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Qualitative data is important to understand certain phenomena. While quantitative data is important to understand the prevalence of attitudes and behaviour in psychosocial medical research, qualitative data can help understand and explain the reasons behind attitudes behaviour. One of the methods of collecting qualitative data is the focus group discussion. During a focus group researchers facilitate a discussion to elicit views and lay theories about the research topic. Researchers use group facilitation skills in order to make participants feel at ease and disclose their thoughts, feelings and perceptions about a particular issue with the least bias possible. This paper discusses how the text obtained after transcribing the discussions can be analysed using thematic analysis.

This paper introduces the key methodological challenges related to working with children within an ethnographic research design. After briefly identifying in-depth contextually rich data as the key aim of any ethnographic research, some of the particular challenges related to working with children are explored. These are described along two key axes: ethical issues related to power dynamics loaded in favour of the adult researcher, and practical, logistical barriers to generating data that are a true reflection of the young child's ideas and beliefs. 'Process assent' and 'ethical symmetry' are flagged as the key means of optimising ethicality, while the benefits of using the 'least adult role', and creative child centric activities are outlined as ways to generating and collecting trustworthy data.

Images can be helpful for eliciting data in the form of responses from participants. Sometimes photographs can help participants speak about issues, events, thoughts and emotions which they find difficult to talk about. This paper discusses how photos can be used and how the data collected through their use in a discussion can be analysed using Correspondence Analysis (CA). Although CA is considered a quantitative tool for analysing data, this paper describes how it can be used with qualitative data.

The nature of qualitative research is fundamentally different from quantitative research. In depth understanding and exploration of specific phenomena or their significance usually involve a data collection process with a select few participants with whom the researcher interacts over an extended period of time. The participants are not anonymous to the researcher and they also reveal personal information. Research participants' rights need to be protected, even more so in this type of qualitative work. This chapter discusses how ethical decision-making needs to be ongoing during the research process by applying the principles of respect for persons, beneficence and justice. Respecting research participants, involves addressing issues related to informed consent, anonymity and confidentiality while also reflecting on the researcher's impact on the participant and the research context while the research is carried out and also when the research is disseminated. The second principle of beneficence calls for the researcher to ascertain that participants' safety is given utmost importance. Finally, justice refers to ensuring that the burdens and benefits of research are shared fairly without any form of discrimination or manipulation. These principles offer the researcher flexible tools to reflect on ethical decisions as they arise throughout the research process.

This paper provides an introduction to set-theoretic methods with respect to their application to research in the qualitative paradigm, as part of a series of papers on qualitative research being featured in Early Human Development. It discusses the philosophical assumptions underlying set-theoretic methods and the properties that make them well-suited for use in the analysis of qualitative data. It also takes a closer look at qualitative comparative analysis (QCA), a cross-case analysis method that has arguably been most widely applied among the set-theoretic methods and has also been gaining ground in the health field. Theoretical underpinnings, sample-size considerations, set calibration, QCA software packages, parameters of fit and limitations of the method are discussed. A description of the analytic output issued by a freely available software package, fs/QCA, is used as an example to illustrate QCA results and their interpretation.

The almost inevitable descriptive statistics exercise that is undergone once data collection is complete, prior to inferential statistics, requires the acquisition of basic descriptors which may include standard deviation and quartiles. This paper provides pointers as to how to do this in Microsoft Excel™ and explains the relationship between the two.

Data collection for the purposes of analysis, after the planning and execution of a research study, commences with data input and validation. The process of data entry and analysis may appear daunting to the uninitiated, but as pointed out in the 1970s in a series of papers by British Medical Journal Deputy Editor TDV Swinscow, modern hardware and software (he was then referring to the availability of hand calculators) permits the performance of statistical testing outside a computer laboratory. In this day and age, modern software, such as the ubiquitous and almost universally familiar Microsoft Excel™ greatly facilitates this process. This first paper comprises the first of a collection of papers which will emulate Swinscow's series, in his own words, “addressed to readers who want to start at the beginning, not to those who are already skilled statisticians.” These papers will have less focus on the actual arithmetic, and more emphasis on how to actually implement simple statistics, step by step, using Excel, thereby constituting the equivalent of Swinscow's papers in the personal computer age. Data entry can be facilitated by several underutilised features in Excel. This paper will explain Excel's little-known form function, data validation implementation at input stage, simple coding tips and data cleaning tools.

