Pilot Study in Research

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A pilot study is a preliminary small-scale study that researchers conduct in order to help them decide how best to conduct a large-scale research project. Using a pilot study, a researcher can identify or refine a research question, figure out what methods are best for pursuing it, and estimate how much time and resources will be necessary to complete the larger version, among other things.

Key Takeaways: Pilot Studies

  • Before running a larger study, researchers can conduct a pilot study : a small-scale study that helps them refine their research topic and study methods.
  • Pilot studies can be useful for determining the best research methods to use, troubleshooting unforeseen issues in the project, and determining whether a research project is feasible.
  • Pilot studies can be used in both quantitative and qualitative social science research.

Large-scale research projects tend to be complex, take a lot of time to design and execute, and typically require quite a bit of funding. Conducting a pilot study beforehand allows a researcher to design and execute a large-scale project in as methodologically rigorous a way as possible, and can save time and costs by reducing the risk of errors or problems. For these reasons, pilot studies are used by both quantitative and qualitative researchers in the social sciences.

Advantages of Conducting a Pilot Study

Pilot studies are useful for a number of reasons, including:

  • Identifying or refining a research question or set of questions
  • Identifying or refining a hypothesis or set of hypotheses
  • Identifying and evaluating a sample population, research field site , or data set
  • Testing research instruments like survey questionnaires , interview, discussion guides, or statistical formulas
  • Evaluating and deciding upon research methods
  • Identifying and resolving as many potential problems or issues as possible
  • Estimating the time and costs required for the project
  • Gauging whether the research goals and design are realistic
  • Producing preliminary results that can help secure funding and other forms of institutional investment

After conducting a pilot study and taking the steps listed above, a researcher will know what to do in order to proceed in a way that will make the study a success. 

Example: Quantitative Survey Research

Say you want to conduct a large-scale quantitative research project using survey data to study the relationship between race and political party affiliation . To best design and execute this research, you would first want to select a data set to use, such as the General Social Survey , for example, download one of their data sets, and then use a statistical analysis program to examine this relationship. In the process of analyzing the relationship, you are likely to realize the importance of other variables that may have an impact on political party affiliation. For example, place of residence, age, education level, socioeconomic status, and gender may impact party affiliation (either on their own or in interaction with race). You might also realize that the data set you chose does not offer you all the information that you need to best answer this question, so you might choose to use another data set, or combine another with the original that you selected. Going through this pilot study process will allow you to work out the kinks in your research design and then execute high-quality research.

Example: Qualitative Interview Studies

Pilot studies can also be useful for qualitative research studies, such as interview-based studies. For example, imagine that a researcher is interested in studying the relationship that Apple consumers have to the company's brand and products . The researcher might choose to first do a pilot study consisting of a couple of focus groups in order to identify questions and thematic areas that would be useful to pursue in-depth, one-on-one interviews. A focus group can be useful to this kind of study because while a researcher will have a notion of what questions to ask and topics to raise, she may find that other topics and questions arise when members of the target group talk among themselves. After a focus group pilot study, the researcher will have a better idea of how to craft an effective interview guide for a larger research project.

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How to design a small research project

  • How to design a small…

A common theme on this blog is an attempt to provide guidance on the things that, as academics, we are meant to know how to do, but on which we rarely receive any explicit training. For today’s June Blog I thought I’d write one of these posts, about designing a small research project.

As people become more independent as academics, there’s a lot of small project supervision required.  By small, I’m talking about a project carried out as part of a taught Masters degree, or smaller. The former mostly takes place over about a 4 month period – though initial planning might happen much earlier – with relatively full-time focus available for the last 2.5 months. Smaller projects might include summer placements for visiting students (anything from 4-10 weeks) and undergraduate mini-projects. For example, at my University, medical students do “student selected components” in their 5th year, which last about 16 weeks but involve about 6 full weeks’ worth of dedicated project time. One thing to note is that some small project designs will need to be created for student groups – I’m not going to tackle the specific elements that apply to group work today, that’ll have to wait for another blog.

So let’s assume you have a single student joining you for something like 6-12 weeks of full-time work.  How do you help them design a project and achieve their goals?

Check the course requirements

I’ve supervised students on degree courses in departments of Psychology, Clinical Psychology, Linguistics, Education, Medicine – and if this has taught me anything, it’s to check the course handbook right at the start! Some programmes have specific rules about the kinds of data you need to work with – for example, whether students are expected to compile their own, new data set or not. You’ll also want to think about the perspective of the second marker.  If they are from a different disciplinary background to you, you want to make sure your student is deploying the kind of questions and methods they will expect to see.  So, for example, when supervising medical students I will try to make sure we are examining a question with clear clinical relevance, even though I’m not a medic myself.

Another key factor is to make sure you are informed about the deadlines – not just for the final project report but any interim milestones.  Some departments will have students present a poster about their project plan, or ask supervisors to confirm that they are satisfied with student progress at the project midpoint. Another rule might concern what you are allowed to comment on in terms of the final report.  Some courses only allow supervisors to comment on one full draft (I personally prefer to see a methods section + detailed outline for other report sections, and then a full draft) or to comment on everything except the discussion.  So make sure you are on the right side of all of this info from the outset.

Keep it small

The single biggest threat to a small student project is over-ambition. Students will often approach the work – understandably, and rightly so – as a chance to discover something important in their field.  But the honest truth is that masters projects rarely lead to important discoveries. The purpose of a masters degree is to learn how to do science, which may be slightly different from actually doing science. Yes, students are learning “on the job” and of course there are plenty of important scientific insights to be gained.  But both of these aims – student learning and scientific insight – will be most effectively achieved if the project design is modest in scale. A petite project delivered to a high standard will be a much better investment of your time and your student’s time than a large project full of compromises, delays and anxiety.

What does “reasonable” actually mean?

Well, here’s a few rules of thumb to help, noting that I and my students have broken these rules multiple times…

1. stick to a single methodology.  Mixed methods studies automatically entail more decision-making and are harder to write up. Also, you’re unlikely to have time to carry out each type of data collection sequentially, and so the end results may just contradict, rather than informing each other.

2. if you want to collect new data face-to-face, collect it from undergrads. Collecting data face to face – running experiments and doing IQ tests – takes a lot of time and effort to organise. If you are also trying to reach a specific population when you do this – neurodivergent children, adolescents with depression, carers of people with dementia – you will have many more hurdles to overcome in recruitment, study design and responsible management of data collection.

3. If you want to work with a particular population, keep it low impact for them. If you want to recruit people from a particular group, you are placing a burden on individuals who probably already have a lot going on in their lives, to also engage with your research. In an ideal world, this kind of work is developed gradually and carefully in partnership with stakeholders, and has a plan for implementation of the findings.  These steps are virtually impossible to squeeze in to a small project and so in-person working with any kind of atypical population needs to be as low impact as possible. Think about phone / video interviews, a (short) online survey or maybe an online focus group.

4. The topic matters too. Yes, you might be interested in the intersection of homophobia and ableism, but do consider whether this small student project is the right forum for addressing such a potentially difficult topic. It might be – a lot depends on the life experiences of the student of course – but as a supervisor, don’t shy away from directing your student down a path carries less risks for participants.

5. Ask a question you can actually answer. I’ve had students come to me before wanting to do a project about something like emotion perception and autism. This is a literature that is absolutely rife with contradictory small studies, none of which do much to enlighten, let alone improve the lives of autistic people. Another small study is unlilkely to resolve the complex debates in the field. So instead try to find an area where even a very small amount of new information might add value.

