Summer in Houston started wet and is ending wet, with hot in between—sorta typical, though it seems wetter and hotter than usual. But the long hot summer hasn’t stopped the work that our SIOP colleagues have been doing, and a lot has been going on this summer.

Analytics. Algorithms. Data Viz. Metrics. It seems like data are all anyone can talk about these days. And for good reason! This is the era of measurement, of prediction, of analysis. This issue of TIPhas a few articles that directly address the theme of how we use and communicate data to others. Check out Crash Course for an introduction to Tableau, a popular data visualization tool. Feature articles from List and McDaniel, Cucina and Berger, and Mandelke et al. discuss various aspects of how we make decisions about data and what the consequences of those decisions are. There are useful bits of advice in here and also important questions to ask ourselves as scientists and practitioners.

A new semester has started at the University of Arizona, which means I spent the past several weeks revising my syllabus. In particular, I was carefully writing my statement about why computers are not allowed during class time, which always creates quite a stir. Because I’m asked about this when I tell fellow academics that I do this, here’s exactly what I say:

Abbreviated versions of the notes for each chapter are available on D2L. You are to print these notes and bring them to class to keep up with note-taking. Importantly, because these notes are made available to you, the use of computers is not allowed during class unless there is an exceptional circumstance that is approved by Dr. Gabriel. This is to create a positive classroom atmosphere of engagement, which cannot be achieved if half the class is sitting behind a computer screen. Students who are caught using their computers will not only be asked to close their computers down, but also asked to leave class for the day.

Exploring the Gap  Between I-O Trends  and the State of Research

The purpose of the “Bridge” column is to provide an additional conduit, building upon SIOP’s current efforts, for connecting science and practice. The column strives to accomplish this by publishing various types of article content on the subject of science and practice integration; for example, case studies of effective practice; discussions between scientists and practitioners on a relevant topic, reviews of the key scientific and practical implications of a topic area; summaries of latest research findings and their implications for practice; summaries of key practice issues and their implications for needed research; and/or, calls for research to help practitioners overcome challenges associated with effective practice (please see Poteet, Zugec, & Wallace, 2016, for more background information on the column).

This issue, I’ll be digging into the daunting world of big data visualization, sometimes called “data viz.”  This represents one of the four major application areas of big data techniques to I-O psychology and HR, alongside data gathering, data storage, and data analytics (Landers, Fink & Collmus, in press). Importantly, I’m distinguishing data visualization in the big data sense (data viz) from data visualization in the traditional SPSS-ish sense.  “Visualizing data” is something we’ve been doing for a very long time with histograms, scatterplots, pie charts and so on.  Data viz, in contrast, is a specific type of data visualization, one that focuses on interactive exploration of highly complex datasets.  When you create a scatterplot, you’re trying to illustrate to someone the relationship between two variables. When you create a data viz, you’re trying to empower the viewers of that data viz to explore whatever particular relationships they’re personally interested in without much, if any, expertise in statistics required.  In either case, the creator of data visualization must have expertise in both the subject matter being visualized and also in the art of visualization itself; historically, the training of scientists has focused more on the former, which may explain why scientists have not generally been very good at creating visualizations (Gelman, Pasarica & Dodhia, 2002).