presentation analysis and interpretation of data in research pdf

Presentation analysis and interpretation of data in research pdf

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Presentation, Analysis and Interpretation of data

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Data selection and bias

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The digital image or photograph you are planning to use in a publication is not as clear as it could be. Is it okay to change the contrast of the parts you want to emphasise, or is this data fabrication or falsification? Working out how to interpret and then present your research material and data is probably the most creative aspect of research, but also an area where it is easiest to compromise integrity. The rules for interpretation and presentation are usually very field-specific and often unwritten.

For example, no clear universally accepted standards exist to distinguish acceptable manipulation of digital images. See the box at the end of this section for guidance in this area, but if you have further questions:. Data interpretation and presentation is a crucial stage in conducting research, and presents three key challenges:. These three challenges form the subject of this section.

Before we launch into further detail, some experts talk about some of the ways in which data can be manipulated. Daniele Fanelli, Research Fellow, The University of Edinburgh: In my research, there is pretty good evidence that the frequency of positive results, as opposed to results that do not support the hypothesis that was tested in the study, have been dramatically increasing over the last twenty years.

The problem behind this has partly to do with probably how journals select results. Presumably they want-, they're increasingly selecting studies based on the outcome, and this in turn, however, clearly will put a pressure on researchers to get those positive results, to get the publication. So it's quite well acknowledged that the temptation, and it's a temptation very few researchers resist, including probably myself, is that once you have your data set, you will look for the kind of patterns you suspect are there.

The tragedy, if you like, nowadays is that you have so many ways to do that, so many statistical techniques at your disposal, and so many technologies that allow you to be more and more clever at mining your data for results, that the risk is obviously that then you end up just seeing whatever you wanted to see in the first place, without actually being anything there.

Adding to that risk is the fact that usually when you do research, you're not only looking and getting one result, but you're looking at several different aspects of a problem.

Then, if you then only choose some for publication, you will only discuss some and ignore the rest, then again the risk is that you're unduly selecting what is the evidence. The extent to which this is perfectly legitimate, or it is an unconscious form of bias, or it is even a dishonest practice, is controversial. Daniele Fanelli: The way out of this, generally speaking, is to be transparent about what you did. I'm not naive enough to think that this is going to be the whole story, because publication space in journals is limited, and you will never be allowed to tell precisely everything that you have done.

So in part, the system does need other ways also to allow researchers to make fully public their data, you know, all the results they obtained, etc. Again the ideal to follow, I think, in any kind of research, is as much as possible, be transparent of the whole procedure.

What were your original research questions, how you collected the data, what eventually was the data that went into this particular study, and so on. Melissa S. Anderson, Professor of Higher Education, University of Minnesota: If you think about it, what's the most important aspect of research and new knowledge?

It's that it's right, it's correct, it's true. Now, it may be wrong because of a mistake or an error, and if that happens, you go back and you fix your mistake, but if it's wrong because someone has intentionally introduced false information, that's inexcusable. That's exactly what happens in the case of falsification or fabrication. If, in fact, somebody introduces false information into the research record, it can be there for a long time, and people may be making bad decisions on the basis of wrong information.

Nick Steneck, Director of the Research Ethics and Integrity Program of the Michigan Institute for Clinical and Health Research, University of Michigan: The assumption has been that falsification, fabrication and plagiarism or, kind of, the very serious offences, are the ones that we ought to pay the most attention to. Those are serious offences. They need to be investigated when they occur. They actually, in my view, don't have the biggest impact on the research record, because although they're more common than we thought, they still are few in number.

It's other practices, such as bias and conflict of interest, kind of small manipulation of the data, improper authorship, those sorts of things that ultimately turn out having the biggest impact on the research record, and then as we use that research record, actually having the biggest impact on society's use of research.

Since you want your work to turn out to be important and well-received, it can be tempting to manipulate results. In fact, studies have suggested that misinterpretation and over-interpretation may be the most significant sources of error in the research record and of bad advice for policy makers Al-Marzouki, Analysis in humanities disciplines generally involves engaging with the texts and ideas of others to define and discover themes and issues.

The researcher enters into an ongoing 'conversation' to contribute to the furthering of knowledge about ourselves, our history and our cultural milieu. Two potential problems arise here which are partly due to the complexity of the phenomena being studied:. The main problem is drawing the line between creative interpretation and misrepresentation. It is important to reflect on the implications of this for your own work. The typical arts researcher works in a significantly different way.

The aim is usually to produce artefacts or creative work e. Research in these kinds of projects means that your own or others' creativity is often the object of the research. In arts research, then, what counts as 'data' or 'research material' is more ambiguous than in most other disciplines. This means you should think carefully about what counts as 'data' in your work when reading through the rest of this section.

As a first step, you need to determine which data are suitable for further analysis and which should be discarded. In the following section, consider a straightforward request regarding data that appears to deviate from the expected trend.

Make a note of your ideas, then move on to our feedback. You have been analysing a set of newspaper articles on the portrayal of contemporary poets. You have discovered that one particular arts correspondent in a leading Sunday newspaper is both female and very supportive of women poets.

This undermines your own argument that women poets are mostly ignored and that when they are covered in the media, it is mostly in very negative terms. Your supervisor suggests you don't include them, justifying the exclusion on the grounds that you could reframe your research question to only focus on weekday newspapers.

What should you do? Feedback: Some of the most common questionable research practices QRP s centre on the analysis and interpretation of data.