Be Creative

All this is not meant to limit you to a “boring” project.  Instead, try to be creative.  Can your student identify an important and under-studied intersection and gain some insights into something barely understood?  Could interviews with autistic teachers, doctors, nurses or psychologists yield useful insights for practice? What are the experiences of parents of autistic children with visits to the dentist? Another fruitful angle is to explore some routine outputs from your field, and extract insights about dominant theory or language. For example, would a systematic analysis of the last ten years of conference proceedings tell you about shifts in the discourse? What about a content analysis of policy documents relating to your field? This can be a really accessible project to do – with no ethics required, the data freely available and straightforward to code – that also delivers important new knowledge. It might be a great option for a student who is also working part-time or has a health condition that impacts their work, who needs to be able to work flexibly.

Be Practical

Getting ethical approval is one of the major barriers for a small project because it can take a long time and cause significant delays. For shorter projects then, I would try to stick to analysis of existing data (where permission is already in place), literature review, or analysis of data in the public domain. That said, the process of seeking ethical approval is very useful – it helps you articulate exactly what you propose to do – and so if you don’t decide to collect your own new data, you might still want to think about writing a protocol for what you will do. Remmeber as well that “analysis of existing data” isn’t always as simple as it sounds.  Getting hold of the data, understanding the data, checking quality, dealing with missing data – all of these things can take time and should not be underestimated. Make sure you scope out the data availability at a very early stage.

Another practical dimension to consider is cost.  Lots of students will be unaware that many assessments – questionnaires etc – cost money.  Even if your department can loan them an assessment kit, they may need to pay for record forms for each participant. Make sure you and the student know what budget is available – if any – and make a plan that fits with what you can afford.

And finally

Once you have your plan in place, work with your student to break it down into manageable pieces, and plan for supervisions at the key turning points in the work. In other words, map your supervision onto the project – as far as you can – rather than sticking to a supervision schedule that is the same for everyone. Hopefully this will mean you step in at the right moment to help them make decisions.

If you can keep your student projects modest in scale, hopefully the end result will be a high quality piece of work that they can be proud of. It’s quality, not quantity, that counts.

Pilot Study in Research: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design.

Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.

How Does it Work?

Pilot studies are a fundamental stage of the research process. They can help identify design issues and evaluate a study’s feasibility, practicality, resources, time, and cost before the main research is conducted.

It involves selecting a few people and trying out the study on them. It is possible to save time and, in some cases, money by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e., unusual things), confusion in the information given to participants, or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling.”

This enables researchers to predict an appropriate sample size, budget accordingly, and improve the study design before performing a full-scale project.

Pilot studies also provide researchers with preliminary data to gain insight into the potential results of their proposed experiment.

However, pilot studies should not be used to test hypotheses since the appropriate power and sample size are not calculated. Rather, pilot studies should be used to assess the feasibility of participant recruitment or study design.

By conducting a pilot study, researchers will be better prepared to face the challenges that might arise in the larger study. They will be more confident with the instruments they will use for data collection.

Multiple pilot studies may be needed in some studies, and qualitative and/or quantitative methods may be used.

To avoid bias, pilot studies are usually carried out on individuals who are as similar as possible to the target population but not on those who will be a part of the final sample.

Feedback from participants in the pilot study can be used to improve the experience for participants in the main study. This might include reducing the burden on participants, improving instructions, or identifying potential ethical issues.

Experiment Pilot Study

In a pilot study with an experimental design , you would want to ensure that your measures of these variables are reliable and valid.

You would also want to check that you can effectively manipulate your independent variables and that you can control for potential confounding variables.

A pilot study allows the research team to gain experience and training, which can be particularly beneficial if new experimental techniques or procedures are used.

Questionnaire Pilot Study

It is important to conduct a questionnaire pilot study for the following reasons:
  • Check that respondents understand the terminology used in the questionnaire.
  • Check that emotive questions are not used, as they make people defensive and could invalidate their answers.
  • Check that leading questions have not been used as they could bias the respondent’s answer.
  • Ensure that the questionnaire can be completed in a reasonable amount of time. If it’s too long, respondents may lose interest or not have enough time to complete it, which could affect the response rate and the data quality.

By identifying and addressing issues in the pilot study, researchers can reduce errors and risks in the main study. This increases the reliability and validity of the main study’s results.

Assessing the practicality and feasibility of the main study

Testing the efficacy of research instruments

Identifying and addressing any weaknesses or logistical problems

Collecting preliminary data

Estimating the time and costs required for the project

Determining what resources are needed for the study

Identifying the necessity to modify procedures that do not elicit useful data

Adding credibility and dependability to the study

Pretesting the interview format

Enabling researchers to develop consistent practices and familiarize themselves with the procedures in the protocol

Addressing safety issues and management problems

Limitations

Require extra costs, time, and resources.

Do not guarantee the success of the main study.

Contamination (ie: if data from the pilot study or pilot participants are included in the main study results).

Funding bodies may be reluctant to fund a further study if the pilot study results are published.

Do not have the power to assess treatment effects due to small sample size.

  • Viscocanalostomy: A Pilot Study (Carassa, Bettin, Fiori, & Brancato, 1998)
  • WHO International Pilot Study of Schizophrenia (Sartorius, Shapiro, Kimura, & Barrett, 1972)
  • Stephen LaBerge of Stanford University ran a series of experiments in the 80s that investigated lucid dreaming. In 1985, he performed a pilot study that demonstrated that time perception is the same as during wakefulness. Specifically, he had participants go into a state of lucid dreaming and count out ten seconds, signaling the start and end with pre-determined eye movements measured with the EOG.
  • Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study (Richins, 1983)
  • A pilot study and randomized controlled trial of the mindful self‐compassion program (Neff & Germer, 2013)
  • Pilot study of secondary prevention of posttraumatic stress disorder with propranolol (Pitman et al., 2002)
  • In unstructured observations, the researcher records all relevant behavior without a system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.
  • Perspectives of the use of smartphones in travel behavior studies: Findings from a literature review and a pilot study (Gadziński, 2018)

Further Information

  • Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004). Design and analysis of pilot studies: recommendations for good practice. Journal of evaluation in clinical practice, 10 (2), 307-312.
  • Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., … & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10 (1), 1-10.
  • Moore, C. G., Carter, R. E., Nietert, P. J., & Stewart, P. W. (2011). Recommendations for planning pilot studies in clinical and translational research. Clinical and translational science, 4 (5), 332-337.

Carassa, R. G., Bettin, P., Fiori, M., & Brancato, R. (1998). Viscocanalostomy: a pilot study. European journal of ophthalmology, 8 (2), 57-61.

Gadziński, J. (2018). Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study. Transportation Research Part C: Emerging Technologies, 88 , 74-86.

In J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70 (6), 601–605. https://doi.org/10.4097/kjae.2017.70.6.601

LaBerge, S., LaMarca, K., & Baird, B. (2018). Pre-sleep treatment with galantamine stimulates lucid dreaming: A double-blind, placebo-controlled, crossover study. PLoS One, 13 (8), e0201246.

Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of psychiatric research, 45 (5), 626–629. https://doi.org/10.1016/j.jpsychires.2010.10.008

Malmqvist, J., Hellberg, K., Möllås, G., Rose, R., & Shevlin, M. (2019). Conducting the Pilot Study: A Neglected Part of the Research Process? Methodological Findings Supporting the Importance of Piloting in Qualitative Research Studies. International Journal of Qualitative Methods. https://doi.org/10.1177/1609406919878341

Neff, K. D., & Germer, C. K. (2013). A pilot study and randomized controlled trial of the mindful self‐compassion program. Journal of Clinical Psychology, 69 (1), 28-44.

Pitman, R. K., Sanders, K. M., Zusman, R. M., Healy, A. R., Cheema, F., Lasko, N. B., … & Orr, S. P. (2002). Pilot study of secondary prevention of posttraumatic stress disorder with propranolol. Biological psychiatry, 51 (2), 189-192.

Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47 (1), 68-78.

Sartorius, N., Shapiro, R., Kimura, M., & Barrett, K. (1972). WHO International Pilot Study of Schizophrenia1. Psychological medicine, 2 (4), 422-425.

Teijlingen, E. R; V. Hundley (2001). The importance of pilot studies, Social research UPDATE, (35)

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What does it mean to ‘scale up’ your research?

‘Scaling up’ is an important aspect of implementation research, defined by the World Health Organisation as ‘[the expansion and replication of] innovative pilot or small-scale projects to reach more people and/or broaden the effectiveness of an intervention.’

While scale up is acknowledged as a key means to disseminate and propagate proven health interventions, there are many practical considerations and challenges that researchers must consider. These include the feasibility of the project, its translatability into different contexts, cost-effectiveness, the appropriateness of the delivery strategy, and socio-political factors.

Scale up in action

In 2018, we dedicated over $50 million to the scale up of evidence-based interventions for the prevention or management of hypertension and diabetes. The call awarded 27 projects in total, uniting researchers from over 40 countries across the world.

One project funded through this call is the INTE - AFRICA project , which aims to determine the feasibility of scaling up pilot studies on the prevention and management of HIV , diabetes and hypertension in Tanzania and Uganda.

By decentralising diabetes and hypertension treatment to community care clinics, it is hoped to prevent complications and reduce the reliance of patients on primary care centres. Researchers will determine the acceptability of this approach to patients and the community, assess the numbers of patients treated and their outcomes, and the cost effectiveness compared to standard care.

Researchers involved in this project were awarded the ‘International Collaboration of the Year’ at The Times Higher Education Awards in 2021 for their collaborative and inclusive research partnerships.

Get involved

In May 2022, we are delivering our first ever Implementation Science Masterclass dedicated to developing capacity among established researchers and implementers in the methodology and science of scale up.

We are inviting applications from teams of two to participate in the free, online Masterclass, where our expert faculty will use experimental learning to guide the development of a project concept. Our faculty include Mark Huffman and Lisa Hirschhorn from the GACD -funded NaSS project , and implementation science expert Brian Oldenburg.

Find out more and apply.

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Sample Size and its Importance in Research

Chittaranjan andrade.

Clinical Psychopharmacology Unit, Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary sample size is set for a pilot study. This article discusses sample size and how it relates to matters such as ethics, statistical power, the primary and secondary hypotheses in a study, and findings from larger vs. smaller samples.

Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population. This is best ensured by the use of proper methods of sampling. The sample must also be adequate in size – in fact, no more and no less.

SAMPLE SIZE AND ETHICS

A sample that is larger than necessary will be better representative of the population and will hence provide more accurate results. However, beyond a certain point, the increase in accuracy will be small and hence not worth the effort and expense involved in recruiting the extra patients. Furthermore, an overly large sample would inconvenience more patients than might be necessary for the study objectives; this is unethical. In contrast, a sample that is smaller than necessary would have insufficient statistical power to answer the primary research question, and a statistically nonsignificant result could merely be because of inadequate sample size (Type 2 or false negative error). Thus, a small sample could result in the patients in the study being inconvenienced with no benefit to future patients or to science. This is also unethical.

In this regard, inconvenience to patients refers to the time that they spend in clinical assessments and to the psychological and physical discomfort that they experience in assessments such as interviews, blood sampling, and other procedures.

ESTIMATING SAMPLE SIZE

So how large should a sample be? In hypothesis testing studies, this is mathematically calculated, conventionally, as the sample size necessary to be 80% certain of identifying a statistically significant outcome should the hypothesis be true for the population, with P for statistical significance set at 0.05. Some investigators power their studies for 90% instead of 80%, and some set the threshold for significance at 0.01 rather than 0.05. Both choices are uncommon because the necessary sample size becomes large, and the study becomes more expensive and more difficult to conduct. Many investigators increase the sample size by 10%, or by whatever proportion they can justify, to compensate for expected dropout, incomplete records, biological specimens that do not meet laboratory requirements for testing, and other study-related problems.

Sample size calculations require assumptions about expected means and standard deviations, or event risks, in different groups; or, upon expected effect sizes. For example, a study may be powered to detect an effect size of 0.5; or a response rate of 60% with drug vs. 40% with placebo.[ 1 ] When no guesstimates or expectations are possible, pilot studies are conducted on a sample that is arbitrary in size but what might be considered reasonable for the field.

The sample size may need to be larger in multicenter studies because of statistical noise (due to variations in patient characteristics, nonspecific treatment characteristics, rating practices, environments, etc. between study centers).[ 2 ] Sample size calculations can be performed manually or using statistical software; online calculators that provide free service can easily be identified by search engines. G*Power is an example of a free, downloadable program for sample size estimation. The manual and tutorial for G*Power can also be downloaded.

PRIMARY AND SECONDARY ANALYSES

The sample size is calculated for the primary hypothesis of the study. What is the difference between the primary hypothesis, primary outcome and primary outcome measure? As an example, the primary outcome may be a reduction in the severity of depression, the primary outcome measure may be the Montgomery-Asberg Depression Rating Scale (MADRS) and the primary hypothesis may be that reduction in MADRS scores is greater with the drug than with placebo. The primary hypothesis is tested in the primary analysis.

Studies almost always have many hypotheses; for example, that the study drug will outperform placebo on measures of depression, suicidality, anxiety, disability and quality of life. The sample size necessary for adequate statistical power to test each of these hypotheses will be different. Because a study can have only one sample size, it can be powered for only one outcome, the primary outcome. Therefore, the study would be either overpowered or underpowered for the other outcomes. These outcomes are therefore called secondary outcomes, and are associated with secondary hypotheses, and are tested in secondary analyses. Secondary analyses are generally considered exploratory because when many hypotheses in a study are each tested at a P < 0.05 level for significance, some may emerge statistically significant by chance (Type 1 or false positive errors).[ 3 ]

INTERPRETING RESULTS

Here is an interesting question. A test of the primary hypothesis yielded a P value of 0.07. Might we conclude that our sample was underpowered for the study and that, had our sample been larger, we would have identified a significant result? No! The reason is that larger samples will more accurately represent the population value, whereas smaller samples could be off the mark in either direction – towards or away from the population value. In this context, readers should also note that no matter how small the P value for an estimate is, the population value of that estimate remains the same.[ 4 ]

On a parting note, it is unlikely that population values will be null. That is, for example, that the response rate to the drug will be exactly the same as that to placebo, or that the correlation between height and age at onset of schizophrenia will be zero. If the sample size is large enough, even such small differences between groups, or trivial correlations, would be detected as being statistically significant. This does not mean that the findings are clinically significant.

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There are no conflicts of interest.

