Treatment of data
To conduct research responsibly, graduate students need to understand how to treat data correctly.
Researchers who manipulate their data in ways that deceive others, even if the manipulation seems
insignificant at the time, are violating both the basic values and widely accepted professional standards
of science. Misleading data can arise from poor experimental design or careless measurements as well as
from improper manipulation. This is particularly important in an age in which the Internet allows for an
almost uncontrollably fast and extensive spread of information to an increasingly broad audience.
Misleading or inaccurate data can thus have far-reaching and unpredictable consequences of a
magnitude not known before the Internet and other modern communication technologies.
The correct treatment of data in research is important in maintaining the authenticity, reliability, and
accuracy of the research. Inaccurate treatment of data can be done in many forms and in different
intensity. A data that has been totally altered or produced without any real experiments is called a
fraudulent data. Presenting fraudulent data in a research will not only result in the rejection of the
research but it can also put the career of the researcher on stake.
The researcher should have a record of the original data that has been recorded when the experiments
or the survey was conducted. In case of a doubt that the data has been mistreated the publication
company or the editor of the journal can ask the researcher to produce the original data. Small
differences between original and final research data shows that the researcher worked carelessly. Having
major difference between original and final data means that the researcher is involved in a fraud of
producing or manipulating data.
Manipulation of data
Manipulation of data means changing the data so that it meets the requirement of the hypothesis or to
prove researcher’s stance about the research question. Small manipulation in data is not considered a
fraud but it lowers the researcher integrity. It shows that the researcher worked in a careless manner
and had not followed the scientific values, norms and standards. Manipulating data in a manner that is
considered fraudulent will only bring rejection to a research.
Researcher who indulge in such frauds at any time in their career only gain disrespect among the
scientific community. There are various reasons why researcher indulge in manipulation of data. They
want to gain prominence through presenting new ideas and by showing that their hypothesis was proved
true. By doing so they introduce knowledge that does not actually exist. Other scientist might follow
their research and will also take the wrong direction.
Data manipulation may result in distorted perception of a subject which may lead to false theories being
build and tested. An experiment based on data that has been manipulated is risky and unpredictable.
Example: The results of the correlational study "Emotional intelligence and Second Language
Performance of grade 7 students in Dapdap High School" shows that the researchers manipulated the
data, and they presented that there is correlation between variables when, in reality, the interpretation
of data reveals that there is a zero correlation it suggests that the correlation statistic does not indicate a
relationship between the two variables. This does not mean that there is no relationship at all; it simply
means that there is not a linear relationship.
As a researcher it is important to be objective and provide the complete picture that has been obtained
from the experiment without hiding any details or overemphasizing something for personal gain. Ethics
in statistics are important to give the right direction to research so that it is objective and reflects the
truth.
Mishandling of data
Mishandling of data means that the researcher worked carelessly. The researcher did not used tools like
peer-review or corroboration to make sure that the data is accurate. There is no place for carelessness in
the research. There are various other tools that could be used to achieve accuracy of data in research.
In qualitative research the researcher can clearly explain the tools and measures used to enhance and
establish the accuracy, reliability, and authenticity of data collection and data analysis. The reader and
the reviewers will thus be able to draw conclusions about the authenticity of data.
Example: A researcher mishandled the data and sensitive information is copied, shared, accessed, stolen,
or otherwise used by someone who isn't authorized to do so.
To avoid mishandling of data the researcher should share data on regular basis among the peers.
Beginning researcher should share and get their data checked by an advisor or mentor. There should
have to be a record of data in the data notebook. The data notebook should be signed each day as it is
maintained. The data can also be stored in a computer software where it can be accessed any time. The
new researcher should know how to write, store, and share data so that they avoid any mistake. The
researcher can record pictures of the instruments and the work done on them. This will add to the
accuracy of data. The researcher should be able to produce all the links to the final data that has been
generated in case there is a need to do so.
Plagiarism/fraud
Plagiarism is using someone else’s data and findings to prove as your own. Plagiarism threatens the
foundations of knowledge It have been addressed and discussed plagiarism as a manipulative research
practice that affects research Integrity.
