My experiences in IS-301 were on the whole good, though not entirely without mistakes. At times I struggled with time management and proper planning, but in the end I grasped the concepts and successfully applied them to the analysis of our data for our final project.

I think I did a number of things poorly, including recording the information I learned from the textbook, leaving some gaps in my blog posts, and planning the final paper with my group. When doing the worksheets, I developed a bad habit of learning what I needed to do for the worksheet, and then not coming back to the material. I would have done better to keep a notebook in which I recorded the steps necessary to perform different types of analyses, along with what types of variables they were designed to analyze. Furthermore, I think I ought to have worked along with the professor in class, instead of working on other projects for other classes as I did so often.

I began the semester with the intent of writing a blog post every week, and I stuck to this for the first month or so. However, as I began to get bogged down in other responsibilities, I let my regularity slip. I think I remember creating a reminder in my e-mail calendar to prompt me to post weekly, but I’m not quite sure if I did in fact do so, or what became of it if I did. I ought to have created more reminders, possibly physical ones and contented myself with less-than-perfect posts for the sake of posting something.

Finally, I think I ought to have established a firmer timeline with my group. We did make efforts to do this, and had planning sessions for that purpose, but we were all so involved with other responsibilities that we ended up leaving a lot of tasks vaguely defined and treating the deadlines that we set for ourselves extremely loosely. I think that we ought to have laid out a long-term schedule for completing the project on a semester level, and then met in person every so often to discuss our findings and assess our progress.

On the other hand, I think I did some things well, such as getting the big concepts down at the beginning of the semester, writing quality blog posts, and conducting the analysis for the final project. I scored very well on the concept-based quizzes at the beginning of the semester, simply because I took a little bit of time to read the material and made a few flashcards. Those quizzes were quite easy for those who had read the material but mystifying to those who had not; I think I exercised good time management in devoting just enough time to studying for them to do well, but not long enough that it detracted from other projects.

My blog posts were generally on the longer side, and I made a conscious effort to relate my content to the course material and the actual practice of computer analysis. For instance, when I was discovering the implications of a particular method of analysis, or when my group discovered an error in our data, I found it a fruitful topic on which to reflect. Thus, although I may not have posted as often as I should have, my posts were generally of good quality.

I think perhaps my greatest success came when I performed all of the analysis for my group. I was able to apply the correct type of analysis to the various types of data that we had collected. For instance, I used a bivariate correlation to analyze the relationship between Chinese FDI and exports (two interval variables) and used two independent samples t-tests to compare the mean FDI of countries that recognized Taiwan with those that didn’t, and the mean FDI of countries that exported petroleum to China with those that didn’t (an interval dependent variable and two nominal independent variables).

In sum, I ought to have organized my study of the statistical methods more systematically, and also planned both my own blog postings and my group’s work more carefully. However, my self-reflections were of good quality and I successfully grasped and applied the concepts being taught. At the end of the day, I firmly believe that I am leaving the class with a much firmer grasp on how to conduct quantitative analyses.

My group learned an important lesson about how easy it is to make mistakes when dealing with large amounts of data. We wanted to examine the effects of FDI on Chinese exports to African countries out to five years in the future. So, at Col. Sanborn’s suggestion, we simply shifted the Export column down one, thus lining up Export values with FDI values from the previous year (or so we thought). Then, we shifted it down two rows for two years in the future, three for three years, and so on. We were excited to discover that, when we ran bivariate correlations using this shifted data, the Pearson’s coefficient increased from .210 the year the FDI was received to .365 five years after the year of receipt. However, our excitement was short-lived, as we discovered that we had ignored the fact that our data came from multiple countries. Simply shifting the Export column down worked within a country’s data, but once it crossed over the line into another country’s data it became misaligned. So, for instance, SPSS was running correlations on the relationship between Chinese FDI to Angola in 2015 and Chinese exports to Benin in 2003, which didn’t provide any insight into whether or not there was a causal link.

