Grad School Series: Applying to Graduate School in Industrial/Organizational Psychology
Starting Sophomore Year: Should I get a Ph.D. or Master’s? | How to Get Research Experience
Starting Junior Year: Preparing for the GRE | Getting Recommendations
Starting Senior Year: Where to Apply | Traditional vs. Online Degrees | Personal Statements
Alternative Path: Managing a Career Change to I/O
Interviews/Visits: Preparing for Interviews | Going to Interviews
In Graduate School: What to Expect First Year
Rankings/Listings: PhD Program Rankings | Online Programs Listing
A career in I/O psychology requires a Master’s degree or Ph.D., and most of the resources I’ve presented in my graduate school series are intended for those on the “traditional path” to graduate school. Most PhD students these days, whether in I/O or otherwise, finish their bachelor’s degree and head straight to Master’s or Ph.D. training.
This is certainly the easiest way. As with any career, the earlier you know what you want to do with your life, the easier it is to set yourself down the path to get there. But that doesn’t mean a career change to I/O psychology is impossible. It just mean it will take a little more work.
As I’ve noted elsewhere, the first decision when considering a career in I/O is whether you will enter a Master’s or Ph.D. program. Normally, you would first consider the sort of job you might want: I/O’s with Master’s degrees tend to be the “technicians” of our field, applying I/O knowledge out in the world, whereas I/Os with Ph.D.’s tend to be the “researchers” of our field, conducting research studies within organizations to apply I/O knowledge out in the world but also to build that knowledge. Practically speaking, however, a career change into a Master’s program is substantially easier than a career change into a Ph.D. program.
The reason for this is the type of experience needed to apply for each degree. Research experience is useful for a Master’s application, but it is only necessary for a Ph.D. application. If you’re already out in industry and don’t see research experience as a realistic option for yourself, you might want to start by targeting a Master’s degree. In that path, experience as an HR professional, and preferably in strategic HR, will be most helpful to your application in lieu of research experience.
If you do want to strive for the Ph.D., you need research experience. The easiest way to get it will be to find a local college or university with psychology researchers that you can easily drive to. Use Google to search for specific people doing work you find vaguely interesting at that university, and then email and call those faculty members. Explain that you are interested in getting research experience and are willing to donate 10 to 20 hours per week of your time, preferably at home, but that you are willing to come in for meetings. This will give you the best chances of being taken on as a volunteer research assistant. Remember that you don’t need I/O experience specifically, although this is certainly better if you can get it. Any experience as a research assistant in psychology will help your application.
One of the biggest challenges you will face is taking the GRE. If you haven’t been in school for a while, studying for the GRE will probably bring back a lot of bad memories. But it’s worth it; I/O psychologists helped design and validate the GRE, so we take it seriously. You should too. If you haven’t studied in a while – potentially years – give yourself plenty of time to prep. I recommend starting at least a year in advance.
Finally, your personal statement is critical. This is where you explain why you want to change careers. Remember that taking you on as a graduate student is a risk for your new advisor too. That person is worried that 1) you don’t really know what kind of commitment you’re getting into and 2) you heard that I/O was a high paying, fast growing field, and that’s the only reason you applied. Remember that a Master’s program will be at least a 40 hour per week commitment, and a PhD program will be 60 to 80. You need to explain, convincingly, that you understand that and undertake the challenge knowingly and willingly. Remember that even online programs require this sort of time commitment.
Once you have all of your preparation and materials in order, the next big challenge in a career change is figuring out where to apply. My most important advice: don’t be lured into applying to a program because 1) the application requirements are easy, 2) the timeline matches yours, 3) they accept credits for “life experience,” 4) it’s cheap, or 5) it’s an online for-profit. All of these are signals that the program is not going to lead you to a new job. They are generally intended for people who already have a job lined up, but someone said, “for promotion, you need a higher degree.” If you don’t already have a job lined up, don’t go into those sorts of programs.
This continues my thoughts on SIOP 2015 about Big Data. In that article, I described how I/O psychologists can meaningfully position themselves as the “meaning-makers” of Big Data. But what I worry about is that I/O psychologists will not take the opportunity to become proactive in regards to innovative technology.
To date, we have almost universally been reactive, and as has been common in this situation, a new innovation is signified by a flurry of conference activity followed by near-silence in the academic literature.
