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Kaitlin Gallagher is a PhD Candidate specializing in Biomechanics at the Department of Kinesiology at the University of Waterloo in Canada, and a permanent author for GradHacker. You can follow her on twitter at @KtlnG.
Back in October I wrote a post on GradHacker about using checklists in research and as promised, I’m updating you on how I’ve integrated them into my data collection. In January I began to collect data for my thesis (yay!) and had the goal of using checklists to minimize common errors. This post will go over how I developed these checklists, their benefits, issues with implementation, and some final advice.
I developed my main checklists in two steps. First I made a list of all the tasks that I needed to do during my four hour data collection and wrote each down on a cue card for flexible organization. In hindsight, this might have been a little overkill but it was a major step to get over the hurdle of managing a lengthy data collection successfully. I used these for my pilot collection and as a way of thinking through my collection before hand.
With the help of my trusty undergraduate research assistant, we began to figure out where issues were arising, the best way to order tasks, and problems we had in our first few collections. For me, my main issue was leaving this wooden stool in the middle of my collection space, ultimately ruining that trial. After this, I came up with a few pause points that had four or five things to check and inserted them into my data collection sheet. I based this off airplane checklists and it would go like so:
Collection space…………..clear
Wires…………..taped
Timer…………..set
Signals…………checked
Amplifier……….on
The cue cards were great, but I remembered most of the steps. It was these pause points that contained the things I would always forget or were so important that I wanted to make sure I didn't forget. I’ll admit that the checklists have saved me on a few occasions, especially when things were hectic at first.
I haven’t been the best at implementing the checklists perfectly, but I am noticing myself and those I work with getting better at using them. I don’t know why, but I always check them one step too late, like right after I press record. Usually, I’ve already checked for everything but once in awhile something is not right and we’ll need to stop, regroup, and then restart. In those instances I don’t loose much time, but I could have caught the issue earlier.
I’ve also noticed that using the checklist is easier when I start the collection because I’m more alert, but after monitoring data for two hours, I’m sent back into action to transfer to the participant to the next condition. Its easy to be lulled into complacency and not ready for the next step, so the RA and myself started to communicate during the end of the trial about who is responsible for what and what the game plan is moving forward.
So all in all, using checklists is definitely worth and as I get more used to using them, I’ll be able to work more successfully with them in other parts of my work. Here is some general advice from what I’ve learned so far:
- Talk to other people in your labs about common mistakes and think of mistakes you've made in previous collections
- You don’t know what you’ll mess up until you start collecting, so don’t try and come up with the perfect checklist before you pilot
- Make sure everyone who is helping you is aware of the checklist and the pause points
- Figure out when your data collection is the most hectic or critical and be more in tune to using the checklist at this point
- Revise as you go based on your experiences and share what you've found with your labmates
What advice do you have for graduate students on minimizing mistakes during large data collections? Leave your thoughts in the comments below!
[Image taken by Flickr user {Guerrilla Futures | Jason Tester} and used under the Creative Commons License]
