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Research Ethics - lecture notes

lecture notes
Course

Data Analysis and Experimental Design (SS227)

18 Documents
Students shared 18 documents in this course
Academic year: 2020/2021
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The University of Notre Dame (Australia)

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Ethics in Research

Areas of Scientific Dishonesty

1- Plagiarism

Claiming the ideas, writing or drawings of others a your own. Stealing someone’s intellectual property. Passive (most common) is when the individual does not cite the original source of information i. citing the citer. This can result in misrepresentation of the information and is a mistake usually made due to lack of awareness. Active plagiarism is the most serious and occurs when the individual knowingly claims others work as their own or stealing an idea, failing to acknowledge the source.

2- Fabrication and Falsification Making up data or delibralty lying about the results. Fraud makes up 60% of retractions over the last 10 years. Joachum Bolt has had 90 retracted papers. Significance testing can lead to all-or-nothing thinking. p-value hacking. There are very few studies published when the p-value is greater than .05 and very many that have a p-value less than .05. p- hacking is the manipulation of data to help promote a significant p-value.

3- Nonpublication of data

Data results which do not support the hypothesis is not report. This extends to non-significant results. Its hard to give evidence because of the nonpublication factor. Results that disprove a hypothesis can actually be more exciting because the tell us what doesn’t work and we can still build from it.

4- faulty methodology or data collection Malfunctioning equipment, Inappropriate treatment of participants/sample sizes, Continuing data collection when subjects violate inclusion criteria, Incorrect recording of data Famous example: 1998 Study by Andrew Wakefield. Study linking autism to measles-mumps-rubella vaccine. Wakefield’s study lacked good scientific process = misleading findings. Led to short and long-term mistrust of vaccine and subsequent outbreaks. There was a small sample size.

5- Data storage and retention

A version of the data must be stored and maintained as originally recorded and should be password and file protected. This is so people ca go back and double-check and make sure the data says what the researcher says it says

6- misleading authorship

The order of authorship indicates the level of contribution from each scientist. though in some journals the last position is reserved for the project/lab manager. Only those who contribute directly to the research development, management and write up should be listed as authors.

peer review abuse selecting peers who you have a previous rapport with.

Data secrecy is circle because we should be able to access the original data.

Protecting Human Participants

1- Informed Consent

informs potential participants about what they are required to do, the risks involved, benefits and how results will be disseminated

2- Deception

complex issue in research. If they know things it will change the results so is the provision of a placebo a deception. We need to avoid deception as much as possible and especially deception that harms the participants.

3- Confidentiality

As few people as possible should have access to the identifying information of the participants. How can we de- identify data? by considering how and where is data stored, who has access to the data and for how long.

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Research Ethics - lecture notes

Course: Data Analysis and Experimental Design (SS227)

18 Documents
Students shared 18 documents in this course

University: The University of Notre Dame (Australia)

Was this document helpful?
Ethics in Research
Areas of Scientific Dishonesty
1- Plagiarism
Claiming the ideas, writing or drawings of others a your own. Stealing someone’s intellectual property. Passive (most
common) is when the individual does not cite the original source of information i.e. citing the citer. This can result in
misrepresentation of the information and is a mistake usually made due to lack of awareness. Active plagiarism is the
most serious and occurs when the individual knowingly claims others work as their own or stealing an idea, failing to
acknowledge the source.
2- Fabrication and Falsification
Making up data or delibralty lying about the results.
Fraud makes up 60% of retractions over the last 10
years. Joachum Bolt has had 90 retracted papers.
Significance testing can lead to all-or-nothing
thinking. p-value hacking. There are very few studies
published when the p-value is greater than .05 and
very many that have a p-value less than .05. p-
hacking is the manipulation of data to help promote
a significant p-value.
3- Nonpublication of data
Data results which do not support the hypothesis is not report. This extends to non-significant results. Its hard to give
evidence because of the nonpublication factor. Results that disprove a hypothesis can actually be more exciting
because the tell us what doesn’t work and we can still build from it.
4- faulty methodology or data collection
Malfunctioning equipment, Inappropriate treatment of participants/sample sizes, Continuing data collection
when subjects violate inclusion criteria, Incorrect recording of data
Famous example: 1998 Study by Andrew Wakefield. Study linking autism to measles-mumps-rubella vaccine.
Wakefield’s study lacked good scientific process = misleading findings. Led to short and long-term mistrust of
vaccine and subsequent outbreaks. There was a small sample size.

Students also viewed

Related documents