To be honest
Right after finishing current research I decided to critically observe it, in order to identify aspects and directions in wich it can be improved. In addition to this, it will give me a clear picture of what is need to improved in my work and in me personally.
I tried to assign problematic , moments in a chronological (from start of the research to its end) order:
01
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I have realised, even thought delegating task is hard - do a high quality work alone - even harder. Therefore, I wish that in the future I will do the majority of my work in a team
Team
Team
02
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I have faced a lack of experienced adviser. I really hope to have a professor on my side to critique my actions and support my ideas
Adviser
Adviser
03
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I have seen this part in a number of high quality publications (as I understood, it is done in order to:
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Firstly, identify and, secondly show the gap in an academic world. Unfortunately, I was not able to fully understand how to do it.
Literature review
Literature review
04
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The questions were highly focused on binary teenagers. It was harder for non straight people to answer some of the questions. Unfortunately I realised it on the stage of analysis.
Questions
Questions
05
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The main way for me to find participants was Instagram. But that,partially, leeds us to the next problem.
Channels of finding participants
Channels of finding participants
06
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Sample overexposure in terms of:
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Geography (More than 80% were from UK or Russia)
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Gender (+70% were females)
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Age (small sample size in the group of under 14 and above 22)
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Educational level bias (even people from developing counties were English speakers, therefore - from more advantaged background and might not have experienced problems of the representative majority of the country)
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Overall sample size (I assume, that 260+ was a decent number for that research, but if you ask me about a perfect/dream number, I would answer 40 000 people (around 200 from each country in the world)
Sample problems
Sample problems
07
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Construction of the dataset (not "yes or no" were need to be done [however it is arguable])
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Tools (I was glad to use Excel for analysis and Powerpoint with WIX for visualisation, but I feel like there is many more to use on much higher level of proficiency [Programming languages like R and Python])
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Methods (I have discovered the regression and confidence intervals as a grate tool to assess how "good" or non-random your data and finding are, but unfortunately these concepts are still "black boxes" for me. I wish to dive deeper into exploring them in my University years)
Data analysis
Data analysis
08
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More interactive graphs would be a good addition to a simple website like this
Data visualisation
Data visualisation