Small-Scale Research Project

SMALL-SCALE RESEARCH PROJECT [Small-Scale Research Project for Real-estate] Table Of Contents Research Methodology: Quantitative Research1 Benefits of Qualitative Research:1 Considerations:1 Mono Method Research Strategy:2 Justification:2 Methods of Data Collection:3 Types of Questions to be designed:3 Open-Ended Questions-Benefits:3 Nominal Data-Benefits:4 Data Sources:4 Gantt Chart6 Discussion of Key Milestones:6 Problems Associated with Research Execution & Recommended Solutions:7 Ethical Considerations in Real-Estate Research Project:7 False or Luring Advertising:7 Respect of Customer Privacy:7 Consensual Research Methodology and Proceedings:7 Ensuring Awareness among the Customers:8 Third Party Research Obligations:8 References9 Appendices10 Small-Scale Research Project for Real-Estate Research Methodology: Quantitative Research The decision of Quantitative or Qualitative research is the basic paradigm that governs the entire inquiry process. For the case mentioned, typically for a management research to gauge “Customer Satisfaction” of a small scale project, I recommend making use of a Quantitative approach. To enable the project manager to grasp an overview of the scenario according to the views of the company and the participants (customers), the use of Ethnographies to study the culture prevalent in the Real Estate industry; specifically in that locality is advised. Benefits of Qualitative Research: As the project is of small scale excessive data collection for all localities is not advised. Budget constraints play a major role in small scale projects and therefore Qualitative approach is most suitable according to critics in such cases where the demand is not subject to unpredictable variations. Also as the company aims to address its already aware clients and customers, there need not be much investment for the research process itself. The results, if favorable, should be used to decide the degree of further investigation. For instance, if the customers show a positive response or inclination towards the growth in the industry; the manager may further pursue the same research problem verification. According to Gall, Borg, and Gall (1996), Qualitative research is beneficial in case where participant intention is a key factor in any scenario. The acceptance of views pertaining to real estate project in this particular locality will be affected by the culture, norms and values prevalent in that society. The method of qualitative research will help the customers attach themselves emotionally to the project. The qualitative research will serve as a way of studying the customer buying behavior and the trend of the potential market. The reason why the use of qualitative research is recommended initially is because it relies on minimum population estimates thus leading to low statistical inference errors. The population size is quite observable and the errors not high due to small sample estimations. Considerations: The method of qualitative research must be reliable and authentic. The manager should opt for a systematic review and find only the most relevant data. Further investigation via observations, interviews of the target market sample and usage of focus groups that shall help assess the societal trend and culture are recommended. Once the data has been collected the manager should analyze the data by judging its effect on the company goals and the market. In case of outliers, the reasons for the same and their clarification must be sought out. Minimum biases should be entertained while analyzing the validity of the ...

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what is a small scale research project

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what is a small scale research project

  • Education, training and skills

Small-scale research projects: summaries

Brief outlines of projects commissioned through the DfE analytical associate pool.

Applies to England

Analytical associate pool: summary of projects, march 2021.

Ref: ISBN 978-1-83870-196-3, DFE-RR991

PDF , 175 KB , 15 pages

Analytical associate pool: summary of projects, November 2020

PDF , 183 KB , 9 pages

Analytical associate pool: summary of projects, July 2019

Ref: ISBN 978-1-83870-051-5, DFE-RR946

PDF , 75.3 KB , 13 pages

Analytical associate pool: summary of projects, June 2019

Ref: ISBN 978-1-83870-037-9, DFE-RR933

PDF , 774 KB , 21 pages

Analytical associate pool: summary of projects, March 2019

Ref: ISBN 978-1-83870-005-8, DFE-RR916

PDF , 2.98 MB , 22 pages

Analytical associate pool: summary of projects, January 2019

Ref: ISBN 978-1-78105-989-0, DFE-RR901

PDF , 272 KB , 16 pages

Analytical associate pool: summary of projects, March 2018

Ref: ISBN 978-1-78105-883-1, DFE-RR798

PDF , 163 KB , 12 pages

Analytical associate pool: summary of projects, August 2017

Ref: ISBN 978-1-78105-793-3, DFE-RR718

PDF , 381 KB , 37 pages

Analytical associate pool: summary of projects, May 2016

Ref: ISBN 978-1-78105-591-5, DFE-RR534

PDF , 157 KB , 12 pages

Analytical associate pool: summary of projects, December 2015

Ref: ISBN 978-1-78105-542-7, DFE-RR488

PDF , 266 KB , 17 pages

The analytical associate pool consists of more than 150 independent academics and researchers who carry out small-scale data analysis, rapid literature reviews, primary research, and peer review, for the Department for Education. Much of the analysis summarised is too small-scale for publishing on its own.

For further information on any of these projects contact:

Analytical associate pool

Email [email protected]

Added 'Analytical associate pool: summary of projects, March 2021.'

Added Analytical associate pool: summary of projects, November 2020.

Added 'Analytical associate pool: summary of projects, June 2019'.

Added Analytical associate pool: summary of projects, June 2019.

Replaced the March 2019 report with a new version that clarifies information in the section 'Promoting integration in schools'.

Added 'Analytical associate pool: summary of projects, March 2019'.

Added 'Analytical associate pool: summary of projects, January 2019'.

Added 'Analytical associate pool: summary of projects, March 2018' report.

Added 'Analytical associate pool: summary of projects, August 2017' report.

Added 'Analytical associate pool: summary of projects, May 2016' report.

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Small scale scrum, about this publication.

How you scale a process like Scrum has received a lot of attention in recent years. Every time we think of scale we think of big numbers, more complexity, more coordination and more headaches to handle. We went and scaled Scrum, only in the opposite direction. Learn how we went about researching and innovating on the Scrum Framework to arrive at a variant of Scrum that we call Small Scale Scrum. Learn how we applied Scrum successfully to projects where we had 1-2 people available. The problems, the modifications and the journey that we undertook are now ready to be shared with the world.

1.     INTRODUCTION

Scrum is increasingly becoming the leading framework of choice when a team embarks on the journey towards the Agile mindset. It brings with it a lot of benefits that teams, their stakeholders and their customers resonate with. Transparency in how things work, faster to market through releasable increments that deliver value early and often and simply having a closer look at how your product is evolving are some of the attractions towards choosing Scrum. Such is the popularity of Scrum, it has become the zeitgeist of our Agile times. Scrum has undergone a number of changes in recent years with modifications to the Scrum Guide changing the recommended team sizes. This has allowed flexibility for smaller teams to follow the Scrum Guide and achieve success. However, the Scrum Guide makes no suggestions as to how to run a Scrum team with less than the minimum recommended number, which currently suggests 3 people!.

Most research and innovations are focusing on how to run Scrum with multiple teams interconnected and coordinating as one. This is scaling in the classic sense, but  how do you ensure the same success and quality in teams that need to scale down below the minimum? How do you deliver the value that the customer wants, while ensuring that you follow a process that they wish to participate in? How do you scale scrum, down? In Red Hat, we have a number of teams that deliberately work in small teams. Our Professional Services or Consulting teams work with partners and customers to deliver value through small, targeted teams. These are short term engagements, with minimal number of people working on them due to the budget being fixed. These projects need to deliver the customers goals, they need to uphold the quality and reputation of Red Hat and they need to start quickly and deliver value immediately.  They also need to adhere to the guidelines and wishes of the Customer, which includes alignment with their process framework of choice. Our style of work was a prime target for innovation and became the main focal point for figuring out how to apply Scrum to small teams.

We also had other opportunities to innovate with smaller teams. Our Open Source contributions are delivered through 1-2 person initiatives on services and mini products in their own right. Our internship program brings us students who are required to complete a capstone project as a lone developer. What has become the norm for us is customers, our community and our partner universities demanding that we use Scrum. The perceived benefits from their perspective as our customers are enough to make this demand when engaging with us. This is particularly true for our paid for consulting work which forms the bulk of our experience. This insistence on using a framework that does not provide guidance for how to operate in such small numbers both surprised us and challenged us. It motivated us to research how Scrum could work in such limited scenarios and the result of this has been a 2-year journey to develop, iterate on and promote a way of working that suits small teams. This is our experience.

2.     BACKGROUND

I will never forget the first experience of working in a Scrum team trying to adhere to the Scrum Guide. I was a fresh graduate developer working in a research center and our team was about to embark on a journey in Agility with Scrum at the heart of it. I, Leigh, couldn’t believe the freedom that this way of working was bringing. I was being heard. I felt empowered and I started questioning why we were deviating from the Scrum Guide and how we could improve as a team. That experience was repeated for several years in different teams and companies and really culminated in me transitioning into a ScrumMaster. I felt I was a natural coach and my PhD was centered around how groups of people and services formed, with an emphasis on the social aspects of team formation. That insight allowed me to grow my career as an Agile enthusiast and over time I fulfilled all roles within the Scrum Guide. My PhD was also a turning point as it was a solo body of work that I simply had to deliver with no real support. I loosely following a Scrum based approach as I knew it would help me focus on Value and keep me honest in retrospectively analysing my papers and thought process with my supervisor. That was the first time that I delivered a Scrum enabled project as a single person.

