Now that I can see the light at the end of the data tunnel and the end of U.Discover is on the horizon, I thought I’d sum up a few things I’ve gotten out of this program.
1. Doing research on public K-12 schools and expecting to complete it during the Summer months is not the best idea.
While I didn’t need IRB approval since I only needed to ask if the school was named for MLK and what year it was so named (basically public information), I vastly overestimated my ability to get people on the phone at the end of the school year and into the summer. If I had it to do over, I would have started with the more available part (sifting through mounds of data) instead of the hard part (contacting real humans). Luckily, I don’t need that data to complete something reasonable for U.Discover and I’ll be able to do the remaining work to expand the U.Discover research during the Fall in time to present at Ideafest.
2. Sifting through data is tedious and time-consuming, especially to ensure a good, solid sample that can maintain integrity for analysis.
I started with so many MLK schools, I thought even after weeding them down to have a consistent sample of neighborhood-style schools, where children attend based on where they live, that I would still have over 100 MLK schools. After excluding alternative/continuation, vocational, selective, magnet and charter schools, as well as schools without 2007-08 data available, my MLK sample dwindled to 75 schools.
3. Coming up with a strong research design takes time and is harder than it looks.
While I spent too much time in the beginning trying to collect data that would be easier (actually possible!) during the Fall, I also underestimated the time needed to ensure consistent sample and a good design. Creating a good design requires thought about what is bad in one’s design, then trying to fix it, repeat that process about 10 times. It takes awhile, but is very satisfying once it’s done.
4. Unexpected data and ideas for future research
Since a majority of the students at MLK schools are minorities (at least it seems that way in the raw data so far), there were a fair number of schools whose students were solidly one minority or possibly two minorites. In many of these schools, there were significant differences in the assessment data that didn’t comport to what one would expect from what is learned in the Sociology core classes. It seems that research on minority students in schools made up solely of that minority group, possibly one more, could show drastically different results than the research done in the past when white students were a majority in most state/county/districts and minority students couldn’t be studied as a majority in a regular public school. Anyways, the point of this is that I have found more areas to research later that also interest me. This is good!
5. One big, huge, invaluable learning experience
The reason I applied for U.Discover (beyond tripping over the sign in the hallway) is that I thought it would be a way for me to learn more about research from the presentations and my fellow students. I was going to try to do this research on my own this summer (before I fell over the sign) and I thought the structure, faculty mentor, and weekly lecture would be an extra bonus. The U.Discover program provided exactly what I had hoped for and also made me think about the projects my fellow U.Discover people were working on and apply it to my interests and discipline, as well as contribute my thoughts on their projects and incorporate their thoughts about mine. While we still have a couple weeks left, I’m ready to call the program and my research a real learning experience and I’m really thankful that I was given the opportunity to participate!
MLK School Research 
I am curious about your research statistics. I believe you said that you left out a demographic in the comments of your last post along. Furthermore, I believe you said during one of the meetings that you had other statistical problems. I am curious how you plan on dealing with them and if you believe they are compromising the integrity of your project.
Good Luck!
I’m going to have to say that the Asian and Islander demographics are not reliable. Each state seems to have its own method of collecting data for these groups that makes it very difficult to have consistent data. If I had much longer and a specific focus on those populations, I could probably straighten it all out one state at a time. However, from Alderman’s research (and my data as well) Asian/Islander populations are not a large part of MLK schools nationwide. The issue didn’t arise until I was well into rounding up the comparison neighborhood schools.
One of the other problems I had was getting a hold of school staff during the summer. This created a big hole for a couple columns of data I was trying to collect and ate up a lot of the time in the beginning of summer.
Since my project is aimed at two goals (1. establishing MLK vs non-MLK academic performance and 2. seeing if that correlates to year founded and level of internet disclosure of MLK status), I’ve simply had to narrow it down to one for U.Discover and then work on the second part during Fall semester (when schools will be more likely to be fully staffed).
That’s the plan and I don’t think it is compromising the integrity of the project as a whole, just the Asian/Islander demographic.
I am curious if you have discovered any reliable data on the socio-economic status of mlk students? Furthermore, did you evaluate the differences in goals that MLK schools have from other schools?
Reliable data on socio-economic status is one of the easier things to obtain since the free/reduced lunch program is a Federal program and used in every public school nationwide. The majority of states with a MLK school also track academic performance based on people who are eligible for free/reduced lunch and people who are not. Eligibility is based on income.
Goals is a bit vague for schools. Every school has its own mission statement or statement of goals and no two are exactly alike. Many schools are also magnet schools or somehow selective, so they are not the average neighborhood school. There is also the flipside of the magnet schools where there are vocational or alternative schools for kids who are either struggling to catch up, need more flexibility or are at-risk of not graduating. Since the catalyst for this project was the stereotype that MLK schools are poor-performing schools, it made sense to limit the sample to regular schools using residential zones. Doing this ended up cutting my sample size significantly as I didn’t realize in the beginning how many of these schools have been changed into magnet/alternative-type programs. In any case, part of the reason my data collection has taken much longer than I had anticipated is because of the wide variety of schools out there. I have done my best to ensure that each MLK school is public, co-ed, pulling students in based on residence, and has data available for 07-08 school year. Additionally, I have pulled a minimum of 3 and max of 8 comparison schools within 3 miles and same district as MLK school, that are also public, co-ed neighborhood schools of the same grade level type (elementary, middle or high school). This means I had to look up each comparison school to ensure it was similar in structure to the MLK school. Due to time constraints, I have had to cut short the data collection so I can (hopefully) get the statistical findings back in time to put them on the poster. All the data is collected for the MLK schools, but the comparison data is short by quite a bit from what I would like to have. However, since the data and design are good, I will have some preliminary findings at least and will have the final set when I present at Ideafest.
I hope that answered your questions somewhat!
Claire,
It looks like you’ll come out of this program with some usable data, and a good idea of how to get better data. (I too have a better idea of how to get reliable data) What are plans for presenting this data outside of USD? Who needs to see your research?
Jami
Whoa there! One thing at a time 🙂 I haven’t even thought past tomorrow as far as presenting the data anywhere! 🙂
I think the final product that will be ready to go for Ideafest will be relevant for people with interest in lingering race biased social structures, racial mainstreaming, education inequality, urban planning, education policy, and the legacy of MLK. My advisor wants me to present it in undergrad papers at the Midwest Sociological Association’s yearly conference in Chicago but I haven’t looked into anything yet.
I think my research has generated more questions than answers for me at least, so I guess we shall see what happens!
I was reading through the list of what you’ve learned from the program and my main thought was, “Right on!” I don’t mean to sound like an after school special, but I’m glad that you were able to gain something from the program other than results and that you were able to share it. I hope that everything goes better for you in the fall and that you’re able to collect your desired data when school is back in session! Good luck!
Thanks Amanda! I’m sure I’ll have all I need by October once everyone is back at their schools. I think that what I gained out of the program was a bit of a surprise. I had hoped to gain a lot when I applied for it, but I had no clue how much I would actually get out of it!