Tuesday, October 25, 2011
There are two reasons for this. The first, as documented last week, is the requirement of testing new participants. This is becoming tricky, as first-years are almost finished their term and thus are in short supply. Luckily, there are other means of gathering participants, and so work continues apace in this regard. Alas, at the current juncture there is little else to report, as all I can do is wait for participants to sign up. Although, I have recently launched a pair of surveys that are a precursor to my third experiment; shameless of me, but if you could take one (and only one) of them, I would be most grateful :)
The second reason is marking. The second round of assignments has struck and we the tutors have responded, and responded with vigor. That is, we have complained, vacillated and generally procrastinated for quite some time before hooking into the marking. What followed was many days of my good self sitting at my computer, listening to a combination of trance music and symphonic heavy metal while peering myopically at the latest lab report to batter itself against the crumbling edifice of my sanity and work ethic. This process continues, and probably will for a few days yet.
I won't say marking is fun. It's a challenge, as marking isn't necessarily easy due mostly to my lack of experience. I also tend to take longer than is necessary as I give a lot of feedback. Mostly I take such time because I remember receiving feedback on assignments which didn't always help, and I wasn't the best undergrad in the first place. Of course on top of that you do actually have to fail people. I'll avoid blithering on about that because this post is already sounding something like a whinge.
Let's tag marking as 'an experience', in the full knowledge that sooner or later, a proper emotional tag will become available to describe what sort of experience it is. Although for the record, it's a nice feeling to occasionally tunnel-vision to the extent where the outside world becomes less than a passing thought for hours on end.
And now, as lovely as writing this blog is, I must return to said marking with all haste, as the deadline looms (as only deadlines can). Next week, I shall hopefully return with more news of my experiments and experiences! Until then, may your learning never cease.
Tuesday, October 18, 2011
Welcome once again to the fetid den of iniquity that is my blog-post, dear readers. As I mentioned last week, I have been conducting analyses on my second experiment, and progress could be called 'rocky'.
Well, if last week was rocky, it is now firmly ensconced in the Himalayas. Initially, I had performed an analysis of my data while eliminating outliers above and below certain thresholds across all participants and conditions. Statistical significance was achieved after this cleaning was done. However, based on previous research we also deleted outliers based on standard deviations of each participant. Statistical significance vanished.
Now, understand that before all this, I had forgotten to perform some of the most basic and elementary forms of data cleaning known to psychology before doing an analysis. I was feeling like a bit of a fool.
It transpired that certain participants had data that did not make the slightest bit of sense; reaction times that were unfeasible, etc, and were placing rather large spanners in the works of the data. Being the overenthusiastic (and also perhaps cerebrally-challenged) lad that I was, I had not noticed this. My supervisor did, and drew it to my attention. Having received this information, I proceeded to pummel my own head into nearby solid objects located in my office. To forget to deal with outliers, AND to forget to observe individual means (a lesson I was taught in no uncertain terms by my Honours supervisor) is an impressive brain failure, I thought. As a quick aside, I am my own worst critic when it comes to my career. These days, I am unforgiving (perhaps to a fault) when it comes to mistakes such as the aforementioned.
Generally I avoid directly giving advice in this blog, as I prefer people to take whatever insight they wish from the posts (e.g. "why are they allowing this coffee-addicted nutcase into the sunlight?"). However, I will say this; it's not in your interest to be an unforgiving disciplinarian when you make mistakes. The PhD learning curve is a steep one, and if you continue in academia it apparently doesn't shallow out that often. Mentally injuring yourself simply hamstrings your ability to learn from your mistakes. It's also why your supervisor is there; they're good at this stuff. My supervisor has been in academia for almost two decades. I have been a PhD student for 8 months. When I took the data to him, he saw the problem after a few minutes of perusal and basic analysis. Go figure.
The outcome of all this is that I must return to testing, both replacing the faulty data (and finding out how it happened) and expanding the sample size. The rest of the week is filled with testing schedules and importing the new data. Lessons have been learned, shoulders squared, heads un-pummeled and egos deflated (at least a little . . . :D), and the science goes on! See you next week, when hopefully I have new results to speak of!
