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Eric m. prager.
1 John Wiley & Sons, Inc., Hoboken New Jersey, USA
Joshua l. plotkin.
2 Department of Neurobiology and Behavior, Stony Brook University, Stony Brook New York, USA
3 Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles California, USA
4 Center for Research in Biological Systems, University of California at San Diego, San Diego California, USA
Maryann e. martone, hadley c. bergstrom.
5 Department of Psychological Science, Program in Neuroscience and Behavior, Vassar College, Poughkeepsie New York, USA
6 Partnership for Assessment and Accreditation of Scientific Practice, Heidelberg Germany
7 Valdman Institute of Pharmacology, Pavlov First State Medical University, St. Petersburg Russia
8 John Wiley & Sons, Oxford UK
A preprint of this paper, which includes a roadmap to follow when preparing original research manuscripts and comments made during the review of the paper can be found at https://osf.io/5cvqh/ .
Progress in basic and clinical research is slowed when researchers fail to provide a complete and accurate report of how a study was designed, executed, and the results analyzed. Publishing rigorous scientific research involves a full description of the methods, materials, procedures, and outcomes. Investigators may fail to provide a complete description of how their study was designed and executed because they may not know how to accurately report the information or the mechanisms are not in place to facilitate transparent reporting. Here, we provide an overview of how authors can write manuscripts in a transparent and thorough manner. We introduce a set of reporting criteria that can be used for publishing, including recommendations on reporting the experimental design and statistical approaches. We also discuss how to accurately visualize the results and provide recommendations for peer reviewers to enhance rigor and transparency. Incorporating transparency practices into research manuscripts will significantly improve the reproducibility of the results by independent laboratories.
Failure to replicate research findings often arises from errors in the experimental design and statistical approaches. By providing a full account of the experimental design, procedures, and statistical approaches, researchers can address the reproducibility crisis and improve the sustainability of research outcomes. In this piece, we discuss the key issues leading to irreproducibility and provide general approaches to improving transparency and rigor in reporting, which could assist in making research more reproducible.
Progress in basic and clinical research is strongly dependent upon asking important research questions, attempting to answer those questions with robust methods, and then communicating the findings. Persuading colleagues that scientific results are objectively obtained and valid involves a willingness to report accurate, robust, and transparent descriptions of the methods, procedures, and outcomes, which will allow for the independent replication, or reproducibility, of those findings (see Box 1 for definitions).
Publishers have the responsibility of providing a platform for the exchange of scientific information, while at the same time it is the responsibility of the authors, journal editors, and peer reviewers to ensure that the published manuscripts are accurate. While many editors and peer reviewers expect that research published in their journals should be potentially reproducible, there are no set procedures to empirically test whether a finding can be independently reproduced. What's more, other barriers to reproducing results exist, including the laboratory environment, apparatus and test protocols, and animal strain. 1 A major source of irreproducibility also includes substantial systematic error, which can occur while scientists are conducting the experiments or during statistical analyses. 2 Systematic error can occur for a variety of reasons, including lack of scientific skill (e.g., two people performing the same experiment may not have the same level of experience) or variability in subject populations or reagents. 3 In addition, when a researcher has inadequate statistical knowledge or there are honest flaws in the experimental design and statistical output, the errors generated might inappropriately influence the interpretation of the results. 4 , 5
Efforts to improve research transparency (and, subsequently, reproducibility) by funders, researchers, and publishers have led to the development of checklists and new author guidelines (see, for example, Cell Press' Structured Transparent Accessible Reporting [STAR] Methods and the Journal of Neuroscience Research (JNR) Transparent Science Questionnaire ). However, checklists often go unchecked or unenforced by the publishers, editors, and/or peer reviewers 6 and compliance by the authors is not always wholehearted (M. Macleod personal communication). Publishers cannot always ensure that the results are reproducible, but they can help the authors to present a transparent account of their work, including providing full details of the experimental and statistical procedures and results. Transparent and rigorous accounts of how an experiment was performed, why the authors used specific statistical approaches, and what limitations arise from such work will allow the reviewers, editors, and subsequently readers to better judge the quality of the science.
In this commentary, we offer an update to basic approaches in reporting a thorough account of the experimental design and statistical approaches and provide an overview of data visualization techniques. 7 It is our hope, as publishers and editors, that these guidelines will help the authors adhere to specific reporting guidelines that promote rigor and transparency in scientific research, which will ensure an accurate and complete account throughout their experiments and discourage publication bias. This, in turn, will promote better, more reproducible science.
Many factors can lead to irreproducibility of scientific results. Oftentimes, these trace back to flaws in the experimental design, statistical analyses (and a lack of understanding of fundamental statistical principles), including low statistical power or inadequate sample sizes, basic reporting of the information essential for labs to independently reproduce results (e.g., biological reagents and reference material), and selective reporting of data/results (e.g., p‐hacking). 4 , 8 , 9 These factors and others might contribute to between 50% and 90% of the published papers being irreproducible. 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 Attempts to reproduce published results costs the United States approximately $28B annually, 9 , 18 yet poor descriptions of the published studies lead to a majority of studies becoming non‐replicable. 11 The next subsections will break down some of the more common barriers to reproducibility.
The Methods and Materials section of the manuscript is an often neglected area. Journals and authors often limit the methods section to brief descriptions of the procedures or place more complete methods into supplemental materials, or for journals moving away from supplemental material, to online methods that are separate from the article; these are not often critically reviewed by referees and can go unread by the experimenters. Furthermore, reviewers might not be able to adequately review methods and tools and subsequently might fail to notice that key details are missing. This can lead to a lack of complete and transparent reporting of the information required for another researcher to repeat protocols and methods. 2 Similarly, journals requiring a subsection on statistical analyses rarely ask the authors to provide a full account of the statistical approaches, and the authors may also fail to include a full account of the statistical outputs in the results section. Without a rigorous description of the methods, materials, and statistical approaches, experimenters lack the necessary information to independently replicate or nearly replicate results with the same protocol under similar conditions. 2 , 13
Current publication trends place emphasis on the pursuit of novelty and innovation, 19 which leads to a collection of reporting problems in how data were obtained. 8 At the most extreme, pressure to publish may lead individuals to rush their experiments, cut corners, make unintentional errors in statistical outputs, or overinterpret the findings, 20 which can lead to irreproducibility of the scientific findings.
To publish in “high impact” journals, scientists may resort to submitting only their most novel and impactful findings and avoid presenting nonsignificant or incremental findings, 19 though the latter also have important implications in driving scientific progress. The pressure to publish sensational findings has even led some “high impact” journals to state in their submission forms: “negative results are not accepted”. 21 This emphasis might encourage scientists to pursue nonlinear lines of investigation in search of statistical significance (e.g., p‐hacking), and may be one driver of scientific misconduct, including falsifying and fabricating data to increase its impact or statistical significance. 5 At the very least, it leads researchers to omit nonsignificant or incremental findings leading to a bias in the literature, and reinforces the perception that negative findings carry a low priority for publication. 22 , 23 This publication bias has led science reporters and the public to declare that it has become more difficult to trust scientific findings. 24 , 25
Even with the most rigorous reporting guidelines and stringent publication standards, including the precise application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of the results, 26 it is not guaranteed the authors will fully comply. Reporting guidelines cannot overcome poor training in experimental design and statistics, both of which may be responsible for many of the challenges leading to irreproducibility. 27 , 28 Indeed, investigators all too often make errors in designing and performing their research, in selecting statistical tests, and in reporting the results. 29 , 30 The problem can be exacerbated by errors being passed down by the primary investigator to students, by reviewers not catching these mistakes, and editors not having the expertise to catch specific errors. However, tools to reeducate scientists at all levels in the experimental design and to employ correct data visualization techniques 31 , 32 are available (see the National Institutes of Health education modules designed to train students or retrain scientists on the responsible conduct of research, https://www.nih.gov/research‐training/rigor‐reproducibility/training or the National Postdoctoral Association's Responsible Conduct of Research Toolkit ). Moreover, many institutions have statistical consultation available to investigators, which should be used; JNR and Brain and Behavior both hired statistical editors to review the submitted manuscripts for statistical accuracy and Current Protocols in Neuroscience recently released a statistical guide that provides general guidelines regarding when, how, and why certain improved statistical techniques might be used in neuroscience research 33 (see also Motulsky, 2014 34 ). These tools helps the authors improve statistical reporting in manuscripts and ensure that the correct approach was used, though statistical reviews may be limited by how much raw data are available.
