What Would I Be if I Wasn’t an I-O Psychologist: Mapping Jobs to Explore the Possibilities
Thomas A. Stetz
National Geospatial-Intelligence Agency
Gary N. Burns
Wright State University
It is not that we don’t think that being an I-O psychologist isn’t great. It pays well. We have reasonable hours. We don’t have to work outside in the heat and cold. But every once in a while, for the briefest of instances, the thought that maybe, just maybe, there is something better out there pops into our heads. Something that pays better. Something in a high-growth occupation. Something with a great number of jobs in many locations. Something that Fortune columnist and best selling author Stanley Bing doesn’t consider one of the “100 Bullsh**t Jobs” (Bing, 2006). Not that we put any credence in what Bing writes. We don’t “turn perfectly serviceable workers into drooling zombies.” Quite the opposite in fact. We became I-O psychologists to make a difference and perhaps prevent some work situations that we personally have experienced. We sincerely try to do what is right for workers and companies together. However, we are actively fighting becoming Bing’s stereotypical I-O psychologist, which is “a skinny, tweedy old fart with hair everywhere but on your head.” We fear that this might be a losing battle and more accurate than not. Thus, if for no other reason than to try to stop becoming a tweedy old fart, we decided to explore other job possibilities.
Being I-O psychologists we felt we didn’t need to bother other (but probably more qualified) professionals, such as career counselors or counseling psychologists. We didn’t want to have them waste their valuable time on us. Instead we set off on our own mission: to explore strange new professions, to seek out a new life, to boldly go where no I-O psychologist has gone before. Of course we weren’t floating off into space without direction. We were I-O psychologists after all. Therefore, we immediately headed to every job analysts’ favorite Web site, O*Net OnLine, and did an occupation quick search for industrial-organizational psychologist. Instantly we had a summary report for 19-3032.00 industrial-organizational psychologists. Now we were getting somewhere. Now we knew what tasks, knowledge, skills, abilities, work activities, work context, job zone, interests, work styles, work values, and wages and employment were associated with being an I-O psychologist. The summary report also included related occupations! Wow. Now we had real information. What a wonderful tool. O*Net told us that we were most related to the following occupations:
11-3040.00 Human resources managers
11-9032.00 Education administrators, elementary and secondary school
13-1072.00 Compensation, benefits, and job analysis specialists
13-1073.00 Training and development specialists
25-9031.00 Instructional coordinators
43-1011.00 First-line supervisors/managers of office and administrative
support workers
Presumably “related occupations” means that there is a high degree of similarity between the job requirements of I-O psychologists and the listed occupations. Thus, if we wanted to, we should be able to easily transfer our knowledge, skills, abilities, and so forth to these new jobs. Of course, with us being I-O psychologists, we way over complicate issues. Related might not be the best word. Smoke is related to fire but not similar to fire. An air traffic controller is related to a pilot. However, I doubt anyone thinks we should select and train the two in the same manner because they are not very similar in terms of knowledge, skills, abilities, and so on. However, putting aside that minor issue, we pressed on.
Now we knew of six occupations that we might be interested in and, more importantly, might be able to do because of our I-O background. Six, however, isn’t very many. Therefore, we selected other occupations that we were interested in and that seemed to make sense given our background and interests. For example, we included 23-1011.00 lawyers because our interests fall on the personnel side of I-O. We included 29-1066.00 psychiatrists just in case we ever wanted to become real doctors. We added 27-3043.05 poets, lyricists, and creative writers. If you have to ask about that one then you haven’t read JAP in awhile. In all, we supplemented our six occupations identified by O*Net with another 12. All of the occupations are shown in Table 1.
Now we were getting somewhere. Where, we didn’t quite know, but we were moving in the right direction, and we weren’t wasting the time of real professionals. After performing this step, we had a list of 18 occupations that we might be interested in. We could also easily access a ton of information about each occupation from O*Net. Unfortunately, to look at it we had to search each occupation individually. Therefore, we downloaded all O*Net information into Excel and put it into really cool tables. This, however, was information overload even for we who love data. To understand the information would require that we sort through all of the cell entries, compare various columns and rows, and so on, which was just too much effort for us. We figured if it required effort, we might as well have some fun. Being I-O psychologists, we looked for a better (more fun) way.