The good news is, once we fixed the problem and only compared FDI and Exports for the same country, we reran the program and found that there was an even stronger correlation with the correct data than with the flawed data. The Pearson’s Coefficient decreased slightly from 0.210 the year of receipt to .183 one year after, but then increased steadily after that to .250 three years after, .384 four years after, and finally .423 five years after. This data shows that, not only is Chinese FDI a good predictor of Chinese exports to a given African country the same year it is received, but it becomes a better predictor as time passes. Indeed, FDI is twice as good at predicting Exports five years after it is received than in the original year of receipt. This supports our hypothesis that Chinese FDI to African countries has a positive impact on Chinese exports to those countries, and thus further suggests that Chinese FDI is indeed serving China’s strategic interests in Africa. The fact that both we and Col. Sanborn failed to realize the error demonstrates just how easy it is to make mistakes when manipulating large amounts of data, and how those mistakes can effect research results.

We have discovered that the student version of SPSS won’t let us analyze the volume of data that we have. So, we are relying on Col. Sanborn to help us use department computers to analyze our data with the full version of SPSS. We have collected data for 50 African countries over a 13 year period (2003-2015), producing 650 total data points for each area of analysis. We have struggled to find good sources for much of our data. Data on Chinese FDI to African countries was extremely difficult to locate, until we finally stumbled upon a data set that had been compiled by the Johns Hopkins University School of Advanced International Studies’s China-Africa Research Initiative. Finding data on yearly natural resource exports to China from African countries was also hard, as we were able to find data organized by country of origin and by year, but not by both simultaneously. We eventually had to go through and manually copy and paste the results from each year one at a time, which was a tedious and time-consuming process. Having professional sources of macro-data on which to draw would have made our project much easier.

During the first class period after Thanksgiving break, my group met to discuss our plan of action for the coming weeks. We outlined our final paper, scheduled a date on which to submit our draft, reexamined our literature review, and discussed the division of work among group members. We left class with a rough plan for producing a finished research paper. We still have a sizable quantity of work to do, as much of our data remains to be gathered, and the analysis itself will need to be run after that. Most particularly, we have discovered that  finding sources of data on Chinese FDI in Africa elusive, although there is plenty of literature that makes broad claims about the nature and purpose of that FDI. This is an interesting trend, one that raises the possibility that perhaps this is an area that could benefit from the application of a careful quantitative analysis of broad trends, such as we wish to conduct.

I am returning to my blog after a long hiatus that has primarily been due to my being swamped with work preparing for Ring Figure. My strategy for preventing this from occurring again is two-fold: I will set a reminder on my calendar to post every week, and will stop being so perfectionistic about what I write. This last part will be especially hard for me, but I will simply write down my reflections in rough form, and edit them later when I have time.

In the interlude, my group has moved towards formalizing our theory. We propose the following: If China increases investment in African countries, then it will further its strategic goals by being be able to extract a greater number of natural resources from these countries (measured in barrels of oil/cubic feet of natural gas exported to China), secure markets for Chinese exports (USD value of Chinese exports per country), as well as boost political legitimacy (measured through the countries’ renouncing of Taiwan). We will likely focus on Sub-Saharan Africa, but are considering including Northern Africa as a control.

I have a couple concerns about our theory at this point: first, we need to further define our independent variable by determining exactly what types of Chinese financial activity we will include in our definition of “investment.” Will we include loans? Investment by state-owned Chinese companies? What about foreign aid? Second, we need to work on the causal mechanism; how exactly does Chinese investment lead to these results? What if, instead of investment causing increases in hydrocarbon imports, trade, and derecognition of Taiwan, Chinese investment naturally flows to African countries that already have these traits? We will also need to come up with a list of control variables.

I have realized that as I learn more and more commands in SPSS, I forget those I have previously learned. I know that this will only get worse as the semester goes on, and I suspect that at some point new commands will be introduced that build on those already taught. Therefore, I have begun keeping a notebook listing the steps for each command, its purpose, and any additional pertinent information. I think that it would have been better had I begun this sooner, but fortunately the material covered so far hasn’t been overwhelming and can be filled in easily.