In addition to the above tools, editorials and commentaries published in various journals attempt to help the authors improve the descriptions of their experimental procedures and results to ensure that the published research is transparently and accurately reported. 35 , 36 , 37 , 38 , 39 , 40 Unfortunately, the authors often fail to incorporate these guidelines into their articles and most journals do not enforce or penalize the authors for not including specific criteria. 6 Refining the steps necessary to ensure quality control during the peer review and publication processes is essential in order to improve transparency and scientific rigor. Adopting the approaches discussed below will better ensure that the experimental designs are accurate and deviations from that design are explained, with the ultimate goal of increasing the reproducibility of the published data. Journals and publishers should continue to provide detailed guidelines to help the authors during the submission process, but if researchers do not adopt a rigorous and transparent approach to scientific design and reporting from the onset of training, these requirements will continue to fall short.
In the following sections, we outline the key steps to improve transparency and scientific rigor that should be considered during the designing stages of experiments, not just before submission for publication. These requirements can be broadly broken down into (a) reporting criteria to ensure rigor and transparency; (b) transparent account of experimental design; (c) improving statistical rigor and transparency; and (d) peer review to enhance rigor and transparency. Encouraging specific descriptions and a full account of the study will ensure transparency and could improve reproducibility efforts. The next four sections will break down these components to elaborate on how each can improve transparency and rigor in scientific reporting.
The following points describe the key characteristics that must be included in any research design to assess the internal validity, reliability, and potential for reproducibility of scientific findings. Many of these recommendations have been discussed in various venues (e.g., ARRIVE guidelines 7 , 18 , 38 , 41 , 42 ), and some might only be appropriate to specific sciences. However, we feel that inclusion of these criteria, when applicable, into research manuscripts will improve rigor and transparency of the experimental design and statistical approaches.
The methods section of each published study begins with a description of the experimental unit; however, in many cases, the information provided falls short. The experimental units are the entity that is randomly and independently assigned to the treatment conditions (e.g., human subject, animal, littler, cage, fish tank, culture dish, etc.). 43 The sample size is equal to the number of experimental units. In considering the sample size, one must ensure that the experimental units are independently allocated to the experimental condition, the application of the condition is applied independently to the unit, and the experimental units do not influence one another. 43 A significant concern in cell biology is determining whether cells or sections, for example, can be considered an experimental unit. In cases where an animal is treated and subsequent testing occurs postmortem (e.g., immunohistochemistry or electrophysiology), then the histological sections, neurons per section, spines per neuron, tumor cells per section etc. are all subsamples of the experimental unit, which is the animal, and should be considered an n of 1. 43 , 44 If data are not independent, one strategy is to analyze clustered data (e.g., convert the replicates from a single subject into a single summary statistic. 44 Alternatively, there are also procedures to accurately model the true variability in data sets using modern statistical techniques (e.g., handling nested data such as cells/animals, littermates). 45 As Stanley Lazic so eloquently concluded in his recent paper, 46
...a few simple alterations to a design or analysis can dramatically increase the information obtained without increasing the sample size. In the interest of minimizing animal usage and reducing waste in biomedical research, 15 , 47 researchers should aim to maximise power by designing confirmatory experiments around key questions, use focused hypothesis tests, and avoid dichotomising and nesting that ultimately reduce power and provide no other benefits.
An appropriately written section describing the experimental subjects must include a statement of ethical approval (Institutional Review Board approval for human research or Institutional Animal Care and Use Committee approval for animals), followed by the total number of participants involved in each experiment. The authors must also include a clear description of the inclusion and exclusion criteria, which should be prespecified prior to the start of the experiments. Reporting the number of experimental units (i.e., subjects, animals, cells) excluded as well as the reason for exclusion is necessary to prevent the researcher from introducing selection bias that favors positive outcomes and distorts true effects. 48 Crucially, studies involving human subjects must not reveal individual identifying information but must contain a full description of the participants' demographics as variations in the demographics can lead to confounding variables if not appropriately controlled. When designing an experiment, one must also account for sex as a biological variable (see below). One should carefully review the extant literature to determine whether sex differences might be observed in the study and, if so, design and power the study to test for sex differences. Omitting this step could compromise the rigor of the study. 49 , 50
Choices made by investigators during the design and execution of experiments can introduce bias, which may result in the authors reporting false‐positives. 13 , 39 , 51 For example, when investigators are aware of which animals belong to one condition or know that a given treatment should have a specific effect, or human subjects become aware of the conditions they are in, the researchers and participants may inadvertently be biased toward specific findings or alterations in a specific behavior. 52 , 53 To reduce bias in subject and outcome selection, the authors should report randomization and blinding procedures. 54 Implementing and reporting randomization and blinding procedures is simple and can be followed using a basic guide, 52 , 55 but to reduce bias, it is essential to report the method of participant randomization to the various experimental groups as well as on random sample processing and collection of data. 38 , 39 Moreover, investigators should report whether experimenters are blind to the allocation sequence and also, in animal studies, report whether controls are true littermates of the test group. 44 Similarly, once the investigator is blind to the conditions, they should remain unaware of the group in which the subject is allocated and the assessment outcome. 39 Blinding is not always possible. In these cases, procedures to standardize the interventions and outcomes should be implemented and reported so groups are treated as equally as possible. In addition, researchers should consider duplicate assessment outcomes to ensure objectivity. 52 Attention to reporting these details will reduce bias, avoid mistaking batch effects for treatment effects, and will improve the transparency of how the research was conducted.
Many life science disciplines use animal models to test their hypotheses. Few studies provide detailed information regarding housing and husbandry and those reports that contain the information typically do not provide any level of detail that could allow for others to follow similar housing procedures. When using animals, care should be taken to adequately describe the housing and husbandry conditions as these conditions could have profound implications on the experimental results. 56 At a minimum, the authors should introduce in the abstract the race, sex, species, cell lines, etc. so that the reader will be aware of the population/sample being studied. However, in the methods section, the authors should carefully describe all animal housing and husbandry procedures. For example, it is normally unclear whether animals were single or group housed, and in most journals, the age and/or weight of the animals are commonly omitted. 57 Other factors that are not commonly reported include information on how the animals were transported from a breeder to the experimenter vivarium (see Good practices in the Transportation of Research Animals, 2006), vivarium temperature, humidity, day/night schedules, how often cages are cleaned, how often animals are handled, whether enrichment is provided in a cage, and cage sizes. 56 Requiring a full description of housing and husbandry procedures will be essential to the rigor and transparency of the published studies and could help determine why some studies are not reproducible.
Sex/gender plays an influential role in experimental outcomes. A common practice within research is that findings in one sex (usually males) are generalized to the other sex (usually females). Yet, research consistently demonstrates that sex differences are present across disciplines. For example, as evidence reveals in a recent issue of JNR (see Sex Influences on Nervous System Function ), sex not only matters at the macroscopic level, where male and female brains have been found to differ in connectivity, 58 but at the microscopic level too. 59 The National Institutes of Health as well as a number of funding agencies mandates the inclusion of sex as a biological variable, yet this mandate is not enforced by most journals. Starting at the study design, the authors must review whether the extant literature suggests that sex differences might be observed in the study, and if so, then design and power the study to test for sex differences. Otherwise, the rigor of the study could be compromised. When publishing the results, the authors must account for sex as a biological variable, whenever possible. At a minimum, the authors should state the sex of the subjects studied in the title and/or abstract of the manuscript. The rationale for choosing only one sex if a single sex study is conducted should also be provided, though discussed as a limitation to the generalizability of the findings. Investigators must also justify excluding either males or females. The assumptions that females are more variable than males or that females must be tested across the estrous cycle are not appropriate as these are not major sources of variability. 60 This policy is not a mandate to specifically investigate sex differences, but requires investigators to consider sex from the design of the research question through reporting the results. 49 , 50 In some instances, sex might not influence the outcomes (e.g., 61 , 62 ), but balancing sex in animal and cellular models will distinctly inform the various levels of research. 49 More specific guidelines for applying the policy of considering sex as a biological variable are also available, 50 , 63 but shifting the experimental group composition should be done in the context of appropriate a priori power analyses. One concern is that sample sizes need to be doubled to identify effects using both female and male subjects, but factorial designs can evaluate the main effects of the treatment and subject sex without increasing the sample size. 64 While the risk of false‐positive errors associated with testing sex differences in this way is present, reporting that these differences may or may not be present is imperative to understanding how sex influences the function of the nervous system. This practice should be extended to all scientific journals using animal/human subjects.
