The open science movement

These past 10 weeks have led me on a slight rollercoaster regarding the efficacy of open science. During the week 1 workshop where we learnt about preregistration, open materials, open data, open analysis and open access, I felt very convinced about how open science would help with the replication crisis and reduce malpractice in research. However, as I worked with my team on replicating our chosen study, I found that there were still many obstacles that bogged down the true potential of open science. In particular, there was a problem of the original authors not clearly labelling how they coded responses. For example, with gender, we had to figure out as a team that 1 coded for males and 2 coded for females. Another big issue was the problem of the flipped graphs between the published graphs and the graphs produced by the codebook. I found this really frustrating since the authors didn’t mention why they did this nor did they provide the code that they used to get the published graphs. This also meant that I had doubts about whether the authors engaged in ethical research practices, even if in actuality the authors didn’t do anything wrong. Going through the original codebook also made me realize that another obstacle were the packages that were used. Specifically, I assume that authors would generally use packages they were more familiar with however, this could run the risk of lowering the reproducibility of the study if certain functions in packages become outdated. Now that I’m in week 10 and have personally experienced these issues, I still see the open science movement as a very good idea for improving the quality of research and removing biases in publication but it is most definitely still a work in progress.

The value of group work

Given that my experiences with group work in the past have not been very good, I feel that these past 10 weeks have really restored my faith in the value of group work. One of the major pros of teamwork was that it made a huge task much easier than if I were to tackle it by myself. The fact that there were 4 people looking for the solution for one issue meant that we were able to fix issues much faster. This also made it less frustrating. I also find that when working in a good team where everyone is equally productive, it makes finishing a project much more satisfying. However, actually getting into a good group is still a bit of a gamble =(. Overall, I feel that this research internship has helped me see future groupwork in a more optimistic light.

How you learn

The past 10 weeks in PSYC3361 have been really eye-opening for me with regards to how I learn. Specifically, it has kind of helped me to overcome the fear of learning via exploration. That is, I was surprised that I could learn many different R functions via Google and QnAs despite only being given the basic functions in Dani’s lectures. I was also surprised that I was able to learn quite a bit of coding in R without any background in coding. The deadlines set for this term were also really helpful in helping me stay motivated and reduce procrastination. Furthermore, I also find that working in a group and setting our own deadlines has helped me with my time management. Overall, I’m really thankful for the opportunity to have enrolled in PSYC3361!