unexpected results in research

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• Charlesworth Author Services
• 14 December, 2020

It’s every researcher’s worst nightmare: Your data isn’t yielding the results you had expected, or your data is showing ‘negative’ results that completely negates your research aims and contradicts your hypotheses.

You might feel like your entire research project is falling apart and that you cannot move forward. However, rest assured that there are always ways to deal with unexpected data that will not only salvage your research, but also make important contributions to your field.

Results are neither ‘negative’ nor ‘positive’

First of all, try not to think of data and results as being either ‘negative’ or positive’. In research, results can always still be useful in some way by telling you something important or interesting about either your data set, methods or methodology.

If a ‘negative’ result means that your data disproves your hypothesis or does not answer your research questions in the way you expected, this does not necessarily mean that the results must be discarded or rendered useless. It is still possible to write up and publish this research, and to extract important information from the results you have obtained.

After all, ‘negative’ or unexpected results are still results – trace your research backwards and try to examine what it is that caused this result. You might find something very interesting and insightful in the methods you used. Or you might discover that the results tell you something novel, even groundbreaking, about that particular data set or the issue you are investigating.

Being able to clearly demonstrate and explain how and why a method does not work, or why a particular method produces undesirable outcomes, is itself a valuable contribution to the field. For example, in research around vaccines or medical treatments, having something not work out is not considered failure. Instead, it can help researchers eliminate what is not effective, narrow down the scope of investigation, and allow them to rule out certain methods so they can proceed to work with others.

Remember that throughout the history of scientific research, unexpected anomalies in results have often brought up surprising new discoveries or prompted scientists to investigate other novel issues. In fact, sometimes the discoveries are the anomalies or accidental ‘mistakes’ from another research project.

Talk it out

It is not uncommon for PhD students to panic when they get unexpected results. They might then try to start their project from scratch or give up altogether. However, before you resort to any extreme measures, it is really important that you speak to your supervisor and/or colleagues from your research project or department.

Your supervisor should be able to offer you more focused advice about what you can do to effectively address the specific issues arising with your data. You can talk to them about what you did during your data collection (in either labwork or fieldwork) and they can help you untangle where things may have gone wrong, how to recollect more data if necessary (and if you have time), and what else you can do at this stage to move forward with the results that you have.

Getting different perspectives and troubleshooting your process with others might also reveal that the issues you are facing with your data are not as disastrous as you think. When you talk to others, they can give you new ideas for how you can work with the data you currently have or offer suggestions for what else you can do in your situation.

It is important not to try to solve everything on your own. Remember that you have the support of your supervisors and the research community in your department and university. Don’t fear being judged for having problems with your data. All researchers understand that it is common for difficulties and issues to arise with research and data, and they are more than likely to have good advice and reassurance to help you.

Prove your mettle

Although it may not seem like it at the time, having to deal with difficult, unexpected results is an excellent opportunity for you to prove your strengths and resourcefulness as a researcher.

As you address diverse and unexpected issues arising in your research, you demonstrate your knowledge of the field and discipline. You show that you understand a range of existing theory, methodology and analytical tools, and showcase your ability to employ and extract from previous work to manage your own research – whatever the challenges. By doing this, you show how resourceful, adaptable and versatile you are as a researcher.

You would also be exercising clear researcher reflexivity, by presenting conscientious awareness of how your decisions and actions affect your data, your analysis and the overall directions of your research. You will be able to show that you have a thorough understanding of what you have done, how you have done it, and what you could have done differently.

These are all excellent and highly desirable traits of an effective researcher, and being able to exercise and prove these qualities is often more important than the findings themselves. Remember that you are being assessed not just on the research you produce, but also in your abilities to do rigorous, thoughtful research. So, take the focus off the ‘negative’ results at hand got and consider how you will effectively respond to and adapt your research instead.

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What to do with unexpected research results

When research results are not as expected, it can be challenging for the investigator, especially for those just starting out. However, as Dr. Thomas Kosten, professor and the Jay H. Waggoner Endowed Chair in the Menninger Department of Psychiatry and Behavioral Sciences at Baylor College of Medicine, reminds us, some of the best work can come from failed research.

“Most research projects have unexpected results and often the main hypothesis is not supported by the data collected. However, the most exciting new ideas are generated in failed experiments or clinical trials where, as you examine the data more and more carefully, you can develop new insights into the disease and its potential treatment,” said Kosten.

Kosten’s work focuses on addiction treatment and its human neurobiology and includes pharmacogenetics, immunology and neuroimaging. He and his collaborators have been working for the past 20 years on an anti-cocaine addiction vaccine. They have also developed nicotine, opiate and methamphetamine vaccines.

A previous study in 2008 found that about one-third of those who received the cocaine vaccine had antibody levels that were sufficiently high enough to block the euphoric effects of cocaine. His latest study of 300 patients at six sites across the United States found that more than 60 percent of those who received a cocaine vaccine reached antibody levels at a therapeutic range, had increased treatment retention and more patients attaining at least two weeks of abstinence, but the overall amount of cocaine use did not decrease compared to those who had lower antibody levels and those who received a placebo

“The fact that more than 60 percent of patients had antibody levels in the therapeutic range is promising,” said Kosten. “However, the fact that the cocaine use did not decrease reflected two problems. First these new study patients appeared to be significantly less motivated to stop using cocaine and most tried to override the antibody blockade by using more cocaine. Second, those who attained the robust antibody response were also the same patients who had substantially greater amounts of cocaine use during the baseline period of 10 weeks.”

Kosten hopes in his next trials to improve the vaccine with a new and better carrier protein and adjuvant. He also hopes to recruit a more motivated population of cocaine dependent patients who can remain abstinent from cocaine for at least two weeks before entering the study. Those in the most recent study had at least 30 percent of their urine samples test positive for cocaine during the month before starting the study, and none were cocaine-free for more than five days before starting the vaccination.

“As a researcher, you just keep trying to improve your product – in this case, the vaccine – and more carefully target your patient group,” said Kosten.

His advice for young scientists?

“Extract every possible insight from your data collection and remember that you can never analyze your data too much or be too careful in looking for new leads,” he said. “Do not be easily discouraged when results are disappointing. The glass is always half-full.”

Kosten is also with the Michael E. DeBakey Veterans Affairs Medical Center in Houston.

 – By Dipali Pathak

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Negative Results: The Data are the Data

The data are the data. We have all used this line repeatedly while conducting research and while teaching others how to do research. Generally these words are expressed with a frustrated grimace, a shaking of the head, a pulling of the hair or an exasperated sigh. Negative data are a regular result from good research questions, solid experimental designs, and weeks/months of reliable data collection. The results are not what we wanted, we often initially cannot explain them, but as we have all been trained, "the data are the data". As frustrating as it is to obtain negative results after weeks/months of work, experienced researchers are aware that negative results are a pretty common occurrence in research. However, for an inexperienced researcher or a junior scientist, negative results can evoke negative emotions such as fear of failure or fear of disappointing their mentor.

The phrase "negative results" is actually somewhat ambiguous. The phrase is generally thought to describe results that are inconclusive due to a failure to reach statistical significance. However, unexpected results or results contrary to the reported literature are also often described as negative. Furthermore, regarding inconclusive results, there can be multiple reasons that data fails to reveal a clear trend or effect. As stated above, it could be that a researcher's hypothesis is incorrect and thus, their experimental manipulations fail to reveal an effect. However, it could also be that the experiment failed to produce clear positive results because there are experimental factors (i.e., drug side effects) that were not considered and thus not controlled by the experimenter. These extraneous factors can contribute notable variability to the data. Finally, and sadly, unexpected negative data can be a reflection of poor or sloppy research practices due to inexperience and/or poor training or supervision of the research team.

Therefore, upon the frustrating revelation that one's data is negative, it is critical that a researcher takes a deep breath and assesses both their team and their data. Assessing the team for awareness of and adhering to experimental protocol can be relatively easy. Simply sit down the members of the team and ask them to describe their procedures. Attentively listen to their description and then note any inconsistencies between their report and the defined protocol and then work to correct any mistakes or omissions. It is frustrating to realize after the completion of an experiment that mistakes have resulted in wasted time and resources but these problems can generally be easily corrected and a valuable lesson will be learned. Furthermore, the team needs to realize that experimental protocol shift is not uncommon, especially when there is regular personnel turnover combined with poor direction or supervision. Learn the lesson, correct the problem and clean up experimental protocols.

However, if you assess your team and cannot find any procedural inconsistencies, the data should also be assessed for both expected and unexpected observations and trends, even if these trends are somewhat variable. Researchers often detect highly variable, but repeatable trends or observations in their data. These puzzles often require researchers to consider alternate factors that may be contributing to data variability so that they can refine their experimental questions and/or protocols. Delving into these puzzles is often the fun part of research. However, it can also be a very costly phase of discovery. It takes time, resources and considerable risk to discover what these undetected factors may be. And a great deal of effort may never see the reward of publication due to the large volume of inconclusive data that is produced in the process of discovery due to the bias against publishing negative data.

During my undergraduate and graduate training, there were research teams in our department focusing on fetal alcohol syndrome (FAS). Through coursework and journal clubs, I learned a bit about the difficult experimental history surrounding fetal alcohol syndrome research. Although researchers, clinicians and even teachers recognized commonalities and consistent observations that were suggestive of a disorder, it took a great deal of time and effort for the experimental data to reveal conclusive FAS data and a clinical profile. The reason that this early work was so difficult is that there were many factors that needed to be worked out in the research. The frequency of binge drinking during pregnancy, the volume of alcohol consumed, the developmental phase of fetal exposure to binge drinking as well as the genetic susceptibility to alcohol exposure all impacted the type and magnitude of impairment produced by alcohol exposure during fetal development. All of these factors needed to be experimentally addressed before researchers successfully and reliably revealed a reliable and reproducible link between fetal alcohol exposure and developmental problems in children. Each of these factors contributed to variability in the data and for a long time, the variation or "noise" in the data hindered recognition of the syndrome. However, throughout these difficulties with the research, scientists persevered through file cabinets full of negative results while recognizing that there was meaning hidden in their observations and inconclusive data trends.

Difficult success stories, such as that experienced by early FAS researchers, serve as a reminder that the agenda of original research is to conduct experiments to explore and explain the unknown. As such, our data often reveal that there are many more unknowns that we have not yet considered. These are critical phases of research and therefore, it is unfortunate that science often fails to reward the effort that goes into acquiring these interim negative or inconclusive results. As you peruse the literature, it is quite uncommon to come across publications that report negative results. Positive results are much more likely to be published as compared to negative results. Most researchers recognize the ethical and scientific importance of sharing and publishing negative results. However, like many issues in responsible research, recognizing that there is a flaw in research practices does not readily or rapidly elicit a change in research practices or resources.

Furthermore, although many researchers agree that there should be increased opportunities to publish negative results, there are arguments against these practices. As stated above, one of the explanations for negative or contrary research results can be due to inexperienced researchers conducting poor research. If this is the case, it is arguable that ready availability to publishing opportunities may fail to reveal and "weed out" poor research practices. However, most researchers do not want wish to publish poor work. Rather, researchers are very particular about work that they want to publish and thus, only want the opportunity to publish negative results when they are collected through well designed and reliably conducted research. Researchers can be so particular about work that they choose to publish that I have had colleagues express reluctance to publish even positive results when they did not trust the reliability or integrity of their team members that conducted the work.

An additional reservation regarding the opportunities to publish negative results addresses the potential for untrained readers to misinterpret data that is consistent with the null hypothesis to be "proof" of the null hypothesis. Scientists are trained that the goal of research is to disprove hypotheses and that data can never prove a hypothesis to be true. However, when untrained readers peruse the scientific literature, they often misinterpret a single research report as proof of a causal or correlative relationship rather than a piece of evidence that fails to disprove a hypothesis. These types of untrained or premature interpretations can cause a great deal of harm if they reach a popular audience. An example is the discredited and retracted Wakefield (1998) report of a link between MMR vaccine and the development of autism. Even though this single study was proven to be fraudulent and that follow-up studies have failed to reveal any link between vaccines and autism (Taylor et al., 1999; Madsen et al, 2002) the popular myth asserting a link between vaccination and autism persists. Scientists are trained to treat even positive results with skepticism. However, public or popular misinterpretation of negative or conflicting results could potentially impact public opinion and/or funding opportunities, especially during difficult phases of discovery as was described above for fetal alcohol syndrome researchers. Scientists are trained to expect conflicting reports and critically assess the methodology and results of conflicting reports to find a seed of truth and as that seed grows, the scientific literature self-corrects. However, the untrained audience can misinterpret these conflicts as wasteful, fail to recognize that discovery is a process and public opinion will often fail to recognize that large amounts of new data are a critical part of the correction process.