A transparent experimental design, meaning how the experiment is planned to meet the specified objectives, describes all the factors that are to be tested in an experiment, including the order of testing and the experimental conditions. As studies become more complex and interconnected, planning the experimental procedures prior to the onset of experiments becomes essential. Yet even when the experiments are planned prior to their initiation, the experimental designs are often poorly described and rarely account for alterations in procedures that were used in the study under consideration. To provide a more transparent and rigorous approach to describing the experimental design, a new section should be placed after the “subjects” paragraph describing, in detail, the experimental design and deviations made from the original design.
The experimental design section should consist of two main components: (a) a list of the experimental procedures that were used to conduct the study, including the sequence and timing of manipulation; and (b) an open discussion of any deviations made from the original design. The description should include an explanation of the question(s) being tested, whether this is a parameter estimation, model comparison, exploratory study, etc., the dependent and independent variables, replicates (how often the experiments were performed and how the data were nested). and the type of design considered (e.g., completely randomized design, randomized complete block design, and factorial design; see 65 , 66 ) for definitions and procedures to implement these designs). Assuming the authors planned the analysis prior to data collection, the authors should describe the specific a priori consideration of the statistical methods and planned comparisons 7 or report that no a priori statistical planning was carried out. If the statistical approach deviated from how it was originally designed (see, for example, Registered Reports below), the authors should also report the justification for this change. This open description could help to improve independent research reproducibility efforts and assist reviewers and readers in understanding the rationale for specific approaches.
A precise description of how methodological tools and procedures are prepared and used should also be provided in the experimental design section. Oftentimes, methodological procedures are truncated, forcing the authors to omit critical steps. Alternatively, the authors may report that the methods were previously described but might have modified those procedures without reporting those changes. Due to current publishing constraints, various caveats that go into the methodological descriptions remain unknown. However, this can be remedied easily by journals requiring a full description or step‐by‐step procedure of the experimental protocol used to test the dependent variables. Two options are available for publishing full protocols. First, the protocol could be published in the manuscript, with the reviewers verifying that the procedures are appropriately followed; second, a truncated version of the methods could be published in the manuscript, but the extended methods must be required as supplemental material (the extended methods will be peer reviewed during the submission process). An alternative approach is to deposit step‐by‐step protocols into a database or a data repository such as Dryad, FigShare, or with the Center for Open Science, where they will receive a DOI and can be linked back to the original research article, which will contain the truncated procedures.
Rigorous descriptions of the experimental protocols not only require a level of detail in the description of the experimental design, but also a full account of the resources and how they were prepared and used. A contributing factor to irreproducibility is the poor or inaccurate description of materials. In order for researchers to replicate and build upon published research findings, they must have confidence in knowing that materials specified in a publication can be correctly identified so that they might obtain the same materials and/or find out more about those materials. Most studies do not include sufficient detail to uniquely identify key research resources, including model organisms, cell lines, and antibodies, to name a few. 67 While most author guidelines request that the authors provide the company name, city in which the company is located, and the catalog number of the material, (a) many authors do not include this information; (b) the particular product may no longer be available; or (c) the catalog number or lot number is reported incorrectly, thus rendering the materials unattainable.
A new system is laying the foundation to report research resources with a unique identification number that can be deposited in a database for quick access. The Resource Identification Initiative standardizes the materials necessary to conduct research by assigning research resource identifiers (RRIDs). 68 To make it as simple as possible to obtain RRIDs, a platform was developed ( www.scicrunch.org/resources ) to aggregate data about antibodies, cell lines, model organisms, and software into a community database that is automatically updated on a weekly basis and provides the most recent articles that contain RRIDs. While SciCrunch is among the founding platforms, these identifiers can also be found on other sites, including antibodyregistry.org , benchsci.com , and others. Similarly, though more involved, PubChem offers identification for various compounds such as agonists and antagonists. Simply find the chemical abstract service (CAS) number from the chemical safety data sheet (SDS), input that number into PubChem, and receive the PubChem Chemical Identifier (CID). RRIDs have been successfully implemented in many titles throughout Wiley and are also in use by Cell Press and a number of other publishers. The authors should provide RRIDs and CIDs when describing resources such as antibodies, software (including statistical software used, as this is rarely reported), and model organisms, or compounds used, allowing for easy verification by peer reviewers and experimenters.
With most statistical software having a user‐friendly interface, students quickly learn how to perform basic statistical tests. However, users all too often choose inadequate and incorrect statistical methods or approaches or cannot reproduce their analyses since they have only a rudimentary understanding to each test and when to use them. 6 , 28 , 69 , 70 What's more, the authors do not appropriately describe their statistical approaches in text, partially because tests are performed only after the study is executed. In designing and reporting the experiments, the authors should report normalization procedures, tests for assumptions, exclusion criteria, and why statistical approaches might differ from what the authors originally proposed, if they developed these approaches prior to the onset of data collection. In addition, the authors must also include the statistical software and specific version thereof, descriptive statistics, and a full account of the statistical outputs in the results section.
Errors in statistical outputs often arise when the authors (a) do not conduct and report a power calculation 70 or do not distinguish between exploratory and confirmatory analyses; 71 (b) fail to state which statistical tests are used or provide adequate detail about the tests, including the descriptive statistics and a full account of the statistical output; (c) fail to state whether assumptions were examined 42 ; or (d) fail to describe how replicates were analyzed. 69 Moreover, it might be difficult to reproduce statistical output when the authors do not report the statistical software and specific version thereof, fail to include in the manuscript the exclusion criteria or code used to generate analyses, or explain how modifications to the experimental design might lead to changes in how statistical analyses are approached (e.g., independent versus non‐independent groups) (additional details about these common mistakes can be found in, 7 , 28 , 32 but it is important to emphasize that failure to report these variables can lead to errors in data interpretation.
Choosing the correct statistical analyses first depends on an appropriate experimental design and mode of investigation (exploratory versus confirmatory 71 ). One must decide whether experimental conditions are independent, meaning that no subjects or specimens are related to each other, 7 , 32 whether the conditions are non‐independent or paired, and whether there are any associations between variables. 72 The second step is that statistical analyses must include specific details about the test statistics, rationale for choosing each test, a description of whether normal distribution parameters are obtained, and a statement about which p‐value level is deemed statistically significant. In addition, a transparent and rigorous statistical analysis section must include the following:
Many studies are rejected for publication because of criticism that a study is underpowered, though many more studies are published despite this. 74 Reporting how a sample size was predetermined based on power analyses conducted during the experimental design stage is a good way to avoid this criticism. Researchers are taught to perform these analyses prior to the start of their experiments, but evidence suggests that researchers and peer reviewers do not fully understand the concept of statistical power, have not been given adequate education about the concept, or do not consider the measurement important in designing the experiments. 75
Reviewers and journal editors are beginning to ask authors to address the question of what the power of the study was to detect the observed effect. 76 , 77 Determining whether a study is appropriately powered a priori or post hoc is a matter of debate. 77 Many argue that post hoc power analyses are inappropriate, especially for nonsignificant findings, while others argue that post hoc power analyses are appropriate since a priori power analyses do not represent the power of the ensuring effect, but rather the hypothesized effect. 75
The a priori power analysis is the most common way of determining the sample size for simple experiments and can be easily computed using freely available software such as G*Power . The sample size depends on a mathematical relationship among the (a) effect size of interest; (b) standard deviation ( SD ); (c) chosen significance level; (d) chosen power; and (e) alternative hypothesis. 54 Yet, as more parameters come into play (for example, within mixed effects modeling), power analysis software becomes more complex (see Power Analysis for Mixed Effect Models in R ). Conducting these analyses allows researchers to confidently select a sample size large enough to lead to a rejection of the null hypothesis for a given effect size. 75 However, one limitation to a priori power analyses is that effect sizes and SD s may not be known prior to the research being conducted and may lead to observed effects that are smaller or larger than the hypothesized effects, 78 , 79 ). Alternatively, if it is conventional to use a specific number of subjects for a particular test, then one can report the calculated effect size for that particular sample size and decide whether more samples would be warranted. Either way, power and sample size calculations provide a single estimate, ignoring variability and uncertainty as such simulations are highly encouraged (see 80 ).