My work in companies brought me into contact with student interns who from a retention perspective we would help support in their final year capstone project. I encouraged all students to follow some form of process to keep them sane and ultimately deliver their project on time and with high marks. Loosely following Scrum and the visuals of Kanban really helped me see the potential for applying a full blown team orientated framework on a single person project. It really piqued my interest but my research career ended as I moved towards pastures new of working in industry on more tangible real world projects with real paying customers. The long and winding road brought me to Red Hat in 2015 where as a People Manager with a specialism in Process Improvement, I have found my career best role. I get to manage a team that follows both Scrum and Kanban to deliver Products to our customers and indeed our Community. I get to work with people on their problems and help them become better versions of themselves and help the team to succeed. And I get paid for it, can you believe that?

This role saw me embark on several Agile transformations across teams and products. It also brought me back to working with college students as Red Hat places a huge value on connecting with local universities and helping students experience our way of working. This sees us engage in multi month work placements and donate our time to mentor final year projects. Seeing the stress and pressure that the students came under and having already had some mild success in this area of delivering a prolonged body of work in a solo mode, I experimented with modifications to Scrum to enable a student to plan, prioritise and deliver value through their final year project. The 6 students who followed this over 3 years received the highest marks in their class, with the reviewers commenting on the professionalism of the project’s planning and execution. I had worked indirectly with Agnieszka in our office and over coffee one day I enquired about how her Masters was going. She was struggling for a topic for her thesis and we had connected about Agile initiatives several times in the past. My light bulb moment came and I pitched the idea of formally researching scaling scrum down. This led me, Leigh, to mentor Agnieszka in her Masters thesis.

I came from a very different background to Leigh and took a winding road into IT as a profession. I graduated with a degree in Law and embarked on legal professional career but never quite had the passion for it. I was always intrigued by software and problem solving and spent most of my secondary school days in the early 2000s programming. Unfortunately, that early experience didn’t convince me to pursue it as a career and also the risk of moving into software development career versus a stable well paying job in Law was something that held me back. My husband had a similar thought process and when he realised that he had a real potential in computer programming career, he left his job to go back and take on a 4 year computer degree. Through his college experience, I was amazed how much software development changed from the early 2000s and that gave me the encouragement to go ahead and try and realise my once unwanted career and transition into software development.

Thankfully with an existing qualification and some prior knowledge of programming, I just had to complete a 1 year Higher Diploma in Computer Science. That allowed me to start in a software company specialising in R&D software projects which eased my entry into the IT industry and fundamentally prepared me for my new career. As a new person to this industry, but with great ambition to succeed and equally the fear and self doubt at times you would expect, I discovered that this team had a really open way of communicating and planning work. I didn’t feel like I was inexperienced and my thoughts and opinions mattered. This Scrum process combined with my prior customer focused mindset was making my life easier and when I moved on in my career and joined Red Hat as a Consultant, I discovered a new way of working. As a paid for Consultant, we are billable by the hour or day. Customers want to maximise their value and their budget so the projects I worked on were now staffed with 1 to 2 people and a part time project manager on oversight. This gave me huge experience with various roles in a team as day to day you need to wear so many hats. My specialism was as a Software Engineer writing frontend code then switch to backend work and as a Quality Engineer every few weeks before final releases, then due to demand I briefly moved into more of a Quality Engineer role but day to day I could be writing frontend code and then switch to some backend work and finally into quality role.

Roles switching is very common and dictated by project needs, giving associates the maximum experience of delivering complete and high quality enterprise software products. Customers employing an outside Consultancy house ultimately have to own that product or functionality that you create. That means contractually we look at minimising the friction between our style of working and that of the Customer. We align on tools, on programming languages and on other technical elements. In recent years we are starting to see an alignment on methodologies. Our Customers often have appointed Product Owners and their teams are running as a Scrum team. As such, they want us to run in a Scrum manner. They love the pause and inspect opportunities as in a finite time project (typically under a month), the chance to inspect and adapt is crucial. Red Hat has worked on a number of improvements in methodology and process and our  Open Innovation Labs  has helped to make a much more fluid and Agile experience for our customers which helps to train teams in order to achieve the shared goals. Given my relatively accelerated entry to this industry I decided to go back to college part time and address some of the gaps that a 1 year transition course could never cover. My MSc gave me a lot of technical skills and coupled with my day to day I really gained the strength and confidence I needed. Now I had to do a capstone thesis, a 6 month or so research project. I really didn’t want to go deep on another language or tool or tech in general as I was at saturation point! A chance coffee with Leigh and I find myself researching Scrum. I thought I knew a lot about Scrum but now I had to figure out how do we adapt it to work with these small teams. I had a ready-made test group in my colleagues and an enthusiastic mentor. What more could I ask for!

2.1        The formalization of Small Scale Scrum

In early 2018 we set about formalising Small Scale Scrum as Agnieszka began her research on her thesis area. We wanted to frame our research around a theme, that theme was Quality. Quality is the hallmark of a good project, it is what companies like Red Hat build their reputation on and sustainability of projects is non-negotiable goal. Agnieszka focused on this from the professional consultancy projects. We looked at the Agile Manifesto and the Scrum Guide and took a pass through it from the perspective of what modifications would be needed to represent the concerns of teams of 1-2 people working on an initiative. Working for a company like Red Hat that has very strong values and principles has really stood to us while designing and thinking our way through this topic. If you are ever trying to make a culture of innovation and Agility work in your organisation, consider what your values and principles are. They can make or break and initiative in our experience of having worked in companies that did not have such a strong guiding hand. Our way of working in Red Hat is both refreshing and novel and we would highly recommend a values-driven approach to how you, our readers, run your team.

Small Scale Agile Manifesto

The manifesto obviously does not recommend team sizes or implementation details and is very much at a philosophical level. Therefore we did not make any modifications and we wished to keep the spirit of the Manifesto intact. However we wished to provide some additional values to the to help emphasise the need to be more aware of the possible pain points and challenges that we had identified. The following are the values that we arrived at but we styled them in the phrasing of the Manifesto for consistency:

Wide Communication over narrow . We found that teams often use information radiators and have a heavy narrow focus within the team. That behaviour is harmful in small teams so defaulting to wider stakeholders to promote transparency is our recommendation here. We were very surprised to note how few communications come from a normal sized Scrum team on a day-to-day basis. We observed communication peaks at sprint boundaries but the teams were very insular outside of that. Our thoughts here were early and often communication to get out in front of any blockers or misinterpretations that might occur.

Team feature delivery over individual responsibility .  In big teams, despite work being on a board and in theory anyone able to take work and progress it, specialists attract to their work area consistently. Leigh in particular observes this daily in his larger teams and mini silos of responsibility and singular points of failure start to emerge unless dealt with by a good ScrumMaster and an even better self-organising team. By promoting a team mentality of overall feature delivery, Agnieszka discovered on her previous projects that the very best Consultants would happily step out of their comfort zones and put the project needs first.

Quality delivery over speed of development . As quality was our main challenge we wanted to enshrine it in our manifesto. Rapid development is often at odds with quality delivery but finding that balance of best practices, good tooling and expectation setting was going to be the key for how we envisioned this working.

