Our group, consisting of Chris J, Chris L and Lawrence Lin, is going to develop an interactive timeline map of Carleton Student Statistics, with a primary focus on where the students are coming from throughout our history with other corresponding statistics such as gender, major, religious affiliation and athletic information.
We hope to find the data we’re looking for in the Carleton Archives, Carleton Admissions, and with help of the the Office of the Deans. We are assuming they will at least have the hometown and major of each admitted student as well as the other information that we are seeking out. We obviously are going to be dealing with massive amounts of data and we hope to use a relational database to store and manipulate our data. We’re hoping that Carleton has digitized their archives but if not we will be looking at a lot of documents and will need to use a scanner to convert them into a PDF and a PDF reader to get the data we need from them. Once we have a relational database set up we’re thinking we want to use the ArcGIS platform to create our interactive timeline map that will offer the user information about each Carleton student and the geographical history of admitted students.
Were going to try and break down our project into three weeks. By the end of week 6 we want to have access to and have digitized all the data that we need to run this project. That includes having scanned any documents that we plan on using. By the end of week 7 we wan to have out relational database set up and able to store our massive amount of data. By the end of week 8 we want to have a rough cut of the final project with the rest of week 9 to clean up the site and figure out our bibliography.
http://legacy.calacademy.org/human-odyssey/map/
Personal Interest In Student Origin Map: http://awesomebeasts.com/personal-interests-in-student-origin-map/
http://thelcontinuum.com/class-stuff/interests-and-hopes-for-dh-project/
http://chardeemacdenniselecticboogaloo.com/uncategorized/final-project/
Team Student Stats,
Your project is very interesting, and I like how you are casting a wide net in terms of data. A group last year did a similar project using the ZooBooks data only, which included hometown but not major. It would be great if you could link these together and tease out some more patterns. See their final project here
That group used Palladio to visualize their data, which might work for you all, but I’d be happy to discuss other options with you all as you narrow down your dataset.
Hi, I wanted to address something Lawrence mentioned in his personal blog post on the topic of your analysis and the scope of it. In his post, he hypothesized as to the cause of certain demographic changes in the Carleton population across time, specifically mentioning finding causation.
I though it important to mention that since your analysis will constitute an observational study (and NOT a randomized/controlled experiment), you cannot infer any causality from the trends you observe. It’s fine to hypothesize about the causes of the trends you see, but your evidence will not support any claims of causation.
I say this not to be critical, but to make sure you guys know the limits of your analysis.