An alternative to the a priori power analysis is a post hoc power analysis (SPSS calls this “observed power”) or confidence intervals. The post hoc power analysis takes the observed effect size as the assumed population effect, though this computation might be different from a true population effect size, which might culminate in a misleading evaluation of power. 75 Post hoc power analyses always show there is low power with respect to nonsignificant findings. 77 Thus, utilizing the post hoc power analysis must be done with extreme care and should never be a substitute for the a priori power analysis. In fact, many in the statistical community see post hoc analyses as a waste of effort and recommend abandoning this approach 81 ; see also https://dirnagl.com/2014/07/14/why‐post‐hoc‐power‐calculation‐does‐not‐help/ and http://daniellakens.blogspot.com/2014/12/observed‐power‐and‐what‐to‐do‐if‐your.html ). If a reviewer or journal requests a power analysis, we recommend that rather than using post hoc power analyses, report confidence intervals to estimate the magnitude of effects that are consistent with the statistical data reported. 76 , 77 , 82 Alternatively, if increasing power is a necessity and/or sample sizes are already at their limits for financial or logistic reasons, one should consider alternative approaches, which are well described by Lazic; these include: (a) using fewer factor values for continuous predictors; (b) having a more focused and specific hypothesis test; (c) not dichotomizing or binning continuous variables; (d) using a crossed or factorial design rather than a nested arrangement. 46
We also advise authors to determine whether a parametric or nonparametric test is the most appropriate for the obtained data. Analogues to ordinary parametric tests (e.g., t ‐test or ANOVA, etc.) can be performed even if data are skewed or have nonnormal distributions; multiple robust analytics are available for these circumstances (see 83 ) as long as the sample size is sufficient. Importantly, parametric tests also generally have somewhat more statistical power than nonparametric tests and are more likely to detect a significant effect if one exists. Alternatively, when one's data are better represented by the median, nonparametric tests may be more appropriate, especially when data are skewed enough that a mean might be strongly affected by the distribution tail, whereas the median estimates the center of the distribution. Nonparametric tests may also be more appropriate when the obtained sample size is small, as occurs in many fields where sample sizes average less than eight per group 48 or when the data obtained are ordinal, ranked, or there are outliers that cannot be removed. 84 Beware, however, that meaningful nonparametric testing with sample sizes too low (e.g., n < 5) contains very little appreciable power to reveal an effect, if indeed one is present; difficulties due to violations of the underlying statistical assumptions of the particular test being used might be present. Bayesian analyses with small sample sizes are also possible, though estimates are highly sensitive to the specification of the prior distribution.
Figures illustrate the most important findings from a study by conveying information about the study design in addition to showing the data and statistical outputs. 7 , 32 Simplistic representations to visualize the data are commonly used and are often inappropriate. For example, bar graphs are designed for categorical data; when used to display continuous data, bar graphs with error bars omit key information about the data distribution (see also 85 ). To change standard practices for presenting data, continuous data should be visualized by emphasizing the individual points; dot plots (e.g., univariate scatterplots) are strongly recommended for small samples, along with plots such as violin plots (or overlaid points on the plots) to provide far more informative views of the data distributions when samples are sufficiently large. Bar graphs should be reserved for categorical data only. Moreover, graphic data plots involving multiple groups are often shown as overlaid, but should be “jittered” across the X ‐axis so that each discrete data point can be visualized. The use of jittering means that when there are fewer unique combinations of data points than total observations, the totality of the data distribution is not obscured. By adopting these practices, readers will be better able to detect gross violations of the statistical assumptions and determine whether results would be different using alternate strategies. 42
When plotting data, it is important to also report the variability of the data. Typically, this is expressed as the SD or standard error of the mean ( SEM ), but it is important to note that SEM does indicate variability. 34 The SD is calculated as part of an estimate of the variability of the population from which the sample was drawn. 86 , 87 The SEM , on the other hand, describes the SD of the sample mean as an estimate of the accuracy of the population mean. In other words, the SD shows how many points within the sample differ from the sample mean, whereas the SEM shows how close the sample mean is to the population mean. 87 The main function of SEM is to help construct confidence intervals, which are a range of values that take into account the true population value (usually an unknown), so that one can quantify the proximity of the experimental mean to the population mean. 88 Yet deriving confidence intervals around one's data (using SD ) or the mean (using SEM ) is premised on those data being normally distributed. Robust estimators are increasingly important as heteroscedasticity (having subpopulations with differing variabilities) is a frequent consequence of real‐world measurement. Traditional data transformations are an attempt to cope with this phenomenon but for many, such transformations may not actually serve to resolve anything and may add a layer of unnecessary complexity.
In determining which estimate of variability to depict graphically, it is important to remember that the SD is used when one wants to know how widely scattered measurements are or the variability within the sample, but if one is interested in the uncertainty around the estimate of the mean measurement or the proximity of the mean to the population mean, SEM is more appropriate. 87 When plotting data variability, it is important to consider that when SEM bars do not overlap, the viewer cannot be sure that the difference between the two means is statistically significant (see 34 ). We also note that it is misleading to report SD 's in the narrative and tables but plot SEM s. Furthermore, unless an author specifically wants to inform the reader about the precision of the study, SD should be reported as it quantifies variability within the sample. 86 , 87 , 88 Therefore, the optimal method to visualize data variability is to display the raw data, but if that makes the graph too difficult to read, instead show a box‐whisker plot, frequency distribution, or the mean ± SD . 34
The probability that a scientific research article is published traditionally depends on the novelty or inferred impact of the conclusion, the size of the effect measured, and the statistical confidence in that result. 21 , 89 The consequence of obtaining negative results can lead to a file‐drawer effect; scientists ignore negative evidence that does not reach significance and intentionally or unintentionally select the subsets of data that show statistical significance as the outcomes of interest. 41 This publication bias skews scientific knowledge toward statistically significant or “positive” results, meaning that the results of thousands of experiments that fail to confirm a result are filed away. 89 These data‐contingent analysis decisions, also known as p‐hacking, 90 can inflate spurious findings and lead to misestimates that might have consequences for public health. To combat the stigma of reporting negative results, we encourage authors to provide a full account of the experiment, to explicitly state both statistically significant and nonsignificant results, and to publish papers that have been rigorously designed and conducted, irrespective of their statistical outcomes. In addition, some organizations such as the European College of Neuropsychopharmacology are offering prizes in neuroscience research to encourage publication of data where the results do not confirm the expected outcome or original hypothesis (see ECNP Preclinical Network Data Prize ). Published reports of both significant and nonsignificant findings will result in better scientific communication among and between colleagues.
Though objectivity of a researcher or group is assumed, conflicts of interest may exist and could be a potential source of bias. Conflicts of interest largely focus on financial conflicts, 91 , 92 but they can also occur when an individual's personal interests are in conflict with professional obligations, including industrial relationships. 93 Conflicts, whether real or perceived, arise when one recognizes an interest as influencing an author's objectivity. This can occur when an author owns a patent, or has stock ownership, or is a member of a company, for example. All participants in a paper must disclose all relationships that could be viewed as presenting a real or perceived conflict of interest. When considering whether a conflict is present, one should ask whether a reasonable reader could feel misled or deceived. While beyond the scope of this article, the Committee on Publication Ethics offers a number of resources on conflicts of interest .
One possible way to incorporate all the information listed above and to combat the stigma against papers that report nonsignificant findings is through the implementation of Registered Reports or rewarding transparent research practices. Registered Reports are empirical articles designed to eliminate publication bias and incentivize best scientific practice. Registered Reports are a form of empirical article in which the methods and the proposed analyses are preregistered and reviewed prior to research being conducted. This format is designed to minimize bias, while also allowing complete flexibility to conduct exploratory (unregistered) analyses and report serendipitous findings. The cornerstone of the Registered Reports format is that the authors submit as a Stage 1 manuscript an introduction, complete and transparent methods, and the results of any pilot experiments (where applicable) that motivate the research proposal, written in the future tense. These proposals will include a description of the key research question and background literature, hypotheses, experimental design and procedures, analysis pipeline, a statistical power analysis, and full description of the planned comparisons. Submissions, which are reviewed by editors, peer reviewers and in some journals, statistical editors, meeting the rigorous and transparent requirements for conducting the research proposed are offered an in‐principle acceptance, meaning that the journal guarantees publication if the authors conduct the experiment in accordance with their approved protocol. Many journals publish the Stage 1 report, which could be beneficial not only for citations, but for the authors' progress reports and tenure packages. Following data collection, the authors prepare and resubmit a Stage 2 manuscript that includes the introduction and methods from the original submission plus their obtained results and discussion. The manuscript will undergo full review; referees will consider whether the data test the authors' proposed hypotheses by satisfying the approved outcome‐neutral conditions, will ensure the authors adhered precisely to the registered experimental procedures, and will review any unregistered post hoc analyses added by the authors to confirm they are justified, methodologically sound, and informative. At this stage, the authors must also share their data (see also Wiley's Data Sharing and Citation Policy ) and analysis scripts on a public and freely accessible archive such as Figshare and Dryad or at the Open Science Framework. Additional details, including template reviewer and author guidelines, can be found by clicking the link to the Open Science Framework from the Center for Open Science (see also 94 ).