Several guidelines have been created in an attempt to raise the quality of different kinds of studies. These also attempt to ensure that research is reported accurately and transparently. Six such guidelines (CONSORT, MOOSE, PRISMA, SPIRIT, STARD, and STROBE) will be briefly discussed in this paper. The purpose of these guidelines is "to create a manual for the authors to follow, which should lead to total transparency, accurate reporting, and easier assessment of the validity of reported research findings." This goal has not been completely reached and it remains uncertain how it can as mandatory and full implementation at the journal end would entail significant and costly man-hours.

As an experienced writer, one is often asked the question "what can I publish" by younger and inexperienced colleagues who must publish. This paper will briefly review the reasons why academics publish and will then outline what kind of papers one may be able to publish with relative ease, using personal experience and citing first hand material as a practical guide. Potential authors must cultivate "a lean and hungry look" as competition from fellow writers is stiff, and editors are choosy. However, do take heart - if we, older colleagues did and can, you can too.

Academics may deliberately hoax colleagues with false research, so as to forcibly and dramatically make a particular point. Moreover, false research may a process of deliberate and outright fraud. This paper will review a few examples as cautionary tales for academics and researchers. While it may seem amusing and clever to entrap colleagues in a hoax, the repercussions may have chronic and unintended consequences. Outright fraud may result in short term gain but will almost inevitably, quite simply, lead to the destruction of one's career, along with the potential for exposure to criminal charges. It is sensible for wise academics to shy away from both.

The increasing demand on academics and researchers to publish has led to the development of fake journals (also known as predatory journals). Such journals lack peer review and precipitate unfair criticism toward legitimate open access journals. Predatory journals tend to bombard a researcher's mailbox on a daily basis, inviting authors to submit a review/manuscript/opinion/short case to their journal while promising expedited publication – against a fee. This study assessed the unsolicited emails received over the period of November 2017 by a young and by a senior researcher. The young researcher received a substantially higher amount of emails (n = 101) compared to the senior researcher (n = 23). The article processing costs for solicited journals received, ranged between $49 and $3019. These journals are almost all only indexed in Google Scholar and do not display any meaningful journal metrics. Furthermore, the majority of the unsolicited emails were not relevant to the researcher's field of study. Therefore authors and readers alike should evaluate emails received with regard to journal legitimacy prior to submission of work to possibly predatory journals.

The calculation of descriptive statistics includes the calculation of standard error and confidence interval, an inevitable component of data analysis in inferential statistics. This paper provides pointers as to how to do this in Microsoft Excel™.

The plotting of data into graphs should be a mandatory step in all data analysis as part of a descriptive statistics exercise, since it gives the researcher an overview of the shape and nature of the data. Moreover, outlier values may be identified, which may be incorrect data, or true outliers, from which important findings (and publications) may arise. This exercise should always precede inferential statistics, when possible, and this paper in the Early Human Development WASP series provides some pointers for doing so in Microsoft Excel™.

Readers are drawn towards tables and figures (such as graphs) because these are perceived as easy reading, when compared to attempting to interpret a verbose account of the same content. Previous publications in these Best Practice Guidelines series have already given some basic instruction in Excel chart creation and in Excel automatic histogram creation. This paper will outline some more advanced instructions with regard to graphs and tables, along with suggestions pertaining to the general formatting of these items. Specific recommendations pertaining to Microsoft Excel will also be highlighted since this is the most commonly used charting software. At all times, graphs "should avoid prolixity, complexity and gaucheness and strive instead for simplicity, brevity, cogency and clarity."

This paper describes the use of Interpretative Phenomenological Analysis (IPA) in qualitative research and its adaptability to medical research. Whilst presenting the main tenets of the approach, it also highlights its' strengths in helping medical professionals gain rich information about their service users' needs, symptoms and experiences, via research. It discusses the application of IPA on various levels such as by contextualising quantitative data or gaining insight into patients' understanding of clinical guidelines. The paper also describes methodological choices of the approach and the reasoning behind them. It outlines the sample, data collection tools and method of analysis utilised in IPA. To help researchers locate the particular use of this approach on the qualitative research landscape, this paper also compares its' use to that of other qualitative approaches. Finally, it discusses ways of evaluating IPA studies and hence, to ensure that such studies respect the philosophical roots of qualitative research in general, and those of IPA in particular.

