Working in a psychological research laboratory requires a great deal of attention to the here and now.  Collaboratively, a lab absorbs information from the literature, shares new ideas, considers the past work of the lab and how it might progress, and plans for the future.  However, this orientation is heavily biased towards the present and to the future but pays almost no attention to the past.  Of course labs review the research literature, but I’m talking about another kind of past:  last year’s data.

In my time at SFSU, I’ve had the opportunity to supervise a number of Masters of Arts candidates as they have 1) reviewed the literature and defended a set of directional hypotheses with a group of faculty, 2) collected original data after obtaining IRB approval, 3) analyzed their data and tested their hypotheses, and 4) defended their data collection, analysis and interpretation.  For the 75% who eventually complete step 4, about 40% never engage in basic research again and the other 60% go off to a PhD program where they collect data in the tradition of their new research lab.  As a result, most MA students abandon their data for greener pastures.  That means orphaned data are everywhere!    Here are just a few examples from my own lab:

1) A 1000+ study of the links between college student attachment to parents and peers and academic adjustment

2) A three-wave longitudinal study of stress and coping among first-year freshman

3) A study of the factors that influence school choice in the San Francisco Unified School District enrollment process

4) A 700+ study of college student relationships with parents and alcohol use

5) A study of the effects of media on gender role attitudes

6) A study of parents’ experiences of receiving a diagnosis for their child’s learning disabilities

And that doesn’t count all of the data I’ve collected, published from, and then left for the newest project on the block.

Although there’s something very attractive about the next study being conducted in the lab, you should ask yourself whether there’s existing data that you can use to ask your questions.  Such secondary data analysis saves a lot of time in finding measures, navigating an original data collection though IRB, collecting that data, and cleaning it for analysis.  Sure, you still have to do the analysis and write it up but secondary data analysis can provide an exceptional jump-start to a young career.  My first paper was a secondary analysis of the National Longitudinal Study of Adolescent Health.  I don’t consider this a great piece but it helped me secure my postdoctoral fellowship at Arizona State University’s Prevention Research Center which launched me to this job.

So the next time you see a Litter Barrel, you don’t have to say thanks but you might ask which data are sitting in the litter barrel waiting to help you start your career.