The authors who practice transparent and rigorous science should be recognized for this work. Funders can encourage and reward open practice in significant ways (see https://wellcome.ac.uk/what‐we‐do/our‐work/open‐research ). One way journals can support this is to award badges to the authors in recognition of these open scientific practices. Badges certify that a particular practice was followed, but do not define good practice. As defined by the Open Science Framework, three badges can be earned. The Open Data badge is earned for making publicly available the digitally shareable data necessary to reproduce the reported results. These data must be accessible via an open‐access repository, and must be permanent (e.g., a registration on the Open Science Framework , or an independent repository at www.re3data.org ). The Open Materials badge is earned when the components of the research methodology needed to reproduce the reported procedure and analysis are made publicly available. The Preregistered badge is earned for having a preregistered design, whereas the Preregistered+Analysis Plan badge is earned for having both a preregistered research design and an analysis plan for the research; the authors must report results according to that plan. Additional information about the badges, including the necessary information to be awarded a badge, can be found by clicking this link to the Open Science Framework from the Center for Open Science.
The process of peer review is designed to evaluate the validity, quality, and originality of the articles for publication. Yet peer reviewers are not immune to making mistakes. For example, several studies were conducted where major errors were inserted into papers. In these studies, no reviewer ever found all the errors and some reviewers did not spot any errors. 95 , 96 While it is beyond the scope of this article to discuss many of the defects of peer review (see 97 ), it is important to note that the changes to the peer review process are ongoing 98 and publishers are working to develop more formal training processes. However, to quickly improve rigor and transparency in scientific research, peer review should emphasize the design and execution of the experiment. We are not saying that reviewers should focus solely on the experimental design; it is important for reviewers to weigh in on the novel insights of a study and how study results may or may not contribute to the field. However, to help ensure the accuracy and the validity of a study, emphasis should first be on the experimental design. To assist the reviewers, the authors should submit as part of their manuscript a Transparent Science Questionnaire (TSQ), or something equivalent, which identifies where in the manuscript specific elements that could aid in reproducibility efforts are found. The reviewers use this form to verify that the authors have included the relevant information and ensure that the study was designed and executed objectively, ensuring the study's validity and reliability. Using this or similar forms will also help reviewers to find the relevant information necessary to ensure the appropriateness of the design, which can then allow them to focus on the experimental outcomes. Adopting forms such as the TSQ or using services such as those offered by Research Square could also speed up the peer review process and reduce the cost in time committed by unpaid reviewers (which, in 2008, was estimated to cost $2.3 billion) ( https://scholarlykitchen.sspnet.org/2010/08/31/the‐burden‐of‐peer‐review/ ).
A multistage review where different parties are concerned with different aspects of the review may be optimal. Because many errors in manuscripts are found in the statistical output, one stage of review should be a statistical review, whereby a statistical editor reviews the statistical analyses of the manuscript to ensure accuracy, but also verifies that the most appropriate statistical tests for that design were used. Upon completion, the editor will then make a decision as to whether the approach and execution is sufficient and is in line with the reported statistical output. By having experts focus on specific aspects of a research report, journal editors will become more confident that the research published is valid and of high quality and integrity.
A challenge in science is for scientists to be open and transparent about the procedures used to obtain results. A major source of irreproducibility is substantial human error, which can occur while scientists are conducting the experiments or during data/statistical analysis. Groups are continuing to develop systems that help researchers cover every aspect of the experimental design (e.g., EQIPD or XDA ), but education and awareness of the key elements in research design and analysis is essential to transparent and reproducible research. By incorporating the specific elements discussed in this document into research manuscripts, researchers can reduce subjective bias, while actively improving methods' reproducibility, which will increase the likelihood of research reproducibility as the two are closely linked. 2 While variability in results is inevitable, ensuring that every salient aspect of a study is reported will help others understand the procedures involved and potential sources of errors during the experimentation process, which will ultimately lead to greater transparency in science.
Dr. David McArthur serves as JNR's paid statistical reviewer and has reviewed in that capacity for other journals, both Wiley and other publishers. Dr. Anita Bandrowski runs SciCrunch, a company devoted to ensuring RRIDs persist in the literature. Dr. Maryann Martone is a founder and the CSO of SciCrunch, which provides services supporting RRIDs and is the Editor‐in‐Chief of Brain and Behavior . Dr. Eric Prager is the Editor‐in‐Chief of Journal of Neuroscience Research . Dr. Nidhi Bansal is the Editor‐in‐Chief of Cancer Reports . Chris Graf works for Wiley, and volunteers for COPE, Committee on Publication Ethics.
All authors take responsibility for the integrity and the accuracy of this manuscript. Conceptualization , EMP and CG. Writing—Original Draft , EMP, KC; Writing—Review and Editing , EMP, KC, JKP, DLM, AB, NB, MM, HCB, AB, CG; Supervision , CG.
Acknowledgements.
We would like to thank Dr. Larry Cahill, Dr. Stanley Lazic, Dr. Hermina Nedelescu, Dr. Tracey Weissgerber, and Dr. Cora Lee Wetherington for valuable comments to this manuscript. EMP and AB acknowledge the contribution of the discussions that took place during the meetings organized by the ECNP Network Preclinical Data Forum ( https://www.ecnp.eu/research‐innovation/ECNP‐networks/List‐ECNP‐Networks/Preclinical‐Data‐Forum.aspx ).
Prager EM, Chambers KE, Plotkin JL, et al. Improving transparency and scientific rigor in academic publishing . Cancer Reports . 2019; 2 :e1150. 10.1002/cnr2.1150 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
This article is simultaneously published in Brain and Behavior ( https://doi.org/10.1002/brb3.1141 ) and in Journal of Neuroscience Research ( https://doi.org/10.1002/jnr.24340 ).
* When describing the data, it is important to differentiate between an exploratory and confirmatory study, as this could have profound implications as to how data are presented. Exploratory analyses are meant to identify patterns in the data without much emphasis on hypothesis testing, but most studies publish confirmatory experiments to test one or a few stated hypotheses.
With agile market research on the rise, and a significant trend towards research methodologies that can be used remotely, interest in Online Bulletin Boards (OBBs) has soared in 2020. Read our post about how you can use OBBs to obtain rich insights from your participants and how FieldworkHub can help you integrate them into your market research practice today.
Online bulletin board (OBB) research is a qualitative approach that gathers a virtual assembly of participants and a moderator to gather insights on topics of interest through interactive discussion.
Unlike a focus group, an OBB will typically last for several days, during which time participants are dipping in and out as their schedule permits. It’s a place for participants and moderators to interact and delve deep into various topics of interest over an extended period. Participants are encouraged to elaborate on ideas, comment and express opinions as best they can, and are often invited to include supporting material in the form of media uploads to help illustrate their thoughts; this helps fuel further discussion with the moderator, and within the group if the material is shared. The OBB may start as a group discussion but later split off into more personal one-one-one interactions as it progresses and research evolves.
Activities and participation typically involve:
Bulletin boards are conducted via an online platform and typically include 10-20 participants, though in theory there’s no upper limit. Groups can be composed of those who meet a broad set of demographics, such as consumers aged 18-65 who live in the UK, or can be more specific, such as B2B decision makers aged 25-65 in the financial services sector who use a specific enterprise software application, or parents with children aged 12-15 who purchase teen trousers at least once every three months.
