There are a lot of different ways to think about comparing two things, or more appropriately perhaps, two sets of things. If they are things we can count, we can easily see which there are more of. If they are more like a score, we can easily see which set has a higher score. We can also fairly easily see what the distribution of the things in each set are, although comparing the distributions is a bit more tricky.
Using some basic statistics measures, we can tell whether or not the two sets of things are different from each other using significance testing. This is typically done with a t-test or an analysis of variance (ANOVA) or a similar measure. These types of measures, based on the mean and variance of a set of data points, are simple and easy to calculate (especially with a basic stats program) and have therefore become commonplace in the research literature. But unfortunately, their simplicity ends up hiding a lot of information and potentially interesting nuance.
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Today I am thankful for my friends and family and being born into a middle class family in a first world country and all of that normal Thanksgiving stuff. But there is something else that I am thankful for that I want to call attention to. I am thankful for the public funding of science.
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I love vectors. I mean, they’re pretty awesome. They have a magnitude and a direction. Two for the price of one. They are also quite helpful when doing physics. [Full disclosure: my dissertation had a big focus on vectors, so I’m a little biased.] I also really like graph paper. So you can imagine how excited I was when I stumbled upon a paper-and-pencil game that used vectors as the main mechanic.
The game goes by many names and has been around for a long time. I first saw it as Graph Racer, but I like Vector Racer too. There’s a whole article on the rules and variants on Wikipedia. The game is always different because you draw the board each time you play. So how does it work?
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There are three important letters that you add to your name when you finish your Ph.D. But there are two other letters that are also important to researchers as they begin their careers: P.I. The Principal Investigator is the person in charge of a research project and it signifies the next step in your career, where a funding agency has selected your research proposal, using a panel of your peers in most cases, as worthy of gaining a substantial amount of external support. It is basically a sign that other people (you know, people who aren’t trying to help you graduate) think that your work is important and interesting. It’s a really good thing and the first time you become a P.I. is an important career milestone.
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What is evidence? In science, usually we think of evidence as a collection of the observations, measurements, and results of data analysis from an investigation of a phenomenon. But I think evidence isn’t just a set of these measurements. In order for it to be evidence and not just data, there also needs to be information about its relevance and appropriateness to answering a question or a claim. You can think of good evidence (data that was collected in a careful and thoughtful way and that supports your claim) or bad evidence (sloppily collected data, incomplete data, and/or data that doesn’t support your claim).
The podcast Serial came up during one of my research meetings today. We were talking through a transcript of a middle school science classroom and debating whether or not to apply one of codes to a particular utterance the teacher made. The topic of evidence was brought up and it made me think of Serial and how evidence is discussed on the show and how it is similar in a lot of ways to how we want students to talk about evidence in their science classes.
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A couple weeks ago at work I gave a presentation as part of one of our lunch-time brown bag meetings. Sometimes we have a researcher come from another institution to talk about their project, but sometimes we have an internal person give a mini-workshop or ask for feedback on a research issue. For this particular meeting, I and another researcher talked about general Excel tips and tricks that we had picked up over the years. We had both realized from talking to others that some people weren’t taking advantage of some of the many advantages of using a spreadsheet program; they were just using Excel like a place for their data instead of a place where they could organize, manage, and analyze their data. (No joke, I have actually seen people count numbers on their screen.) The basic idea was to share ways that Excel can help us do our work – sometimes just knowing that something is possible to do lets you know that you can search out ways to improve data organization/analysis.
So, here are some of the things that I shared in that meeting. Most of them are things that I figured out through either hard work, a friendly colleague, or a quick Google search. They mostly assume a more than basic understanding of Excel. There are a ton of Excel forums online so I would suggest if you’re ever doing something in Excel that seems like it could be done more easily, you should search and see if a solution comes up. You will usually be able to save lots of time and headaches.
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In August 2004 I went to Rotterdam, the Netherlands for a conference. I was presenting my work on a recent meta-analysis of STEM simulations for learning. (You can read more about it here and can download the report here.) The conference was a meeting for two special interest groups of EARLI (the European version of AERA) – Instructional Design and Learning & Instruction with Computers. It was a small conference with no concurrent sessions (i.e., we were all in the same room for the entire conference) which was really nice because a) I didn’t have to make any decisions and wonder if I chose the wrong concurrent session and b) I was exposed to a bunch of interesting research that was a bit outside my normal area.
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