Fagpressenytt

Every year around this time, I set aside a few hours in my work schedule to prepare a seminar on data analysis for students writing a master's thesis in the Language Acquisition and Language Processing Lab. Almost all theses involve data collection in the lab, in kindergartens or in schools. Very often these data come from simple surveys, questionnaires or standardized tests. But occasionally students design and conduct their own experiments using more advanced measures, such as eye movements or brain potentials. For all intents and purposes, these are 'miniature' research projects, with their own peculiar challenges and rewards. The training we offer to our students is tailored to the requirements of their respective theses. Like other forms of individualized tutoring, it falls under the rubric of 'student supervision'.

Still, my seminars do involve quite a bit of teaching in classical forms. One has to cover the basics of probability and statistics to explain students how to arrange the data, how to choose and apply appropriate tests, and how to interpret their results. Interaction with the students in these 'foundational' seminars has taught me that they need more training in this area, and that they are well prepared to receive it. They understand how data are relevant to their future professional and personal lives. They want to learn to think correctly and critically about other people's data. Possibly, they want to be able to generate and use their own data sets, too. Surveys I have conducted yearly as a lecturer in 3000-level courses, as well as the growing number of attendees at my data analysis seminar each spring, have convinced me that the students' demand for training in quantitative methods is real, and does not just