Multiple project responsibilities over fixed assignment .  Agnieszka in particular wanted to reinforce that you can’t have a singular role in a project where the full software development life cycle is to be experienced. As students, Leigh wanted to emphasise that the success of the project is all about switching mindset into another role and fulfilling that.

Accelerating innovation over marginal request driven thinking.  Our jobs as Engineers is to help interpret what a customer wants and help them arrive at what they need. The temptation is to deliver exactly what they requested and follow that strictly. Innovation is what customers pay companies like Red Hat for and having the seeds of innovation planted within the teams thinking was going to be critical to the overall success and sustainability of the project completed.

Customer growth over customer engagement.  A successful engagement is obviously important but we wanted our customers to grow and learn on this journey as well, given they are a key stakeholder. Growth or creation of business is what our customers highest priority is and it should therefore be the teams highest.

Small Scale Scrum Principles

We created four guiding principles based on experiences in small scale consulting projects to complement our Manifesto:

Value-Based Communication  – With finite time, all communication should be valuable.

Quality-First Development  – This principle focuses on taking a quality approach to software development each Sprint.

Delivery Ownership  – This principle is about taking initiative in driving software delivery by a Development Team on a Sprint-to-Sprint basis.

Iterative Sign Off  – Identifying gaps in requirements through an iterative sign off approach is something we found very important.

Small Scale Scrum Framework

Now that we had a guiding manifesto, principles and guidelines, we set about designing modifications to the Scrum Framework to arrive at our version of the Small Scale Framework presented in Figure 1.

what is a small scale research project

Let’s focus on the changes that we had to bring in. First off, there is no Sprint Backlog and Project Backlog. In a small scale project, the duration of the project is finite, so we amalgamated those concepts into one singular Project Backlog. It is a list of everything that is known to be required in the project and is interspersed with User Stories and the technical tasks that are required to support and realise them. For us, this was hugely important for our consultancy projects as it highlighted to the customer how much work is actually involved in realising a solution. Often with our user stories we want to stay at the What and Why level of discussions but the How is really important here to make informed decisions in a time sensitive manner. The team really appreciated having their viewpoint shared so widely and knowing that the entire backlog was the known scope gave them a great sense of progression as we burned down towards our goals.

The sprint here is something that we recommended with an upper bound of 2 weeks but may be the order of days instead of a minimum of a week. The recommendation of no less than 1 week and no more than 30 days is not really workable in a small project where the timeline may actually be 30 days or less. The fluidity is needed by the team and the lightweight nature of the ceremonies around it allows for this shorter turnaround.

An interesting addition was that of the Proof of Concept (PoC) / Demo. Now you may be thinking that is the Sprint Review right? Sprint Reviews are where our stakeholders come in and have the accept/reject goals of the Sprint or more accurately the increment that is created. As the team may have very short and variable sprint durations to tackle key pieces of the project backlog, we wanted to have some flexibility here to have an internal PoC or Demo. This is very much a rough prototype phase to ensure that our direction is sane and that the team has much more flexibility in their creativity and can take chances that they ordinarily would not want to try with the more formal demo where a release or drop of the functionality is expected. As such, this additional step is important for the confidence of the team and enables us to innovate in a safe environment.

2.2        Informing our Approach

In parallel to our creation of the values and principles, we decided to take a broader look at Agile and Scrum to allow our findings here feed into our inspect:adapt loop. The actions taken included conducting interviews with Agile Practitioners running Scrum and Agile variants in large projects within Red Hat and running a “Small Scale Scrum vs Regular Scrum” survey in order to understand how people felt about using Scrum in their teams of varied sizes. We wanted to extract Scrum in its large context to see what could work for small teams in short term engagements. We were interested in what worked, what didn’t work, what could we change/improve. We then fed survey and interviews results directly into Adaptation / Test of selected Scrum Ceremonies and Principles step of the framework’s formulation process.

Some responses within the survey came as a surprise to us. A big one, which we have seen first hand in our own projects, is that teams do not adopt a Definition of Ready (DoR). This resulted in work commencing that lacked all of the information which ended up being gathered in flight. That presented obvious challenges which from our own personal projects that we analysed resulted in missed requirements and even bugs. It was refreshing to see that more mature teams had not adopted this and we dug a little bit deeper. Scrum teams that had not adopted it were generally not on a tight timeframe. It was fine for them to start work and use the sprint increment to try and sharpen up on the acceptance criteria. Their Product Owner was not a traditional Product Owner role. On going deeper again we found that a lot of Scrum teams simply don’t have a full time dedicated Product Owner. One of the 3 core roles was simply not being fulfilled so the teams were following the framework but lacking the oversight, direction and value setting that a Product Owner brings. Pulling that simple thread of the DoR led us to realise that teams that self identify as being a Scrum team are not adhering to the Scrum Guide definitions. That gave our Small Scale Scrum adaptation a real boost as the modifications and tweaks we had to make would never be viewed as pure Scrum. Yet our version of Scrum seemed to have a lot more stability, value and quality than the traditional approach taken by the developers, Quality Analysts, ScrumMasters, Product Owners and management teams that formed part of our survey of Agile practitioners.

2.3        Our capstone project

After a lot of research, data gathering and trialing aspects of the approach through a number of projects, we had the version of Small Scale Scrum outlined above. We wanted to tackle a project in a capstone kind of manner to really get a sense of the benefits that Small Scale Scrum could bring. We led a final year college project from September 2018 through to April 2019 with a student called Ciaran Roche. Ciaran wrote this in his final project write up in relation to the up front planning and stocking of his project backlog:

“It showed me the importance of initially understanding a problem. While the process of splitting a problem into small tasks can be quite time consuming and tedious, doing this at a granularity at which I never had to sole responsibility to do before proved to pay dividends as the project progressed. This exposed and opened new and previously unthought of avenues throughout the project. Overall this increased the project’s value and when it came time to developing these tasks there was zero confusion allowing the completion of these tasks happen in a fluid and consistent manner.”

Ciaran had some strong reflections on the process:

“Numerous times throughout this project I questioned was this the correct methodology to suit a Final Year Project (FYP). Now retrospectively looking back I feel it was the correct choice, while my arguments against scrum revolved around it not being dynamic enough, as college circumstance change so quickly, it was often hard to adapt sprints to suit. While this is a valid argument, I feel the commitment of Small Scale Scrum is needed for a project like this, in that as college circumstances change you are obligated to finish all tasks in a current sprint. This ensures work gets completed regardless, and progress is consistently made. I feel being more dynamic would lead to less completed work with added pressure on the final deadline.”

The comments about being dynamic and flexible are something that Leigh is going to use to continue the adaptive nature of evolving Small Scale Scrum and in part has contributed to our thoughts on the Sprint duration being possibly as low as days versus a full week.

2.4        What We Learned

For Leigh, as someone who works in the coaching mindset area, the journey through Small Scale Scrum was incredibly challenging. It really highlighted the gaps in thinking and mindset that would ordinarily go amiss in a larger team, with a longer runway to work with. The time pressure combined with a finite number of people to work on the project magnified issues that larger teams would bat off. For example, having the luxury of an already established test bed and infrastructure were not present in any of the projects we worked on. They became key requirements to try and bring forward and interspersed with the real customer requirements and it was a battle of wills to dismiss them as implementation details when in reality they would help dictate the success of the project. The observations of how our Agilists faired while put into a more confined team have confirmed to me that knowledge of the Scrum Guide and the overall guiding principles of Agile and Scrum are simply not known by the teams that practice that way of working. I feel that teams get the mechanics right for the most part and the improvements seen have moved the team forward far beyond where they began. Teams have gotten comfortable and accepted changes and limitations that ultimately change what Scrum, the framework, purports. Running a team without an available Product Owner, accepting stories that are not ready to be consumed, and even accepting and finishing stories that were simply not complete were just some examples that I observed. Modified scrum has gained on ‘pure’ Scrum in each state of Agile report over the last few years. I’m now questioning whether the modifications are really modifications or are they simply an abandoning of the principles and guidance the Scrum Guide provides? Are they a cheap way out instead of tackling a challenge? This has motivated me to spend more time in the Small Scale Scrum world and continue to evolve the model and gain more of an understanding into why deviances happen to the guidelines issued.