The Wakefield fraud case is a good example of an inaccurate positive result being proven false (false positives). In contrast, negative results can also be proven false (false negatives). However to prove any results to be false, one must have access to the published results. I clearly remember during my graduate training, during a late night study session, the night before a statistics exam, a friend of mine sounded off in frustration regarding everyone's focus on using statistics to avoid false-positive results. She expressed that this concern was ridiculous because science is self-correcting, thus a published false-positive would be replicated and failure to replicate would correct the literature. She expressed that our concern should be focused on false-negative results because once published data failed to support a hypothesis, the hypothesis would be abandoned because researchers would not waste time and resources working on negative findings. Therefore, she asserted, good ideas could rapidly and mistakenly be abandoned once a negative result was reported, even if that negative result was false.

In a way she was correct. However, her rant was based on a few assumptions. First, she assumed that a false-negative result would be reported in the literature. As stated above, there are file cabinets full of negative results that have never been submitted for publication and even more that have been submitted for publication only to have been rejected. Second, although the ideal model is that the literature is self-correcting, that assertion is based on the assumption that follow-up studies, failing to replicate false-positive results, are actually submitted for publication and that these negative or contrary results are actually published. However, due to the tendency of researchers to hesitate wasting time and resources constructing manuscripts that largely contain negative or contrary results and the publication bias toward rejecting publications containing negative or contrary results it is hard to readily assert that our system of scientific reporting is in fact, self-correcting. Failing to publish negative results impairs the self-correcting design of research.

Independent of the above controversies, most researchers agree that "the data are the data" and as such, negative results are as important as positive results. We need those negative results to alter our hypotheses and redesign our experiments. We need access to negative results to guide us on the path to positive results. Therefore, we need to address the bias against publishing negative results and come up with a system that enables us to share those valuable negative results that were produced from solid research questions and reliable data collection techniques. Historically, much of this data has been shared unofficially. We meet at conferences and discuss our ideas and frustrations and we often find that our colleagues are addressing similar questions and encountering similar frustrations. However, those casual conversations often do not reveal all aspects of their methodology so that they can be utilized to modify experimental design. Thus, there needs to be a focus on increased resources that enable researchers to share their negative results so that wasted time and resources are minimized.

There are valid concerns regarding an agenda to increase opportunities to publish negative or inconclusive research results, but many of these concerns can be addressed through the system of peer review that currently exists in academics. In contrast, maintaining practices that are biased against publication of inconclusive or contrary results has too much potential to negatively impact scientific progress. These practices could impact research decisions made by junior scientists, working to build a career, because they may perceive positive results as more valuable than maintaining objectivity. Furthermore, publication bias against reporting negative results also limits researcher access to evolving data and methodology and potentially biases the information that is available in the literature. Finally, bias against publishing negative results slows the self-correcting virtue of science. It is great to encounter increased dialogue regarding the importance of negative data and discussion on how best to share the methods and results associated with inconclusive yet intriguing data. These discussions are a first step toward improving our system of reporting and acknowledgement for the efforts of investigators that struggle with the difficult phases of discovery.

Marianne Evola is senior administrator in the Responsible Research area of the Office of the Vice President for Research. She is a monthly contributor to Scholarly Messenger.

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Q&A: What happens when you discover unexpected results in research

In a long and distinguished career, QCGC Research’s head of research Professor Andreas Obermair has never once had to deliver unexpected results for a clinical research trial – until LACC. Here he explains the value of the unexpected.

Q: How did you react when you heard there were unexpected results for the LACC trial ?

A: I was shocked and deeply concerned. I am a researcher, but I’m also human. As a trial leader, I have to be ‘blinded’ to the outcomes while the team conducts the trial so that I don’t influence the results. So, this made the news about LACC even more difficult to comprehend. It was unexpected.

To get a clinical trial up and running, you demonstrate ‘equipoise’. That means you must justify that the proposed treatment, in this case, laparoscopic surgery for early-stage cervical cancer, is theoretically better and at least no worse than the alternative. You can’t prove it, and that’s why you need to conduct the trial, but you have a sense of the likely outcome.

Q: How did the LACC trial come about?

A: QCGC Research had shown the benefits of a laparoscopic approach with the LACE trial  for endometrial cancer. We wanted to investigate if the same was true for cervical cancer. We had an international team that thoroughly reviewed the literature and prepared the protocols. The trial involved 33 gynaecological oncology centres in 14 countries and enrolled 631 women. We expected, and of course, would have preferred different results.

Q: What happened after you realised there was a problem?

A: Based on the recommendation of the LACC Safety Committee, we called for trial recruitment to be placed on hold. The trial team then began an intensive review of the results. At this stage, although I had no visibility of the problem, I could see that the issue was serious and immediate action needed to be taken for the safety of future patients. During a three-month period, the trial team did every check required to make sure there was no way to misinterpret the data. All due diligence was done.

I would like to make special mention of the critical role that the independent data safety committee played in alerting the trial team about a patient safety issue. The committee’s role is to ensure the safety of all patients on the trial.  The LACC Safety Committee absolutely fulfilled this role and acted in the best interests of trial patients.

Q: Did the review find a mistake?

A: No unfortunately, as the review results came in, it was clear there was no mistake. Laparoscopic surgery was not the best treatment option for early-stage cervical cancer. In fact, the research showed survival outcomes were worse. We had to share the news  quickly with the community and empower as many women as we could with this new and vital information.

Q: As a researcher, what’s it like to explain that your original assessment was wrong?

A: It was very difficult to do, and many medical professionals reacted with anger. But I’m such a fierce proponent of laparoscopic surgery that people took notice – here I was telling them that for early-stage cervical cancer, this approach was not the answer.

Although it is not what we expected or hoped, this result is critically important. It shows yet again that clinical trials are irreplaceable. You cannot get high-quality evidence any other way. To write the textbook for gynaecological cancer treatment, you must conduct clinical trials.

Q: Do unexpected research results lead to other innovations and discoveries?

A: Yes, for sure. In the LACE trial, we learnt that even with laparoscopic surgery for endometrial cancer, obese women do not do well. This led us to the FeMMe trial where we are investigating a kinder and more effective treatment for these women.

Also, during LACE, we established that although laparoscopic surgery was the preferred treatment option, surgeons weren’t using it. That lead to the LIgHT study , which showed surgeons needed greater training support.

Q: If LACC needs further studies to understand the results, will you take it on? 

A: After a decade of LACC, we will still be writing up the manuscripts for another five years. Someone else may take up further research related to LACC, but my energy is focused on two potential trials for different gynaecological cancers. The first is a follow-up to FeMMe and the second is about the role of sentinel nodes in the treatment of uterine cancer. I will never walk away from research – it is my opportunity to make a real difference.

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How to Write About Negative (Or Null) Results in Academic Research

Researchers are often disappointed when their work yields "negative" results, meaning that the null hypothesis cannot be rejected. However, negative results are essential for research to progress. Negative results tell researchers that they are on the wrong path, or that their current techniques are ineffective. This is a natural and necessary part of discovering something that was previously unknown. Solving problems that lead to negative results is an integral part of being an effective researcher. Publishing negative results that are the result of rigorous research contributes to scientific progress.

There are three main reasons for negative results:

• The original hypothesis was incorrect
• The findings of a published report cannot be replicated
• Technical problems

Here, we will discuss how to write about negative results, first focusing on the most common reason: technical problems.

Writing about technical problems

Technical problems might include faulty reagents, inappropriate study design, and insufficient statistical power. Most researchers would prefer to resolve technical problems before presenting their work, and focus instead on their convincing results. In reality, researchers often need to present their work at a conference or to a thesis committee before some problems can be resolved.

When presenting at a conference, the objective should be to clearly describe your overall research goal and why it is important, your preliminary results, the current problem, and how previously published work is informing the steps you are taking to resolve the problem. Here, you want to take advantage of the collective expertise at the conference. By being straightforward about your difficulties, you increase the chance that someone can help you find a solution.

When presenting to a thesis committee, much of what you discuss will be the same (overall research goal and why it is important, results, problem(s) and possible solutions). Your primarily goal is to show that you are well prepared to move forward in your research career, despite the recent difficulties. The thesis defense is a defined stopping point, so most thesis students should write about solutions they would pursue if they were to continue the work. For example, "To resolve this problem, it would be advisable to increase the survey area by a factor of 4, and then…" In contrast, researchers who will be continuing their work should write about possible solutions using present and future tense. For example, "To resolve this problem, we are currently testing a wider variety of standards, and will then conduct preliminary experiments to determine…"

Putting the "re" in "research"

Whether you are presenting at a conference, defending a thesis, applying for funding, or simply trying to make progress in your research, you will often need to search through the academic literature to determine the best path forward. This is especially true when you get unexpected results—either positive or negative. When trying to resolve a technical problem, you should often find yourself carefully reading the materials and methods sections of papers that address similar research questions, or that used similar techniques to explore very different problems. For example, a single computer algorithm might be adapted to address research questions in many different fields.

In searching through published papers and less formal methods of communication—such as conference abstracts—you may come to appreciate the important details that good researchers will include when discussing technical problems or other negative results. For example, "We found that participants were more likely to complete the process when light refreshments were provided between the two sessions." By including this information, the authors may help other researchers save time and resources.

Thus, you are advised to be as thorough as possible in reviewing the relevant literature, to find the most promising solutions for technical problems. When presenting your work, show that you have carefully considered the possibilities, and have developed a realistic plan for moving forward. This will help a thesis committee view your efforts favorably, and can also convince possible collaborators or advisors to invest time in helping you.

Publishing negative results

Negative results due to technical problems may be acceptable for a conference presentation or a thesis at the undergraduate or master's degree level. Negative results due to technical problems are not sufficient for publication, a Ph.D. dissertation, or tenure. In those situations, you will need to resolve the technical problem and generate high quality results (either positive or negative) that stand up to rigorous analysis. Depending on the research field, high quality negative results might include multiple readouts and narrow confidence intervals.

Researchers are often reluctant to publish negative results, especially if their data don't support an interesting alternative hypothesis. Traditionally, journals have been reluctant to publish negative results that are not paired with positive results, even if the study is well designed and the results have sufficient statistical power. This is starting to change— especially for medical research —but publishing negative results can still be an uphill battle.

Not publishing high quality negative results is a disservice to the scientific community and the people who support it (including tax payers), since other scientists may need to repeat the work. For studies involving animal research or human tissue samples, not publishing would squander significant sacrifices. For research involving medical treatments—especially studies that contradict a published report—not publishing negative results leads to an inaccurate understanding of treatment efficacy.

So how can researchers write about negative results in a way that reflects its importance? Let's consider a common reason for negative results: the original hypothesis was incorrect.

Writing about negative results when the original hypothesis was incorrect

Researchers should be comfortable with being wrong some of the time, such as when results don't support an initial hypothesis. After all, research wouldn't be necessary if we already knew the answer to every possible question. The next step is usually to revise the hypothesis after reconsidering the available data, reading through the relevant literature, and consulting with colleagues.

Ideally, a revised hypothesis will lead to results that allow you to reject a (revised) null hypothesis. The negative results can then be reported alongside the positive results, possibly bolstering the significance of both. For example, "The DNA mutations in region A had a significant effect on gene expression, while the mutations outside of domain A had no effect. Don't forget to include important details about how you overcame technical problems, so that other researchers don't need to reinvent the wheel.

Unfortunately, it isn't always possible to pair negative results with related positive results. For example, imagine a year-long study on the effect of COVID-19 shelter-in-place orders on the mental health of avid video game players compared to people who don't play video games. Despite using well-established tools for measuring mental health, having a large sample size, and comparing multiple subpopulations (e.g. gamers who live alone vs. gamers who live with others), no significant differences were identified. There is no way to modify and repeat this study because the same shelter-in-place conditions no longer exist. So how can this research be presented effectively?

Writing when you only have negative results

When you write a scientific paper to report negative results, the sections will be the same as for any other paper: Introduction, Materials and Methods, Results and Discussion. In the introduction, you should prepare your reader for the possibility of negative results. You can highlight gaps or inconsistencies in past research, and point to data that could indicate an incomplete understanding of the situation.

In the example about video game players, you might highlight data showing that gamers are statistically very similar to large chunks of the population in terms of age, education, marital status, etc. You might discuss how the stigma associated with playing video games might be unfair and harmful to people in certain situations. You could discuss research showing the benefits of playing video games, and contrast gaming with engaging in social media, which is another modern hobby. Putting a positive spin on negative results can make the difference between a published manuscript and rejection.

In a paper that focuses on negative results—especially one that contradicts published findings—the research design and data analysis must be impeccable. You may need to collaborate with other researchers to ensure that your methods are sound, and apply multiple methods of data analysis.

As long as the research is rigorous, negative results should be used to inform and guide future experiments. This is how science improves our understanding of the world.

