Landing page for PSYC790, Statistics for Behavioral Sciences I (Regression Analysis).
Previously, I was teaching PSYC650 POLS706 and PSYC90 rolled together into one course. Now these things are separate. Sometimes you will run into outdated material and links that don't fit anymore. I'm trying to clean all that up.
I keep all my lecture material under the general topical structure in http://pj.freefaculty.org/guides. Feel free to dig around in there. I found a Web program called H5ai that allows me to just show the files. No fancy HTML code going on here. This makes it easy for me to rename directories and files without breaking any pages. See a file, click on it, you can view or download.
Almost all of these are "literate program documents". The R code is woven into the documents raw code, and then the code is processed when the PDF or other output file is produced. The folders include the source code--everything needed to re-do the work. So if you want to revise some lectures for me, feel free. Send a "diff" file with your edits :)
In case you are interested in learning how to write papers that incorporate runable R code, there are 2 ways. I've written an R package called "stationery" and it comes with HOWTO essays about different startegies for doing this. I'll show some examples from time to time. Your choice is to use either Rmarkdown or Sweave to do this. If you want PDF output, for me the choice is clear: Use Sweave. However, other people like Rmarkdown. The overview essay in the stationery package explains. I started lecture notes on this for a workshop, stored under https://pj.freefaculty.org/R
If you think you might like to take the class, but are unsure if you are well prepared, I made a Checklist of topics. I just updated that for 2020.
If you have had one or two social science stats and methods courses, I expect you will be ready.
I've gravitated toward computing and computer science over the years. I can help you understand that transition process. It seems to me that many social scientists today can find good careers in modern "data science" if they are willing to learn a bit about programming.
Before the class begins, you should exert yourselves to prepare your personal computers. Whether you use Linux, Macintosh, or Windows, install R (free software), and at least one reasonable editor. If you only have a ChromeOS device, I'm sorry, but I think you need to get a full-sized OS in order to make any progress. Also, if you have an early version of Windows Surface, you are going to have trouble.
For an editor, I still use Emacs with ESS to interact with R, but many Windows users do well with Notepad++ (and the NPPTOR plugin). Many Mac users do well with the editor called R.app. There is a newer program called "R Studio" which I actively dislike, but I cannot deny it is popular with people under the age of 30.
In the CRMDA at KU (when it existed), we made instructional videos about installing R on Windows and Mac. I just copied those guides to a subdirectory called crmda_guides in the Rcourse folder. Look for folders "46.windows_R_setup" (the *.ogv file is a video, click to watch in browser) and "47.mac_install".
Years ago, I put up similar video for Windows users on YouTube, but they are dated now. Should figure out how to remove them. Until 2015 or so, R required a working verstion of Perl. You don't need to install that anymore.
At the bare minimum, please try to install the R program (http://www.r-project.org). Since "R Studio" is free for the moment, you might as well install that.
Name Last modified Size Description
Parent Directory - Syllabus-2019.pdf 2019-08-25 10:17 194K Syllabus-2020.pdf 2020-08-31 12:34 194K archive/ 2020-07-28 08:10 - psyc790/ 2019-11-25 12:39 -