Using the downloaded occupational data, we took the importance ratings of the 35 O*Net skills and computed the Euclidean similarity between all pairs of occupations.1 Thus, the result was a 19 x 19 (18 occupations plus I-O psychologist) occupation similarity matrix that we could use to explore the relationships among the occupations, thus determining which occupations would be the best alternatives for us to consider. We imported the similarities into a network analysis and graphing program—Pajek2—so we could explore the relationships visually. A 19 x 19 fully connected graph will show 171 lines connecting the graph, which is quite dense with clutter hiding the important links and the underlying structural relationships of the occupations. Therefore, we had to systematically reduce the number of lines (or links) in the graph. There are many techniques to do this; however, we used a simple threshold approach to help make the graph more understandable. We calculated the average similarity then removed all links that were above this average (Chen & Morris, 2003).
1 See Cronbach and Gleser (1953) for a review of computing similarity between profiles.
2 For more information on Pajek or to download the program, go to http://pajek.imfm.si/doku.php.
Next a graphing algorithm was used to determine the placement of the occupations on the graph. We used a spring or force directed algorithm (Kamada & Kawai, 1989). A spring algorithm acts to minimize the variation in line length by pulling and pushing the vertices until they are in a state of equilibrium, just like springs would do. Imagine that the lines between jobs are springs with attraction and repulsion forces that are based on the weight (or strength) of the connection that ties the jobs together. Because I-O psychologist was our focal occupation, and we believe all work should revolve around I-O psychology, we fixed that node in the center of the graph and made the node diamond shaped.
Now we had a graph showing the similarity of occupations. This was a great step forward. However, it still wasn’t enough information for us. We also needed to know important things like salary and job growth. Stetz, Button, and Porr (2008) showed how these types of graphs lend themselves nicely to incorporating other pieces of useful information. We didn’t want to change jobs to make less money or be downsized. Because money was very important to us, we made the size of the node that corresponds to each occupation proportional to the occupation’s median annual wage (obtained from O*NET). In addition, we were interested in job growth for each occupation. Therefore, we colored each node based on projected job growth making the nodes increasingly darker as expected job growth increased. Thus, darker nodes represented greater job growth. We where also interested in the total number of jobs expected as a result of job growth. For example, I-O psychologist has “much faster than average job growth,” but it is an extremely small occupation with only a total of 1,000 new jobs expected. In contrast, lawyers are expected to have an average job growth, which is a total of 228,000 new jobs. To take this into account, we annotated the occupation title with an exclamation point if there were greater than 200,000 new jobs expected. Finally, we weren’t interested in taking a lot of time retraining. Thus, if there was a high entry barrier, such as a license requirement, we drew a line across the occupation’s node.
Figure 1 presents the final result of our efforts. We call this a “jobs map” because, like a map, it shows useful information about how to traverse the job terrain to easily get from one point to another point. However, rather than a graphic representation of the physical features of the Earth by means of signs, symbols, and a specified projection, it represents the income, growth features, and similarity between occupations using signs, symbols, and a specified projection technique.
Figure 1. Map of jobs similar to I-O psychologist.
Examining the graph it was immediately clear that we are not too bad off. First, the I-O node is pretty darn big, meaning that we get a pretty good salary in comparison to most of the selected occupations. Second, the I-O node is dark. This means the occupation is growing much faster than average. Maybe we like being an I-O psychologist more than we realized! However, we still weren’t convinced that there were not better opportunities. We, therefore, continued our examination of the graph. Right away we were able to eliminate 13-1071.01 employment interviewers and 13-1071.02 personnel recruiters because, despite good job growth, the sizes of their nodes were pretty small relative to I-O psychologist.