For Agnieszka, being hands on Software Developer and Quality Engineer at the same time as an MSc Student investigating and implementing Small Scale Scrum was demanding but rewarding. Engineers are passionate about problem solving and she was excited to take on the challenge of finding new ways of working for small teams in small consulting engagements. Her research, found her yet again performing an uneasy multirole act. She observed that context switching proved to be the most difficult, challenging and time consuming for her and for small teams in general. Too many uncertainties from the very start of the project, dynamically changing, unclear requirements and varying personalities, both team members and customers could result in project failure, if not managed timely and respectively. Having team members without overlapping roles was very positive in this case and having the customer fully onboard with a change from Waterfall to Scrum-like process was significant and key to the creation of Small Scale Scrum. Having projects with varying number of team members and different scope proved difficult from statistics point of view and made drawing conclusions challenging. Unfortunately every project is different and finding the most similar projects was difficult. Having access to more small size projects for different customers would benefit and strengthen the framework. On the positive note, encouraging the entire team to participate in the creation of the Small Scale Scrum framework in line with  Red Hat Open Decision Framework (ODF)  through surveys, interviews, random chats ensured that the framework was truly created “for the team and by the team”. This was critical to make the framework a success. Today, the benefits of Scrum and Small Scale Scrum are openly and transparently acknowledged in Red Hat Consulting through Red Hat trainings and offerings such as Red Hat Open Innovation Labs integrating and sharing some of the practices and tools mentioned in the  Red Hat Open Practice Library .

One of the more intangible results of executing Scrum in small teams is the mentality of Agile that emerged. Often in a regular sized team, you would have a small number of Agile Champions within the team. That is to say, you have people who have not just bought into the process of adhering to Scrum and Agile principles, but whom project that onto the team. These champions often mask the lack of Agility in the rest of the team as they tend to lead initiatives and be more vocal. From our experience, this means that teams who practice a form of Agile are often mechanically Agile. Those teams and individuals are able to follow ceremonies, they know how to story point, know how to break down tasks, are able to hold Sprint Planning and Reviews and the mechanics of Scrum or Kanban are more or less textbook implementations. The bulk of a team fall into that follower mode, where they are able to be a participant and in some cases drive it. However, the reasons why we perform these ceremonies are often lost. The values and principles behind them are practically invisible to the individuals practicing them. In Red Hat, our Scrum Teams who later branched out to try and experience other Agile approaches such as Kanban, really struggled to grasp the concepts. The muscle memory that came with following Scrum only extended to the mechanics versus the principles. This really shocked Leigh, as a coach of the team who had worked extensively with them for almost 3 years. When presented with Small Scale Scrum, the members who worked with students and whom ran mini internal projects in this manner got a much more rounded experience of Scrum and the Agile principles that underpin it. When brought back into their teams, the change in approach was noticeable but in an intangible way. They challenged assertions around how we tested and looked at Quality. They actively pushed back on the Product Owner for having incomplete stories flowing into the team and rigorously enforced a DoR that ultimately protected the team. A noticeable spike in communications came from the team with a noisy environment being generated that ultimately had to be addressed because it went too far into the over communicating sphere. It did however showcase that a happy medium could be found for the teams to radiate relevant and timely information. While the individuals had that growth mindset, they propagated it at a team level by acting as additional Agile Champions. The team around them became much more self-organising with decisions being made with discussions that centered around the 3 Pillars of Scrum and the values behind Scrum. In the 3 years of coaching the team, conversations that centered around those topics were infrequent. The team, through the empowered individuals who undertook Small Scale Scrum projects, now had a stronger understanding of why we make certain decisions and why certain actions are taken. In one of our longer running Engineering teams, the ScrumMaster found a remarkable change in the team. They no longer required as much coaching in the day-to-day sense that she previously would have found herself engaged in. The team became a lot more self-organisaing and aware of their communications and their actions to the point that the team’s reliance on their ScrumMaster lessened. She found herself with a lot more free time to focus on other teams that needed her help more and this really helped us benchmark where our Agile teams should be from a maturity perspective.

3.     ACKNOWLEDGEMENTS

We would like to thank our colleagues and peers in Red Hat for their support and patience with us while we asked questions and matured our work. Our students, in particular Ciaran, and lecturers in Waterford Institute of Technology played an equally key role. Rebecca, thank you for your time and patience as we juggled our day–to-day work and took on your amazing feedback, we are indebted to your guidance in making this report a possibility.

Gancarczyk, Agnieszka. “Small Scale Scrum: A Framework for Successful Implementation of the Scrum Methodology for Small Sized Teams”. MSc thesis. Waterford Institute of Technology, Waterford, Ireland. 2018.

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5 photos, left to right: a tractor in a field, power lines, two people in hard hats, a tractor trailer on a highway and a flooded road

Flood mitigation, rural transportation, supply chains: NSF awards 9 new people-focused research grants

Flood mitigation measures, rural transportation systems and pharmaceutical supply chains are among the subjects being examined by nine new research projects receiving backing from the U.S. National Science Foundation. NSF is investing more than $8 million in the projects through its Strengthening American Infrastructure program. The program supports research that utilizes advances in behavioral and social science to improve the value and usefulness of infrastructure in people's lives, from U.S. roads and highways to state and local power grids.

The projects are centered on social and behavioral science being conducted at institutions across the U.S. in collaboration with researchers from a wide range of other fields, including computer science, engineering, geosciences, mathematics and physical sciences.

"These projects can reveal new ways to enhance safety, reduce pain points in our everyday lives and enable greater prosperity and security for future generations," says Marc Sebrechts, director of NSF's Behavioral and Cognitive Sciences Division. "NSF's Strengthening American Infrastructure program is using the illuminating power of social and behavioral science to examine issues and opportunities at the heart of practically every type of infrastructure that people in the U.S. depend on."

"Strong infrastructure stimulates U.S. job creation, improves our quality of life and protects the well-being of our communities for many years into the future," says NSF Assistant Director for Social, Behavioral and Economic Sciences Kellina Craig-Henderson. "The projects we're supporting are taking science out of the proverbial lab and into a broad new array of areas so communities large and small can directly benefit from their discoveries."

The projects' diverse goals include improving air quality in rural areas with high levels of highway traffic, enabling communities and neighborhoods to retain power during large-scale power outages and helping states and cities effectively plan for increasingly frequent floods and storm surges. In addition to conducting fundamental research, NSF-supported projects will support participation and outreach activities with the residents of several local communities, the creation of new tools and resources that will be freely available to others and multiple educational and training opportunities for STEM students.