Quantitative and qualitative analyses and research are conventionally regarded as fundamentally different techniques. However, their objectives and applications overlap, and the decision of which of the two methods to apply depends not only on the nature and volume of data that is to be collected, but also the actual type of data and the resources at hand. Indeed, the outright combination of the two methods is becoming increasingly common. This collection will cover some aspects of qualitative analyses techniques.

This WASP (Write a Scientific Paper) Best Practice Guidelines (BPG) will wrap up all of the aspects pertaining to qualitative analyses as well as some more theoretical aspects of paper writing. These include types of studies and their importance in the hierarchy of evidence base as practiced in medicine. In addition, two papers review very practical aspects of paper writing including which journals to target (and why), and realistic methods for dealing with editors. Another paper also deals with fraud and hoax in science. Since the next BPG collection pertaining to WASP will deal with qualitative methods of analysis, by way of introduction to different ways of doing things, this collection will also review the practical differences between writing and presentation in the humanities and in the sciences. A paper will also very briefly review interdisciplinarity, including the depiction of interdisciplinarians as envisaged in future, in the (albeit speculative) science fiction genre. A final paper will demonstrate several precepts that have been highlighted over these BPGs, utilising as a practical example, a novel study that uses past requirements in order to estimate anticipated trends in cardiology service requirements at Mater Dei Hospital, Malta.

Publishing scholarly work is a requisite in academia. Identifying a suitable journal for a particular paper can be difficult. Authors need to initially establish whether they want to publish in a subscription-based or an open access journal, which requires a clear understanding of the pros and cons of both options as well as issues relating to copyright licences. Establishing specific journal selection criteria ranging from whether to publish in a general or a specialised journal to the publication frequency of the journal is essential. A number of web-based tools are available that can facilitate this selection process.

Interdisciplinarity, the combining of two or more academic disciplines into one activity, is generally encouraged, but there are many pitfalls in the path of the would-be interdisciplinarian. Science fiction, has repeatedly warned of indulgence in super-specialization. This paper will review the genre's depiction of some protagonists who have reified the interdisciplinary paradigm within science fiction, and some lessons that may be learned from these narratives.

Once an author/s submits a manuscript to a journal, editorial and review processes are initiated which will determine acceptance or rejection of a manuscript. Understanding the editor's perspective and role enables authors to appreciate the factors that can ensure that the submitted manuscript meets the editor's expectations. This increases the chances that the manuscript passes the initial scrutiny and is forwarded for peer review. The reviewers' comments and recommendations make or break the manuscript and will take the form of comments for the authors as well as confidential recommendations to the Editor. Rejection should not lead to dejection. Authors need to digest the reasons for rejection, review comments and suggestions and incorporate them into the revised manuscript prior to identifying another journal for possible submission. If the manuscript is accepted with minor or major revisions, the authors need to make sure all comments and recommendations are dealt with. Once the manuscript satisfies the editor's and reviewers' expectations, it is on its way to publication.

Introduction The requirement for medical services fluctuates. This study was carried out in order to attempt to extrapolate the service requirements for various cardiology services at Mater Dei Hospital, Malta over the coming five years, based on service demands from previous years. Methods Past annual data was obtained from hospital records for various services (to 2017). Linear regression was carried out using a bespoke Excel™ spreadsheet in order to extrapolate possible services requirements up to 2022. Results All services are expected to increase, with forecasts ranging between 41 and 354%, depending on services being considered. Discussion It is easy to “get on with it” and perform the work required at the workplace but this study has shown that it is equally important to anticipate demands lest lack of planning leads to long and important waiting lists for critical diagnostics and treatments. Health care provision requirements are increasing worldwide. Even using conservative estimates and in the absence of the creation of new services, the demands for extant services are likely to continue to grow. Unless medium term plans are made for hardware, software, physical space and staffing, and the funding thereof, waiting lists for investigations in this speciality are bound to rise. This may be mitigated by novel treatments but since these cannot be predicted, it would be safer and wiser to plan ahead lest we are overwhelmed. This paper has also shown how WASP (Write a Scientific Paper) precepts can be applied to elegantly study a problem and write up a paper.

