Here are the links to week 5’s comments
Here are the links to week 5’s comments
The main thing to consider when being responsible with research is ETHICS.
Psychologists should follow and respect ethical for a number of reasons: ethical standards provide knowledge and truth of a research piece and they help to prevent errors – ethics set up trust, respect and equality – researchers are more likely to get funded for their research if they know they are trustworthy – ethics provide health and safety issues due to moral values and human rights!! (without ethical standards, research may not have any respect for our human rights, and none of us want to lose a limb or .. you know.. maybe even DIE, due to taking part in a risky study). *
Shamoo and Resnik** suggested that vital Ethical principles in psychological research are:
Honesty Researchers must not lie!!!!! No one likes a liar. Research is to be reported honestly due to what results have been produced, no sneaky data removing so you can prove your hypothesis (unless of course, there are valid reasons like the outliers discussed last week). Also, lying to others is obviously forbidden, everyone involved in the study should be aware of the research.
Objectivity Researchers should avoid being bias, if they have any personal attachments or interests in something to do with the study this should not interact and affect with the research. (I dunno if anyone has seen the latest Plant of the Apes film? but the main character is a great example of this, he is a scientist and his father is suffering with Alzheimer’s.. he steals drugs from the work and also ends up stealing a chimp all in order to cure his dad..)
Integrity Researchers should be consistent and stick to any agreements made.
Carefulness Researchers should basically.. be careful. Meaning they should keep records of everything happening in the research process, this will prevent careless errors.
Openness Ideas, data, results and opinions should be shared between researchers.
Respect for Intellectual Property Plagiarism is not cool… Do not use other people’s work and publishings without citing and referencing their work.
Confidentiality Keep individual’s data confidential. Their data is allowed to be shown and analyzed but the public do not need to know the individual person’s name, age, address and dog’s name to look at the data!
Responsible Publication Researchers should only publish their research in order to help others and advance research, not just for personal gain of looking good with publications!
Responsible Mentoring Help to mentor research students but researchers should not tell them what to do, students should be able to make their own decisions.
Respect for colleagues Self-explanatory really haha.
Non-Discrimination Anything about a person that is NOT related to the study should not even be thought about: such as their race, sex, ethnicity, age etc.
Legality Well researchers shouldn’t break the law!
Animal Rights Animals should be treated with respect too (Again, Planet of the Apes.. was it really fair that the chimp was stolen and used as scientific research??). Researchers shouldn’t purposely harm animals in order to complete their research. Unfortunately, animals are usually given the nasty experiments as in terms of research humans are looked at as a lot more important creatures.. 😦
Human Rights Personally, I would say this is the main ethical principle. Researchers should be VERY careful when dealing with risky studies, they should always respect a person’s privacy and human dignity! Harm should be avoided!!!
A very famous example of piece of research that did not follow ethical issues was of course, Milgrams experiment on obedience and authority. I wont go into detail about the study as I’m sure all you psychology geeks know ALLLLLL about it. Anyway, Milgram caused serious emotional harm for the participants as they thought they were electrocuting someone else! A study like this would never ever be done again due to this. But you could argue, as disgusting and as upsetting it was to emotional harm the participants.. if Milgram didnt break the ethics, we would never have that huge piece of research! We would never have thought that us as humans would harm (and up to 450 volts? maybe even kill!!!) another human just because an authority figure told us to do so?? So thats the only benefit I can think for not having ethics… lol. Oh no, also without ethics, conducting a study would be a lot quicker as we wouldnt have to faff around with consent forms and all that!
To summarize, ethics may restrict researchers from researching exactly what they wanted to, which unfortunately can lead to us missing out on some interesting and scientific findings. However, I do believe that ethical principles are a great thing to have in place, without them.. who knows what kind of studies we might be conducting. It’s not justifiable to have an experiment where someone could potentially lose their life just so we can have some good research, we can still find results with ethics in order :-). Also, being responsible in research is important as these guidelines provide a basis on how to conduct a study responsibly, atleast we know all research is being conducted fairly in the same way 🙂
* Ethical Principles of Psychologists and Code of Conduct according to APA.
** Adapted from Shamoo A and Resnik D. 2009. Responsible Conduct of Research, 2nd ed. (New York: Oxford University Press).
