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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.", (Copyright © 2019 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Data may be normally distributed in which case standard and relatively familiar tests may be used for inferential testing. Or data may be non-normally distributed, such as ordinal data, in which case, non-parametric tests are usually advocated. This paper will review current thinking with regard to these tests, and the appropriateness of their application/s., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Spreadsheet analysis software allows easy data management, as has been shown by the WASP (Write a Scientific Paper) sets of papers in these Best Practice Guidelines (BGP) in the previous issues. However, commonly available spreadsheet software, such as Excel, is weaker on the more advanced statistical techniques. Potential alternatives include the creation of custom macros, the utilisation of an Excel add-in, or using a standard, stand-alone, dedicated statistics package. This does not imply that Excel need be abandoned. Indeed, many users prepare their data in Excel and later transfer it into a statistical package for advanced analysis. Results may then potentially be returned into Excel for graphical depiction. Some examples of such advanced statistical techniques are given in this Best Practice Guideline (BPG) collection. The upcoming non-statistics papers in the WASP BPG series should be found equally useful to researchers who are new to paper writing, such as the one included in this particular set which outlines the writing of a research proposal., (Copyright © 2018 Elsevier B.V. All rights reserved.)

This paper outlines Binomial and Poisson distributions which are both used to measure the occurrence of a number of random events within a certain period., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The calculation of various test characteristics may be required as part of a data analysis exercise. This paper explains how to set up these calculations in Microsoft Excel in order to obtain sensitivity, specificity, positive and negative predictive values, diagnostic accuracy and prevalence., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

t-Testing is a common component of inferential statistics when comparing two means. This paper explains the central limit theorem and the concept of the null hypothesis as well as types of errors. On the practical side, this paper outlines how different t-tests may be performed in Microsoft Excel, for different purposes, both statically as well as dynamically, with Excel's functions., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Contingency tables may be required to perform chi-test analyses. This provides pointers as to how to do this in Microsoft Excel and explains how to set up methods to calculate confidence intervals for proportions, including proportions with zero numerators., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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™., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The calculation of descriptive statistics after data collection provides researchers with an overview of the shape and nature of their datasets, along with basic descriptors, and may help identify true or incorrect outlier values. This exercise should always precede inferential statistics, when possible. This paper provides some pointers for doing so in Microsoft Excel, both statically and dynamically, with Excel's functions, including the calculation of standard deviation and variance and the relevance of the t-distribution., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Data analysis at the descriptive stage and the eventual presentation of results requires the tabulation and summarisation of data. This exercise should always precede inferential statistics. Pivot tables and pivot charts are one of Excel's most powerful and underutilised features, with tabulation functions that immensely facilitate descriptive statistics. Pivot tables permit users to dynamically summarise and cross-tabulate data, create tables in several dimensions, offer a range of summary statistics and can be modified interactively with instant outputs. Large and detailed datasets are thereby easily manipulated making pivot tables arguably the best way to explore, summarise and present data from many different angles. This second paper in the WASP series in Early Human Development provides pointers for pivot table manipulation in Excel™., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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™., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Plotting data into graphs is a crucial step in data analysis as part of an initial descriptive statistics exercise since it gives the researcher an overview of the shape and nature of the data. Outlier values may also be identified, and these may be incorrect data, or true and important outliers. This paper explains how to access Microsoft Excel's Analysis Toolpak and provides some pointers for the utilisation of the histogram tool within the Toolpak., (Copyright © 2018. Published by Elsevier B.V.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: The ability to publish is a career-critical skill but requires the acquisition of a wide and disparate skill set. The Write a Scientific Paper (WASP) course was created in Malta in 2010, an intensive, three-day event. WASP is an accredited event held in Malta, London, and Bahrain. The COVID pandemic forced WASP to go online. This study compared satisfaction with WASP online as opposed to in-person by analyzing course feedback pre- and post-pandemic. Methods: Google forms are used to collect anonymous feedback on a Likert scale for various aspects of each WASP. The period 2017 to 2022 was used to compare four courses on-site and five courses online. Feedback on: Rate lectures, handouts, WASP overall and how likely are you to recommend WASP was compared. Results: Response rates were >60% and almost all Cronbach's Alpha values were >0.7. High satisfaction scores were achieved in all four questions (>4/5). There were no significant differences except in lectures, which scored well but fared slightly worse overall online. Conclusions: Migrating online does not necessarily lead to change/s in presentation contents but transforms delivery. Our results indicate that WASP is accepted online but the slightly lower lectures score implies that WASP might be better delivered in-person than online. However, students remained happy to recommend WASP and this accords with other studies that overall, student satisfaction with online education is common. It is hoped that as the pandemic recedes, webinars complement and not continue to totally replace traditional in-person meetings. [ABSTRACT FROM AUTHOR]

The effective presentation of scientific work in abstracts, posters, and PowerPoint presentations resembles popular newspaper writing far more than it does traditional paper writing. In the former three, a good "hook" is essential and must be obvious and apparent up front. A slight element of truthful sensationalism is therefore often helpful. Research preparation and presentation and skills of all sorts are increasingly critical in the incrementally competitive world of academia. This paper will offer factual and practical guidelines with regards 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.". [ABSTRACT FROM AUTHOR]

Slide show presentations have become integral to the workplace, and PowerPoint is ubiquitous since it ships with the Microsoft Office Suite. However, the increasing sophistication of such software may encourage presenters to privilege form over content. It is possible to apply scientific research from Educational Psychology to slide shows so as to not only avoid this fate but also to optimise presentations. This paper applies several Educational Psychology concepts to PowerPoint, most notably those of Mayer's Multimedia Learning Theory, and will emphasise and demonstrate that the quality of a presentation is dependent on the quality, relevance, and integrity of its content. Slide shows should supplement, and not substitute a presentation. Presenters should avoid prolixity, complexity and gaucheness and strive instead for simplicity, brevity, cogency and clarity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data protection (DP) protects crucial and humane fundamentals - the respect of human rights, particularly protecting aspects of privacy and confidentiality for living and identifiable persons. DP is enshrined in legislation, and this paper will outline the duties of potential data controllers (researchers) when applying for access to data, when processing said data, and what to do with it at the end of the study. [ABSTRACT FROM AUTHOR]

The ability to write a scientific paper (WASP) is becoming progressively more critical because the "publish or perish" mantra is increasingly valid in today's world where success is judged by number of publications and quality of publications based on journals which publish the researcher's work. These metrics are used to gauge applicants in often cut-throat competitions for jobs and/or career advancement. However, the science and art of paper-writing comprise a vast panoply of different skills, from writing a proposal, to ethics and data protection applications, to data collection and analysis, to writing and dealing with editors and authors, and so on. Over the next few issues, Early Human Development will embark on a series of Best Practice Guidelines that will outline and explain the various requisite WASP skills while providing practical guidelines for paper writing. The purpose is to impart the authors' collective experience to trainees in this crucial aspect of career progress. This first set of WASP papers will mainly focus on statistical analysis using Excel™. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]