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Reporting an Unexpected Adverse Event

Research is not always predictable; in fact, some of the best discoveries have happened when things didn't go according to plan. However, there are instances in which you need to report unexpected events, particularly if the results are adverse. The purpose of reporting unexpected adverse events is not to be punitive but rather to provide an opportunity for the principal investigator to revise the protocol to prevent further issues and for the Board to understand where they can better assist researchers in the future.

The following three categories define undesirable research events. These definitions are important to understanding what needs to be reported to the IRB-SBS and the Office for Human Research Protections (OHRP).

Unexpected Event

An unexpected event is an event that is not listed as a risk in the protocol and/or consent forms or was not previously documented at the intensity/frequency observed in your study population. The unexpected event also places the participant at greater risk than was previously known or recognized. For example, as a researcher in a typical school, you are interviewing children about a math test. During one of the interviews, the student tells you that his dad abuses him when he does poorly in school. Abuse is not a topic that you intended to cover in your study and the possibility of talking to students about abuse was not listed as a risk in your protocol. However, you are obligated to report child abuse and this is an unexpected event that changes the level of risk to the participant.

Adverse Event

An adverse event is any “undesirable and unintended, although not necessarily unexpected, effect of the research occurring in human subjects as a result of (a) the interventions and interactions used in the research; or (b) the collection of identifiable private information under the research” (Adapted from the 1993 OPRR IRB Guidebook). In other words, a participant is harmed as a result of participating in the study but the harm was described as a possibility in the protocol. For example, your study involves clinically depressed participants with a high suicidal rating. After attending intervention therapy as part of the study, one of the participants later in the evening reports a suicide attempt. This event was described as a risk in the protocol and in the consent form.

Serious Adverse Event

The potential harm for most studies fits in a scale of severity as related to probability. For example, the risks for a study measuring one’s happiness after exercising (low-impact moderate jogging) might include fatigue and soreness. Depending on the athleticism of the participants, the probability may be high for this adverse event to occur, though for normal, healthy adults this would not be considered a severe adverse event. A serious adverse event would be an event where the probability is not high for the event to occur and it resulted in harm to the participant. Continuing with the exercise example, one of the participants trips while performing the exercise and sprains an ankle, resulting in the need for medical assistance. Although the event is serious, it is still within the defined level of risk outlined in the protocol. However, if five of the fifteen participants receive the same injury, the frequency of the event changes the probability predicted in the protocol. The protocol should be reviewed to determine if the procedures need to be altered to better protect participants (i.e. select another jogging route) and if participants need to be better informed about the study (i.e. increased risk for injury, advice for safe jogging).

Adverse Event Related or Possibly Related to the study

It is easy to develop examples where an adverse event is clearly related to a study. However, it is not always clear that an injury or harm is directly related to a study and may require your best assessment of the situation. If you have questions about a potential situation, please contact our office. We would be happy to provide consultation.

Noncompliance Event

Noncompliance and more specifically, a "protocol violation," results from any change or departure from the study design or procedures of a research project that is not approved by the IRB-SBS prior to its implementation. Also, a protocol violation is thought to be any departure from established code of ethical conduct from the researcher's professional organization, or from applicable local, state, national and/or international regulations, policies and procedures. For example, a data recording procedure is not followed properly and confidential data about a participant is accidentally published on a website, or a laptop is stolen with participants’ social security numbers and home addresses, putting the participants at risk for identity theft. The act may be an unexpected problem or misfortune, a careless act or mistake, or a deliberate act against what the Board recommended.

In addition, failure to properly maintain a protocol is also considered a noncompliance event. For example, letting a protocol lapse without submitting a continuation request or modifying a protocol and failing to notify our office of the change are considered noncompliance events. Repeated disregard for maintaining a protocol is a serious noncompliance event. It is important that you keep accurate records and an updated protocol in order to maintain compliance with the IRB-SBS and federal regulations. If the study is complete, don't let it lapse but close the study instead.

OHRP recognizes that adverse events are a normal part of conducting a study and while they should be recorded and monitored as part of the data collection process, they don’t necessarily need to be reviewed by the IRB and OHRP. OHRP does define three categories where reporting an adverse event is necessary:

• Adverse events that are serious, unexpected, and related or possibly related to participation in the research.
• Serious adverse events that are expected in some subjects, but are determined to be occurring at a significantly higher frequency or severity than expected.
• Other unexpected adverse events, regardless of severity, that may alter the IRB’s analysis of the risk versus potential benefit of the research and, as a result, warrant consideration of substantive changes in the research protocol or informed consent process/document.

Essentially, if the resulting event changes the level of risk that was previously approved by the Board, the Board and researcher needs to revisit the protocol’s procedures and recommend appropriate changes to accommodate the new level of risk. These changes could include changes to the protocol, consent forms, and other documents used in the study. If the protocol received an expedited review or was exempted in the initial review (because the protocol was considered minimal risk at the time), a change in risk may require that the protocol receive full board review for any future reviews. In addition, the Board has the authority to terminate or suspend approval for a study if they feel that the risk to participants is too great for the study to continue.

Noncompliance events  that change the probability of harm to a participant must be reported to the IRB, even if a participant was not harmed as a result of the problem.

• Locate the protocol associated with the unexpected adverse event in the iProtocol Protocol Management page. Select the "create a copy" link below the protocol's title.
• Select the first radio button "I am copying in order to submit a Modification..."
• You will see a “Protocol was successfully updated” message. Click on the “return to protocol management” link to return to the Protocol Management page to find the new copy of the protocol. 
• Select "edit" below the new copy of the protocol.
• Review the protocol. Please note that you can edit the protocol if needed but make sure to mark "yes" to the "Modifications" question below the "Continuation" question.
• In the "Unexpected Adverse Event" section near the end, complete the questions regarding the unexpected adverse event.
• When you are done making edits, select "submit" at the bottom of the page. In order for the submit button to be revealed, all of the "required" fields must be complete.

Trying to make a copy of the protocol for an unexpected events report but iProtocol won't allow it? iProtocol only allows one "in development" copy of the protocol for the same protocol number. Look for the protocol copy in the Protocol Management page. If you don't see it in your Protocol Management page, check the  Trash/Recycle Bin  for a copy of the protocol. Once you locate the modification copy, you have two options:

• Revise the copy that you already made for your current unexpected events report needs and submit it.
• Move the copy to the "Trash/Recycle Bin" and select the "permanent delete" button to delete the version (see  Trash/Recycle Bin  for directions). Once the copy is deleted, you can create a new copy from the most recently approved version of the protocol. 

If you are completing the protocol for the first time and you do not need to report an unexpected adverse event, you should mark “no” to the first question and continue with the rest of the protocol. If you need to report an unexpected adverse event, mark "yes."

Did a negative event associated with the research occur and does it meet one of the following conditions:

• is not described as a possibility in the previously approved protocol  OR
• did not occur within the parameter described (i.e. an increase in frequency or severity)

In this section, simply describe what occurred and provide details.

What was done to negate the incident or minimize risk? If no action was taken, describe why this was the case.

Provide the Board with any additional information that will help them understand the unexpected adverse event better. If you know what changes need to be made to negate or minimize risk going forward, it may be appropriate to modify the protocol. If this is the case, select “yes” to the modification questions and complete that section as well. You are not required to make a modification and it may not be appropriate to do so for every unexpected adverse event case.

Is the negative event the result of not following what was described in the protocol?

Including straightforward responses will help the Board understand the best course of action for the protocol. If the protocol wasn’t followed and the deviation resulted in the unexpected adverse event, following the protocol is a likely first solution. Part of the issue may be that the protocol isn’t practical or that there are issues with the original plan that need to be addressed so that it can be followed. Again, it may be appropriate to proactively modify the protocol in addition to completing this section and the two can be done together.

• Modifications

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• 09 May 2024

Daily briefing: ‘The ugly side of science’ — how to report negative results

• Katrina Krämer

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In principle, a nuclear clock should be more precise and more stable than an optical clock (pictured). Credit: Andrew Brookes, National Physical Laboratory/Science Photo Library

Ultra-precise ‘nuclear’ clock in sight

Researchers have used a laser to prompt tiny energy shifts in an atomic nucleus — a major step towards building nuclear clocks. These could be around 10 times more accurate than the world’s current best timekeepers, known as optical clocks, and less sensitive to disturbances. “We will be able to probe scenarios of dark matter and of fundamental physics that are currently inaccessible to other methods,” says theoretical physicist Elina Fuchs. To turn the system into an actual clock, physicists will need to build higher-resolution lasers that nudge the nucleus with more precision.

Nature | 5 min read

Reference: Physical Review Letters paper

Hot super-Earth has atmosphere

Investigations using the James Webb Space Telescope have confirmed that the exoplanet 55 Cancri e has a carbon-based atmosphere — the first time an atmosphere has been detected surrounding a rocky planet similar to Earth outside the Solar System. The planet orbits very close to its Sun-like star and can’t support life as we know it, in part because it is probably covered by a magma ocean. “Earth probably went through at least one magma-ocean stage, maybe several,” says planetary geologist Laura Schaefer. “Having actual present-day examples of magma oceans can help us understand the early history of our Solar System.”

Nature | 3 min read

Reference: Nature paper

55 Cancri e is a little bigger than Earth, but much smaller than the Solar System’s giant planets, such as Neptune. Credit: NASA, ESA, CSA, Dani Player (STScI)

Brazilian universities hit by strikes

Academic workers at some Brazilian institutions are entering their fourth week of strikes for better wages and more university funding . They say the country’s president has come up short in his promise to boost science and education funding, in part because of opposition from legislators. “We are not against the government,” says botanist Thiago André. “We are in negotiation with the government.” The strikes have halted classes on many campuses, although many scientists are continuing their research. It’s unclear when the strike will end.

Features & opinion

The scientist who fled aleppo.

“If you’re passionate about research, about science, I think my advice is to never give up,'' says Syrian biochemist Aref Kyyaly. He recalls being close to abandoning his work when, in 2013, he decided to flee war-torn Aleppo with his family . The Council for At-Risk Academics helped Kyyaly secure a visa and job in the United Kingdom. “It was like a door was opened for me.” Kyyaly has been granted permission to stay in the country and has secured a permanent job as a biomedical science lecturer, but his immigration status has hindered his career: “I have friends who started four or five years after me and now they’re way ahead of me.”

Nature | 8 min read

‘Nanopore’ sequencing: now for proteins

By squeezing a protein through a nanopore — a tiny opening created by another protein — researchers are starting to decipher the string of amino acid building blocks that proteins are made of . This nanopore sequencing is mostly used for DNA whose building blocks can be ‘read out’ as it passes through the nanopore, driven by an electrical current. Proteins can’t be moved consistently by a current, so researchers have found ways to push or pull them through a pore using water, enzymes or molecular motors. “All the pieces are there to start with to do single-molecule proteomics and identify proteins and their modifications using nanopores,” says chemical biologist Giovanni Maglia.

Nature | 6 min read

How to illuminate the ‘ugly’ side of science

Data repositories, workshops and alternative journals allow scientists to share and discuss negative results, which could help to solve the reproducibility crisis and give machine learning a boost . Publishing negative results is often seen as not worth the time and effort, yet “understanding the reasons for null results can really test and expand our theoretical understanding”, says psychologist Wendy Ross. And highlighting negative results can help students to see that “you are not a bad researcher because you fail”, adds computer scientist Ella Peltonen.

Nature | 11 min read

Image of the week

Credit: Fabrizio Villa/Getty

A newly formed crater of Italy’s Etna volcano puffs out perfect 'smoke rings' . These volcanic vortex rings form when cold air above the volcano causes hot gases travelling up the walls of a round vent to condense. Such displays are rare: the vent must have a circular shape and sides of the same height for such well-defined rings to form.

See more of the month’s sharpest science shots , selected by Nature ’s photo team.

QUOTE OF THE DAY

“we’re living in the golden age of birding, and like any good cult member, i’m recruiting people to the cause.”.

Technology that makes it easier than ever to identify birds and become part of the bird-watching community played a big part in science writer Kate Wong picking up the hobby. ( Scientific American | 14 min read )

doi: https://doi.org/10.1038/d41586-024-01394-w

Today, I’m excited to discover that blasting coffee with ultrasound while it is brewing gives a surprisingly smooth taste similar to a cold brew — but taking only minutes rather than an entire day. “It’s now my favourite way to drink coffee,” says chemical engineer and study co-author Francisco Trujillo.

Please send me your coffee hacks (alongside any feedback on this newsletter) to [email protected] .

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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Research Tips and Infromation

How to Write the Results Section of your Dissertation or Thesis?

Introduction

Organizing your results, providing context, presenting the data in results section, describing statistical analysis, reporting the findings in results section, supporting the findings, visual representation in results section.

As you progress on your journey towards completing your PhD or Post Graduate dissertation, one of the most critical sections that holds immense significance is the results section.

