Why I Lost Faith in Political Science

I've been meaning to write this for some time, but I never really had the time, so I thought I would finally get around to doing it.

A few years ago, I changed my field completely from political science to communication. Whenever people asked me why, I would often say that it was because political science was interested in large movements of statistical data, whereas communication was more about the specific acts of communication between individuals. Well, that's somewhat true, although there are variations in both sciences that offer alternatives, but for the most part, that's generally how it is. But that's really not the reason why I abandoned political science for communication.

It was political science itself that caused me to realize I'd never be able to answer the questions I had within that discipline. You see, political science has taken a direction since the 1960s that puts it more into a self-reflective paradigm where its members are scared to death to appear to not be doing science that they are going around creating science for the sake of creating science rather than creating science for the sake of answering questions. Most disciplines borrow from other disciplines, and we all accept that. Political science borrowed punctuated equilibrium from biology. Communication borrowed identity theory from sociology and psychology. Each made these concepts their own, so much that they might not be recognized by the original discipline. I was fine with that. However, at some point political science became so engrossed in wanting to appear scientific that it stopped being very relevant.

I was taking a course on Congress from a professor at Western Michigan University when I realized that political science was finished for me. BS (the professor) was all about making science out of political science, and he loved his data files. It was all about manipulating those data files and then publishing his results. This was also the first time I came across the dirty tactic where professors latch onto the work of their students and then sign onto the project so that they can up their publication numbers; that's one of those incest-like behaviors of academia that I've never really found myself to be very comfortable. It's one thing to have a student approach a professor and want to write a paper with that professor, but when a professor acts like a vulture and scavenges off of grad students for material, that just seems so wrong. But that's for another essay, I guess.

Anyway, what I started to discover at this time is that political science has been overrun by the desire to publish material that comes from massive survey data. National Election Studies, student evaluations, and all that sort of tripe is used to make major inferences in the discipline. Every major election is followed by tons and tons of published reports about what scientists have found based on the question and answer sessions at polling booths by science-thinking professionals. And then a bunch of people across the planet keep making connections based on whatever statistical process they think to question in their research. If the numbers don't give them the results they expected, they change the variables, or they manipulate the way they question the variables. At least until they get the results they desire. In other words, we're not a bunch of scientists curious about finding something out, but we have a theory and we use the data to prove it. And then we publish it. And then we continue to publish about it, regardless of whether or not it's really true. We all have heard the joke about statistics (99% of all statistics is made up), but we keep accepting it as canon that it's good information. And we keep publishing it over and over again, and it makes major careers out of people who then call themselves scientists, because they can claim to use mathematics as part of their academia research.

In the 1950s, after World War II, the Ford Foundation sort of changed political science as we know it. In order to receive those elusive grants that were coming from the foundation, you had to show that you were doing "science". The hard sciences, like physics and biology, had an easy time because they were doing actual science. The softer sciences, like sociology and political science, had a much more difficult time doing the same thing. They had to make their social experiments look more scientific, and one of the ways they did it was to start using a lot of statistical information because that looks and sounds very scientific. But statistical data is very misleading. Let me explain why.

There are two types of statistical data. One is hard data, and the other is survey data. The first is actual science. Things happened, they were recorded, and you can use that data to explain natural phenomena. An example is one I did early in my career. I gathered data for a ten year period to display how many violent revolts took place in the world, and I categorized them by the amount of violence that occurred (deaths, financial GDP losses, etc.). I then compared that to the types of governments, the amount of legislation that took place in those areas, the education levels of areas in those countries and the countries at large. I tracked a few other variables I had to gather. I then plugged them into a statistical formula to eventually surmise a few things, such as "as more legislation occurs in a yearly period in countries with low to mid levels of education, they tended to suffer more violent outbursts". There were a bunch of other findings, but that's the basic style of what I was trying to do.

The other type is survey data, and that's where you question a lot of people and try to make some type of statistical connection in the data, like "how does education reflect whether or not someone feels good about a particular political figure". To me, this kind of data is somewhat useless because I've never been a fan of the opinions of people because I don't believe people really know what they believe. It's like the mass communication theory that states that people are influenced by media because they think their friends are influenced by the media, but they don't think they are personally influenced. The theory shows that they are obviously mistaken about themselves and right about how they feel about their friends. The fact that they might be mistaken about their friends never seems to creep up into the literature, and that sort of interpretation is why survey data is such a problem for me. People interpret it as they desire.

I had a conversation with a friend of mine yesterday, and it reminded me of this. She's worried about her data efforts because others have suggested that using another command in the statistical program might achieve different results. That's where we're so focused on the "how" rather than the "what" or "why". Scientists aren't doing the statistical work anymore by hand, which means that the software has become so complicated that they may be making mistakes just because they don't have every proper button pushed when running the data. I had someone try to tell me that once in a statistical data set I was running, and I asked what are the implications of that "other" command he was discussing. He said he didn't know, but that he learned about it from another professor. I'm not kidding about this. I asked if the calculus was still correct, and he had no idea there was even calculus involved. For my friend, I would just like to say: Continue doing what you've been doing, unless someone can prove to you otherwise that your data manipulation is an error FOR A SPECIFIC REASON.