To illustrate, I remember when the idea of “maybe social media is important” hit the SIOP conference maybe 3 or 4 years ago. The first year, there were a couple of presentations on social media. The next year, there were at least five or six, several standing-room only. This year, there were two again. I imagine next year it will decrease to one or none. In all these years, the I/O research literature has added maybe two papers on the topic, although a fair amount of research on workplace social media has appeared in non-I/O journals. And – surprise – social media is still a concern for those in the field.
I think what this represents is interest in the practitioner community – specifically, someone has asked what their informed, I/O opinion is on this new technology – yet the research literature remain mostly silent. In the absence of empirical research, the practitioner community develops its own internal understanding of the technology and then stops talking about it at SIOP. Thus the issue remains a concern in the field while few contribute empirical research. Decisions are made based upon rules of thumb and gut reactions.
This situation is not tenable if I/O hopes its research to be applied to the modern workplace. We risk the same pattern followed by newly minted MBAs and teachers – a brain full of knowledge from school that is discarded within the first year of “real” work. If we want I/Os to continue applying research on the organizational front lines, we need to craft more relevant research.
I don’t mean to imply by this that the responsibility lies solely with the academics. We seem to have forgotten the scientist-practitioner model, which does not imply that there are both scientist and practitioners, but rather that all I/O psychologists should be both scientists and practitioners. The harsh realities of billable hours make this balance difficult for people in the field, but more must be done. We must increase academic-practitioner partnerships for the good of our field, to produce research at the forefront of new organizational phenomena.
And perhaps it goes without saying, but if you have or are considering such a technology, give me a call!!
Somehow, every year, SIOP rises a little bit in intensity for me. There are a few more sessions I need to attend, a few more people I need to connect with, and a few more drinks I need to space out better throughout each evening. This particular year, things reached a fever pitch when I got a bit of long-term news mid-conference: I have been granted tenure at ODU! I suppose that means this blog wasn’t such a horrible idea after all.
One of the reasons for this ramp-up in conference intensity for me is the increasing focus at the SIOP conference on technology. It is a change I honestly was skeptical would ever happen; in fact, in our primary journals, it really still hasn’t. But at our annual conference, at least, there is a growing recognition and appreciation for technology research related to workplace behavior. This year, the focus was clearly Big Data, with about a dozen presentations on Big Data specifically and another dozen or so on related approaches, such as workforce analytics. One thing that became apparent quite quickly was that Big Data presentations all had essentially the same content, which I can summarize for you here: Big Data is complicated, Big Data provides answers to questions we didn’t know we had, Big Data can’t answer causal questions, and Big Data is so complicated that I/Os don’t really understand how it works. Depending upon whom you ask, this last part is either a huge problem or a huge opportunity.
The problem is obvious, but the opportunity is complicated. On one hand, there is a perception there is an opportunity for I/O to “take over” Big Data. There were many presentations about how Big Data is just an extension of what we already do, how we’ve already been doing Big Data for years, how Big Data is nothing new, etc. These are all based upon false assumptions, primarily driven by a misunderstanding of what Big Data actually involves. On the other hand is the viewpoint I endorse,which is that Big Data is an opportunity either 1) for I/O to enter a new era of interdisciplinarity, cooperating with computer/data scientists in a way we never have before or 2) for I/O to begin training graduate students in computer science so that can eventually be in a place to contribute meaningfully. Or maybe both.
There is some movement on both fronts. In terms of I/O training, one of the questions asked at one of the panels I was on was if computer programming should be taught to new I/O PhD students. I think it should, and I’ve been teaching it for a few years now. But one doctoral I/O program is not enough to make a dent in Big Data, and definitely not any time soon.
In terms of interdisciplinarity, businesses are beginning to realize that Big Data is not the solution to every problem, and this is a very promising shift for our field. As evidenced in part by this article in the New York Times, Big Data must be paired with might be called “small data” in order to fully understand any particular organizational phenomenon. The definition of “small data” could very well be “what I/O psychologists do.” It involves carefully validated surveys, a strong command of the research literature, and the interpretation of data based in expertise surrounding both. If we take that approach – that we are the meaning-makers for the Big Data folks – I think I/O will find a very comfortable position within the Big Data movement, one which neither sacrifices how we define ourselves nor pretends that change isn’t in the water.