Results section serves as the pinnacle of your research, where you unveil the outcomes of your exhaustive efforts and shed light on the answers to your research questions. In this blog post, we will delve into the intricacies of the results section and explore how to effectively present and interpret your findings to leave a lasting impact.

Whether you’re conducting research in the field of biology, psychology, computer science, or any other discipline, the results section is where your data takes center stage. It is a space where you showcase your meticulous analysis, statistical methods, and the discoveries you’ve made along the way. By understanding the key components and best practices for constructing a compelling results section, you can present your findings in a manner that resonates with both your academic peers and the wider research community.

In this comprehensive guide, we will walk you through the fundamental elements of the results section, from organizing your data to choosing the appropriate visual representations. We will explore the importance of clear and concise reporting, emphasizing the significance of providing contextual information and highlighting any unexpected or groundbreaking discoveries.

Furthermore, we will discuss strategies for effectively interpreting your results, discussing their implications, and connecting them back to your research objectives. By mastering these skills, you will be able to demonstrate the significance of your work, contribute to the existing body of knowledge, and potentially pave the way for further research in your field.

Throughout the blog post, I will provide concrete examples from various disciplines to illustrate the implementation of these techniques. Additionally, I will offer valuable tips on avoiding common pitfalls, ensuring the accuracy and reliability of your results, and seeking feedback from your advisors or peers to enhance the quality of your analysis.

If you are in paucity of time, not confident of your writing skills and in a hurry to complete the writing task then you can think of hiring a research consultant that solves all your problems. Please visit my article on Hiring a Research consultant for your PhD tasks for further details.

Organizing the results of your study in a logical and coherent manner is crucial for effectively communicating your findings. By presenting your results in an organized structure, you enhance the clarity and readability of your dissertation. Here are some key considerations for organizing your results:

• Research studies often involve complex algorithms, software implementations, experimental data, and performance metrics. It is essential to organize these diverse elements in a cohesive manner to make it easier for readers to follow your research. A well-structured results section enables readers to understand the progression of your experiments and the relationship between different findings.
• Begin by reminding readers of the research questions or hypotheses that guided your study. This alignment helps establish a clear connection between the objectives of your research and the subsequent presentation of results. For example, if your research question focuses on evaluating the efficiency of a new sorting algorithm, you would present the experimental data, performance metrics, and comparative analyses specific to that algorithm in relation to the research question.
• Subsubsection 1.1: Experimental Setup
• Subsubsection 1.2: Experimental Results and Analysis
• Subsubsection 2.1: Performance Metrics
• Subsubsection 2.2: Comparative Results and Discussion

Remember to tailor the organization of your results section to the specific requirements of your research. The key is to provide a logical flow and structure that enables readers to easily comprehend and interpret your findings.

Providing context for the results of your study is essential to help readers understand the significance and implications of your findings. By offering background information and study design details, you establish a foundation upon which the results can be properly interpreted. Here are some key considerations for providing context:

• Before delving into the results, it is important to provide readers with relevant background information about the topic or problem being addressed. This may include a literature review of existing research, theories, or methodologies in the field. By doing so, you situate your work within the broader landscape of and demonstrate its relevance. Additionally, explain the design of your study, such as the specific algorithms, software frameworks, datasets, or hardware setups used. This ensures that readers understand the context in which your results were obtained.
• Provide a brief overview of the current state-of-the-art in image recognition algorithms and their limitations.
• Explain the specific challenges or gaps in the existing methods that motivated your research.
• Describe the design of your study, including the choice of machine learning techniques, datasets used for training and evaluation, preprocessing steps, and any hardware or software configurations.

By providing context, you allow readers to understand the background, motivation, and methodology behind your research. This sets the stage for better comprehension and interpretation of your results. Contextualizing your findings, as it helps establish the relevance, novelty, and potential impact of your research within the larger field.

Presenting data in a clear and organized manner is crucial for effectively communicating your results. The way you present your data can greatly impact the reader’s understanding and interpretation of your findings. Here are some key considerations for presenting data:

• Presenting performance metrics of different algorithms using a table to allow for easy comparison.
• Using a line graph to depict the improvement in accuracy over training iterations in a machine learning model.
• Employing a bar chart to compare the execution times of different algorithms on a specific dataset.
• Clear labelling and formatting of your data ensure that readers can easily understand and interpret the information presented. Label each table, figure, chart, or graph with a concise and descriptive title. Ensure that axes, legends, and labels are clearly labelled and units of measurement are specified. Use appropriate fonts, colours, and styles to enhance readability. Consider providing captions or footnotes to provide additional context or explanations where necessary.
• In the text, refer to a specific table presenting the accuracy results of different algorithms and explain how these results support your research hypothesis or contribute to the field.
• Discuss a figure showing the relationship between the number of training examples and the performance of a machine learning model, emphasizing its implications for scalability and generalization.

By presenting data in a visually appealing and well-organized manner, you enhance the clarity and accessibility of your results. Proper labelling, formatting, and referring to each table or figure in the text help readers navigate the information and grasp its significance. Remember to choose the most appropriate format for your data and use visuals to support and reinforce your findings.

The inclusion of statistical analyses in the results section is crucial for providing objective and quantitative evidence to support your findings. Statistical analyses help you draw meaningful conclusions from your data and determine the significance of observed results. Here are some key considerations for describing statistical analyses:

• Statistical analyses play a vital role in determining the reliability and significance of your findings. They provide a systematic and objective framework for interpreting the data and testing hypotheses. Discuss the importance of including statistical analyses in the results section to demonstrate the rigour and validity of your research.
• Describe using a t-test to compare the means of two groups in a user study, as it is appropriate for assessing the statistical significance of differences.
• Explain employing logistic regression to model the relationship between independent variables and a binary outcome in a predictive analytics study.
• Report the p-value as 0.032, indicating a statistically significant difference between the two groups at the 0.05 significance level.
• Interpret an effect size of 0.40 as a medium-sized effect, highlighting its practical importance in the context of the research.

By describing the statistical analyses conducted, explaining the rationale behind the chosen tests, and accurately presenting the statistical values and interpretations, you strengthen the validity and reliability of your findings. Statistical analyses provide an objective framework for drawing conclusions from your data and lend credibility to your research in the computer science domain.

Reporting the findings of your research in an objective, concise, and clear manner is essential for effectively communicating your results. Here are some key considerations for reporting the findings:

• Summarize the key findings of a machine learning study by stating that “the proposed algorithm achieved an average accuracy of 85% on the test dataset, outperforming existing state-of-the-art methods by 10%.”
• For a research question about the impact of different programming languages on software performance, present specific metrics such as execution time or memory usage for each language, along with a comparison and interpretation of the results.
• Instead of using overly technical language, communicate the results in a more accessible way: “The experimental results showed a significant correlation between the number of training samples and the accuracy of the model, indicating that a larger training dataset leads to improved prediction performance.”

By guiding readers on summarizing the results objectively and concisely, addressing each research question or hypothesis, and using clear and concise language, you ensure that your findings are communicated effectively. This approach allows readers to understand the core contributions of your research and how they align with the research questions or hypotheses you set out to investigate.

Providing strong evidence from the data to support your findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations are essential components of reporting research findings. Here are some key considerations for supporting the findings:

• Present empirical evidence from a user study, such as participant feedback or performance metrics, to support the usability and effectiveness of a proposed user interface design.
• If a software system performed unexpectedly poorly in certain scenarios, discuss potential factors such as data bias, implementation issues, or limitations of the evaluation methodology that could have influenced the results.
• Acknowledge limitations such as a small sample size, limited dataset availability, or computational constraints that might affect the generalizability or robustness of the results.
• Discuss potential explanations for unexpected results, such as issues with data quality, algorithmic complexity, or model assumptions.

By providing evidence from the data to support the findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations, you demonstrate a rigorous and reflective approach to your research in the computer science domain. This allows readers to assess the strength and reliability of your findings and gain a deeper understanding of the nuances and implications of your work.

Using visual representations, such as tables, graphs, and figures, alongside the text can greatly enhance the understanding and impact of your findings. Here are some key considerations for visual representation:

Visual representations offer several benefits in presenting research findings. They provide a concise and intuitive way to convey complex information, trends, and patterns. Visuals can help readers grasp key insights at a glance, enhance the overall readability of the document, and make the findings more memorable. Visual representations also facilitate effective comparisons, highlight important relationships, and aid in storytelling. Example:

When creating visual representations, consider the following tips to ensure clarity and effectiveness: a. Choose the appropriate visual format: Select the most suitable format, such as tables, line graphs, scatter plots, or heatmaps, based on the nature of the data and the message you want to convey.

b. Simplify and declutter: Avoid overwhelming the visuals with excessive data points, labels, or unnecessary decorations. Keep the design clean and focused on conveying the essential information.

c. Label and title clearly: Provide descriptive and informative titles for tables, graphs, and figures. Label the axes, data points, or components clearly to facilitate understanding.

d. Use colors and visual cues purposefully: Utilize colors and visual cues to highlight important information or differentiate between categories. Ensure that the chosen colors are distinguishable and accessible. e. Provide legends and captions: Include legends to explain symbols, colors, or abbreviations used in the visuals. Provide informative captions or annotations to guide readers in interpreting the visuals accurately. Example:

By incorporating clear and effective visual representations alongside the text, you enhance the presentation and understanding of your research findings in the computer science domain. Well-designed tables, graphs, and figures can simplify complex information, facilitate comparisons, and enhance the visual appeal of your dissertation. Remember to choose appropriate formats, keep the visuals uncluttered, label clearly, and use colors and visual cues purposefully to maximize their impact.

Writing the results section of a dissertation or thesis is a critical task that requires careful attention to detail, organization, and effective communication. Throughout this blog post, we have explored key elements to consider when crafting this section.

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• Baylor College of Medicine

Scientists turn unexpected results into research tool

Graciela Gutierrez

713-798-4710

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Puzzled by their experimental results, a team of scientists from Baylor College of Medicine and Texas Children’s Hospital investigated why a research tool that was expected to suppress neuronal activity actually was stimulating it. Their findings led them to modify the research tool in ways that minimize the undesired effects, transforming it into a more useful technique to study neuronal function. The study appears in eLife .

“One of the research goals of our laboratory is to understand how different classes of neurons in brain circuits interact with each other to perform their functions,” said corresponding author Dr. Mingshan Xue , assistant professor of neuroscience and of molecular and human genetics at Baylor College of Medicine, Caroline DeLuca Scholar and member of the Cain Foundation Laboratories and the Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital. “One way to study neuronal function is by altering the activity of the neurons and then observe the outcomes, just like in genetics where scientists modify a gene to determine what it might do.”

Xue and his colleagues initially wanted to study the effect of inhibiting the activity of specific neurons in the visual cortex of mice. They used an optogenetic approach by which they genetically introduced into specific neurons a light-sensitive protein called light-gated chloride channel GtACR2, which was assumed to be an inhibitor of neural activity. The researchers then activated the inhibitory effect of GtACR2 by shining a light on the modified cells. It was expected that once it was light-activated, GtACR2 would inhibit the output of the neuronal activity, which the researchers measured as release of neurotransmitters.

“We expected that GtACR2 would inhibit the release of neurotransmitters, but surprisingly the neurons did just the opposite,” said first author Jessica Messier , who is a McNair M.D./Ph.D. Scholar in the Medical Scientist Training Program at Baylor College of Medicine. “We were puzzled by these unexpected results and investigated the causes.”

How neurons work

Neurons receive signals from other neurons through a part of the cell called the cell body, commanding it to either ‘fire’ or ‘not fire’ signals. If the command is ‘fire,’ then the cell body will send the signal down the axon, the long threadlike extension of the cell that connects the neuron with others. Neurotransmitters will be released from the axon’s endings passing on signals to the next neuron. If, on the other hand, the cell body receives a ‘no fire’ signal, then it won’t send a signal down the axon.

“When we used the light-gated channel GtACR2, we expected to silence the cell body, so no matter which signal it received, the cell body would not send a signal down the axon. But we found that even though the cell body was indeed silenced, signals still ran through the axon and neurotransmitters were released,” Messier said.

When activated, channel GtACR2 opens a door on the cells through which negatively charged chloride ions flow, from where their concentration is higher toward where it is lower. The flow of chloride ions from inside the neuron toward the outside triggers a ‘fire’ signal, while the opposite, flow of negative ions from the outside to the inside of the cells, results in a ‘no fire’ signal. Usually, chloride concentration is higher outside of the cell than in the inside, so when channel GtACR2 opens, ions flow toward the inside of the cell, which results in a ‘no fire’ signal. That’s why chloride channels usually inhibit neuronal activity.