Some clients recruit their participants from existing customer or prospect lists. Alternatively recruitment can be outsourced to a specialist agency such as FieldworkHub which has access to a broader range of potential participants and can screen them to find people who meet the client’s precise requirements.
OBBs empower researchers, executives, developers and brands to make decisions regarding strategy or next phases of research in a rapid and cost-effective manner. Within a few days, clients can obtain initial insights on topics of interest, allowing them to make decisions and tailor their next steps much more quickly than with traditional research methods. There is also more anonymity with participants in an OBB, making it especially useful when discussing more sensitive topics.
Benefits to OBBs:
The last five years have seen a rise in the number of OBB software platforms, which can make the process of choosing which platform to use or license more difficult. Here are some of the most important things to consider when making your selection:
FieldworkHub can support a number of your OBB needs and has the capability to run a qualitative study from start to finish, including:
Get in touch with us today to understand how we can help you with your OBB research needs.
Market research agency, market research audiences.
Research in progress presentations.
What makes a good research presentation? A set of skills that some people seem to develop intuitively. However, most of us have to work at it. Here are a few tips:
CONTENT: Think about ……
The key skill is to include exactly the right amount of material. Most speakers prepare too much and find there is little time left for an interesting Q and A session at the end.
STRUCTURE and COHERENCE: Think about …
The key skill is to organize your presentation so that the information and arguments will be presented in a balanced and accessible way. You can achieve this through a combination of spoken words, Powerpoint slides, handouts, pictures, charts, maps and diagrams.
COMMUNICATION SKILLS : Think about …
The most important skill is the last one. Audiences will appreciate it when they feel that you are talking to them rather than to your notes, Powerpoint slides or the ceiling. It is important to look at your audience to check for positive or negative feedback signs. Nodding the head in agreement is a good sign. Looking at a watch or a mobile phone is usually not. To achieve rapport you may have to depart from your intended script from time to time: it is never a good idea to prepare your words 100% in advance and then deliver them without modification in response to audience feedback.
A TYPICAL RESEARCH in PROGRESS PRESENTATION
Although there is no fixed format for this type of presentation, you will probably wish to include a mix of the following:
Picture this: You're a project manager juggling multiple tasks, deadlines, and team members. Keeping the balance between different tasks is hard but very important.
Enter the progress report, your secret weapon in conquering chaos and ensuring smooth sailing.
But what exactly is a progress report, and how do you craft one effectively? In this blog post, I'll demystify progress reports and guide you through the process of writing one.
From daily progress reports to weekly progress reports, using practical progress report templates and a tried-and-true format.
A progress report is a vital tool in project management , designed to keep different types of stakeholders informed about the ongoing status of a project.
It's a concise document highlighting current achievements, challenges, and goals, allowing the project manager to track progress and make necessary adjustments.
Project progress reports are one of the most important types of project management reports . They help maintain transparency, communication, and accountability within a team, ensuring everyone is on the same page. They also provide valuable insights for decision-makers, helping them gauge the project's overall health and success.
Here's what you can expect to find in a typical progress report:
Writing a progress report can seem daunting, but it doesn't have to be. You'll create a valuable document that keeps everyone informed and aligned by breaking it down into manageable sections and using clear, concise language.
Embrace the progress report writing skill and watch your team's productivity and communication soar.
Progress reports play a vital role in project management, serving as a communication tool to keep stakeholders updated. Let's delve into why progress reports are crucial for the success of any project or business.
Progress reports eliminate ambiguity and promote transparency. By regularly sharing project updates with stakeholders, the project team is held accountable for their work. This accountability ensures everyone is on track to meet the project milestones and objectives.
Progress reports help identify potential problems before they escalate. Team members can spot bottlenecks, delays, and other issues by examining project data and analyzing the progress report.
Early detection enables the team to take prompt action and prevent these issues from derailing the project.
Armed with accurate and timely information from progress reports, project managers and stakeholders can make informed decisions.
When a project progresses smoothly, management can allocate resources more efficiently or plan for future phases. On the other hand, if a project encounters challenges, swift decisions can be made to reallocate resources or change course.
A progress report's important aspect is maintaining momentum. When team members see their progress documented and shared, it fosters a sense of accomplishment and motivation.
This positive reinforcement encourages teams to keep pushing forward and maintain their productivity.
Progress reports facilitate better communication and collaboration among team members. By sharing updates and insights, the entire team stays informed, reducing the chances of miscommunication or misunderstandings.
Moreover, progress reports provide a platform for team members to ask questions, provide feedback, and offer support.
Business progress reports, such as quarterly, monthly, or annual progress reports, help track performance over time.
By comparing past reports, management can gauge the business's overall health and identify trends or patterns. This historical data can inform future strategies and drive continuous improvement.
Step 1: define the purpose.
The first step in writing a progress report is understanding its purpose. Progress reports inform stakeholders about the project's status, including what has been accomplished, any challenges encountered, and future planning. This allows project managers to keep everyone in the loop and make informed decisions.
The purpose of this monthly progress report is to update the management team on the project's status. It presents an overview of completed tasks, in-progress tasks, upcoming tasks, and any challenges faced during the reporting period. This report will also provide insight into key performance metrics and future planning .
Determine who will read the progress report. Is it for higher-ups, clients, or team members? Tailor the language, tone, and level of detail accordingly.
Decide the reporting period – weekly, monthly, or quarterly. Choose a timeframe that best suits your project's pace and stakeholder expectations.
Gather data on tasks completed, team members involved, and any obstacles faced. Consult previous progress reports, project documentation , and team members for accurate information.
Break down the report into logical sections. Here’s what we suggest:
Craft a concise summary that provides a snapshot of the report. Mention key achievements, challenges, and plans for the future. Keep it brief but informative.
This progress report covers our team's accomplishments during Q1, with a particular focus on the completion of the website redesign and the initiation of our social media marketing campaign. We've encountered some challenges in coordinating with external vendors, but we've implemented solutions to overcome those obstacles .
List all tasks completed during the reporting period. Include the following information:
Outline ongoing tasks, their current status, and expected completion dates. Explain any delays and their impact on the project timeline .
Identify tasks scheduled for the next reporting period. Provide details such as:
Discuss any challenges encountered during the reporting period. Describe how they were resolved or any plans to address them in the future.
Highlight key project management performance indicators and progress toward project goals. Use visuals like charts or graphs to make the data more digestible.
Discuss plans for the next reporting period, including any adjustments required. This may involve reallocating resources, revising timelines, or redefining objectives.
In the next reporting period, our focus will shift to improving user retention and engagement. We plan to implement new features based on user feedback and optimize the onboarding process.
Review the report for clarity, accuracy, and readability. Ensure all information is presented in a clear, concise manner.
Submit the progress report to the relevant stakeholders, ensuring they have ample time to review and provide feedback.
Use this template as a starting point for your progress report:
Project Title | [Project Name] Report |
Summary | Brief overview of the report's contents, key achievements, and challenges |
Completed Tasks | Task 1: Description, team members, start and end dates, relevant metrics Task 2: … |
In-Progress Tasks | Task 1: Description, current status, expected completion date Task 2: … |
Upcoming Tasks | Task 1: Description, assigned team members, estimated start and end dates, dependencies Task 2: … |
Challenges | Challenge 1: Description, resolution, or plan to address it Challenge 2: … |
Key Metrics | Metric 1: Description, current status, target goal Metric 2: … |
Future Planning | Plans for the next reporting period: any adjustments or changes required |
Conclusion | Recap of the report's contents: final thoughts or recommendations |
By following these steps and guidelines, you'll be well-equipped to write an effective progress report that keeps stakeholders informed and drives project success. Clear communication is key to maintaining momentum and ensuring everyone is on the same page.
1. business progress report.
A business progress report helps track company growth, accomplishments, and areas for improvement. It includes:
These reports offer a snapshot of a project or business every three months. They cover:
Monthly progress reports provide more frequent updates on projects or departments. They highlight:
Project status reports focus on a specific project's progress. They showcase:
Personal progress reports help individuals track their growth and development. They include:
When you create a progress report, start by identifying your target audience . Project stakeholders, team members, and future decision-makers should all benefit from your report.
Write in such a way that it is easy for them to understand. Avoid technical jargon and explain industry-specific language so everyone stays on the same page.
Establish a reporting frequency for your progress reports. Whether weekly, bi-weekly, or monthly, maintain consistency. Include report dates and the expected completion date of the current project to provide a clear timeline.