In this case, it may have been tempting to ignore the data which bucked your expectations, made your own argument more difficult, or which you found difficult to align with your own theoretical or personal position. Responsible researchers should have solid and unbiased justification for ignoring data which presents such problems.

Researchers should be mindful of the bias that their perspectives and goals bring to the research setting. This can be a bias toward our own ideas, career pressures or external pressures for example, funding agencies that influence our decision-making. Being conscious of these influences is a first step towards addressing them. Another important aspect of interpreting and presenting findings is estimating their significance. For example, consider the following questions:. It is important to recognise the limitations of any research study and to interpret the findings within these constraints.

In the following section, you will be presented with a list of research components and a list of potential limitations. For each component, make a note of the limitation you think it might cause, then continue to our suggested answers. A final aspect of data interpretation involves making decisions on how to present and explain your findings to others.

Data presentation overlaps into the subjects of reporting and publishing, covered elsewhere in this course — however, we mention it here because some of the decisions you make in relation to presentation will be critical to your analysis. A great deal of freedom and creativity can be employed in data presentation in order to convey information that seems to suggest a particular conclusion.

In the following section, consider the alternative versions of the same information, and in each case reflect on the significance of this difference in relation to interpretation. Our thoughts: If you use citations, you need to consider the full context in which they appear.

Our thoughts: Inserting, removing or enhancing certain elements of an image is a form of misinterpretation and would be considered falsification. Whilst these are relatively simple examples, it is clear that the tools available to researchers open the door to a variety of manipulations that can overstate the significance or underplay the limitations of findings.

Data interpretation and presentation raise many challenges for responsible behaviour. Although these guidelines relate in particular to scientific images, some of the principles transfer across to other disciplines. Reference - Fanelli, Fanelli, D. Screen challenge The digital image or photograph you are planning to use in a publication is not as clear as it could be.

See the box at the end of this section for guidance in this area, but if you have further questions: Check carefully with your supervisor, colleagues or publication editor before making any changes Be honest and upfront in letting others know what changes you have made.

Data interpretation and presentation is a crucial stage in conducting research, and presents three key challenges: Selecting which material will be used for drawing conclusions about your work Establishing the significance or otherwise of material and identifying potential weaknesses and limitations Deciding how to present your findings and observations.

Media - Video. All meanings, we know, depend on the key of interpretation. George Eliot Useful links. Historian Michael Bellesiles accused of misrepresenting evidence: www.

Presentation, Analysis and Interpretation of data

Profile of the Research Participants A total of faculty members and administrators were invited to participate in this study. Of those invited, responded to this invitation or an equivalent of Table 3. Figure 4. Profile of Participants According to Employment Status In terms of academic rank, the research participants had varied qualifications ranging from a bachelors degree for Lecturers and Instructors to a doctoral degree for Full Professors and some Professional Lecturers. In addition, a total of 38 research participants were not able to indicate their academic rank, hence their responses were not captured in the summary of analysis.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy. See our Privacy Policy and User Agreement for details. Published on Jul 24, A presentation on how to prepare the fourth chapter of a thesis. SlideShare Explore Search You.

When creating chapters 4 and 5 of your study, always go back to your Statement of Purpose to determine what data needs to be displayed and what results you need to have to achieve the purposes of your study. Shorten a paper by stating points of discussion clearly and directly to avoid a long paper. Confining the discussion to the specific problem under study, combining or deleting data displays, elimination of repetitions across sections, and writing in the active voice are ways to shorten a paper. We tested three groups: a low performers, who scored fewer than 30 points; b moderate performers, who scored between 30 and 60 points; and c high performers, who scored more than 60 points. Referencing is an important part of any research paper.

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Contents - Previous - Next. Stages of analysis and interpretation of findings Establishing the trustworthiness of information Presentation of findings Implementation of findings. This chapter deals with the processes of conducting overall analysis of all the information gathered and reviewed; checking its trustworthiness by triangulation; interpreting or making sense of findings; presentation and use of findings.

Chapter-4-Presentation-Analysis-and-Interpretation-of-Data.pdf

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Data selection and bias

The digital image or photograph you are planning to use in a publication is not as clear as it could be. Is it okay to change the contrast of the parts you want to emphasise, or is this data fabrication or falsification? Working out how to interpret and then present your research material and data is probably the most creative aspect of research, but also an area where it is easiest to compromise integrity. The rules for interpretation and presentation are usually very field-specific and often unwritten. For example, no clear universally accepted standards exist to distinguish acceptable manipulation of digital images. See the box at the end of this section for guidance in this area, but if you have further questions:. Data interpretation and presentation is a crucial stage in conducting research, and presents three key challenges:.

Компьютер однократно пискнул. На экране высветилось: СЛЕДОПЫТ ОТПРАВЛЕН Теперь надо ждать. Сьюзан вздохнула. Она чувствовала себя виноватой из-за того, что так резко говорила с коммандером. Ведь если кто и может справиться с возникшей опасностью, да еще без посторонней помощи, так это Тревор Стратмор. Он обладал сверхъестественной способностью одерживать верх над всеми, кто бросал ему вызов. Шесть месяцев назад, когда Фонд электронных границ обнародовал информацию о том, что подводная лодка АНБ прослушивает подводные телефонные кабели, Стратмор организовал утечку информации о том, что эта подводная лодка на самом деле занимается незаконным сбросом токсичных отходов.

Chapter IV PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

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