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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

This chapter examines strategies to overcome many of the obstacles to writing that make the process seem harder than it should be, including writing strategies for all stages of the dissertation process. Topics covered include the dissertation structure, proofreading, revising, and editing, and suggestions for dissertation content. Also examined are issues related to qualitative data presentation, quantitative data presentation, tables, charts, and graphs as well as strategies for structuring the presentation of dissertation results.

This chapter is a synthesis of selected issues relating to the analysis and presentation of dietary data, building on the experiences of those working in this field. The underlying objectives of data analysis and presentation are to learn as much as possible from the available data and to present what has been learned so far to readers completely and with maximum clarity. Particularly for presentation, the approaches should vary depending on the intended readership. For example, simpler analytic approaches and greater use of figures may be appropriate for a general medical journal, whereas more complex methods and primarily tabular results may be best for an epidemiologic publication.

Transparency of the review process acts as a driving principle when considering how to organize and present the results of qualitative synthesis within systematic reviews. In this chapter, we illustrate strategies for reporting systematic qualitative synthesis. Findings in a systematic review approach should detail a clear process of the review. A comprehensive presentation of the review means keeping detailed and accurate records throughout the review process. Record keeping means recording the following: all decision points made during the review, key questions for the review, search terms used, the time period for conducting the search, the number of hits located, details of the screening process and decisions to include or exclude studies, the included studies, and clear articulation of the steps taken for the data analysis and report writing.

F&F theorists claim H&B theorists have merely exposed laboratory frailties in judgment and decision-making; the findings do not imply poor performance in natural environments. H&B experimenters purportedly often present problems in a cognitively intractable form rather than the more tractable form they would take in natural environments, and they often ask people to solve, through abstract methods, problems of no practical significance that formally resemble important problems that they solve without using formal logic. Moreover, at times, subjects will substitute a pay-off structure from the real-world variant of the “game” that resembles the “game” the experimenters have established with its own unfamiliar pay-off structure and, at other times, people will reinterpret the language of instructions they are given because they draw implications from the quasi-conversation with the experimenter that are not literally present. F&F scholars further believe that the heuristics that the H&B scholars have identified are both under-theorized—there is no adaptationist account of why any of the cognitive mechanisms they identify would have developed—and under-defined.

This chapter presents an online lexical database and shows how some of its data categories have been converted to make them more accessible to users. The database is called DiCoInfo and contains English, French and Spanish terms related to the fields of computing and the Internet. Entries are compiled according to the principles of Explanatory Combinatorial Lexicology, ECL (Mel’čuk et al. 1995). It first describes the basic structure of the entry, focussing on the encoding of collocations (based on lexical functions). Then, it shows how the meaning of collocations can be described with natural language explanations, how actantial structures can be better reflected in these explanations, how users can browse collocations in order to find collocates that express specific meanings, and search for translations of collocations. Finally, it explains that, even though the encoding performed by lexicographers is semi-formal and proves necessary for all the new functionalities described, it can still lend itself to adaptations defined according to specific user needs.

Understanding information is complex as it depends on the source and reliability of the initial data and the way information is presented, as well as our own ability to make the correct interpretation. Each aspect of an information chain can lead to failure, including when the presenter does not correctly understand, or is deliberately trying to mislead, and from errors because the audience lacks the expertise of the source. Statistics and political examples of information errors are common, not least as many will have no ability or familiarity with graphical or mathematical steps involved in the process. Several types of presentation are included, where we are likely to draw totally opposite conclusions from the same information, or the aim was intentionally to mislead. The topic is relevant in daily life, from politics and health, as well as more complex issues such as wealth statistics, climate change or control of pandemics.

Quantitative risk analytical research is about measurements. Statistics and econometrics are the most widely used branch of mathematics in quantitative research. Various descriptive statistics are useful in risk analysis. The application of summary statistics, central tendency (mean, median mode), dispersions (such as standard deviation, skewness, and kurtosis), and data distribution (percentile, Lorenz curve) in risk measurement has been demonstrated with examples in this chapter. The analysis of these metrics will enable a risk manager to get idea about the nature of risk inherent in the business. Information about of body and spread of the data series are essential in estimating frequency and severity of losses. This chapter addresses the problem-solving side of the risk analytical work to equip the risk manager to get idea of a data series and analyze it patterns to understand the nature of risk inherent in the asset portfolio and trace problems.