Focus on the project's scope and stay within the project's purpose. Don't digress or include unrelated details. A concise report ensures that readers remain engaged and informed.
Refer to the previous report to identify any changes or developments. Highlight the work completed, project deliverables , and any updates to the project plan. Doing so will maintain continuity and keep stakeholders informed about the department's progress.
Arrange project priorities logically, focusing on the most critical aspects first. Organize the information in a clear, easy-to-follow format. Use headings, subheadings, and bullet points for better readability.
Don't shy away from discussing problems or challenges. Addressing issues helps stakeholders understand the project's status and any hurdles that may affect successful completion. Offer potential solutions or workarounds to demonstrate proactive thinking.
Use relevant data to support your progress. Figures, charts, and percentages can provide a quick overview of the project's status. Make sure your data is accurate, up-to-date, and presented in an easy-to-understand format.
Acknowledge team members who have made significant contributions to the project. This recognition boosts morale and encourages continued excellence.
Discuss what's next for the project, such as upcoming tasks or milestones. This helps stakeholders understand the trajectory of the project and anticipate the work ahead.
Present complex ideas in a simple, easy-to-understand language. Break down complicated concepts into manageable chunks. Offer actionable insights and practical takeaways, so stakeholders can quickly grasp the project details.
Create a database to store all progress reports. This repository helps stakeholders access past reports and provides valuable insights for future projects. It also ensures that information is preserved and easily accessible when needed.
Before sharing your progress report, proofread and edit for clarity, consistency, and accuracy. This step ensures that your report is polished, professional, and easy to understand.
A progress report is most valuable when you're working on a long-term project. It's a way to keep stakeholders updated on progress and share important insights.
The primary purpose of a progress report is to provide a clear and concise overview of a project's status. This includes: – Communicating progress toward goals – Identifying potential issues and solutions – Demonstrating accountability and commitment to the project – Providing a step-by-step guide of completed tasks and upcoming work – Offering visual aids, like charts and graphs, to illustrate data A well-crafted progress report keeps stakeholders informed and fosters collaboration. It's also valuable for maintaining momentum and motivation throughout the project.
So, you've reached the end of this blog post. You're now equipped with the knowledge and tools to make progress report writing a breeze. Remember, it doesn't have to be a daunting task.
Keep it simple, stick to the facts, and let your progress shine. Talk about what you achieved, any challenges you faced, and how you overcame them. Use a clear, concise, structured format to ensure your message is easily understood.
To simplify the process, check out our guide on project reporting tools .
Ask yourself:
Considering these questions will make your progress report informative, actionable, and engaging. And don't forget, practice makes perfect. The more progress reports you write, the easier and more efficient the process will become.
Martin luenendonk.
Martin loves entrepreneurship and has helped dozens of entrepreneurs by validating the business idea, finding scalable customer acquisition channels, and building a data-driven organization. During his time working in investment banking, tech startups, and industry-leading companies he gained extensive knowledge in using different software tools to optimize business processes.
This insights and his love for researching SaaS products enables him to provide in-depth, fact-based software reviews to enable software buyers make better decisions.
6-minute read
A progress report is a business document that provides updates on a project’s progress toward meeting a goal. Typically, you’ll provide a progress report for a supervisor/manager, team member, or business client to summarize a project’s status and what still needs to be completed or improved.
But how do you write an effective progress report for your business’s projects ? In our guide below, we set out the typical structure of a progress report.
A progress report should start with a header that includes key details about the report and the project. Typically, this will include the:
This will help the recipient to understand the contents of the report at a glance.
The introductory paragraph of a progress report should outline the purpose and timeframe of the project, plus any other important details or insights.
You can also include an overview of what the rest of your progress report will cover.
The next section of your report should be titled “Work Completed.” Here, you can provide a chronological list of the project tasks that you have already completed and their corresponding dates. You can also include key findings from those tasks.
The next section should outline any problems encountered in the project so far. You should then explain either how those problems were solved or how they will be solved, and whether any extra help will be required to do so. You will also need to mention if those problems prompted any changes to the project.
To highlight the goals for the remainder of the project, the next section of your report should outline any future project tasks with their corresponding dates or deadlines, anticipated problems, and/or ideas for the project as you move forward.
End your progress report with a brief summary of key completed tasks, ongoing tasks, and major issues encountered. You don’t need to go into too much detail here, though. Stick to the essential details.
We also have some helpful tips you can use when writing a progress report:
Finally, to be sure your report looks and sounds professional, have it proofread. You can try our proofreading services by uploading a trial document for free today!
To see what a progress report might look like, check out our example report below:
Subscribe to our newsletter and get writing tips from our editors straight to your inbox.
Date: September 24, 2021 To: J. Seymour, Head of Planning From: A. Boleyn, Planning Assistant Subject: Migration to new planning software
Since November 2016, Exemplar Inc. has used the PlanULike package to manage the company’s everyday operations. However, when we expanded to new territories in July 2021, the limitations of the software became evident, especially with regard to currency conversions when budgeting for projects in Europe. As a result, in August 2021, the decision was made to migrate to new planning software. This report covers the progress in this project made up until September 24, 2021.
Work Completed
Problems Encountered
The key problem encountered thus far has been a compatibility issue between the new software and some of the company’s existing hardware. Head of IT, Simon Robinson, reports that this was due to PlanZone including graphical features that Exemplar Inc. does not use and had not been factored into the initial planning.
Due to speedy delivery and installation of new hardware, this has not significantly affected the timeframe for the migration. But the unexpected expense does mean that the project is now significantly over budget.
In addition, the testing of the in-house training program took longer than anticipated to complete. Key staff are now familiar with the new software, but the deadline for company-wide training has been extended to November 15, 2021.
Future Plans
The improved training program will continue until November 15, 2021, when all relevant staff are expected to be familiar with the new software, after which all operational planning will use PlanZone, and the PlanULike systems will be deprecated by November 30, 2021. Due to exceeding the budget allocated for this project, a meeting will be scheduled for heads of department to discuss how the extra expenses may impact budgeting for other projects.
The company has acquired and installed new planning software (PlanZone), which is projected to enhance project planning and ease operations in new territories. However, unexpected hardware and training issues have slowed progress. Deadlines for the migration have thus been extended. Meanwhile, implications of the extra expenses will be factored into budgeting for upcoming projects.
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I have always loved the beginning of the school year. It is a time filled with excitement, and new beginnings. It is also a great opportunity to establish a strong school culture that engages and excites students about mathematics.
While on the surface, school culture seems like a purely non-academic factor, it can strongly influence students’ academic achievement. Through the Every Student Succeeds Act (ESSA), non-academic factors are now being included in statewide accountability plans . While assessments tend to be the focus when student’s academic performance is discussed; the inclusion of the non-academic factors, specifically school culture, highlights the importance of an engaging learning environment.
Being intentional and strategic in designing school culture positions students for greater academic success.
The visual environment of your classroom and schools are great places to start. How could you use bulletin boards and hallway displays to establish a school culture that embraces math?
Schools all around the country who use ST Math, a game-based visual and instructional software program, are using bulletin boards to celebrate student growth and success in mathematics. Bulletin boards are great ways to show student progress and at the same time they can be great tools to get your students thinking and communicating their learning.
Check out these great bulletin boards from various schools with ideas on how to add interactive elements that engage students in math in ways that are approachable, exciting and meaningful!
In this example, students created penguin avatars that they move from postcard to postcard as they make progress. Each postcard represents 10% progress.
Bulletin board display by teacher Cortni Brunty at Liberty Union-Thurston Elementary in Baltimore, Ohio
Ideas to make it interactive: As students move to a new postcard ask them to research postcard locations and create math problems based on the information. The math problems don’t need to be traditional, but can be things they would like to explore (e.g., the distance between two places, the shape of the structures in the location, etc.).
Students’ names are written on airplanes that travel to the postcard's destination as they make progress in ST Math.
Bulletin board display by Viki Cooper at Tussing Elementary School in Pickerington, Ohio
Ideas to make it interactive: As students reach a new destination, have them write a “flight plan” to share areas they were stuck on and how they were able to learn from their mistake and solve the problem.
In this example, students made their own penguins and shared facts they learned about penguins.
Bulletin board display at Herbert Mills STEM Elementary in Reynoldsburg, Ohio
Ideas to make it interactive: Students can share strategies they are using to solve puzzles or explain the math they are learning with JiJi. They can post these on the bulletin board beside their penguin.