This has gotten me to the point where I don't do data manipulations now unless I understand the science behind what I'm doing. I've been to conferences where I realize that's not the case with others because I'll ask such a simple question based on the mathematics involved, and they stare at me as if I just asked the location of the abominable snowman.

For the record, I don't do survey research anymore. I abhor it and have no faith in it. To be honest, I don't care what people think (the ones you survey). I care what people do, what people say and how they carry out what they intended. For projection projects, I now use what I call an iterative approach, involving computer modeling. This is a complicated way to say I use a computer to continue to throw the same independent variable at the dependent variable and then reverse it for effect. I see things over massive periods of time, involving tens of thousands to millions of iterations, to see how things effect based on continuous influence. An example is the Friendship Over Time (FOT) Theory. Rather than focus on one attempt at communication, I focus on change over time as two entities share something in common over generations, until the results start to approach each other (people become a lot more alike as they share common behaviors), and then use that as an additive process to other behaviors to explain why friendships grow over time between nations, or devolve.

I'd probably like to use this process to challenge a lot of political theory, but to be honest, I don't feel welcome in political science anymore, so I have to find my own place in the sphere of science, even if I don't know where that is yet.

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Scientists prove that scientists really need girlfriends

I'm sure everyone remembers the infamous, age old question asked by such philosophers as Socrates and Machiavelli: "How many licks does it take to get to the center of a Tootsie Roll Pop?" Well, as for the Tootsie roll center question, Purdue did an experiment utilizing a "licking machine" they invented to simulate the human tongue, and they discovered it took an average of 364 licks to get to the center of a tootsie pop. Using human subjects, the study determined it took an average of 252 licks. Not willing to let Purdue be the "decider", Michigan State then conducted its own experiment and determined that their customized licking machine took 411 licks to get to the center. What does all of this evidence tell us? Scientists at Purdue and Michigan State really need to get girlfriends.

So, I'm kind of at a crossroads of what to be more concerned about. That these institutions took a great deal of resources to answer this "age old" question, or that they both invented "licking machines" to do so.

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STOP THE PRESSES! Scientists have just discovered that cost affects perception

Scientist Antonio Rangel and his colleagues at the California Institute of Technology have discovered that if you tell people the cost of a wine is more expensive than another wine, people will believe the taste of the more expensive wine is better. They showed this to be the case even when the two wines were identical (aside from the price). According to Rangel, "We were shocked. I think it was because the flavor was stronger and our subjects were not very experienced."

Okay, ground-breaking study aside, here's why I'm talking about it. It is not ground-breaking, nor is it an indication that they've discovered something new. This is called priming, which has been around psychology and communication for a very long time. In this study, they "primed" the subjects by telling them that one wine was expensive, and thus, it resulted in a response that sees the more expensive wine as a higher quality wine. Not really all that surprising.

This is one of those things about science that really bugs me. It happens all of the time. An example: Every five years or so, some science report comes out that they've discovered planets around another sun, and suddenly, it's the first time this has ever happened. They'll use a different method (or sometimes the same method but from a different telescope or radiation emitter), and suddenly the news reports this as groundbreaking because "it's never happened before."

We see this with drug breakthroughs. A pharmaceutical company comes out with the new drug that is going to cure diabetes, or something like that. Or it will make it so people no longer have to diet, and they'll just melt pounds off their body. But it never comes to light, or it turns out to be a mere shadow of what they claimed it to be.

For the social science problem, I have a few theories as to why this happens. I've seen it by being part of the environment. I think one of the culprits is laziness. People do lousy research when they conduct their literature reviews. It's like observing the research techniques of Ann Coulter who checks Lexis Nexis using a search term and then states "there is no information of this ever happening in the news". The science of "searching" previous studies involves knowing how to search for it. Using a couple of key words or phrases is not enough. Sometimes, you have to do a literature review on the key terms themselves so that you understand what it is you're searching. An example: If I was researching emotional intelligence, it is not enough to look up all instances of emotional intelligence, but I would then need to completely analyze emotional intelligence to understand what other terminology might be used to explain this phenomenon. I'd start with simple things like self-perception and then work myself from that literature to find more obscure processes like personal enrichment or process-driven behavior. Each new study I found would then fuel the next study I found until I completely exhausted the literature that I could find. Then I would start on the next term and continue from there.

That doesn't happen anymore. People do crappy research these days. They'll look up one term and then somewhat hope that no one calls them on their lack of work to completely exhaust the subject. These scientists at CIT really should have covered this subject from a primer effect, to see if there was further value in knocking down this theory, but instead, they did this study and somewhat ignored the obvious studies in lieu of covering what sounded more interesting, like saying they're studying the minds of people rather than the stimuli that effect the minds of people.

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