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		<title>Guiding virtues in data presentation</title>
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				current-module = "Data Selection, Analysis and Presentation"
				page-title = "Guiding virtues in data presentation"
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							<p>The final step in preparing for publication involves making decisions on how to <glossary placement="bottom" content="The way you choose to communicate your findings to others.">present</glossary> your results to others. Presentation is part of communication and publication, but it is a process that is closely linked to, and grows out of, data selection and analysis.</p>
							<p>Presentation is a skill that must be learnt because there is no point in conducting research if you do not tell others about your results. The main way of presenting research is through written reports.</p>
							
							<h3>Virtues underpinning data presentation</h3>
							
							<p>As with the other topics covered in this module, the details of best practice for presentation should be defined by the professionals in your field of study. You should spend time reviewing how research similar to your own is presented, paying special attention both to examples that model what you <em>should</em> do and to those that suggest what you <em>should not</em> do.</p>
							<p>There are five virtues that underpin best practice in data presentation.</p>
							
							<instructional-text><p>Click on each card for an explanation of how each virtue should be applied in relation to data presentation.</p></instructional-text>
							<instructional-text-accessible><p>Continue on for an explanation of how each virtue should be applied in relation to data presentation.</p></instructional-text-accessible>
							
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									<flashcard-front variant="heading">Transparency</flashcard-front>
									<flashcard-back variant="text"><p>You should always provide a clear explanation of how you reached your conclusions. It is important that you are able to describe your decision-making process and the choices you made. In some fields, transparency means making this data publicly available (e.g. in registry databases).</p></flashcard-back>
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									<flashcard-front variant="heading">Reliability</flashcard-front>
									<flashcard-back variant="text"><p>To confirm that your presentations are reliable, you should always retain a copy of your original data/information and a clear record of your methods. This allows others to check for any errors or biases you might have missed.</p></flashcard-back>
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									<flashcard-front variant="heading">Rigour</flashcard-front>
									<flashcard-back variant="text"><p>Take the time needed to make sure your presentational materials are clear, complete and accurate. While mistakes happen, they should be rare occurrences and should not arise from careless or sloppy work.</p></flashcard-back>
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									<flashcard-front variant="heading">Impartiality</flashcard-front>
									<flashcard-back variant="text"><p>Presentational materials should be chosen to clarify your findings, not sell them. Do not use words or graphics to exaggerate (hype) your findings.</p></flashcard-back>
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									<flashcard-front variant="heading">Honesty</flashcard-front>
									<flashcard-back variant="text"><p>The material you present should accurately and honestly convey your findings.</p></flashcard-back>
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							<h3>Summary</h3>
							
							<p>Properly prepared presentational materials are an essential part of your research. You and your research team are the only ones who are completely familiar with your findings; everyone else relies on the materials you present to understand what you have done and accomplished.</p>
							<p>It may be in your best interests to prepare these materials in a way that will engage others, but the desire to engage an audience should not override your duty to follow the virtues of rigour, reliability, transparency, impartiality and honesty.</p>
							
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										<p>As part of a research project, you are taking 10 measurements. For example, you could be recording the number of times a politician used a key phrase in 10 speeches leading up to an election, or the number of tremors in an active volcano over 10 days.
										<br><br>For this activity, the measurements are not important, but the way in which they are presented is. You gathered these results:</p>
										<br>1 -->
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													<th>Measurement</th>
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													<td>1</td>
													<td>8</td>
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													<td>2</td>
													<td>7</td>
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													<td>3</td>
													<td>9</td>
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													<td>4</td>
													<td>8</td>
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													<td>5</td>
													<td>8</td>
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													<td>6</td>
													<td>9</td>
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													<td>7</td>
													<td>10</td>
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													<td>8</td>
													<td>9</td>
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													<td>9</td>
													<td>10</td>
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													<td>10</td>
													<td>11</td>
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										<!--<p>Assuming the results can be shown to be statistically significant, you must next decide how to present them in a publication. You have two options:</p>
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										<action-link goto-panel="1"><p>Use the full range of values covered (1-12) for the y-axis</p></action-link>
										<action-link goto-panel="1"><p>Limit the y-axis to a specific range of values (7-11)</p></action-link>
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										<p>Both these forms of presentation suggest that there has been some level of change over the course of the 10 measurements, but that change is more obvious if you choose Option 2. Which factor would you consider when you made a decision on which option to choose?</p>
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									<action-link goto-panel="2"><p>The extent to which my analysis supports the interpretation of the level of change suggested by the second chart</p></action-link>
									<action-link goto-panel="3"><p>The significance level required for my publication to be published in the journal I have intended it for</p></action-link>
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										<p>If your analysis supports the conclusion that the change is highly significant (depending on how significance is established in your field of study), then choosing Option 2 could be justified. However, if the results are only slightly significant, then going with Option 2 could be seen as "hyping" your findings (i.e. exaggerating or inflating the results beyond what the evidence supports).
										<br><br>There are other ways in which supposedly hard quantitative data can be hyped or exaggerated through presentation, as illustrated in the two graphs below. Here, an arrow has been added to illustrate the change over time.</p>
										<img class="img-responsive" src="../images/activity_images/course_specific/dap_1_40_img2.png" alt="" />
										<p>Which of these two options could be an example of additional hyping or exaggeration?</p>
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									<action-link goto-panel="4"><p>Option 1</p></action-link>
									<action-link goto-panel="4"><p>Option 2</p></action-link>
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										<p>Focusing on the significance level you believe is required to get your publication in the journal you have targeted could easily lead you to hype your results (i.e. exaggerate or inflate your findings beyond what the evidence supports), and if this is what motivates you to plot your data in a certain way you could be accused of irresponsible conduct. Instead of considering this, your primary concern should be whether your analysis supports the conclusion that the change is highly significant (depending on how significance is established in your field of study); if this is the case, choosing the presentation displayed in Option 2 could be justified.
										<br><br>There are other ways in which supposedly hard quantitative data can be hyped or exaggerated through presentation. The two graphs below have added an arrow to illustrate the change over time.</p>
										<img class="img-responsive" src="../images/activity_images/course_specific/dap_1_40_img2.png" alt="" />
										<p>Which of these two options could be seen as an example of additional hyping or exaggeration?</p>
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									<action-link goto-panel="4"><p>Option 1</p></action-link>
									<action-link goto-panel="4"><p>Option 2</p></action-link>
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										<p>Since Option 2 shows the greatest change, it is strongly suggesting that a significant change has been detected and is thus most likely to be suspected of plotting the data with the specific goal of hyping the findings.
										<br><br>Looking back at the different presentation options, it is worth comparing the least and most bold ways of presenting your information:</p>
										<img class="img-responsive" src="../images/activity_images/course_specific/dap_1_40_img4.png" alt="" />
										<p>Which presentation most accurately and honestly conveys the results of your research? The answer will usually depend on the research project, but will always need to be carefully considered.</p>
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