“However, we found that, in the particular neurons we were studying, the chloride ion concentration inside the cell body is lower than the concentration outside of the cell, but inside the axon it is the opposite, the concentration of chloride ions is higher inside than on the outside,” Xue said. “This difference in chloride ion concentration between the cell body and the axon of the same cell explained why channel GtACR2 triggered a ‘no fire’ response in the cell body and a ‘fire’ response in the axon.”

To minimize the ‘fire’ signal running through the axon, the researchers modified channel GtACR2 so it would be mostly expressed in the cell body, and not in the axons.

“Relocating channel GtACR2 to the cell body significantly reduced the signals running through the axon, but there is still room for improvement,” Xue said. “This approach also enhanced the inhibitory effect in the cell body and resulted in increased inhibition of the activity of the neuron. A take home message for us is that this light-gated inhibitory channel can be used, but it’s important to take into consideration the effects it can have in different parts of the cell.”

“The phenomenon we describe here also has other implications. One is that this channel itself has now become a tool to study chloride concentrations in different compartments within neurons, such as the axons, which are very small and hard to study,” Messier said. “Second, the improvements that we made to channel GtACR2 to minimize the undesired effects also gave us future direction toward how to further improve the tool.”

Other contributors to this work include Hongmei Chen and Zhao-Lin Cai at Baylor College of Medicine, the Cain Foundation Laboratories and the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital.

This work was supported in part by a Whitehall Foundation research grant (#2015-05-54), a National Institutes of Health grant (R01NS100893), the Baylor College of Medicine Medical Scientist Training Program, the McNair M.D./Ph.D. Student Scholar supported by the McNair Medical Institute at the Robert and Janice McNair Foundation and a Caroline DeLuca Scholarship.

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• Neurol Res Pract

How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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• ACS Publications

Call For Papers: Lessons Learned in Organic Process Chemistry

• May 9, 2024

This Special Issue will highlight important details on negative results and unexpected troubles encountered in day-to-day research and development activities. Submit your manuscript by April 1, 2025.

This Special Issue aims to share process researchers’ expertise in overcoming challenges associated with negative outcomes and unexpected results, addressing troubles caused by a lack of understanding of reactions and physical properties, emerging new impurities due to changes in manufacturing methods, examples of catalytic reaction deactivation during scale-up studies, difficulties in establishing global supply chains, and more.

Organic Process Research & Development is a journal that serves as an authoritative source of scalable procedures for synthetic chemists. As such, it is a communication tool between industrial chemists and chemists working in universities and research institutes. It reports original work from the broad field of industrial process chemistry but also presents academic results that are relevant, or potentially relevant, to industrial applications. Process chemistry is the science that enables the safe, environmentally benign, and ultimately economical manufacturing of organic compounds that are required in larger amounts to help address the needs of society.

Topics include, but are not limited to :

• Pharmaceuticals
• Agrochemicals
• Flavors and Food Additives
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Submit your manuscript by April 1, 2025.

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Accepted manuscripts for consideration in this Special Issue can be formatted as Articles, Reviews, or Perspectives. For Reviews and Perspectives, we ask that you discuss the proposed topic with us by sending an inquiry to [email protected] . Papers accepted for publication for this Special Issue will be available ASAP (as soon as publishable) online as soon as they are accepted. After all submissions have been published, they will then be compiled online on a dedicated landing page to form the Special Issue. Manuscripts submitted for consideration will undergo the full rigorous peer review process expected from ACS journals.

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AirPods Pro 3 just got even more appealing for an unexpected reason

A pple on Tuesday shared preliminary results from a large-scale Apple Hearing Study conducted with the help of University of Michigan researchers. The research started in 2019 and focused on tinnitus, a hearing condition that many people suffer from.

Tinnitus is a ringing sound that people can experience in one or two ears. It can be temporary or permanent, and it might need treatment so one's hearing isn't impacted.

As someone who deals with hearing issues, including tinnitus, Apple's Hearing Study is exciting, particularly because I'm aware of what AirPods Pro 3 rumors say . Apple might turn the AirPods Pro into better hearing aids . It might be unexpected for some people, but this is something that will make Apple's next-generation AirPods Pro far more appealing to so many people out there.

On top of that, the iPhone might support more advanced hearing tests that might help users screen for hearing issues before they visit a doctor.

The Apple Hearing Study started in 2019 and is ongoing. Researchers enrolled more than 160,000 participants, who answered surveys and completed app-based assessments to asses their tinnitus.

The researchers found that 15% of participants experience tinnitus on a daily basis. The ringing sound in one's ears can impact various aspects of life. It's not just about hearing the other sounds and noises around you more clearly. Constant ringing in your ears can affect sleep quality and concentration.

The preliminary conclusions offer some insights into tinnitus, which could help researchers discover better treatment options for those experiencing the phenomenon. Hearing ringing in the ears is quite common, though it'll impact people differently:

The study found that 77.6 percent of participants have experienced tinnitus in their life, with the prevalence of daily tinnitus increasing with age among many. Those ages 55 and up were 3x more likely to hear tinnitus daily compared to those 18-34 years old. Additionally, 2.7 percent more male participants reported experiencing daily tinnitus compared to females. However, 4.8 percent more males stated they had never experienced tinnitus.

As for treatment, there are ways to manage tinnitus, but not a definitive treatment:

In the Apple Hearing Study, participants reported mainly trying three methods to ease their existing tinnitus: using noise machines (28 percent), listening to nature sounds (23.7 percent), and practicing meditation (12.2 percent). Less than 2.1 percent of participants chose cognitive and behavioral therapy to manage their tinnitus.

Given Apple's interest in this particular condition, I'd expect the company to also consider new ways to address tinnitus with its products. That's where the AirPods Pro 3 might come in. 

If rumors are accurate, the AirPods might offer better hearing aid modes than their predecessors. Current AirPods Pro models also serve as decent hearing aids, though that's not their main purpose. Also, I'd love to see a hearing aid test built into the iPhone's Health app come iOS 18 this summer.

Apple does have a test for tinnitus, as seen in the screenshot above. Participants in the study used an app to characterize their tinnitus. They had to wear AirPods (Max, Pro, or regular) or EarPods to listen to the sounds the app made.

Thanks to the app, the researchers have an idea of what sort of sounds people suffering from tinnitus are most likely to hear: 

The majority of participants described their tinnitus as either a pure tone (78.5 percent) or white noise (17.4 percent). Among those who described a pure tone, 90.8 percent reported a pitch at 4 kilohertz or above, similar to the tones in a songbird's call. Additionally, for those who described a pure tone, 83.5 percent identified their tinnitus as a single tone and 16.5 percent identified it as a teakettle tone - a high-pitched, whistling sound.

Apple also says that its current tech can help with tinnitus. For example, enabling the Noise app on the Apple Watch will notify you when environmental levels rise. The Health app also monitors and tracks the sound levels on headphones and AirPods to help you reduce exposure to loud sound.

Wearing AirPods Pro can be beneficial in situations where loud sounds are present . The earbuds play anti-noise to cancel external sounds and reduce exposure to loud sounds.

I expect AirPods Pro 3 to build on these features. Moreover, it'll be interesting to see Apple bring some of these hearing health features to future versions of regular AirPods. But we'll have to wait for new hardware announcements for that.

It's unlikely that Apple will unveil new AirPods models soon. However, Apple could always announce new software features for existing AirPods versions at WWDC 2024 next month, which could explain why Apple shared these preliminary results from the Apple Hearing Study this week.

The post AirPods Pro 3 just got even more appealing for an unexpected reason appeared first on BGR .

The resilient self-employability of women and senior people after sudden economic shocks

• Empirical articles
• Open access
• Published: 28 May 2024

Cite this article

You have full access to this open access article

• David B. Audretsch 1 , 5 ,
• Seham Ghalwash   ORCID: orcid.org/0000-0001-8415-5525 2 , 3 &
• Iñaki Peña-Legazkue   ORCID: orcid.org/0000-0002-5398-4168 4  

In recent times, various crises have negatively affected the financial stability, job security, and health of countless individuals. According to research, different factors that operate at both the individual and contextual levels can play a prominent role in influencing people's self-employment during periods of economic downturn. This study investigates the changes in the likelihood of self-employment by gender and age across various contexts following sudden shocks. By analyzing the intersection of occupational choice and business cycle theories, this research offers insights into how crises affect people's ability to work for themselves. Using a sample of over 250,000 individuals from sixteen countries and applying a multilevel binary logistic regression analysis, the results confirm that women and older individuals are less inclined to be self-employed in general. However, unexpected economic recessions may lead to a slight increase in their likelihood of self-employment. Furthermore, when women and seniors live in a higher GDP per capita context, this context positively moderates the relationship between gender, age, and the propensity for entrepreneurship. These findings offer valuable insights for policymakers seeking to promote resilient self-employment among vulnerable individuals for post-crisis recovery.

Avoid common mistakes on your manuscript.

Introduction

In times of crisis, many people face significant challenges, such as losing their jobs, income, and overall well-being. Sudden layoffs or reduced work hours can be quite overwhelming for those experiencing them. The global financial crisis, the COVID-19 pandemic, and the War in Ukraine have all resulted in disproportionate worldwide impacts on the lives of many people. Several scholars argue that there is a recurring gender and age-based asymmetrical response to crises in the labor force (Vorobeva & Dana, 2021 ). Moreover, the effects of economic downturns are also devastating for most aspiring entrepreneurs (Bailey et al., 2020 ; Zahra, 2021 ). These disruptive shocks are expected to have long-lasting adverse economic effects, particularly on those who are most vulnerable and disadvantaged (Naudé, 2020 ). However, crises can sometimes present new opportunities for self-employment and personal change.

The unexpected shocks affect markets, institutions, and people, although not everyone experiences the same level of suffering or recovery (Doern et al., 2019 ). Indeed, the distribution of resources for entrepreneurship is unequal, particularly during periods of scarcity. This shortage results in the under-representation of certain groups, such as women and senior citizens, often called the "missing entrepreneurs" (OECD, 2021 ). Self-employment becomes the only viable way for many women and people over 50 to recover economically from crises during market contractions (Pines et al., 2010 ; Young et al., 2017 ). Despite its relevance, very few studies have investigated how sudden shocks affect the occupational decision to pursue self-employment (Davidsson & Gordon, 2016 ). While previous research has shown that the relationship between macroeconomic cycles and entrepreneurship is complex, there is little information on changes in the propensity of women and senior people to self-employ after a major disruptive event (Fairlie & Fossen, 2019 ; Parker, 2011 ). Understanding the significance of a sudden variation in these segments of society is crucial because the magnitude of such a deviation may determine the level of effort required for a more inclusive and fairer self-employment activity during a subsequent recovery period (Shepherd et al., 2020 ).

To address this glaring gap in the literature, we undertake an under-explored view of disruption in the external context and investigate how crises affect the intention and realization of self-employment of vulnerable people across different places. To truly understand why some people are more resilient than others, we must consider the unique circumstances of each crisis, as well as the personal characteristics of those considering self-employment. It is important to recognize that the likelihood of someone pursuing self-employment can vary greatly based on specific moments, locations, and demographic groups. Failing to account for these factors can lead to a limited understanding of the motivations and challenges faced by potential self-employees (Quintillán & Peña-Legazkue, 2020 ). Abrupt shake-outs influence differently distinct segments of (aspiring) entrepreneurs, and to the best of our knowledge, research to date has not investigated enough this “persona causa” behind these processes (Hessels et al., 2011 ). Thereby, this investigation undertakes a more nuanced approach to investigate resilient entrepreneurial behavior by considering the effect of recent shocks (i.e., the global financial crisis, hereafter GFC, and the coronavirus crisis, COVID-19 hereafter) on people who live in dissimilar socioeconomic environments and face different sorts of vulnerability (i.e., gender-related and age-related vulnerability).

The purpose of this study is to examine how the gender-related and age-related likelihood of self-employment varies after economic crises. To achieve this, we look at the probability of self-occupation by gender and age in response to two recent shocks, drawing upon the economic cycle and occupational choice literature. In so doing, we expect to contribute to the extant research on self-occupation during economic recessions in several ways. First, we take a dynamic view, analyzing the variation of the gender and age propensity of self-employment for specific periods preceding and succeeding two different crises. This approach complements previous studies focusing mainly on static cross-section research to examine the influence of factors linked to the institutional (spatial) context (Estrin & Mickiewicz, 2009 ; Klyver et al., 2013 ; Welter, 2011 ). Moreover, our study compares two critical points of the entrepreneurship process (i.e., the intention and realization of self-employment), distinguishing it from previous studies. Secondly, this investigation challenges the notion that economic shocks are homogeneous (Congregado et al., 2012 ; Koellinger & Thurik, 2012 ), recognizing that every shock is not identical in terms of duration, scope, and degree of intensity. Since different shocks elicit different responses from people, we compare the effects of an endogenous shock and an exogenous shock to observe the extent to which the likelihood of having an intention and the realization of self-employment varies by focusing on women and seniors. Thirdly, this study extends previous work on opportunity and necessity-driven entrepreneurship by examining how dissimilar contexts moderate the vulnerability for self-employment of women and senior people in the aftermath of crises (Hechavarria & Reynolds, 2009 ). The results suggest that the national wealth level moderates the effect of disruptive shocks since not all places provide the same advantageous conditions to recover and (re)engage in self-employment for the most fragile segments of the population.

