Utilitarian Views of Science

James Williams recently wrote about training science graduates to become science teachers in the New Scientist (free registration required). He has surveyed 74 of his graduates and found that many fail to understand basic scientific concepts despite being good students and well versed in their respective disciplines.

For example, only 11% of his students knew what constituted a scientific fact. Many (76%) thought that scientific facts were the same as the words "truth" and "proven." On the other hand, most (61%) understood the definition of a hypothesis and its provisional nature.

Williams interprets his results as being due to a lack of history or philosophy of science courses. Most of his students were good scientists, he maintained, but were not aware of how science fit into the big picture. Williams also worries that the lack of historical and philosophical awareness may affect how scientists counsel policymakers about issues such as global warming and cloning. If the scientists cannot understand these issues, they will not be able to advise others.

In our first chapter, we explicitly attempt to situate psychology, historically and topically, within the broader framework of science. We also briefly cover the philosophy of science and feature sections on Karl Popper, Thomas Kuhn, and Paul Feyerabend. We strongly agree with Williams, science is more than learning methods and techniques. Scientists must be aware of how their discipline evolved and how their data fit into larger and important contexts. In other words, scientists should adopt something more than an utilitarian view of science.

Experimental Golf

I have been playing golf for over five years. Because I teach research methods, I think of golf as a long-term research project. In other words, I'm always changing things: stance, grip, swing, speed, and who knows what else. From a research methods point of view, those things could all be be independent variables. Me, I'm always searching for a better combination of those variables so as to lower my score.

The score, of course, is the main dependent variable in golf. In stroke play (the most common form of keeping score) the player who takes the fewest strokes wins. In match play, the player who wins the most holes wins.

Over the last five years, I have discovered other golf dependent variables. One is the net number of balls lost or found. If I find more lost balls than I lose, I win. Another dependent variable is the number of pars (birdies if you are good or bogeys if you are not) per round. Other interesting dependent variables are how straight the ball flies or how far it goes.

Lately, my experimentation is progressing nicely. Golf being what it is, I should expect a sudden and rapid rise my stroke count.

Golf is also a good way to approach statistical topics. In class, I like to show the difference between my golf game and Tiger Woods'. Tiger has a MUCH lower standard deviation than I do for both direction and distance.


The good news is I am bringing my SD down for both distance and direction. Last week, I told a colleague about playing in the early morning fog. He asked me if I could tell whether or not the heavy, foggy air was making my shots shorter. I replied that my SD was still too big for me to answer that question. In other words, the distance I hit the ball is still too variable to conduct an experiment using the independent variable of air density.

To answer his question, I'd have to hit the ball the same distance and direction every time. Then, if I did that when the air was dry and the air was foggy, I could answer his question. There is a way to do that; use a machine to hit the ball consistently.

One machine that does that is called the Iron Byron:

It is named after famous golfer Byron Nelson who holds the record for most consecutive wins on the pro tour (11 wins). He also possessed an exceptionally smooth golf swing.

Now to figure out how to acquire an Iron Byron and get some one to pay for my golf research.

Einstein: The Rest of the Story

In chapter 1, we briefly discuss the history of science and use John Horgan's idea of scientific surprises as an explanatory device. One of those scientific surprises is Einstein's theory of general relativity. We describe (pp. 15-16) how Eddington empirically confirmed Einstein's predictions:

In 1905 Albert Einstein (1879–1955) (Figure 1.6), an obscure Swiss patent examiner who was also a PhD candidate in physics, surprised the scientific world by publishing three extraordinary papers on physical phenomena. The first paper on the particle nature of light won him the Nobel Prize in 1921. One of the other two papers explained Brownian motion, the previously unexplained movements commonly observed in microscopic systems. Molecules themselves were causing the movement. The third paper eventually made him a worldwide celebrity; its topic was special relativity. In it, he demonstrated that time was a necessary fourth dimension to the three dimensions of space and that energy and mass were equivalent (E = mc²). When he extended that paper in 1915 to include gravity (general relativity) and when his theoretical predictions were later empirically confirmed, Einstein became a worldwide celebrity.

Figure 1.6 Albert Einstein

Einstein’s surprises were startling. His equations showed that time and space were not invariant, but that they changed depending on the motion of the observer. The equations also indicated that gravity warped space itself, a prediction confirmed by Edington’s observations of stars during a solar eclipse in 1919. At the atomic level, Einstein’s definition of light as quanta (small packets of light energy), led to the development of quantum mechanics, which was yet another scientific surprise. Like Galileo and Darwin before him, Einstein prompted a completely new worldview in which very small or very fast particles followed rules unlike any in the observable world.

Last night, the History Channel, broadcast a television documentary on Einstein which added much detail about the efforts empirical scientists underwent in order to confirm his predictions. That detail is provided by a new book, Einstein's Jury, by Jeffrey Crelinsten. In it, he shows how astronomers attempted to measure the predicted deflection of light caused by the Sun's immense gravity. Attempts were made in Crimea in 1914, but were interrupted by World War I. Another attempt was made by the Lick Observatory in America during the war. After the war ended, Eddington, a pacifist and a Quaker, thought that confirming Einstein's theory would do much to alleviate the deep discords between European scientists.

