Minor musings on measurement

This is the life story before a recipe that you can skip

Last year I hit my New Year’s resolution of writing 3 blog posts, but just barely. I think this year is going to be very similar. I’ve written 1 out of 3 blog posts, ahhh! Mid-November means I better get hustling.

There is a lot of writing I do for my research that never ends up seeing the light of day. And that’s a shame because the perfectionist in me agonized over it just the same. A lot of this cast-aside writing is the result of trying to teach myself about topics or methods that are already well-accepted and known. So, nothing novel that deserves publication, really… 

… but it makes great blog post material! It seems to be reaching people as well, and that is cool. From my blog analytics, I can see that certain posts seem to be particularly handy from mid-October to early November. Hmmm… intro statistics, machine learning, or math stats midterms, methinks? Anyhow, here’s another lil’ ditty along those same lines. Yes, I am phoning in these last few blog posts of the year.


This blog post is about how we translate real-world phenomena into numbers that we can gain insight from. In other words, measurement! I think that measurement is both widely taken for granted and still hotly debated on several fronts. Measurement is something that has been around for much of human history in different forms, yet also something that is presently at the forefront of modern psychological and medical sciences (think patient-reported outcome measures, etc.) An interesting topic with a rich history, to say the least.

I’m going to focus specifically on different ways of classifying measurements that we commonly see in statistics. These classifications are not always widely accepted and, again, the source of debate in some cases. I also probably won’t get everything right. But it’s useful to think about what implications measurement carries for analysis, interpretation, and beyond. Hopefully, this inspires you to dig deeper into this topic! And if you disagree, promptly locate the comments section at the bottom of this page.

Continue reading Minor musings on measurement

Hello world!

Hello world! 

I started blogging back when I was a Master’s of Applied Statistics student making my way through some very heavy journal articles. It always took me a while to work my way through journal articles as I often found myself wanting to semi-prove all the results in a paper for myself. Having “wasted” an obscene amount of paper and time working these things out, I decided that I would attempt to translate these scribbles into complete notes… and so, Statisticelle was born!

Another big part of my learning process is applying what I’m reading or deriving to an actual data set. Having based my Master’s degree on clinical trial research and now working in industry, I don’t think that I can actually use/discuss the data relevant to my past research or career without breaking a few laws. However, I think this blog presents itself as a great opportunity to delve into the massive collections of open data now available. I’m going to make an effort to provide references to these data sets and any R code I used – even if no one uses it, I’m hoping that putting it out in the open will encourage me to be a more diligent statistician!

It’s been a while since I’ve written any blog posts but I’ve recently found myself staring at my statistics bookshelf, thinking that I really miss delving into new topics and working my way through all the messy bits. I’m not sure that anyone will really read this but on the off-chance that they do, I hope these blog posts are helpful and not a collection of misinformation! I am trying to understand these topics in a way that makes sense to me and I can only hope that I am doing so correctly. If you do find any errors, do not hesitate to let me know.

Anyways, that’s my mission statement in so many words. Part 2 – lets see how this turns out!