Curiosity. Dedication. Surprise. Hesitation. Confirmation. Jubilation. And so the life cycle of a research assignment goes. A research assistant spends countless hours (nights?) reading relevant papers, learning how to conduct a particular experiment, practicing how to filter and process data and hoping to discover something revolutionary. Being a researcher is not a “choice”; it´s a lifestyle. Just like debating. Let me take you on a journey to the Psychology Department of Tilburg University, where one study is being born…
Me and my classmate are going to conduct a study on how implicit and explicit motivations (explanation here) of people clash and how that impacts their work productivity. And one of the most important and challenging parts of this research is the analysis of short stories people write when presented with a number of different pictures. More specifically, we will be looking for the motivation for achievement, affiliation and power.
Here comes the tricky thing: In order to be able to “code” these three motives, we need to learn the method to identify them in a text. However, such coding cannot be an exact science: it all depends how you interpret all those fine shades of meaning. “Do they really like each other, or is it just a game?”, “Is he intentionally trying to influence her?”, “Is it her job to check on other people, or is exercising extra power?” and so on.
As you might have noticed in some of the earlier posts, I am a natural overachiever and tend to have very specific (and very high) criteria on what constitutes “success”. Hence, I have great difficulties coding this motive, since most of the actions in the practice stories are simply “not good enough” for my standards.
On the other hand, I dare say I am a caring and reliable friend who has already gone though many tough periods of separation and loneliness, which makes it easier for me to spot even slight hints of warmth and affiliation in the stories. I can relate to the feelings of depicted characters longing for friendship or cherishing it; it almost feels “natural”.
This systematic “bias” led the professor who oversees my and my friend´s preparation to a conclusion that it might actually be more interesting not to study the motivations of “normal” citizens, but rather those of researchers (of coders, in this case) – and where their biases come from.
To be quite honest, I think it is a brilliant idea: If only to show that researches are people with inner inconsistencies like anyone else, prone to errors and occasional failures. After all, to err is human. But realizing your failures before you conduct a study and draw conclusions may help to rule out the “human factor” – a step vital especially in social sciences.