Q21.
Investigating related material sources on the
internet,
discuss how bias can be avoided in scientific
research.
Learning outcomes:
5.3
helpful links :
en.wikipedia.org
In
experimental science, experimenter's bias, also known as research
bias, is a subjective bias towards a result expected by the human
experimenter.[1] For example, it occurs when scientists unconsciously
affect subjects in experiments.[2]
www.ncbi.nlm.nih.gov
This
narrative review provides an overview on the topic of bias as part of
Plastic and Reconstructive Surgery's series of articles on
evidence-based medicine. Bias can occur in the planning, data
collection, analysis, and publication phases of research. ...
explorable.com
Research
bias, also called experimenter bias, is a process where the
scientists performing the research influence the results, in order to
portray a certain outcome.
blog.efpsa.org
Every
scientific discipline is determined by the object of measurement and
the selection of appropriate methods of data collection and
statistical analysis. Faulty methodology can lead to incorrect...
Help on Research Bias
Some
bias in research arises from experimental
error and
failure to take into account all of the possible variables.
Other
bias arises when researchers select subjects that
are more likely to generate the desired results, a reversal of the
normal processes governing science.
Bias
is the one factor that makes qualitative
research much
more dependent upon experience and judgment than quantitative
research.
Quantitative
Research Bias:
Denial of any Bias
Denial of any Bias
Qualitative
Research Bias:
Acceptance and Acknowledgment of Bias.
Acceptance and Acknowledgment of Bias.
For
example, when using social
research subjects,
it is far easier to become attached to a certain viewpoint,
jeopardizing impartiality.
The
main point to remember with bias is
that, in many disciplines, it is unavoidable. Any experimental
designprocess
involves understanding the inherent biases and minimizing the
effects.
In quantitative
research,
the researcher tries to eliminate bias completely whereas,
in qualitative
research,
it is all about understanding that it will happen.
Design
BiasDesign
bias is introduced when the researcher fails to take into account the
inherent biases liable in most types of experiment.
Some bias is inevitable, and the researcher must show that they understand this, and have tried their best to lessen the impact, or take it into account in the statistics and analysis.
Some bias is inevitable, and the researcher must show that they understand this, and have tried their best to lessen the impact, or take it into account in the statistics and analysis.
Another
type of design bias occurs after the research is finished and the
results analyzed. This is when the original misgivings of the
researchers are not included in the publicity, all too common in
these days of press releases and politically motivated research.
For example, research into the health benefits of Acai berries may neglect the researcher’s awareness of limitations in the sample group. The group tested may have been all female, or all over a certain age.Selection/Sampling BiasSampling bias occurs when the process of sampling actually introduces an inherent bias into the study. There are two types of sampling bias, based around those samples that you omit, and those that you include:Omission BiasThis research bias occurs when certain groups are omitted from the sample. An example might be that ethnic minorities are excluded or, conversely, only ethnic minorities are studied.For example, a study into heart disease that used only white males, generally volunteers, cannot be extrapolated to the entire population, which includes women and other ethnic groups.Omission bias is often unavoidable, so the researchers have to incorporate and account for this bias in the experimental design.Inclusive BiasInclusive bias occurs when samples are selected for convenience.
This type of bias is often a result of convenience where, for example, volunteers are the only group available, and they tend to fit a narrow demographic range.
For example, research into the health benefits of Acai berries may neglect the researcher’s awareness of limitations in the sample group. The group tested may have been all female, or all over a certain age.Selection/Sampling BiasSampling bias occurs when the process of sampling actually introduces an inherent bias into the study. There are two types of sampling bias, based around those samples that you omit, and those that you include:Omission BiasThis research bias occurs when certain groups are omitted from the sample. An example might be that ethnic minorities are excluded or, conversely, only ethnic minorities are studied.For example, a study into heart disease that used only white males, generally volunteers, cannot be extrapolated to the entire population, which includes women and other ethnic groups.Omission bias is often unavoidable, so the researchers have to incorporate and account for this bias in the experimental design.Inclusive BiasInclusive bias occurs when samples are selected for convenience.
This type of bias is often a result of convenience where, for example, volunteers are the only group available, and they tend to fit a narrow demographic range.
There
is no problem with it, as long as the researchers are aware that they
cannot extrapolate the results to fit the entire population.
Enlisting students outside a bar, for a psychological
study, will not give a fully representative group.
Procedural
BiasProcedural
bias is where an unfair amount of pressure is applied to the
subjects, forcing them to complete their responses quickly.
For
example, employees asked to fill out a questionnaire during
their break period are likely to rush, rather than reading the
questions properly.
Using students forced
to volunteer for course credit is another type of research bias, and
they are more than likely to fill the survey in quickly, leaving
plenty of time to visit the bar.
Measurement
BiasMeasurement
bias arises from an error in
the data collection and the process of measuring.
In
a quantitative experiment, a faulty scale would cause an instrument
bias and invalidate the entire experiment. In qualitative research,
the scope for bias is wider and much more subtle, and the researcher
must be constantly aware of the problems.
- Subjects are often extremely reluctant to give socially unacceptable answers, for fear of being judged. For example, a subject may strive to avoid appearing homophobic or racist in an interview.
This
can skew the results, and is one reason why researchers often use a
combination of interviews, with an anonymous questionnaire, in order
to minimize measurement bias.
- Particularly in participant studies, performing the research will actually have an effect upon the behavior of the sample groups. This is unavoidable, and the researcher must attempt to assess the potential effect.
- Instrument bias is one of the most common sources of measurement bias in quantitative experiments. This is the reason why instruments should be properly calibrated, and multiple samples taken to eliminate any obviously flawed or aberrantresults.
Interviewer Bias
This
is one of the most difficult research biases to avoid in many
quantitative experiments when relying upon interviews.
With
interviewer bias, the interviewer may subconsciously give subtle
clues in with body language, or tone of voice, that subtly influence
the subject into giving answers skewed towards the interviewer’s
own opinions, prejudices and values.
Any experimental
design must
factor this into account, or use some form of anonymous process to
eliminate the worst effects.
See
how to avoid this:Double
Blind Experiment
Response Bias
Conversely,
response bias is a type of bias where the subject consciously, or
subconsciously, gives response that they think that the interviewer
wants to hear.
The
subject may also believe that they understand the experiment and are
aware of the expected findings, so adapt their responses to suit.
Again,
this type of bias must be factored into the experiment,
or the amount of information given to the subject must be restricted,
to prevent them from understanding the full extent of theresearch.
Reporting Bias
Reporting
Bias is where an error is made in
the way that
the results are disseminated in theliterature.
With the growth of the internet, this type of bias is becoming a
greater source of concern.
The
main source of this type of bias arises because positive research
tends to be reported much more often than research where the null
hypothesis is
upheld. Increasingly, research companies bury some research, trying
to publicize favorable findings.
Unfortunately,
for many types of studies, such as meta-analysis,
the negative results are just as important to the statistics.
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