Occupation 29-1066.00 psychiatrist looked promising. First its node is the biggest on the graph, indicating that it is the highest paid occupation presented. Second, it is quite dark, indicating faster than average job growth. Third, it is annotated with an exclamation point, indicating that the projected need is over 200,000 additional jobs. Fourth, it was more similar to 19-3032.00 industrial-organizational psychologist than other jobs, as seen by its proximity to our diamond and the direct connection. Had we found our true calling? Unfortunately, as illustrated by the slash through the node, psychiatry has a hard degree requirement, meaning that to work in the field an individual must hold a license as a medical doctor. The prospect of going to medical school was not that appealing to us so psychiatry was now out as an alternative career for us and we will never know what is it like to be a real doctor. Strike one.
Next we saw occupation 23-1011.00 lawyers. Again this looked promising—good money and job growth with a lot of additional employees projected. Unfortunately, again there was a line across the node indicating a high barrier to entry, a license requirement. Strike two.
What else looked promising? Marketing managers 11-2021.00 looked good—high pay, good job growth, and no high entry barriers. It didn’t have a huge number of job openings projected, but it had to be more than the 1,000 for I-O psychologists. In fact, when we looked at our tabular data, it showed 61,000 projected job openings. There wasn’t a direct line from I-O psychologist to marketing manager, indicating that there were a lot of other jobs that were more similar, but it was management. How hard can that be? After all, I-O psychologists study management and actually teach in management departments. Sure, we have heard of that old saying about those who can’t teach. At first we thought that the saying only applied to others. However, on second thought, we realized that there was not a direct line with our node for a reason. We really weren’t too interested in relying on our lowly developed O*Net skills like “management of financial resources,” “management of material resources,” and “management of personnel resources.” Strike three. At last, we finally decided that we were pretty happy being I-O psychologists.
Even though we decided against a job change, our map of jobs shows a new way to present information about jobs. Research has shown that tables require slow serial processing of cell entries (Cleveland & McGill, 1985). In contrast, graphs display a great deal of information to the users giving them an immediate impression of the overall trends in the data (Kosslyn, 1994). This is highly useful for job changers who want an overall view of the data. In addition, with a little bit of programming, interactive components could be added to give job changers control over what information to display, such as which jobs to include and what pieces of information to display (i.e., salary, training required, etc.). In addition, the graph could allow the user to click on two jobs, and the shortest path between them would be highlighted.
Although this article was written in a playful manner, we think that there are powerful implications for this type of analysis and presentation of data. Stetz, Button, and Porr (2009), Stetz, Button, and Scott (2009), and Stetz and Burns (2009) have argued and shown that the visual presentation of data is effective in the presentation of job analysis data and the identification of job clusters. There is a cliché about a picture being worth a thousand words. It may be a cliché, but it is true. People are particularly well suited to comprehend images, forms, and patterns. A visual representation allows the user a penetrating look at the structure of data without the corresponding mathematical complexity or difficulty understanding large amounts of tabular data. It allows the user to easily sort through and understand large amounts of information quickly. Although this article focused on mapping relationships among jobs, we believe that this approach has broader application than career exploration. Any I-O psychologist who is trying to communicate with management or other decision makers should consider the greater use of graphical displays of information and study findings.
References
Bing, S. (2006). 100 bullshit jobs…and how to get them. New York: Collins Business.
Chen, C. & Morris, S. (2003, October). Visualizing evolving networks: Minimum spanning trees versus Pathfinder networks. IEEE Symposium on Information Visualization, Seattle, WA.
Cleveland, W. S., & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, 229, 828–833.
Cronbach, L. J., & Gleser, G. C. (1953). Assessing similarity between profiles. Psychological Bulletin, 50, 456–473.
Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31, 7–15.
Kosslyn, S. M. (1994). Elements of graph design. New York: Freeman.
Stetz, T. A. & Burns, G. N. (2009, April). Visual job classification: A neural network illustration. Poster session presented at the 24th Annual Conference of the Society for Industrial and Organizational Psychology, New Orleans, LA.
Stetz, T. A., Button, S. B., & Porr, W. B. (2009). New tricks for an old dog: Visualizing job analysis results. Public Personnel Management, 38, 91–100.
Stetz, T. A., Button, S. B., & Scott, D. W. (2009, April). Creating occupational groups using visual job analysis. Poster session presented at the 24th Annual Conference of the Society for Industrial and Organizational Psychology, New Orleans, LA.