In writing the first draft of my group’s literature review, we found that dividing the work among group members lightened the load but made it more difficult to coordinate and encouraged procrastination. Since the nature of the project demands that we divide the burden of researching and writing, we will have to schedule better in future, allowing time to compile and edit our individual material into a coherent whole.

After delving more deeply into the literature surrounding our topic, we are in a much better position to define our variables. We found that our dependent variable, Chinese political gains in Africa, was summed up by RAND in four categories: “access to natural resources, particularly oil and gas; markets for Chinese exports; political legitimacy; and sufficient security and stability to continue its commercial activities.” Our independent variable, Chinese FDI in Africa, was defined by Blackwill and Harris as investment by state-owned enterprises (SOE’s), sovereign wealth funds (SWFs) and state-owned banks.

Moving forward, we are thinking about focusing on support in the UN and derecognition of Taiwan as two components of our dependent variable (political gains for China). It looks like the most challenging part will be fine-tuning the exact mechanism by which Chinese FDI leads to political gains. For instance, is a particular threshold of FDI needed to get a particular benefit? Do countries intentionally try to milk China for all they can before they give up a concession? Additionally, what non-financial things might China be doing in tandem with its economic efforts? All of this will hopefully become clearer as we continue to research and start gathering data.

Over the past week, my group has been working on narrowing down our area of focus and choosing a research question. This process ended in us making a tentative decision last Friday. Picking a research question was not as easy as I had thought it would be, due to the need to sort through existing literature and find an area that had a body of research from which to draw our data. Also, picking a question specific enough to be manageable, but broad enough to be meaningful, was also a challenge.

We started off knowing that we were generally interested in China’s use of economic tools for political ends, especially in the One Belt, One Road initiative. We struggled to narrow down which region we wanted to focus on, what types of political concessions to look at, and how to quantify political gains. Figuring out how to quantify our question has probably been our most difficult problem thus far. Right now, we are considering coding concessions on a scale of 0-4, with “0” being “none” and “4” being “significant concessions.”

We eventually chose to focus on Africa to make our task more manageable, and are asking: “is there a relationship between increased levels of Chinese FDI in Africa and strategic concessions?” We postulate that increased Chinese FDI gives China the leverage needed to acquire access to natural resources, preferential treatment for Chinese companies, and infrastructure usage. We certainly plan to modify our hypothesis and make it more specific as we do more research. I think the main challenge we will face going forward is how to accurately quantify our variables.

My first experiences with SPSS have been good! So far, we have learned how to analyze basic descriptive statistics, specifically measures of central tendency like mean, median, and mode. We have also been able to create graphic representations of distributions with histograms, which makes it easy to visually see how data might be skewed to one side or the other.

Skewness is itself an interesting concept, as it measures the effect of outliers on the mean. I found it helpful that we can use skewness to test hypotheses about how data is distributed. For instance, if we theorize that a few states receive disproportionately higher defense spending, while most others receive a moderate amount, there should be a few positive outliers, meaning that the data will be skewed positive. Thanks to SPSS, we can test this in a matter of seconds.

I have a feeling that this is going to get a lot more complicated as we learn to run more advanced types of analyses. The key for me will be to make sure I read the chapter and do the examples ahead of time, so that I can work on the problems effectively in class. To help with this, I plan to make it a habit to read the chapter for the coming week every weekend.

I am both excited and apprehensive as I begin my study of Computer Analysis. I certainly think that it will benefit me to gain a better understanding of the methodology of Political Science in general, and specifically to learn how to collect and analyze data using SPSS. These skills will be applicable to my future studies in the discipline, and to my National Security Minor and Institute Honors theses in particular.

However, I am a bit concerned about the group project that will be due at the end of the semester. I have never done research on this scale before, and I understand that others who have taken this class before have found it difficult. I think the keys to success will be to select a manageable research question, divide the work among the members of my group, and start work early to avoid procrastination.