Therefore, to avoid such plagiarism the unpublished data should be kept in confidentiality unless there is
a need to share it. The need for sharing an unpublished data may occur in fields where the advancement
of knowledge is preliminary and should be done as fast as possible.
Plagiarism is fraud and it once proved the manuscript that is presented for publication is rejected. The
researcher might lose his/her job, degree, grant, and integrity off course. Though it is hard to find out
that the data presented is mishandled or mistreated but it is much easier to find out a fraud or
plagiarism. Luckily now a days there are software and other tools that can help in finding any plagiarism
in data.
Example: There have been cases of plagiarism in past where a researcher used another researcher’s data
before the original researcher published it.
A statement developed by the U.S. Office of Science and Technology Policy, which has been adopted by
most research funding agencies, defines misconduct as “fabrication, falsification, or plagiarism in
proposing, performing, or reviewing research, or in reporting research results.”
According to the statement, the three elements of misconduct are defined as follows:
Fabrication is “making up data or results.”
• Falsification is “manipulating research materials, equipment, or processes, or changing or omitting data
or results such that the research is not accurately represented in the research record.”
• Plagiarism is “the appropriation of another person’s ideas, processes, results, or words without giving
appropriate credit.”
In addition, the federal statement says that to be considered research misconduct, actions must
represent a “significant departure from accepted practices,” must have been “committed intentionally,
or knowingly, or recklessly,” and must be “proven by a preponderance of evidence.” According to the
statement, “research misconduct does not include differences of opinion.”
A crucial distinction between falsification, fabrication, and plagiarism (sometimes called FFP) and error
or negligence is the intent to deceive. When researchers intentionally deceive their colleagues by
falsifying information, fabricating research results, or using others’ words and ideas without giving credit,
they are violating fundamental research standards and basic societal values.
For example, because trust in language research depends so heavily on the assumption that the origin
and content of theory, concepts, and ideas about language will be treated with respect, plagiarism is
taken very seriously in linguistics, even though it does not introduce spurious results into research
records in the same way that fabrication and falsification do.
But someone who plagiarizes may insist it was a mistake, either in note taking or in writing, and that
there was no intent to deceive. Similarly, someone accused of falsification may contend that errors
resulted from honest mistakes or negligence.
Researchers draw conclusions based on their observations of nature. If data are altered to present a case
that is stronger than the data warrant. They mislead their colleagues and potentially impede progress in
their field or research. They undermine their own authority and trustworthiness as researchers. And
they introduce information that could cause harm to the broader society. This is particularly important in
an age in which the Internet allows for an almost uncontrollably fast and extensive spread of information
to an increasingly broad audience. Misleading or inaccurate data can thus have far-reaching and
unpredictable consequences of a magnitude not known before the Internet and other modern
communication technologies.
Mistakes and Negligence
All researchers are human. They do not have limitless working time or access to unlimited resources.
Even the most responsible researcher can make an honest mistake in the design of an experiment, the
calibration of instruments, the recording of data, the interpretation of results, or other aspects of
research. Despite these difficulties, researchers have an obligation to the public, to their profession, and
to themselves to be as accurate and as careful as possible.
Mistakes that mislead subsequent researchers can waste large amounts of time and resources. When
such a mistake appears in a journal article or book, it should be corrected in a note, erratum (for a
production error), or corrigendum (for an author’s error).
Research Misconduct
A statement developed by the U.S. Office of Science and Technology Policy, which has been adopted by
most research funding agencies, defines misconduct as “fabrication, falsification, or plagiarism in
proposing, performing, or reviewing research, or in reporting research results.”
According to the statement, the three elements of misconduct are defined as follows:
Fabrication is “making up data or results.”
• Falsification is “manipulating research materials, equipment, or processes, or changing or omitting data
or results such that the research is not accurately represented in the research record.”
• Plagiarism is “the appropriation of another person’s ideas, processes, results, or words without giving
appropriate credit.”
