I had occasion to need to open a Quattro Pro spreadsheet file from 1999 today. OpenOffice did not know what to do with it, but then I remembered Gnumeric. It worked like a charm! This file was so old, the extension is *.wb3. Now I can get the old data into R!
FYI, this was on current Debian stable.
Here’s a description of a new course I’m teaching next quarter (Spring, 2019).
PSYCH 548 ADV QUANT PSYCH: Exploratory Data Analysis in Psychology
From very early, Psychology as a discipline has emphasized hypothesis driven research. At the same time for decades, exploratory statistical approaches and algorithms, such as exploratory factor and principle components analysis have been key analytical approaches. Additionally, machine learning and other exploratory algorithms are being embraced across many scientific domains, including Psychology. What about this disconnect between a historical disdain for exploratory analysis with the current interest in complex exploratory computational procedures? How have and could we think about exploratory data analysis in Psychology? How should exploratory work be used to extend Psychological knowledge and theory?
In this seminar, I hope we will consider these issues, as well as some exemplar models and approaches. We’ll read historical and more recent papers on exploratory analysis generally, as well as focus on some specific models (some possibilities include exploratory factor analysis, canonical correlation, cluster analysis, some machine learning approaches, exploratory SEM; suggestions welcome). Classes will focus on discussion of the material, implications for research (broad, as well as specific to a single research area). You will be encouraged to work with your own data for the class, and we’ll strive to work some of those analyses into class meetings. I’m considering brief weekly reaction papers or a brief analyses using some focal model on your own data. A final paper will compare the scientific upshot of a few exploratory approaches applied to your data.
My goal is that we come out of this seminar thinking more broadly and critically about exploratory analysis in Psychology.
In my graduate class on path analysis, we do a lot of analysis on our own data. This year, I suggested that people consider analyzing simulated data based upon the statistics of their data. This way they’ll use a data set that looks like their data, but they aren’t doing a lot of model fitting on data they care about and what to use in real research. Thus, today I typed up a quick guide to simulating multivariate normal data in R for use in our class.
If you find typos, errors, etc., please let me know.
You have to enjoy the introduction of Sander Greenland, et al.’s article in the supplemental material posted with the American Statistical Association’s statement on p-values (full text here):
“Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature.” (p. 1, emphasis mine)
Working scientists should be able to handle this.
Every fall, I get email that read something like this:
I’m writing to inquire about pursuing a Ph.D. in Quantitative Psychology at UW. I’ve always been interested in statistics (data analysis, measurement, etc.). I think that studying quantitative with you would prepare me very well to do research in [insert substantive area here].
I believe I understand the reasoning these students have. “If I learn stats. very well, I’ll be able to do really cool research in social/memory/psychopathology/etc.” Here’s what I wrote a couple weeks ago to someone:
Thanks for your inquiry. You have the right person. However, before I talk about the program, let me ask you something about your email. You wrote that you are interested in stats, but your research interest is on learning and memory. That suggests to me that you should go to a cognitive program where you can focus your research on learning and memory. One of your criteria for choosing a grad. program then might be your ability to get good training in quant. methods and research design.
Why do I raise this? I do because grad. school in Psychology is largely about learning how to do good research. If you were to come to grad. school in Quant. with me, you’d learn how to do research in psychometrics, modeling, and applied stats. That research is performed rather differently than substantive research on learning and memory. Getting a PhD in quant. would not then necessarily put you in a good position to do research on learning and memory. Whereas, if you go to a program and work with an expert in some domain of learning and memory that interests you, you should come out with a solid basis for doing that work. And if you are motivated, you’ll get training in methods that you want. I hope this makes sense. If it doesn’t, feel free to ask clarifying questions.
Let’s make this clearer, If you want to study some substantive psychological domain, you want to learn how to design studies in that domain, how to measure the behavior in question, and how to analyze the acquired data. In Quant. Psychology, you’ll learn how to set up and conduct a simulation study, math stats. and probability theory underlying statistical decisions, computer programming, etc. You’ll also likely do analyses of real data, but often primary interests in quantitative research are about how a quantity or algorithm performed, less on the substantive implications of the specific results. The skills and knowledge for doing good substantive research do not (in my opinion) largely overlap with the skills and knowledge for doing good quantitative research.
In summary, I don’t think pursing a Ph.D. in Quant. is necessarily a good way to try to do great research in some substantive scientific domain. However, if you want to do research on how we measure, model, and study behavior, that is the methods and models used to design our studies and analyze our data, then I think a Ph.D. program in Quant. sounds like a good fit.
As UW-IT removes the old Catalyst tools, I’m moving stuff to new places, as well as updated all the pointers to that material. This blog site is my replacement for what was my Catalyst Commonview faculty webpage. I’ll be learning WordPress, as well as moving and adding information here over the coming months.
…” ‘interaction’ in contingency tables enjoys only a few of the fortuitously simple properties of interactions in the analysis of variance.” (from Gohkalke and Kullback, The Information in Contingency Tables)