Here are the links to the comments that I wrote this week (week 3).
Wow, this week we can talk about about anythiiiiiiiiiiiiiing we want to (stats related of course), so I have been inspired by this week’s lecture and I’m going to chat about outliers. Are they important? Incase you are unsure as to what I mean by “outlier”, you can basically sum up an outlier as something that is very different from everything else. (Like this guy in the image below!!)
So in terms of statistics, an outlier is a data point that is extremely numerically different from all the other data points in the sample, it doesn’t follow any of the patterns that the other results may show and they can really change a researcher’s results due to the impact they have on the mean! When we’re doing psychological experiments, outliers can occur for many different reasons. It could be due to the researcher, did they not have a big enough sample? Were there flaws in their design? Did they measure incorrectly?? Although, the outlier data point could have been developed from the participant! Was the participant not listening to the instructions of the task? Did they fake their answers? ….. or was it complete chance?
One thing to consider with outliers, is.. is it wrong to remove them from your data? Some may say it’s wrong to remove an outlier from your data, because you are messing with the natural results from the study.
However, I think in some circumstances it is the correct thing to do!
Having outliers in a set of data can have a really dramatic influence on the outcome of the study. The value obtained for the correlation can be seriously affected.. you may end up thinking your research has been really successful (or really disastrous) just because of one single participant changing the mean!
I’ll show you an example experiment so I can try and show you what I’m rambling on about
My example experiment is testing the levels of hyperactivity in children against how many fizzy drinks they have consumed that day (I’ve used the exact same numbers from out of a book* to make sure I get the sums right haha).
The first image shows a set of data points where the correlation is almost 0 (r=-0.08) meaning there isn’t really a relationship between the two so the amount of fizzy drinks most likely doesn’t affect a child’s hyper behaviour. The outlier example has been added into the second dataset, and there is a huuuuuuge change in the correlation value! Due to this ONE participant, the correlation is now r=0.85, suggesting there is a strong positive correlation, and that fizzy drinks do affect how hyper a child behaves! So the entire outcome of the experiment has changed just due to this one person, is that fair?
In conclusion, I think it is acceptable to remove outliers from your dataset as they can have a serious effect on the end result, and for the majority of the time it is an unnecessary effect. In some cases when outliers occur it is important that the research looks into it, it could be due to they didn’t understand the task, therefore the researcher should probably reconsider their experiment! 🙂
*statistics for the behavioural sciences, eighth edition
Hmm, I personally think this week’s topic is quite a tricky one! Of course, statistics are very important and they do help us to understand data, but do we NEED them?
In terms of Psychology, the main purpose of the statistics is to show us whether or not our experiment has been successful… so whether or not our theory is worth mentioning. With the stats, we produce graphs so we can show off what we have found out from our experiment, we can use line graphs, bar charts, box plots, histograms or scatter graphs depending on what kind of data we have been dealing with (Ooo would you look at that, I was listening in week 2 small groups ;-)). With statistical graphs and tables everything is laid out nice and neatly infront of us, we can see correlation in the data, we can easily find averages, we can make sensible comparisons etc. The statistics basically allow us to make sense of theories.
…But, isn’t this only really relevant in terms of quantitative data?
Statistics don’t really play a huge part with qualitative research. Qualitative researchers are more interested in why things are the way they are, rather than how they are. The main focus is people’s behaviours, attitudes and lifestyle. Interviews, emails, notes, case studies, photos, videos (and all that jazz ) are used for qualitative research… Rather than “yes or no” answer questions on questionnaires, which quantitative researchers like to use. So because qualitative data isn’t just simple numbers that can be added up (like “yes or no”, “male or female” answers on a questionnaire) it doesn’t always tend to end up in statistics. So obviously, statistics isn’t needed in understanding qualitative data.
So, I guess the answer to this question is.. No, We don’t need statistics to understand data. It sure does help us in terms of quantitative data, but not with qualitative. As I don’t consider myself to be the best statistician, I personally prefer the way qualitative researchers work. To me it feels like I’m actually getting to the point of the problem being investigated, rather than just using a load of numbers and sums to come out with one final figure which is supposed to be the answer to my problem!!