better the expression effect. Their formulas for the calculation are as follows:

Time and space complexity are two basic metrics to measure the performance of network. This evaluation method separately calculates the number of parameters and the floating-point operations (i.e., FLOPs) to measure the complexity of the algorithm.

The smaller the spatiotemporal complexity metric, the less resources required for model training and the higher the model performance.

Parameter number

\({k}_{w}\times {k}_{h}\) represents the kernel size of the convolution layer; \({n}_{in}\) indicates the number of input channels and \({n}_{out}\) indicates the number of output channels. \(H\) and \(W\) represents the height and width of the output feature map.

4.4.4 Improvement in Classification Performance

In this evaluation method, ResNet-18 [ 11 ] is chosen as the classifier, and the augmented image set is used for the classification task. The validity of the model can be judged intuitively by comparing whether the classification accuracy and the precision are improved before and after the augmentation of the image set. The above metrics can be defined as follows.

\(TP\) , \(TN\) and \(FN\) represent the samples belong to True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN) of the category, respectively.

5.1 Ablation Study

In this section, we conducted detailed ablation experiments to demonstrate the individual effects of the pyramid connection module and the perceptual loss component on the overall model.

5.1.1 Generated Image Visualization

Checkerboard artifact refers to the grid-like pattern of varying color intensity that appears in generated images, resulting from uneven overlapping of deconvolution operations [ 20 ]. Figure 6 illustrates a comparison between the generated images of SSGAN before and after the introduction of pyramid connections. The left side of Fig. 6 shows the images generated by SSGAN without pyramid connections, while the right side shows the images generated by SSGAN with pyramid connections. It can be observed that the introduction of pyramid connections effectively alleviates the checkerboard artifacts.

Pyramid connection alleviates checkerboard artifacts

5.1.2 Evaluation of Generated Image Quality and Diversity

Table 1 presents the FID and IS scores of SSGAN, SSGAN without Perceptual Loss (PL), SSGAN without Pyramid Connection (PC) and SSGAN without Pyramid Connection and perceptual loss. The IS score measures the clarity and diversity of generated images, where a higher score indicates better performance. On the other hand, the FID score reflects the distance between generated and real images, with a lower score indicating better similarity.

According to Table 1 , the complete SSGAN achieved the best FID and IS scores, demonstrating that it generates images with the highest quality and diversity. The Perceptual Loss resulted in an FID reduction of 11 and an IS improvement of 1.58. The Pyramid Connection led to an FID reduction of 19.3 and an IS improvement of 1.7. The combination of perceptual loss and pyramid connections in SSGAN led to a decrease of 24.3 in FID and an increase of 2.17 in IS. This confirms the positive impact of both components in enhancing the overall performance of the model.

5.1.3 t-SNE Visualization

t-SNE is employed as a metric to assess the similarity between generated and original images in terms of their distribution. A well-clustered distribution of generated and original images in the t-SNE space indicates high-quality generated images. Moreover, if the generated images exhibit significant dispersion, it signifies a greater diversity in the generated image set. Figure 7 showcases the augmented results of several image classes in the “Flower” dataset.

T-SNE visualization results of ablation experiments

It can be observed that the generated images by SSGAN exhibit the highest overlap with the real images and demonstrate good dispersion. The SSGAN without perceptual loss generates images with lower dispersion, indicating a lower diversity in the generated image set. Similarly, the SSGAN without pyramid connection generates images with comparatively lower dispersion compared to the SSGAN.

5.2 Comparison Experiment

In this section, we trained the proposed method along with several classic approaches such as WGAN, SAGAN, DCGAN, and WGAN-GP on a small-sample dataset. We compared the augmented effects among these methods and demonstrated the effectiveness of the proposed approach. The augmented effects among these methods were compared, and the effectiveness of the proposed approach was demonstrated.

5.2.1 Generated Image Visualization

Figure 8 presents the generated images from several methods, providing an intuitive impression of their respective generation performances. It can be observed that SSGAN produces images with superior clarity, diversity, and finer details in terms of edges and textures compared to other methods. Following SSGAN, WGAN-GP and SAGAN exhibit relatively good generation results, while DCGAN and WGAN perform less favorably in generating high-quality images.

