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Data Presentation And Analysis In Research Pdf

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Learning Skills:. Subscribe to our FREE newsletter and start improving your life in just 5 minutes a day. Which you choose will depend on your research questions, your underlying philosophy of research, and your preferences and skills.

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends.

Data analysis

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.

Research Guide: Data analysis and findings

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.

Planning is a process that designs a plan of action or evaluates the impact of a proposed action to achieve a desirable future. During this process planners often obtain the necessary data from different sources, analyze them efficiently and comprehensively, and present the results in easily understandable forms. The rationale for such a process is that public policy and decision makers derive their decisions based on the anticipated future from knowledge about the present and the past of a community. The three-step procedure—data collection, analysis, and presentation has the goal of accurately presenting the information to reflect what has happened and what may happen. Unable to display preview. Download preview PDF.

Use, analysis and presentation of qualitative data

The uses of qualitative data are broad and varied and have been discussed throughout the chapter. Qualitative findings may be published in peer reviewed journals, in non-peer reviewed journals, and in reports for funders and decision-makers. However, the raw data obtained from interviews and focus groups transcripts of what was said , and observations field notes on what was observed by the researcher must first be analysed.

Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision making, and are key instruments in data interpretation. Data interpretation refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion. The interpretation of data assigns a meaning to the information analyzed and determines its signification and implications. The importance of data interpretation is evident and this is why it needs to be done properly.

Home Consumer Insights Market Research. Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. This data is any quantifiable information that can be used for mathematical calculations and statistical analysis, such that real-life decisions can be made based on these mathematical derivations. This data can be verified and can also be conveniently evaluated using mathematical techniques.

What Is Data Interpretation?

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4 Comments

Adrienne B. 14.05.2021 at 13:49

WHO Library Cataloguing-in-Publication Data: Implementation research toolkit. Contents: Facilitator guide, Workbook, Brochure and Slides. drugtruthaustralia.orgch. 2.​Delivery.

Luke C. 15.05.2021 at 01:41

Contents - Previous - Next.

Pierrette P. 15.05.2021 at 12:06

This works best for simple observations, such as: "When viewed by light microscopy, all of the cells appeared dead.

Megan G. 18.05.2021 at 05:48

Data analysis is a process of inspecting, cleansing , transforming , and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

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