The following section presents a theoretical framework that analyzes the impact of economic shocks on gender and age disparities in self-employment in various contexts. This is followed by a description of the data and methodology used in the empirical tests. The main results and discussion of findings are then summarized, and the study ends with the main conclusions, limitations, and implications.

Theory background

The relationship between crises and self-employment.

It is widely accepted that the choice (or need) for self-employment is influenced by the personal features of individuals and by the context surrounding them (De Clercq & Dakhli, 2009 ). A comparative study by examining different global shocks and the interface between the micro-level (i.e., personal characteristics) and the macro-level of self-occupation (i.e., different cycles and contexts) can enhance our understanding of how some people respond and self-employ resiliently at times and places of adversity (Klyver et al., 2013 ; Langowitz & Minniti, 2007 ). The entrepreneurial process usually starts with an idea, whether intentional or accidental. This idea can either be acted upon and turned into a new business or simply discarded. Several personal or contextual challenges may arise during this challenging process, preventing the idea from becoming a feasible business. For this reason, we differentiate the intention from the realization of self-employment.

The theory of planned behavior suggests that multiple individual and contextual factors influence the intention of individuals to become entrepreneurs and that intention is a reasonable precursor to action (Cardella et al., 2024 ). In this study, intention refers to a voluntary and purposeful desire to engage in self-employment, driven by personal motives. On the other hand, realization means the complete engagement in self-employment. Simply intending to start a business does not guarantee that people will engage in self-employment. Entrepreneurs often encounter obstacles that hinder their entry into the market, while at the same time, unexpected opportunities or needs may trigger individuals to become self-employed. This mismatch creates a disconnect between their intention and the realization of self-employment. These scenarios highlight the significance of studying the intentions and realization of self-employment separately. We contend that sudden shocks can have varying effects both on self-employment intentions and realization , particularly among the most vulnerable individuals.

While some studies on the linkage between economic cycles and entrepreneurship claim that recession times are pro-cyclical, and therefore, detrimental to entrepreneurship (Bartz & Winkler, 2016 ; González Pernía et al., 2018 ), other authors hold that new firms are more likely to survive and to remain in business during crisis times due to the upraised opportunity cost for abandoning their activity (Simón-Moya et al., 2016 ). Indeed, not all economic recessions are identical and produce the same effects on self-employment (Yu et al., 2024 ). For instance, the global financial and coronavirus crises are two recent shocks with very different features. A key difference between both crises is that the GFC results from an endogenous shock while the coronavirus crisis emerges as an exogenous shock. The economic and institutional system caused the GFC. Financial markets froze and liquidity dried up, resulting in a deep economic recession and a long recovery process. In contrast, the COVID-19 pandemic appeared from a virus outside the economic and institutional system. The sudden onset of a global health crisis caused a rapid decline in worldwide markets and institutions, resulting in lockdowns and mandatory business shutdowns. The recovery process has been uneven, with a “V-shape” recuperation advancing more quickly once the health problem has been brought under control. Therefore, it is important to analyze how different types of shocks impact the self-employability of vulnerable groups.

The propensity of self-employment of women after economic shocks

Although men and women entrepreneurs are both critical for generating wealth, men still outnumber women in self-employment in most countries, and this gender gap has persisted for decades (Alkhaled & Berglund, 2018 ; Reynolds, 1997 ). Historical evidence suggests that the size of the gender gap has varied over time, and one reason for this variation could be that institutions' efforts to reduce gender disparities weaken or disappear during unexpected crises. This recurring phenomenon in history partially explains why women's entrepreneurial activity rates rarely surpass those of men.

According to the theory of business cycles, when the economy is doing well, it creates favorable conditions for entrepreneurs to start new businesses. However, when the economy struggles, the appeal of starting a business decreases, especially for women facing limited financing access during tough times. Social norms and stereotypes can also discourage women from pursuing self-employment, especially when economic instability and risk are high (Lanchimba et al., 2024 ). All these pro-cyclical factors combined make it even more challenging for women to consider the desire for self-employment during periods of adversity.

H1a: The probability of self-employment  intention  of women compared to that of men decreases in response to adverse economic shocks.

The connection between economic cycles and entrepreneurship is complex and influenced by various factors at both macro and micro levels. Indeed, this relationship can be unclear, particularly when considering the motivations behind self-employment. Vulnerable individuals with necessity-driven motivations may be impacted differently than those with opportunity-driven motivations (Fairlie & Fossen, 2019 ), especially after unexpected shocks. A more nuanced study is necessary to better understand how the realization of self-employment behaves during times of crisis.

Studies have shown that unexpected crises like the GFC or the COVID-19 pandemic have made women more susceptible to challenges in the job market compared to men (Gezici & Ozay, 2020 ; Verick, 2009 ). This is because women are overrepresented in industries hit hardest by economic downturns, such as retail, hospitality, and services (Belitski et al., 2022 ; Manolova et al., 2020 ). Women who have lost their jobs, as well as other women, may choose to pursue self-employment as a means of achieving greater autonomy and flexibility in their careers. The theory of occupational choice examines the factors that affect the career path of individuals (i.e., such as their income potential, skills, preferences, and own needs). In this vein, women may adjust their career aspirations or occupational preferences based on the prevailing labor market and their personal conditions (Minniti & Nardone, 2007 ).

Despite the obstacles women face in the job market, such as gender discrimination, unfair pay, and limited career advancement opportunities (known as the glass ceiling), a shift towards self-employment can provide them with an alternative path to achieve their professional aspirations, financial independence, and personal goals. Especially during times of crisis, self-employment can offer women better work-life balance, job schedule flexibility, and manageable responsibilities, making it a viable option (or need) for those with caregiving obligations or other commitments (Litsardopoulos et al., 2023 ; Murgia & Pulignano, 2021 ). While it may seem counterintuitive, crises can sometimes present new opportunities for women to become self-employed and maintain their resilience in the face of challenging circumstances in the job market. This is especially true for women who have been excluded from the labor market and rely (unintendedly) on self-employment as their primary source of income. Thus, we hypothesize that while a gender gap in self-employment still exists overall (with women having lower chances of being self-employed compared to men, regardless of economic cycles), unforeseen shocks can trigger an increase in women's self-employment relative to men´s and reduce this gap.

H1b: The probability of women's  realization  of self-employment compared to men increases after economic shocks (i.e., although the coefficient remains negative, reflecting a gender gap).

The propensity of self-employment of senior people after economic shocks

This research also investigates how unexpected crises affect senior citizens' self-employment decisions. The population's age structure is rapidly changing, which has significant implications for labor force participation, health service provision, and other societal issues. Discrimination based on age, unequal treatment of senior employees in the labor market, and the self-employability of older workers have become important topics recently for scholars and policymakers (Stypińska & Nikander, 2018 ).

Senior individuals may be less inclined to take on the financial risks associated with starting their own business, particularly if they have high costs such as mortgages, healthcare expenses, and dependents to support. The theory of occupational choice suggests that older individuals have a shorter time frame to achieve their career and financial goals than younger individuals. Due to this time pressure, seniors´ desire to self-employ could be perceived as more daunting (Kautonen et al., 2014 ). In fact, transitioning to self-employment may require acquiring new skills or adapting existing ones, which makes the intention to launch a new business more arduous and risky for senior people during an economic downturn. Hence, it is reasonable for seniors to prioritize stability and security in their career choices during difficult times.

H2a: The probability of self-employment  intention  of senior people compared to their younger counterparts decreases in response to adverse economic shocks.

Evidence shows that crises tend to reduce employment opportunities for workers over 50, and senior women are particularly affected (Goda et al., 2021 ). The economic cycle significantly impacts occupational decisions, as vulnerable population groups may turn to self-employment during economic uncertainty. Therefore, self-employment can also be a feasible alternative for older adults during economic downturns. There is a widespread belief in society that people over 50 years old become less valuable for social and economic reasons, regardless of their skills and health status (Choi et al., 2018 ). Employers often have a biased view of age and prefer younger candidates, leaving older ones disadvantaged during the hiring process (Backman et al., 2021 ). Even if they are employed, senior employees often receive lower pay than their younger colleagues, and they may also be overlooked for promotions, training opportunities, and other important changes within the company. Economic push factors such as layoffs, downsizing, or age-related discrimination in the labor market may induce seniors towards self-employment to earn a living (Halvorsen, 2021 ). These burdens tend to be more pronounced during economic downturns, and can negatively affect the self-esteem and financial security of older workers. As advocated by occupational choice theory, senior individuals may self-employ to maintain control over their professional careers and generate their own income after being unexpectedly displaced from the labor market.

Resilient senior self-employed individuals become relevant not only because they can manage themselves economically during times of crisis but also because they can serve as inspiring role models for others (Soto-Simeone & Kautonen, 2021 ). Human capital theory holds people can leverage their skills, experience, and resilience to navigate economic uncertainty. While some human capital assets may become obsolete in the labor market, other assets amassed by seniors can be helpful for self-employment. As individuals age, they accumulate valuable knowledge, networks, and sometimes savings throughout their careers (Mair & Marti, 2009 ). Additionally, older adults who have experienced previous economic downturns may be more adaptable and resilient when faced with new economic shocks. Self-employment can be an appealing option for seniors transitioning into retirement or seeking a better work-health balance after a crisis (Halvorsen, 2021 ).

Although the literature recognizes an age gap, in general, when it comes to self-employment (Kautonen et al., 2017 ) (i.e., with senior individuals being less likely than younger ones to pursue entrepreneurship), a sudden shock might encourage the realization of self-employment of senior people, particularly of those who are driven by necessity after being displaced from the labor market.

H2b: The probability of the  realization  of self-employment of senior people compared to their younger counterparts increases after economic shocks (i.e., although the coefficient remains negative, reflecting an age gap).

The after-shock self-employability of women and seniors across countries with different levels of prosperity

The context matters in shaping entrepreneurial behavior, as spatial and social contexts create essential conditions at the macro and meso levels for occupational decisions (Avnimelech & Zelekha, 2023 ; Brush et al., 2009 ; Estrin & Mickiewicz, 2011 ). Recent research suggests that women face more challenges when initiating and expanding their businesses in areas where resources are scarce (Aparicio et al., 2022 ). In contrast, the conditions for entrepreneurship may be favorable for women in more prosperous locations (Elam et al., 2021 ; Zhao & Yang, 2021 ). In other words, the level of economic prosperity in an area could influence women's decision to pursue self-employment after facing unexpected shocks. However, how the context (i.e., the family, temporal, spatial, or sectoral contexts) moderates the relationship between economic crisis and the choice of self-employment by vulnerable segments raises an interesting research issue widely ignored in the entrepreneurship literature (Brutton et al., 2021 ).

This study emphasizes the moderating role of GDP per capita, a widely used indicator of a place's prosperity, in the relationship between different crises and self-employment variation (Wennekers et al., 2005 ). Several factors can influence the relationship between GDP per capita and the level of self-employment among women following economic shocks. In countries with higher GDP per capita, the population tends to have higher education and skill development levels. Women in these countries may possess the necessary knowledge, skills, and entrepreneurial capabilities to pursue self-employment opportunities, even in the face of economic shocks. Countries with higher GDP per capita also have greater technological advancements and digital infrastructure, creating new opportunities for women to start and operate businesses, particularly in online markets (Irmatova, & Akbarova, 2023 ). These sectors may be more resilient to economic shocks in wealthier economies, which typically recover faster from crises. Moreover, some countries with higher GDP per capita may have more progressive attitudes towards women's economic empowerment (Naveed et al., 2023 ). In such countries, women may have greater access to support programs, funding opportunities, business networks, and mentorship programs, which can facilitate their entry into self-employment following economic shocks. Therefore, living in areas with higher GDP per capita positively influences women´s self-employment intention and realization during crises.

H3a: Living in countries with higher GDP per capita moderates positively the relationship between being a woman and the probability of having the  intention  to self-employ after a sudden shock.

H3b: Living in countries with higher GDP per capita moderates positively the relationship between being a woman and the probability of the  realization  of self-employment after a sudden shock.