Visualization of generated images from several methods

5.2.2 t-SNE Visualization

The distribution of generated images in t-SNE space can reflect the quality and diversity of the images. A higher degree of overlap between the distributions of generated and original images in t-SNE space indicates higher image quality. Additionally, if the distribution of generated images itself exhibits good dispersion in t-SNE visualization, it indicates better diversity of generated images.

Figure 9 displays the t-SNE visualization results of the generated images by various methods on the “Flower” dataset. It can be observed that compared to other methods, SSGAN exhibits better dispersion in the distribution, indicating superior diversity in the generated images. Additionally, the distribution of SSGAN shows the highest degree of overlap with the distribution of the original data, confirming the highest quality of the generated images.

t-SNE Visualization of generated images from various methods

5.2.3 Quantitative Evaluation

We compared the generation performance of our proposed SSGAN with six classical GANs on the “Flower” dataset, and the results are shown in Table 2 , the optimal performance is highlighted in bold. Our SSGAN achieved state-of-the-art performance in terms of both FID and IS metrics. Specifically, the FID score decreased by 18.6 compared to the second-best method, and the IS score increased by 1.39 compared to the second-best method. This indicates that the images generated by SSGAN exhibit better clarity and diversity. Compared to other models, SSGAN has lower spatiotemporal complexity.

5.2.4 Classification Improvement

We utilized the augmented datasets to train the ResNet-18 [ 11 ] classifier and evaluated the improvement in classification performance. The training set of the augmented dataset consisted of two variations: 400 real images combined with 200 generated images, and 400 real images combined with 400 generated images. Table 3 presents the classification performance of the classifier trained on the augmented datasets using different methods, the optimal performance is highlighted in bold.

Our proposed SSGAN method demonstrates the best performance in enhancing the accuracy of downstream classification tasks. In both variations of the augmented datasets, it achieves state-of-the-art results in terms of Accuracy and Precision. Specifically, compared to the second-best method, SSGAN improves Accuracy by 2% and Precision by 2.3%.

5.3 Model Generalization

To demonstrate the generalization performance of our model, we applied several different methods to augment the “Animal” dataset and compared their generated results.

5.3.1 Generated Image Visualization

We randomly selected six images from each method’s generated image dataset for visualization, two images per class. The results are shown in the following figure.

Based on Fig. 10 , it is evident that the images generated by SSGAN exhibit the best clarity and edge texture features. WGAN-GP follows closely in performance.

Visualization of generated results by various GANs on the “Animal” dataset

5.3.2 Quantitative Evaluation

Similarly, we performed sample augmentation using various GAN methods on the Animal dataset and compared the corresponding generated images based on their FID and IS results. Please refer to Table 4 for detailed information, the optimal performance is highlighted in bold.

According to Table 4 , SSGAN also achieves state-of-the-art performance on the Animal dataset. Specifically, it exhibits a decrease of 6.6 in FID compared to the second-best method (WGAN-GP), and an increase of 3.2 in IS. This demonstrates the strong generalization capability of the proposed SSGAN method.

5.3.3 Classification improvement

We once again trained the ResNet-18 classifier using the augmented datasets generated by different methods and compared their performance. The results are shown in Table 5 , the optimal performance is highlighted in bold.

According to Table 5 , the classifier trained on the Animal dataset augmented by SSGAN achieved the best performance. It outperformed the second-best method, WGAN-GP, with improvements of 0.53% in Accuracy and 0.5% in Precision. The classifier’s overall performance on the Animal dataset was generally higher than on the Flower dataset. This can be attributed to the Animal dataset being a relatively simpler classification task. However, the performance improvement achieved through training on the augmented dataset was limited. This further demonstrates the good generalization performance of SSGAN.