However, the solar eclipse of 1919 was observed by several groups of astronomers other than Eddington. Also, the Lick data, collected with second-rate equipment (the state-of-the-art telescopes were still in Russia, having been seized in 1914), showed that Einstein's prediction was wrong. News of Eddington's confirming but preliminary analyses made it to London at the same time that the Lick astronomers were about to announce the lack of agreement with the theoretical prediction. When they heard of the discrepancy, William Wallace Campbell, the head of the Lick group, delayed publication.

When Eddington fully analyzed the data, he confirmed Einstein's prediction that gravity did indeed deflect light, thus undermining classical Newtonian mechanics and making Einstein world famous, nearly instantly.

Here is the link to the December 2, 1919 New York Times story on Einstein and general relativity.

Irritating Phrases

A new book, Damp Squid, lists the most irritating phrases in English. Written by Jeremy Butterfield, it documents many interesting facts about English words. Recently, the Telegraph listed Butterfield's collection of the ten most irritating phrases. Here they are in order:

1. At the end of the day (very British)
   
2. Fairly unique (It's either unique or not)
   
3. I personally (As opposed to...)
   
4. At this moment in time (Now)
   
5. With all due respect (I'm about to disrespect you)
   
6. Absolutely
7. It's a nightmare
8. Shouldn't of (Shouldn't have)
   
9. 24/7
10. It's not rocket science (Does anyone remember when it was rocket science?)
    

Avoid phrases like the above in your writing. Think of other phrases that are equally irritating or trite. One of my pet peeves is "chomping at the bit," it should be "champing at the bit."

Finding the mot juste or just the right word is one of writing's pleasures.

Archive Fever

Marianna Torgovnick recently wrote about archives in the Chronicle of Higher Education (Volume 55, Issue 2, Page B1). We cover archival research in chapter 10.

In her article, she reviews books, exhibits, and video materials and their use as archives. Her view of archives goes beyond our coverage of archives as sources of data for research.

She notes that Foucault's, The Archeology of Knowledge and the Discourse on Language and Derrida's, Archive Fever both note how dependent all of us have become on culturally-based archives such as driver's licenses and visas.

Torgovnick notes how much more archivally creative we have become in our electronic era. The ease by which all of us can now create video materials and post them on the Internet via YouTube and similar outlets has led to an explosion of new archival material.

In addition to these new archival outlets, she points out another phenomenon--pseudodocumentaries, a genre as old as Swift's Modest Proposal and as new as Borat.

So, archival research is much more than simply locating an archive and mining it for its data. On top of that, archives are disappearing too. Torgovnick shows that the usual suspects for the loss of archives, "fire, flood, war, and the passage of time" are also accompanied by other sources of loss such as the unrealized greater fragility of microfilm and microfiche compared to paper. The losses are compounded by the necessity for researchers to sift out "real" archives from those, somehow, less real.

Researchers desire archives that harbor no fiction, and such archives exist. However, lack of fiction is not the same as truth. Torgonovnick shows that people are a necessary link to archives and the story they tell. She worries what false truths our archives might reveal were we no longer around to interpret them.

Like other types of research, archival research requires a careful and diligent research to illuminate the data uncovered and to show what has not been uncovered or what still remains to be found.

I See Dependent and Independent Variables

I see dependent and independent variables all day long. Years of study both at the undergraduate and graduate level combined with many more years teaching experimental psychology and research methods have worked their effect on me.

The other day, I was driving to Texarkana and saw cattle egrets along the way. In one field with cattle, there were many more egrets than in other fields. That observation started me wondering why that one field had so many more egrets than the others.

Were I to follow up on my observation, then the number of cattle egrets in each field would become a dependent variable in my research.

A few days later, I was walking around campus and I noticed that some parked cars had one current campus parking permit affixed to their rear windows. However, other cars had two or more parking stickers affixed. Now, an independent variable came to mind: the number of parking stickers on each window. In other words, I could now use those two groups to divide drivers into two naturally-occurring groups.

Very quickly, hypotheses began to form in my mind. Were the drivers with more stickers students who were illustrating their tenure on campus? Or, were they simply unable to easily remove the old stickers?

After being exposed to research methods classes and after designing and conducting research projects, the world takes on a new look. Dependent and independent variables pop out everywhere.

Confused about the difference between dependent and independent variables? I like to find the dependent variables in a study first. The dependent variables are the measurements in research. Also, all of the participants will be measured.

On the other hand, independent variables divide the research into groups. The groups can occur naturally or be operationally defined by the researcher. The levels of the independent variable will not be the same for all participants. In the example above, some drivers have one parking sticker while others have two or more. See?

Care and Feeding of Editors

A recent column by Lynn Worsham in the Chronicle of Higher Education (September 8, 2008) discusses what academic journal editors look for in submissions to their journals.

She identifies several oft-made mistakes by authors:

• submitting the wrong type of article to a journal
• failing to proofread the submission for grammatical and careless errors
  
• citing sources incorrectly
• being unaware of previous similar research in the same journal and not citing it
• not following specific submission requirements (e.g., # of copies or no return envelope)
• submitting a conference presentation without rewriting it as a paper
• arguing with editor over rejected submissions

Other suggestions included knowing the appropriate style manual followed by a particular journal, following the journal's rules exactly, and realizing that rejection was a fact of academic life.

In our chapter 12, we cover some of the steps necessary to take a research project to completion. Finishing a research project can mean more than one thing. It could mean presenting your results orally in class or in public. Ultimately, it could mean publishing your research in a peer reviewed journal. If that's your goal, pay attention to Worsham's suggestions.