Phase One: Complete.

So FURSCA’s finally over and while most of the people who participated have left campus and gone home, I have not. More on that in a second.

In terms of the end of my FURSCA project, really it’s just Phase One thats over. This summer, I completed the first part of the research I wanted to get done – profiling a voter and a protestor in Lansing, MI. I also was able to profile a registered non voter and a non voter in general. The next steps are pretty clear for me. In the spring, I plan to complete my directed study and tie up some loose ends from this summer as well as prep for next summer. Then, for next summer’s FURSCA, I plan to take what I know about voters and protestors and come up with marketing techniques to attract the underrepresented populations of voters in the polling booth.

About still being on campus – I haven’t permanently went home since school got out. I went home for two nights near the beginning of the summer and was just home for a night a week ago, but I haven’t moved back home. I’ve been living all around campus and honestly the experience of being on campus all summer has been awesome. I’ve learned a lot about what it’s like to be “all grown up” and live on your own – moving out of my college dorm on my own, or having to rent a storage unit, for example. I’ve gotten the opportunity to learn to cook better than I could before. Budgeting has become one of my big goals for the summer and I’m doing well on that note.

Not to mention being on campus by myself all summer has its benefits. I work at McDonalds on the side and President Randall came through the drive thru once. I get to spend as much time as I want in places talking to people. It’s been a relaxing summer, all things considered.

Here’s to FURSCA 2013!

Closing Time

I can’t believe there’s only two weeks left of FURSCA. As the research portion of my summer starts to come to a close, I’ve been reviewing the work I’ve completed thus far.

I started FURSCA this summer with three goals – to identify the profile of a voter for the city of Lansing, to identify the profile of a protestor for Lansing, and to see if I could correlate voting and protesting within the city. I planned to use Census data and Facebook protesting data to achieve these goals, analyzing the data in SPSS.

The first two weeks were spent reading four different books – We The People, The Politics of Power, Introduction to Linear Regression Analysis, and the Michigan and United States sections of The Statesman’s Yearbook – 2011. This reading allowed me to have a better understanding of voting and protesting as it has existed and evolved in the United States, dating to pre-revolutionary times. This reading also allowed me to understand what SPSS (or Statistical Package for the Social Sciences) would do as it analyzed my data.

The third and fourth weeks were spent mainly in Lansing and Detroit, collecting data, information, and perspectives needed to complete my research. Lansing City Clerk Chris Swope was instrumental in getting me voting data – as he provided me with over 90,000 data points that I was then able to clean and organize into usable data for my FURSCA project. The Census Bureau office in Detroit provided perspectives on the national voting “scene”, which allowed me to later correlate my Lansing data to the national averages. The Capitol library in Lansing helped me get data and information on Lansing that would help me predict what the voter and protestor profiles should look like.

The fifth and six weeks of FURSCA were perhaps the most crucial to my research project. As I began to clean out the data, I noticed variables listed in Mr. Swope’s table that I couldn’t identify. Through conversations with him, I was able to identify them and further sort the data by the variables I had outlined in my FURSCA proposal. Through doing research on the Lansing area school districts, I was able to sort my data not just by age, gender, or location, but education as well. Additionally, through Senate and House district numbers, I was able to get a picture of what each voter’s elected official’s views were and see if that had any influence on whether people chose to vote or not.

After I had identified all the variables listed in the data, I began to run reports. I was able to get the profile of a voter and a registered non-voter for the city of Lansing, as well as a rough profile of a protestor (See attachment 1). I was not able to correlate voting and protesting within the city, as there was nearly 100x as much voter data as there was protestor data. I was able to correlate the voter and registered non-voter data for Lansing to national data.

As I was running these reports, I realized two very important variables I had not considered. As I was predicting what my voter would look like, I was considering what I thought to be the “population” of Lansing. However, because of Capitol Hill, my “population” thought was skewed. The majority of the people working in the Capitol or in the Congressional office are commuters. Additionally, many people that protest in Lansing aren’t actually from Lansing. Because Lansing is Michigan’s capital, many people come from all over the state to protest. However, in spite of these variables, I was still able to find conclusive data about the residential population of Lansing in terms of voting.

The data to the left is data for a registered voter (whether they voted or not). To input data into SPSS, it had to be numerical, so I changed the actual words for many of these categories into numbers. For example, under gender, female became 1 and male became two.

The State House and Senate codes indicate what Michigan legislative district the registered voter lives in, and the US Congress code does the same but on a national level. This allowed me to gain a picture of what area of Lansing the registered voter lives in.

The school code indicates what school district the registered voter lives in. There were four school districts represented in the data I had and I ranked them from 1-4 (one being best, four being worst) based on the school districts’ data.

Average age is computed using the descriptive statistics function instead of the frequency function, so I didn’t include that table here.


Average Age: 47

Gender: Female

State House Preference: Democratic

State Senate Preference: Democratic

US Congress Preference: Republican

School District: Mainly from Lansing Public Schools area

My Playbook…of sorts

I’ve gotten a lot of data that I’ve started to process in both the voting and protesting realms.

For the voting data, I worked with the Lansing City Clerk’s office to get the most accurate data they had. I have data from the November 2010 election that I’ve started putting into SPSS (a data analysis software), coding it using my codebook – or playbook.

For the protesting data, I’ve scoured Facebook to find every single Occupy Lansing facebook page, group, etc, that I could, and then compiled data from those sources. This data has also been coded and put into SPSS.

Now that I’ve got a large portion of the data I’ll be needing, I have to play catch-up and wait for some final data points that I need. While I’m doing this, I’m reading a book on Linear Regression Analysis prepping for the data work I’ll have to do. I’ve also started on my literature review, using my notes from the three books and one thesis I read prior to starting data collection.

Here’s hoping I get the rest of my data soon!

It’s getting a little heated…

While the weather gets warmer, things are heating up on campus too!
My research project on voting and protesting is turning out to be a lot more interesting and complex than I had planned! I’ve completed my lit review (reading four textbooks on voting, protesting, and the data analysis style I plan to use) and I’m on to data collection. The voting data is rather straightforward, just taking Census data and recategorizing it for my purposes but it’s the protesting data that’s driving me crazy!

I’ll share some photos soon!