5.4 Model Robustness

Robustness in deep learning refers to the model’s ability to maintain stability and effectiveness in the face of subtle modifications or perturbations to network parameters, as well as when input data is affected by noise (which may obscure critical information). Robustness evaluation is an important consideration to ensure that a model can maintain high performance when confronted with various data perturbations and noise. These approaches collectively contribute to evaluating the robustness of deep learning models:

Data Distribution Shift Assessment: In practical application scenarios, deep learning models may encounter data distributions that differ from those in their training data. Therefore, evaluating a model's robustness to data distribution shifts is of paramount importance. Our training data consists of noise that adheres to a normal distribution. To assess the model's performance across various distributions, we introduce noise conforming to different data distributions, such as Poisson distribution and random distribution, as input. This enables us to evaluate the model's performance under diverse distribution settings.

Noise and Interference Robustness Evaluation: Assessing the model’s robustness to various types of noise and interference is essential. Random noise can be added on top of the original input, and the model's performance change can be observed.

Sensitivity Analysis: Sensitivity analysis evaluates the model’s sensitivity to variations in input parameters. Analyzing the response of the model to small changes in input parameters helps understand the model's responsiveness to input variations. In SSGAN, we set hyperparameters μ and λ to 0.01 and 1, respectively. By perturbing the hyperparameter settings, the model's robustness can be assessed.

Based on the three aspects mentioned above, we conducted comparative experiments, and the experimental results are presented in Tables 6 and 7 .

Table 6 presents the performance of SSGAN when different distributions are used as inputs. It can be observed that using different data distributions as inputs has minimal impact on the model's performance. Additionally, adding random noise on top of the training data distribution also has very little effect on the model's performance. This demonstrates the robustness of the model to data distribution shifts and noise interference.

Table 7 shows the performance of SSGAN with different hyperparameter settings. It can be observed that the further the hyperparameters are set from their optimal values, the faster the model's performance decreases. However, the model continues to function normally without any crashes. This demonstrates that the model maintains stability and effectiveness when facing minor modifications and perturbations in network parameters, highlighting its robustness.

6 Discussion and Conclusion

In real-world scenarios, the problem of small samples in image datasets is widely prevalent. This limitation hinders the accuracy of recognition tasks, particularly in applications based on deep learning techniques such as fault image detection, critical medical image diagnosis, and endangered species recognition. Small sample image augmentation techniques can augment the image dataset, thereby improving the accuracy of downstream image learning tasks. Thus, these techniques hold significant research value.

The paper proposes a novel image small sample augmentation method called SSGAN based on semantic similarity. The key innovations are as follows:

The design of a relatively shallow GAN backbone structure to adapt to small sample sizes. This allows the model to effectively learn from limited data.

The introduction of a pyramid connection structure to enhance the model's feature extraction capability and alleviate the checkerboard artifact issue.

The optimization of the loss function using an image high-dimensional semantic feature filtering module, which enhances the model’s learning ability by focusing on important semantic features.

These innovations collectively contribute to the effectiveness of the SSGAN method in addressing the challenges posed by small sample sizes in image augmentation tasks. We conducted extensive ablation and comparative experiments on the “Flower” dataset. The results of the experiments demonstrate that SSGAN achieves state-of-the-art performance in the task of small sample image enhancement. It outperforms the best-known methods by improving the FID and IS metrics by 18.6 and 1.4, respectively. The dataset enhanced by SSGAN contributes to achieving state-of-the-art performance in downstream classification tasks, with a 2.2% increase in accuracy compared to the best-known methods. In addition, transfer experiments were conducted on the 'Animal' dataset, and promising results were achieved, demonstrating the good generalization performance of the model. Through comparative experiments, we demonstrated that the model exhibits good robustness. Due to hardware limitations, we did not perform augmentation experiments on high-resolution images. In the future, we will continue to research methods for augmenting small sample high-resolution images.

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Congcong Ma, Jiaqi Mi, Wanlin Gao & Sha Tao

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CM: Conceptualization, Methodology, Software, Visualization, Writing—Original Draft. JM: Data curation, Validation. WG: Formal analysis, Resources, Supervision, Writing—review & editing. ST: Formal analysis, Investigation, Supervision, Writing—review & editing.

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Ma, C., Mi, J., Gao, W. et al. SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation. Neural Process Lett 56 , 149 (2024). https://doi.org/10.1007/s11063-024-11498-z

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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?

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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