Want more bulletin board ideas? Visit our board on Pinterest , and remember to think about how you can promote school culture through your visual environment.
ST Math teachers can download the JiJi postcards on the Teacher Resource Site and create their own bulletin board!
Share your bulletin boards and your own ideas on how you are using your visual environment to excite and engage students in mathematics.
Looking forward to sharing your ideas!
Top image credit: bulletin board by Holly Antonelli at Liberty Elementary in Worthington, Ohio
Twana is Vice President of Curriculum and Instruction at MIND Research Institute. Follow her on Twitter @TwanaYoung .
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16 Sep 2024
87 million people in sub-Saharan Africa may not have access to electricity, despite being counted as such
Research published today in Nature Energy calls for standardisation of what ‘counts’ as energy access to track meaningful progress
Research published today in Nature Energy by an international team led by the University of Oxford, compares data from two key agencies tracking progress towards SDG7 on energy and finds that their estimates differ for at least 87 million individuals. This represents almost half (45%) of total reported progress towards electrification in the region.
The discrepancy is due in large part to widely varying interpretations in what counts as access. "If a village has a transformer, everyone in the village may be counted as having access to electricity – even if there is no physical connection to their home," explains Associate Professor Stephanie Hirmer , a lead author from the Energy and Power Group in the Department of Engineering Science and member of the Climate Compatible Growth programme which funded the project.
"We need an agreed standard of what ‘counts’ as access to electricity that can be updated over time to reflect the reality on the ground", she adds. "As a first step, agencies could provide explicit metadata documenting access definitions. Without these changes, the international community can’t track progress in a meaningful way: we’re left in the dark in more ways than one."
The authors add that while energy access is therefore uncertain for this group and may be overreported, these statistics are often taken at face value and used to guide significant policy decisions. "These data discrepancies have deep implications for addressing the electrification challenge," says Associate Professor Julia Tomei, a lead author from University College London. "In Togo for example, if policy makers used the World Bank data, they might stick with their current policy mix for electricity access – since the numbers look very positive. But if they used IEA data instead, which show a decline in access, they might choose a very different electrification strategy."
The authors conclude that it’s now crucial to look more critically at the data and ensure it is accurately reflecting realities on the ground in order to continue making progress towards SDG7: 'ensuring access to affordable, reliable, sustainable and modern energy for all'.
The research team is part of the Climate Compatible Growth (CCG) programme , and brings together researchers from the Department of Engineering Science and Smith School of Enterprise and the Environment, University College London, University of Wuppertal, Imperial College London, Technical University of Munich and KTH Royal Institute of Technology.
Inconsistent measurement calls into question progress on electrification in sub-Saharan Africa is published in Nature Energy. The work was funded by the Climate Compatible Growth Programme.
18 Sep 2024
Engineering Professors elected as Royal Academy of Engineering Fellows
13 Sep 2024
Robots being used to help research the importance of touch
09 Sep 2024
Professor Clive Siviour announced as Department’s new Professor of Materials Engineering
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1. Present early and often. Better to reconsider your design before submitting the IRB, collecting data, or writing the manuscript. 2. Present weeks or months before key deadlines. You'll be more willing to incorporate major changes and have time to present again. 3. Invite faculty. Ask your mentor (s) to come.
The correct answer is CAS.. Key Points. Research in Progress Bulletin: It is a type of current awareness service (CAS). A research-in-progress bulletin usually contains information about the laboratory at which the project is being done, names of principal and associate researchers, funds and sources of funds, duration of the project, and special equipment in use, if any.
7.4 Research in Progress Bulletin . It is an alerting service which alerts the users about the new research projects and the progress made in the projects already in progress. This type of service generally requires joint efforts of more than one organisation or institution working in similar or closely related research areas.
I would give a research-in-progress paper the same format as a typical research paper (using a typical format such as introduction-methods-results-conclusion). However, the content of some of the sections would be different.Using this format as an example: Introduction This would probably be similar to the introduction on your final paper, but it should also give a bit of context on the ...
an alerting service. isAwarenessThe four types of CAS described in this Unit are: Contents-by-Journal, Documentation Bulletins, Research-in-Progres. and Newspaper Clipping. Services. These are non-specific. The which is geared to specific user information needs, is based on.
ESRI Research Bulletins provide short summaries of work published by ESRI researchers and overviews of thematic areas covered by ESRI programmes of research. Bulletins accessible to a wide readership. This Bulletin summarises the findings from: Cristina Iannelli, Emer Smyth and Markus Klein (2015), Curriculum differentiation and social ...
These short communications differ in the following ways: Bulletin or Brief: This offers a very short, "brief" format - either a frequently-circulated update on project progress or a short presentation of evaluation results. It can also be used to present changes decided - for example in a "policy brief" or a bulletin summarising ...
Here are some tips that will get you started with your research progress report. 1. Write the Title of Your Report. The title of your report should at least be about what your research is about. It does not have to be something too fancy that the whole point of the report is lost or too obvious that would make the report redundant. 2.
However, most prior work has focused on the extension of full papers in academic venues [such as a simple conference to journal extension (Eckmann et al. 2011)] and discussion surrounding specifically work in progress papers is not explored. For example, previous research (Montesi and Owen 2008) has investigated the tendencies, habits and ...
The example of the Chemical Titles of the Abstract Service and Current Chemical Papers of the Chemical Society of the British Chemical Society of Britain is an example of this. 3. Research in progress bulletins - In this type of service, a bulletin is issued from time to time about the progress of research in the same field. This service is ...
LitReviewlinear2 by LKaras Work. The process of writing a literature review is not necessarily a linear process, you will often have to loop back and refine your topic, try new searches and altar your plans. The info graphic above illustrates this process. It also reminds you to continually keep track of your research by citing sources and ...
Progress in basic and clinical research is slowed when researchers fail to provide a complete and accurate report of how a study was designed, executed, and the results analyzed. Publishing rigorous scientific research involves a full description of the methods, materials, procedures, and outcomes. ... For example, it is normally unclear ...
Online bulletin board (OBB) research is a qualitative approach that gathers a virtual assembly of participants and a moderator to gather insights on topics of interest through interactive discussion. Unlike a focus group, an OBB will typically last for several days, during which time participants are dipping in and out as their schedule permits ...
The nurse determines the strength of evidence for application to practice, based on study design. Rank order these types of reports based on the Hierarchy of Evidence. 1)Systematic review of all relevant RCTs. 2)Evidence obtained from well-designed RCTs. 3)Evidence from controlled trials without randomization. 4)Evidence from single descriptive ...
Precise wording of motions, including the vote and action taken. The name of the group as well as the date, time, and place of the meeting. Your name and signature. The names of attendees and absentees. Old business, new business, and reports. The name of the group as well as the date, time, and place of the meeting.
A TYPICAL RESEARCH in PROGRESS PRESENTATION. Although there is no fixed format for this type of presentation, you will probably wish to include a mix of the following: Your research topic or draft title. Your reasons for choosing this topic (and for rejecting other possibilities) Your research questions and claims.
A progress report is a vital tool in project management, designed to keep different types of stakeholders informed about the ongoing status of a project. It's a concise document highlighting current achievements, challenges, and goals, allowing the project manager to track progress and make necessary adjustments.
You can also include an overview of what the rest of your progress report will cover. 3. Work Completed. The next section of your report should be titled "Work Completed.". Here, you can provide a chronological list of the project tasks that you have already completed and their corresponding dates.
Check out these great bulletin boards from various schools with ideas on how to add interactive elements that engage students in math in ways that are approachable, exciting and meaningful! Celebrate Success in Math. In this example, students created penguin avatars that they move from postcard to postcard as they make progress.
Research Elements. This journal enables the publication of research objects (e.g. data, methods, protocols, software and hardware) related to original research in Elsevier's Research Elements journals. Research Elements are peer-reviewed, open access journals which make research objects findable, accessible and reusable.
Research published today in Nature Energy by an international team led by the University of Oxford, compares data from two key agencies tracking progress towards SDG7 on energy and finds that their estimates differ for at least 87 million individuals. This represents almost half (45%) of total reported progress towards electrification in the region.
The objective of this study is to determine the extent of universities in India, uploading research. synopsis to the ShodhGangotri repository. It aims to analyze, region-wise, discipline wise ...