The three-year projects will be led by 12 institutions in nine states, including three states within the Established Program to Stimulate Competitive Research which supports areas in the U.S. that have historically received less federal support for research and development:

  • A collaboration between Texas A&M University and the University of Michigan will examine the community benefits and risks involved in a proposed new coastal storm barrier designed to protect people in the Houston-Galveston area of Texas from increased storm surges and flooding due to hurricanes.
  • Northeastern University researchers will explore how to design a new information infrastructure that can ease the supply-chain disruptions that lead to recurring shortages of medicines and other pharmaceutical products in the U.S.
  • Clemson University and the University at Buffalo, State University of New York will collaborate on developing a new cyberinfrastructure system that will provide scientists, cybersecurity professionals and others with a more rigorous way to use artificial intelligence to detect harmful online behaviors such as cyberbullying and inciting violence.
  • Purdue University researchers will conduct experiments focused on accurately predicting the long-term community-level impacts of retrofitting older buildings to withstand future earthquakes and other natural disasters. Their study includes buildings such as hospitals and residential complexes.
  • University of Virginia and Northeastern University will collaborate on an investigation of how physical infrastructure and social factors affect access to safe drinking water during hurricanes and other natural disasters.
  • University of Texas at Austin researchers will investigate how water-utility companies can keep critical water systems operating by more effectively communicating emergency messages to their customers before and during catastrophic events such as winter storms.
  • George Mason University researchers will analyze how electrical infrastructure is distributed across the U.S. to understand how the potential costs of supporting an increasing number of electric vehicles can be fairly distributed, particularly for those who do not use electric vehicles and for low and middle-income families.
  • University of Vermont researchers will study how investments in rural communities' travel infrastructure, such as roads and bridges, influence people's travel-related decisions and how transportation planners and engineers can most effectively use that information.
  • Iowa State University researchers will examine the social and economic benefits of microgrids — small, autonomous electrical grids that deliver power to a local area during natural disasters and other emergencies — for low-income families and other groups who face energy insecurity.

In addition to these nine research projects, NSF is also providing smaller planning grants to other institutions to study the feasibility of future infrastructure-focused research. For additional information, please visit NSF's Strengthening American Infrastructure program webpage .

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  1. The Good Research Guide: For Small-scale Social Research Projects

    3 Project researchers can safeguard against making elementary errors in the design and execution of their research by using checklists to assure them- selves that they have attended to the ...

  2. PDF The Good Research Guide: For Small-scale Social Research Projects

    Action research is normally associated with 'hands-on', small-scale research projects. Its origins can be traced back to the work of social scientists in the late 1940s on both sides of the Atlantic who advocated closer ties between social theory and the solving of immediate social problems.

  3. Small-Scale Research

    The advice on when and how to use small-scale methods is pragmatic, recognizing that small-scale researchers are usually short on time and resources. Yet behind this pragmatism is the principle that research is, above all, about thinking. Whatever needs to be done in a research project has to be for the purpose of providing research audiences ...

  4. What Pilot Studies Are and Why They Matter

    A pilot study is a preliminary small-scale study that researchers conduct in order to help them decide how best to conduct a large-scale research project. Using a pilot study, a researcher can identify or refine a research question, figure out what methods are best for pursuing it, and estimate how much time and resources will be necessary to ...

  5. The Good Research Guide for Small-Scale Research Projects

    It is worth noting at this point, that phenomenology methodology as described by Denscombe (2014) is suitable for small-scale research projects because is economic and the main resource is the ...

  6. Small-Scale Research

    The advice on when and how to use small-scale methods is pragmatic, recognizing that small-scale researchers are usually short on time and resources. Yet behind this pragmatism is the principle that research is, above all, about thinking. Whatever needs to be done in a research project has to be for the purpose of providing research audiences ...

  7. How to design a small research project

    These steps are virtually impossible to squeeze in to a small project and so in-person working with any kind of atypical population needs to be as low impact as possible. Think about phone / video interviews, a (short) online survey or maybe an online focus group. 4. The topic matters too.

  8. EBOOK: The Good Research Guide: For Small-Scale Social Research Projects

    This bestselling introductory book offers practical and straightforward guidance on the basics of social research, ideal for anyone who needs to conduct small-scale research projects as part of their undergraduate, postgraduate or professional studies. The book provides:• A clear, straightforward introduction to data collection methods and data analysis• Jargon-free coverage of the key ...

  9. 'It's really making a difference': how small-scale research projects

    Following an internal evaluation exercise, using Action Research, this paper identifies the positive impact of small-scale research projects on teaching and learning at a single case study UK ...

  10. Small-Scale Research: Pragmatic Inquiry in Social Science and the

    Timely, assured and written with the needs of students uppermost, Small-Scale Research is a direct, comprehensive guide for students doing theses, dissertations, papers and projects. It ...

  11. Small studies: strengths and limitations

    A large number of clinical research studies are conducted, including audits of patient data, observational studies, clinical trials and those based on laboratory analyses. While small studies can be published over a short time-frame, there needs to be a balance between those that can be performed quickly and those that should be based on more subjects and hence may take several years to ...

  12. Pilot Study in Research: Definition & Examples

    Advantages. Limitations. Examples. A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design. Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.

  13. 'It's really making a difference': how small-scale research projects

    Following an internal evaluation exercise, using Action Research, this paper identifies the positive impact of small-scale research projects on teaching and learning at a single case study UK University. Clear evidence is given of how the projects benefited students and staff, and enhanced institutional culture. ...

  14. What does it mean to 'scale up' your research?

    Category General. 'Scaling up' is an important aspect of implementation research, defined by the World Health Organisation as ' [the expansion and replication of] innovative pilot or small-scale projects to reach more people and/or broaden the effectiveness of an intervention.'. While scale up is acknowledged as a key means to ...

  15. Small-scale research design

    The design of your small-scale teacher research will offer a framework to help you to choose your research methods. It will be driven by the research question that you refined in last week's content. Let's think about the evaluative research question, 'To what extent is [strategy x] effective at improving or strengthening [outcome y]?'.

  16. Student teachers' collaborative research: Small-scale research projects

    The student teachers spend half of their time on an internship or job at a school and the other half on activities for the teacher education institute. One of the activities in the second semester is a small-scale educational research project, in which groups of student teachers collaborate, mostly without teacher educators being present.

  17. Sample Size and its Importance in Research

    The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary ...

  18. PDF SMALL-SCALE RESEARCH PROJECT SCHEME

    About the Small-Scale Research Project Scheme The British Council Small-Scale Research Project Scheme provides funding (up to £5,000) for early career researchers in the UK and countries in South Asia to work collaboratively on a joint research project. The duration of the research project should be between 6 and 12 months.

  19. Small-Scale Research Project

    Small-Scale Research Project. The decision of Quantitative or Qualitative research is the basic paradigm that governs the entire inquiry process. For the case mentioned, typically for a management research to gauge "Customer Satisfaction" of a small scale project, I recommend making use of a Quantitative approach.

  20. Small-scale project management

    Small-scale project management is the specific type of project management of small-scale projects.These projects are characterised by factors such as short duration; low person hours; small team; size of the budget and the balance between the time committed to delivering the project itself and the time committed to managing the project.They are otherwise unique, time delineated and require the ...

  21. Small-scale research projects: summaries

    Much of the analysis summarised is too small-scale for publishing on its own. For further information on any of these projects contact: Analytical associate pool. Email associate.pool@education ...

  22. Small Scale Scrum

    2.3 Our capstone project. After a lot of research, data gathering and trialing aspects of the approach through a number of projects, we had the version of Small Scale Scrum outlined above. We wanted to tackle a project in a capstone kind of manner to really get a sense of the benefits that Small Scale Scrum could bring.

  23. The Good Research Guide For small-scale social research projects Fifth

    The Good Research Guide provides practical and straightforward guidance for those who need to conduct small-scale research projects as part of their undergraduate, postgraduate or professional ...

  24. (PDF) Pilot Study, Does It Really Matter? Learning Lessons from

    A Pilot Study (PS) is a small-scale research project conducted before the final full-scale study. A PS helps researchers to test in reality how likely the research process is to work, in order to ...

  25. Flood mitigation, rural transportation, supply chains: NSF awards 9 new

    Flood mitigation measures, rural transportation systems and pharmaceutical supply chains are among the subjects being examined by nine new research projects receiving backing from the U.S. National Science Foundation. NSF is investing more than $8 million in the projects through its Strengthening American Infrastructure program.