The opportunities for self-employment for senior people also vary across different countries. Those who live in wealthier countries often have pension plans to fund their retirement, so they do not have to depend entirely on their families or the government. Advanced economies with higher GDP per capita usually have more diverse and dynamic markets, providing economic opportunities for individuals of all ages. Seniors in countries with higher GDP per capita may have greater well-being, financial independence, and stability, which enables them to pursue self-employment. Higher savings, retirement funds, and access to financial resources may provide seniors with the necessary capital to start and sustain businesses (Kautonen et al., 2017 ). Moreover, countries with higher GDP per capita often have more robust social safety nets and welfare systems in place, providing seniors with a safety net in case their self-employment ventures do not succeed. This safety net can mitigate some of the risks associated with entrepreneurship and provide seniors with the confidence to pursue self-employment opportunities.

High-income countries (i.e., such as Switzerland, the United Kingdom, and the Netherlands) offer flexible labor markets for seniors, accommodating alternative work arrangements, including partial self-employment (OECD/European Commission, 2021 ). For instance, these countries may have a greater demand for specialized expertise in sectors such as consulting, coaching, or mentoring, which can create opportunities for self-employment among seniors. In contrast, less prosperous countries have been slower in addressing aging as a public policy concern and older people may find it difficult to choose self-employment as an occupation (Lloyd-Sherlock, 2000 ). Moreover, elderly individuals living in resource-constrained settings face severe challenges in self-employment due to limited financial support and low literacy rates (Henriquez-Camacho et al., 2014 ). Hence, we hypothesize that seniors' intention and realization of self-employment during an economic downturn are positively affected by residing in areas with a higher GDP per capita.

H4a: Living in countries with higher GDP per capita moderates positively the relationship between being a senior and the probability of having the intention to self-employ after a sudden shock.

H4b: Living in countries with higher GDP per capita moderates positively the relationship between being a senior and the probability of the  realization  of self-employment after a sudden shock.

As pointed out by Welter ( 2011 , p.165), “the rules of entrepreneurship do change dramatically from one time and place to another.” We sumnarize that despite the permanent gender and age disparities, women and senior individuals who are resilient and wish to start their own businesses following unanticipated crises are more likely to succeed in locations that are more favorable for entrepreneurship. Our conceptual framework is illustrated in Fig.  1 .

The moderating role of the context on the self-employability of women and senior people

Methodology

The Global Entrepreneurship Monitor (GEM) provides valuable data for examining occupational preferences worldwide. The annual data collection offers insights into the current state of self-employment in various contexts. While there may be some limitations (i.e., inadequate data infrastructure in some countries resulting in incomplete or inaccurate information, and small sample sizes in certain countries), the GEM data has multiple benefits for investigating our topic. The GEM consortium gathers data from numerous countries, enabling comparisons of self-employment rates across borders (Ouazzani et al., 2021 ). Additionally, the consortium has been collecting information over several years, allowing for tracking trends in self-employability (Simmons et al., 2019 ). In particular, data for the pre-shock and post-shock periods for the endogenous crisis (GFC) and the exogenous crisis (COVID-19) have been used to test our hypotheses.

This study emphasizes the need to examine two different early stages of the process of entrepreneurship: the intention and the realization of self-employment. Therefore, it is crucial to have representative and diverse samples that account for different crises, national contexts, and individuals with distinct profiles. The representativeness of the data depends not only on the quantity of the sample size but also on the quality and appropriateness of the sampling method used. GEM employs reasonable sample sizes in each country to increase the accuracy of the data collected (i.e., the GEM consortium selects a minimum of 2000 adults aged 18 to 64 years old each year from every participating country). The selection process follows a stratified sampling technique to ensure that the survey respondents are representative of the population. In this study, over 250,000 individuals from sixteen countries Footnote 1 were interviewed to provide empirical evidence on the gender and age disparity in self-employment for the GFC (2008–2010) and the COVID-19 pandemic (2019–2021).

Dependent and independent variables

Two dependent variables are used to describe the two stages of the process of self-employment. On the one hand, we measure the intention of people for self-employment (i.e., ex-ante stage of self-occupation); on the other hand, we account for the factual realization of self-employment (i.e., ex-post stage of self-occupation). The variable intention describes the intent to self-employ in the next 3 years at the moment of the interview. If the answer is “yes”, the variable intention takes the value of one (1), and zero (0) otherwise. The variable realization describes whether a respondent owns a new business and has recently earned a wage from self-employment for up to three months after paying business expenditures. If the answer is “yes”, the variable realization takes the value of one (1), and zero (0) otherwise. The effects on the dependent variables for the period right before and after each shock are compared (i.e., results for the Pre-shock and Post-shock models). The literature has used these dependent variables to analyze the subject of nascent entrepreneurs (Wennekers et al., 2005 ).

The explanatory variables are classified into two levels. Level-1 variables reflect the personal characteristics of individuals, while Level-2 variables account for the variation by country. Among the Level-1 variables, two main variables are highlighted to test for gender and age disparity: Female and Age . Female is a dichotomous variable taking a value of one (1) if the respondent is a woman and zero (0) otherwise. Age is also a dichotomous variable taking a value of one (1) if the respondent is over 50 years old and zero (0) otherwise. These variables have been previously used in studies related to the literature on women and senior entrepreneurship l (Estrin & Mickiewicz, 2011 ; Kautonen et al., 2014 ; Simmons et al., 2019 ).

Two more Level-1 control variables have been added to measure human capital -  College and Experience- and the perception of fear of business failure. College is a binary variable that takes the value of one (1) if the respondent has a college degree and zero (0) if not. The variable Experience determines whether individuals have prior entrepreneurial or self-employment experience, regardless of the outcome, since learning occurs from both positive and negative entrepreneurial experiences (DeTienne et al., 2015 ; Guerrero & Peña-Legazkue, 2019 ). If the respondent has shut down a business within the last twelve months, the variable Experience takes the value one (1) and zero (0) otherwise. Previous studies have included similar variables to examine the relationship between individual attributes and entrepreneurship (Dimov, 2010 ; Ouazzani et al., 2021 ).

The Level-2 variable is based on country data and represents GDP per capita data for sixteen countries provided by the World Bank. This variable has been used in other studies to represent the context for entrepreneurship and its level of prosperity (Bosma et al., 2018 ; Wennekers et al., 2005 ). The variable GDP has been converted to US dollars and adjusted for inflation across the pre-shock and post-shock periods to equate the purchasing power of people in both periods. This moderating variable representing the wealth level of each context interacts with our main explanatory variables to test the variation after different crises of the likelihood of self-employment of women and seniors, as exhibited in Fig.  1 . Table 1 summarizes all the variables used in this study, including the additional variables used for robustness tests.

A multilevel binary logistic regression analysis is applied to test our hypotheses. Multilevel regression is a powerful tool for understanding complex data structures and relationships. The hierarchical linear method is commonly used when data has a nested structure and there is a natural grouping of observations. In our sample, we have level-1 individual data and level-2 country data. We use a two-level model to account for the differences in contexts and to measure the effect of both within-country and between-countries variations. Our tests are conducted in two stages. Firstly, we examine an unconditional null model to identify the effect of country variation and compute the intra-class correlation index. Secondly, we test a full model with the main and moderating variables. This approach has been previously used in studies by Guerrero and Walsh ( 2023 ) and Chen et al. ( 2022 ).

Table 2 displays the descriptive statistics and correlation matrix of the entire sample. Of all respondents, 16% intend to become self-employed within three years and 5% have already become self-employed in the past three months. Approximately 53% of the whole sample are women, and nearly 30% are above 50 years old. Roughly 42% of the participants hold a college degree, and about 3% have previous self-occupation experience. The mean GDP per capita across all countries is approximately 28,000 US dollars. We computed the variance inflation factor (VIF) scores for all the variables, and none of the VIF scores crossed the threshold of 5.0, indicating no risk for multicollinearity among the explanatory variables.

Table 3 shows the results of the multilevel binary logistic regression tests for the intention of self-employment. Table 4 , on the other hand, displays the results for the subsequent stage of the realization of self-employment. In both cases, we first analyze the null models to check how the grouping by countries is related to the dependent variables, intention and realization of self-employment. Standarized coefficients have been used for comparability. In general, the intraclass correlation (ICC) coefficients of the null models are acceptable, falling within the range of 5% to 25%. As a next step, the fixed and random effects are estimated by incorporating both the level-1 and level-2 explanatory variables into the full models.

Results on the likelihood of women to self-employ after sudden shocks

The negative and significant coefficients for the variable Female consistently show that women are less likely than men to self-employ, which aligns with existing literature. In Table  4 , our analysis further reveals that the probability of women's realization of self-employment compared to men increases slightly after both the GFC shock and the COVID-19 pandemic (though the coefficient is still negative as predicted). In particular, the increase is from β = -0.042 to β = -0.019 after the GFC shock and from β = -0.029 to β = -0.020 after the COVID-19 pandemic. In contrast, the results in Table  3 show that the probability of women intending to become entrepreneurs decreases slightly after both economic shocks. Specifically, the decrease is from β = -0.095 to β = -0.101 after the GFC shock, and from β = -0.060 to β = -0.072 after the COVID-19 pandemic. Overall, these results support H1a and H1b and consistently show a gender disparity in the intention and realization of self-employment in the pre-shock and post-shock periods for each crisis (i.e., GFC and COVID-19). Interestingly, the gender gap in entrepreneurial intention widens for women after each shock (i.e., H1a), but their resilience leads to a less severe impact on self-employment realization (i.e., H1b).

Results on the likelihood of senior people to self-employ after sudden shocks

The results also reveal that older people are generally less likely to become self-employed than their younger counterparts. As expected, the negative coefficients of the variable age in all the models suggest an age disparity exists when pursuing and materializing self-employment. However, two outcomes are noteworthy. On the one hand, the likelihood of older people intending to become self-employed compared to younger people increases a little from β = -0.149 to β = -0.146 during the GFC crisis, and a bit more from β = -0.145 to β = -0.127 during the COVID-19 pandemic. Therefore, these findings do not support H2a, which holds that older people's intention to become self-employed would be worsened after sudden shocks. On the other hand, the probability of the realization of self-employment increases for older people from β = -0.024 to β = -0.017 during the GFC shock, but it decreases from β = -0.045 to β = -0.049 during the COVID-19 pandemic, possibly due to the weaker health condition of many seniors during the pandemic. Hence, H2b is partially supported. In brief, senior individuals exhibit distinct entrepreneurial behavior compared to women. While the age gap in entrepreneurial intention narrows for seniors after each shock, they become less resilient when realizing self-employment during the pandemic crisis.

Results on the moderating effect of the prosperity level of the context

The development and wealth conditions of a particular context can either encourage or restrict individuals from seeking self-employment opportunities (Brutton et al., 2021 ). The results of the null models and the coefficients for the moderating variables of the full models demonstrate that context plays a significant role. The tests reveal that the interaction terms have significant coefficients, all with positive values ranging from β = 0.001 to β = 0.007. These values suggest that in contexts with higher GDP per capita, the self-employment probability of women and senior individuals further increases after different crises. As the coefficients are consistently positive and statistically significant for both the intention and the realization of self-employment in the GFC and COVID-19 crises, we find support for our hypotheses (i.e., H3a, H3b, H4a, and H4b).

In sum, the results suggest that women and seniors are less likely than men and younger people to pursue and materialize self-employment. However, women and seniors tend to respond more resiliently after crises (i.e., with a slight increase in their probability of self-employment), and in particular, those living in prosperous countries with higher GDP per capita are more inclined towards self-employment. On the contrary, women and senior people from poorer regions become less entrepreneurial and more vulnerable. Therefore, the impact of crises varies depending not only on the characteristics of individuals and the type of crisis, but also on the context in which people live. This additional burden of self-occupation is what we call augmented vulnerability .

Results for the robustness tests

Robustness tests were conducted to check the existence of gender and age disparity by changing certain conditions at both level-1 and level-2 in our models. Specifically, we expanded the full models with three perceptual variables at level-1 that complement the human capital profile and perceptions of the individuals. The dichotomic variable opportunity represents respondents' perception of good business opportunities in the coming six months, while the variable fear failure describes whether or not the fear of business failure would prevent the respondent from pursuing self-employment. Fear of failure is measured by a 5-likert scale centered around the value of zero. Additionally, we nested the countries into several regions at level-2, including northern-Europe, southern-Europe, Middle East-North Africa, and Latin America. The results consistently support our hypotheses and previous outcomes, as shown in Table  5 . In general, both the gender and age disparity persist for both the intention and realization of self-employment after sudden shocks, and this effect is even more pronounced in regions with lower averages of GDP per capita (i.e., augmented vulnerability ).