In addition, the federal statement says that to be considered research misconduct, actions must
represent a “significant departure from accepted practices,” must have been “committed intentionally,
or knowingly, or recklessly,” and must be “proven by a preponderance of evidence.” According to the
statement, “research misconduct does not include differences of opinion.”
A crucial distinction between falsification, fabrication, and plagiarism (sometimes called FFP) and error
or negligence is the intent to deceive. When researchers intentionally deceive their colleagues by
falsifying information, fabricating research results, or using others’ words and ideas without giving credit,
they are violating fundamental research standards and basic societal values.
These actions are seen as the worst violations of scientific standards because they undermine the trust
on which science is based.
However, intent can be difficult to establish
Mistakes and negligence
Research miscoduct
THE TREATMENT Of DATA
In order to conduct research responsibly, graduate students need to understand how to treat data
correctly. Researchers who manipulate their data in ways that deceive others, even if the manipulation
seems insignificant at the time, are violating both the basic values and widely accepted professional
standards of science.
Misleading data can arise from poor experimental design or careless measurements as well as from
improper manipulation. Over time, researchers have developed and have continually improved methods
and tools designed to maintain the integrity of research. Some of these methods and tools are used
within specific fields of research, such as statistical tests of significance, double-blind trials, and proper
phrasing of questions on surveys. Others apply across all research fields, such as describing to others
what one has done so that research data and results can be verified and extended.
Because of the critical importance of methods, scientific papers must include a description of the
procedures used to produce the data, sufficient to permit reviewers and readers of a scientific paper to
evaluate not only the validity of the data but also the reliability of the methods used to derive those
data. If this information is not available, other researchers may be less likely to accept the data and the
conclusions drawn from them. They also may be unable to reproduce accurately the conditions under
which the data were derived.
The best methods will count for little if data are recorded incorrectly or haphazardly. The requirements
for data collection differ among disciplines and research groups, but researchers have a fundamental
obligation to create and maintain an accurate, accessible, and permanent record of what they have done
in sufficient detail for others to check and replicate their work
Depending on the field, this obligation may require entering data into bound notebooks with
sequentially numbered pages using permanent ink, using a computer application with secure data entry
fields, identifying when and where work was done, and retaining data for specified lengths of time. In
much industrial research and in some academic research, data notebooks need to be signed and dated
by a witness on a daily basis. Unfortunately, beginning researchers often receive little or no formal
training in recording, analyzing, storing, or sharing data. Regularly scheduled meetings to discuss data
issues and policies maintained by research groups and institutions can establish clear expectations and
responsibilities. Most researchers are not required to share data with others as soon as the data are
generated, although a few disciplines have adopted this standard to speed the pace of research. A period
of confidentiality allows researchers to check the accuracy of their data and draw conclusions.
However, when a scientific paper or book is published, other researchers must have access to the data
and research materials needed to support the conclusions stated in the publication if they are to verify
and build on that research.
Many research institutions, funding agencies, and scientific journals have policies that require the
sharing of data and unique research materials. Given the expectation that data will be accessible,
researchers who refuse to share the evidentiary basis behind their conclusions, or the materials needed
to replicate published experiments, fail to maintain the standards of science.
In some cases, research data or materials may be too voluminous, unwieldy, or costly to share quickly
and without expense. Nevertheless, researchers have a responsibility to devise ways to share their data
and materials in the best ways possible.
For example, centralized facilities or collaborative efforts can provide a cost-effective way of providing
research materials or information from large databases. Examples include repositories established to
maintain and distribute astronomical images, protein sequences, archaeological data, cell lines, reagents,
and transgenic animals.
New technology of high capacity and memory computers hve been developed in recent years which help
this issue.
Example: In 2002, the editors of the Journal of Cell Biology began to test the images in all accepted
manuscripts to see if they had been altered in ways that violated the journal’s guidelines. About a
quarter of the papers had images that showed evidence of inappropriate manipulation. The editors
requested the original data for these papers, compared the original data with the submitted images, and
required that figures be remade to accord with the guidelines. In about 1 percent of the papers, the
editors found evidence for what they termed “fraudulent manipulation” that affected conclusions drawn
in the paper, resulting in the papers’ rejection.