Tuesday, October 11, 2011
This was because my data analysis had not gone as planned.
Subsequent to unleashing the linguistic beast that was my stream of profanity (read: giant man tantrum), I girded myself and got back to work. It's tempting at times like this to emotionally flagellate yourself for not seeing the issue beforehand, for feeling like a fool etc etc. It is important to remember that a PhD student is the merest babe in the academic world, superior only to the academic zygote that is an Honours student, and you will make mistakes on occasion.
So let's talk a bit about precisely what happened.
My experiments at present are purely behavioural, and utilise a MatLab program to present stimulus. The program records reaction times and outputs that to two separate files, one of which is nicely formatted to be imported into Excel and SPSS. This particular file did not write properly for some reason, and I realised I would have to go through each file individually and import the data trial by trial. That's 360 trials per participant, for 20 participants.
It was at this moment I felt a bizarre and yet entirely understandable urge to strap my beloved Apple computer to the front of some archaic cannon, and then fire said cannon at the laptop utilised in the experiment. This did not occur; I suspect the Psych department frowns upon wanton destruction that is not ethically approved. The lesson here is to test the hell out of any program that is so central to your experiment, and to ensure that it works EXACTLY as needed before you hurl participants at it.
The silver lining is that despite having to spend many hours at my computer manually importing data (a task to melt the brain of the most devoted of nerds), I also managed to catch up on listening to about 4 different podcasts, as well as several hours of my favourite music. So it's not all bad. Once the red mist had cleared, I realised that I should see the whole episode not as an illustration of my own foolishness, but as an experience for future experiments. I also learned that under no circumstances do you go to Merlos and request 'a coffee so strong you could waterproof an ocean liner with it'. They take that seriously. You will electrocute small insects if you drink it, and your brain will fizzle into some kind of homogenous neural goo.
As of writing this, I am at the SPSS analysis stage of this experiment, and the subject of next week's blog will be the results of this analysis, and what it means for my PhD. As always, I hope I have presented a good/informative read, and I shall see you all next week!
Monday, October 3, 2011
This week, I find myself preparing for data analysis, and thus I shall write about it as well.
There are many different words and phrases for data analysis in the sciences. For instance: 'A love affair with statistics packages', 'Listening to music while attempting to break your keyboard', or more simply 'being a COLOSSAL nerd'. Any and all of the above are appropriate. The funny thing about it is that should you have any passion for science, you'll find yourself strangely immersed in not only entering the data, but then running each successive analysis, reviewing the results, interpreting said results and so forth.
It can feel like an anticlimax; you've worked and experimented (for weeks, months or perhaps even years) and now you have this great pile of data that you have to compile and work into something understandable. Not for yourself, as you should hopefully know already what you're doing. But going from having that data to creating a story that is understandable and interesting to other people is a skill, and the entire process is both a privilege and a pleasure. That's not to say it's not stressful and difficult as hell, as it certainly can be. Sometimes things won't go according to plan and you will wish to create a fist-sized and -shaped hole in your computer screen and the wall behind it (much to the shock of the inhabitants of the next room). You will almost certainly have a few late nights in the office. In spite of (and perhaps because of) this, it's totally worth it.
I personally doubt that many of us do this because it's a challenge. I certainly don't. It is absolutely a challenge, and I am expected to run experiments, to publish, etc. But as one of the senior academics said earlier this year, academia is/should be as much a vocation as anything else. So I do it because I love it, and the aforementioned necessities just serve as to legitimise the whole affair as a job.
Interestingly, people look at me funny from time to time and say 'but PhD's make no money' or even more amusingly 'that sounds so boring!'. Regarding money, it's cliche but I'd vastly prefer to do a job I enjoy rather than one that paid well, assuming I had to choose. I once tried working in an office job that paid well. I lasted 4 weeks before my brain melted out one ear from boredom. As for a PhD being boring, each to their own.
I have gone from talking about data analysis to a discussion of my perspectives on PhD's. Rather amusing shift, but I hope it proves enjoyable/interesting/informative nonetheless. I am off to finalise an Ethics proposal, so I shall see you all next week!