Evidence-based published data is the prime source used for clinical decision making, the issuance of guidelines and the drafting new policies. A number of different study designs are used to perform and publish research studies, corresponding to the research question being investigated. Over the decades, a hierarchical system of evidence has been established. This provides an indication of the level of evidence each study design contributes to the research community. Policies and clinical guidelines should naturally be based on the highest level of evidence data available. However, the highest level of evidence study designs may not always provide an adequate answer to a research question. Thus, when utilising published evidence-based data, one should first understand the clinical question that needs to be answered, and then critically appraise the published data accordingly.

The global contemporary shift from paper-based to digital online resources has led to a revolutionary change in medical training. A multitude of online resources have been developed over the years that complement the medical professional's education, with greater access to knowledge, while enabling continuous medical education and development. These sources also allow us to keep up-to-date with medical news. The development of medical mobile applications (apps) has also facilitated medical training by providing versatility, flexibility and portability of updated medical resources.

Writing and publishing scientific papers have become requisites for all scientists (researchers and academics alike) to maintain their professional career. The prospects of writing a scientific paper are often regarded as somewhat daunting to the uninitiated. However a universal, well established structure format known as "IMRAD": i.e. Introduction, Methods, Results And Discussion has been established. This paper details this methodology.

The presentation of scientific work resembles popular media narratives far more than it does the traditional paper writing that scientists are trained in. In the former, a good 'hook' is essential so as to hang a paper that is presented as a slide show, an abstract, a poster or text directly addressed to the media, and this hook must be obvious and apparent up front. A slight element of truthful sensationalism is therefore often helpful. This set of Best Practice Guidelines will offer factual and practical guidelines with regard to preparing research in these formats since self-presentation is essential for career progression. It is not only what we do but how we are seen to be doing it, acceding to Batman's aphorism: "it's not who I am underneath but what I do that defines me". Research preparation and presentation and skills of all sorts are thus increasingly critical in the increasingly competitive world of academia.

Writing for the media is a task faced by many professionals at some point during their academic or clinical life. The style and purpose of writing for the media is strikingly different from that of an academic manuscript. When writing for the media, the author presents information and persuades the mass audience about a particular topic or issue. The author needs to bear in mind that information sent out to the media will make today's story but will be yesterday's news, so time is of the essence. The format of newswriting follows an inverted pyramid structure with the most important message taking center stage at the beginning of the article followed by any additional messages. The use of graphics enhances the message. While embracing the concept of modesty, the author needs to present the newsworthy information in a complete, accurate, efficient and precise manner.

The calculation of odds ratios and relative risks may be required as part of a data analysis exercise. This paper explains how to set up these calculations in Microsoft Excel from a two by two contingency table.

The ancient Greeks honoured the concept of kleos (κλέος) aphthiton, eternal renown. An ancient Greek hero earned kleos through great deeds, up to and including his own death. However, the Greeks also believed that kleos could be earned through feats of intellectual procreation. Publication is thus the modern avenue for an academic to earn kleos. The young academic must perforce make an Achillean choice, to go with the mainstream flow and pragmatically publish only that which is needed or required for career advancement – the more facile path – or to strive to produce respectable papers over and above that which is strictly required. The latter is a much more onerous path, but one that is ultimately satisfying and eventually even becomes pour le sport: for fun.

Correlation and regression measure the closeness of association between two continuous variables. This paper explains how to perform these tests in Microsoft Excel and their interpretation, as well as how to apply these tests dynamically using Excel's functions.

Linear regression is the equation which provides of straight line that best describes the association between two continuous variables, x and y. However, it is often the case that the dependent variable y is influenced by more than one variable and not just a single x variable. Multivariate analysis is a statistical modeling technique wherein multiple x variables are analysed simultaneously for their effect on y, resulting in an additive model (via an equation) that explains the observation/s and corrects for confounding association/s using one dependent and several independent variables, assigning a gradient to each of these independent variables, and with all product terms of gradient and magnitude of the independent variables adding up to estimate 'y'. This paper outlines these various techniques and applications.

Analysis of variance (ANOVA) may be required by researchers as an inferential statistical test when more than two means require comparison. This paper explains how to perform ANOVA in Microsoft Excel.