Scholars widely agree that studying the self-employment of individuals during recessions is essential, given that inclusive entrepreneurship can promote a fair and rapid economic recovery (Liñán & Jaén, 2020 ; Shepherd et al., 2020 ). Some people become self-employed by necessity, while others see it as an opportunity to start a profitable venture during crises (van Stel et al., 2023 ). Only recently have researchers started exploring how the most vulnerable people cope with disruptive events and natural disasters and how such crises affect women and older individuals' pursuit of self-employment (Bullough et al., 2014 ; Li et al., 2019 ; Marshall et al., 2015 ). However, little is known about the reasons why some individuals succeed while others do not and whether marginalized groups have equal access to resources and opportunities for self-employment in successive periods of adversity (Bird & Brush, 2002 ; Minniti & Nardone, 2007 ). In response to the claim in the scholarly literature that further research is needed on this relevant issue, this study investigates the factors contributing to gender and age differences in self-employment during various crises (Davidsson & Gordon, 2016 ; Marlow & Swail, 2014 ).

Unlike in other studies, we explore the extent to which crises differently impact individuals during two early stages of the process of self-employment, namely intention and realization . This research contributes new evidence to the existing literature on the asymmetric effects of disabling shocks on different genders and age groups in various phases of the process of entrepreneurship (Guerrero & Peña-Legazkue, 2019 ; Simmons et al., 2019 ). More precisely, we investigate both the intention and realization early stages of self-employment, providing a more comprehensive understanding compared to studies that investigate only one stage of the entrepreneurship process. Our findings reveal that the effects of any crisis (i.e., either endogenous or exogenous shocks) point consistently in the same direction (i.e., against women and senior people) in both phases (i.e., intention and realization of self-employment).

Our novel approach - blending personal, temporal, and spatial dimensions - helps us better identify individuals who exhibit resilience towards entrepreneurship during distinct periods and contexts of adversity. Indeed, our results confirm differences in gender and age disparity across contexts, as suggested by previous studies that take a static cross-sectional perspective (Brush et al., 2019 ; Shinnar et al., 2012 ). However, in our analysis, we go beyond a static view and take a dynamic approach by considering the potential changes in the likelihood of self-employment over time, particularly in response to various unexpected events. By incorporating a temporal perspective, we observe that the rate of self-employment can also vary due to resilient responses by vulnerable segments such as women and senior entrepreneurs to sudden shocks. Through a comprehensive investigation of how self-employment varies among people from diverse backgrounds, we reveal the untapped potential of women and seniors for entrepreneurship.

Our findings complement previous findings of the theory of occupational choice (Kautonen et al., 2014 ) in that from a temporal perspective, the intention is distinguished from the realization of self-employment. Unlike in previous studies on occupational choice, differences in both stages of the process of entrepreneurship are identified. Findings in the literature suggest that crises affect people's self-employability, and most of the time, this impact is to the detriment of most vulnerable people (Lim, 2000 ; Rubery & Rafferty, 2013 ; Xie & Lv, 2016 ). Our findings suggest that although the gender gap in self-employment intentions may worsen after economic shocks, it tends to narrow when realizing self-employment. Additionally, senior entrepreneurs tend to have a higher intention to self-employ during crises. However, their likelihood of doing so decreased only during the recent COVID-19 pandemic (i.e., probably due to more severe health problems experienced by older people). Our findings emphasize the strong entrepreneurial spirit of women and seniors in the face of unforeseen external challenges. The insights gained from their intention and resilient realization of self-employment can provide valuable guidance to policymakers in designing effective support programs for vulnerable segments during times of hardship.

Business cycle theory analyzes the causes and effects of economic expansions and contractions. Complementing previous findings (Parker et al., 2012 ), we argue that crises' effects on people's self-employability are not uniform. Crises differ in origin, nature, duration, and intensity; therefore, the impact of each crisis on self-employment varies across distinct segments of the population. Our research on the effects of the two most recent global crises, namely the GFC and the COVID-19 pandemic, shows that there is still a continuous disparity in self-employment rates based on gender and age, regardless of the type of crisis (whether it is an endogenous or exogenous shock). We extend the work by Brush et al. ( 2019 ) and Elam et al. ( 2021 ), and claim that the intention and realization of self-employment vary for each group depending on the specific characteristics of the crisis.

It is well known that the context is important for understanding when , how, where, and why entrepreneurship happens and who becomes involved (Giménez & Calabró, 2018 ; Welter, 2011 ). The level of wealth in different locations largely explains the variation in desirability and feasibility of self-employment, as context shapes entrepreneurial intentions and attitudes (Zhu et al., 2021 ). Recent studies on inequality and entrepreneurial thresholds suggest that a country's uneven wealth distribution affects the availability of resources and opportunities, which can influence the success of aspiring entrepreneurs (Constantinidis et al., 2019 ; Sarkar et al., 2018 ). Our findings show that the after-shock resilient behavior of women and seniors is enhanced in more prosperous places. It is well known that differences in markets, institutions, and wealth levels in countries have significant implications for economic inequality (Brutton et al., 2021 ) and also for gender and age disparity in self-employment. We contribute to the literature on economic development and entrepreneurship by arguing that the recovery from any crisis is typically unequal, with economically disadvantaged groups and emerging economies needing much more time and support to recover from each shock (i.e., what we call augmented vulnerability ). The most recent historical evidence indicates that self-employment gender and age gaps tend to converge towards parity through cycles, with sudden drops occurring at shocks and gradual improvements taking place over longer periods. A new disruptive event has the potential to undo the institutional efforts made to promote gender and age parity. That is, abrupt shocks can cause a “sawtooth effect”, leading to a severe reduction or complete elimination of the positive impacts of policies on parity implemented over many years, especially in less favored regions (See Fig.  2 ).

The global gender-gap ratio of self-employment intention (Women/Men, %)

This research confirms the existence of a continuous disparity by gender and age in the pursuit and achievement of self-employment during crises, and the difficulty for resilient entrepreneurship is more pronounced for women and seniors who live in less prosperous environments. Although these vulnerable segments are more exposed to crises, they appear more resilient since, according to our results, their likelihood of turning to self-employment increases slightly after sudden shocks. Hence, the findings suggest that to achieve a faster recovery and more inclusive entrepreneurial activity, it is essential to enhance the conditions of the context for entrepreneurship for the most vulnerable yet resilient segments of society.

Implementing universal policies in response to various disruptive crises, to promote equality and accelerate economic recovery may not be effective (Estrin & Mickiewicz, 2009 ). Our research suggests that the impacts of each shock on the likelihood of women and senior citizens becoming self-employed differ, and these effects vary across different regions. Numerous studies consider shocks to be uniform events, but our research recognizes the diverse nature of crises and their varying effects (Parker et al., 2012 ). Investigating how and why different types of crises lead to distinct self-occupation outcomes is an intriguing research area we reserve for future studies. Additionally, the recuperation process after a crisis is not identical, as susceptible individuals and settings require more time and support to recover. Moreover, since the personal profiles of women and older people are very diverse, policymaking is even more complex. Therefore, we emphasize the relevance of considering the unique circumstances of each crisis , place, and individual when formulating policies to promote equality and accelerate economic recovery.

Limitations and avenues for future research

The investigation is not exempt from limitations. First, it is important to note that during times of crisis, the impact is not evenly distributed among all countries and does not occur simultaneously. For this study, we have used a 2-year time frame between the pre-shock and post-shock points for the GFC and COVID-19 crises. However, this time frame may not be accurate for all countries. Adjusting the time frame for each country and each crisis would provide more precise results.

Secondly, despite focusing on two early stages (i.e., self-employment intention and realization ), our study did not consider subsequent stages of the venturing process (e.g., firm survival, growth, internationalization). It would be interesting to investigate gender and age gaps in the later stages of new ventures following disruptive shocks, as in the study by van Stel et al. ( 2023 ). This type of analysis would require panel or longitudinal data, which is not easy to obtain, especially in more disadvantaged regions. Undoubtedly, the new findings on this issue would improve our understanding of the fragility of women and senior self-employment derived from crises across the whole spectrum of the venturing process.

Thirdly we only focused on the crisis without delving into the specifics of each one. Our study fell short in measuring the extent and main features of each crisis. By understanding the characteristics of each type of crisis, we would gain new insights into how individual features of shocks (i.e., such as changes in the unemployment rate, financial market plunges, and inflation rates) impact the inequality of self-occupation.

Lastly, it is worth noting that this study only investigated sixteen contexts by looking at the prosperity level of each country. Despite having similar GDP per capita, there may be significant institutional differences between countries in terms of gender and age barriers. These differences may arise due to variations in norms, religious and social customs, and ethical standards. Furthermore, there may be regional (sub-national) differences within a country that contribute to such variations (González et al., 2010 ). To gain a more in-depth understanding of the levels of vulnerability of individuals for self-employment during crises, it would be crucial to analyze the evolution of the gender and age gaps in a more extensive range of contexts, including information for both formal and informal institutions, at macro and mezzo-environments (Crecente et al., 2022 ; Simmons et al., 2019 ).

Theoretical implications

In order to gain a better understanding of the factors that drive or prevent women and senior individuals from pursuing self-employment, analyzing individual and cross-sectional factors alone may not be sufficient. Firstly, it is necessary to consider the cyclical effects of different shocks on vulnerable segments of society and their access to resources during times of adversity. Secondly, not all crises are the same, and different crises may result in varying levels of resilience and dissimilar entrepreneurial responses from people. Thirdly, the probability of self-employment of women and seniors is not uniform across locations as they tend to be more vulnerable in less wealthy contexts.

Hence, individual, locational, and temporal lenses are needed to further investigate the complex self-employability of people under adversity. Such a broader conceptualization, by bridging together all these perspectives, may prove appropriate for future theorizing and empirical testing on the unfulfilled potential of women and senior entrepreneurs.

Policy implications

The findings of this research offer guidance and several implications for policymakers and other stakeholders of entrepreneurial ecosystems. The first implication for policymaking has to do with the complexity of crises. To avoid a higher level of inequality and to reduce a structural gender and age poverty gap, entrepreneurial empowerment of the most needed ones should be promoted to pursue equal economic opportunities and to enhance social inclusion across industry sectors and different institutional contexts (Konon et al., 2018 ; Korosteleva & Stępień-Baig, 2020 ). Bearing our results in mind, policy decision-makers should tailor their policies considering a mix of several factors such as the nature of each shock, the personal attributes of the most needed people for entrepreneurship, and the singular characteristics of each place and its institutional context (Backman et al., 2021 ; Bird & Brush, 2002 ; Verheul et el., 2006 ). The policies to promote self-employment of the most vulnerable people after crises would not achieve the expected outcomes (i.e., productive entrepreneurship), unless such support programs recognize the specific complexity of self-employment of different people for different shocks and in different environments. Without a more nuanced understanding of the factors that lead to an augmented vulnerability , any policy misfit would weaken entrepreneurial activity and slow economic recovery from crises.

A second implication has to do with the support programs designed by policymakers. Since crises do not affect everyone and every sector in the same manner, policies must strive to ameliorate the damaging effects by supporting the most affected vulnerable groups across different industries and ensuring the provision of equal access to (scarce) resources (i.e., specialized training, mentoring, seed finance, etc.). Equitably tailored policies would help minimize the harmful effects of gender-gap and age-gap for self-employment both in the short run and in the long run. Some policies may focus on the short-run by delivering financial resources to the most needed ones (i.e., temporary tax reliefs, elimination of administrative red tape, direct cash grants, etc.). In contrast, other policies could be oriented in the long-run to upgrade the skills and capabilities needed to better explore and exploit new business opportunities in unusual contexts (i.e., through specific education and training programs tailored for women and senior people).

Finally, we further recognize the heterogeneous nature of women and seniors, and thus, contend that they should not be treated as two single homogeneous groups (Stirzaker & Sitko, 2019 ; Poggesi et al., 2016 ). This may be key to understanding why universal entrepreneurship policy programs seeking gender egalitarianism accomplish contradictory results (Cheraghi et al., 2019 ; Shahriar, 2018 ). Thereby, policies to support more inclusive self-employability must be revised and implemented rigorously, especially in periods of higher economic and social instability (i.e., periods of worldwide crises). Indeed, the gender and age gaps for self-employment do not only appear in crises. Instead, these gaps persist invisibly with little attention until the shocks increase the size of such a disparity, making it more difficult to ignore. The crises make the iceberg peak over the water. Our study attempts to explain this phenomenon by providing a perspective distinguishing between crisis and normalcy periods. From a positive angle, it should be noted that the COVID opened new windows of opportunities for self-employment to some segments (i.e., new telematic jobs from home), suggesting that a new normal of self-employability may be underway. But the “others” engaged in “everyday entrepreneurship” should not be ignored, people who are also protagonists of their own lives, families, communities, and contexts (Welter et al., 2017 ).

The countries included in this study are Norway, Netherlands, Latvia, United Kingdom, Germany, Spain, Italy, Croatia, Slovenia, Greece, Egypt, Iran, Israel, Brazil, Chile, and Colombia.

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Audretsch, D.B., Ghalwash, S. & Peña-Legazkue, I. The resilient self-employability of women and senior people after sudden economic shocks. Int Entrep Manag J (2024). https://doi.org/10.1007/s11365-024-00982-6

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