It’s all about understanding their process.

A little over five years ago, I took a university administrator (staff) position after 15 years working at public and private companies. This change was somewhat of a homecoming as I had left a faculty position before moving into my industry work, but that is another story.

My goal here is to help guide folks on how to be successful in graduate school. 

First, I recognize there are significant differences between STEM fields and non-STEM fields—I come from the former. STEM fields include engineering, life sciences, physical sciences, and mathematics; non-STEM areas include social sciences and the humanities. A Council of Graduate Schools presentation from 2007 showed Ph.D. completion rates for STEM fields between 48% and 57% after seven years, with graduate students completing non-STEM degrees at 29% for Humanities and 41% for Social Sciences. At the 10-year mark, approximately 64% of engineering and life sciences graduate students completed the Ph.D.; social sciences and physical sciences topped out at 55%; the humanities failed to break 50%—showing only a 49% Ph.D. completion rate after ten years. (https://nanopdf.com/download/phd-completion-project-nsf-agep_pdf#)

A summary of the STEM master’s completion rates after four years was 66%. (https://cgsnet.org/masters-completion-project)

This article isn’t a research paper, so I didn’t dig further, but I would wager these numbers haven’t improved. 

How do you make sure you’re in the group that makes it?

Do the math.

If you’re applying for a Ph.D. program, you should expect a stipend and a tuition waiver, and you should look at this as a full-time job. For first-year graduate students in STEM fields, the department will likely tie compensation to being a teaching assistant (TA). In other words, you’re working for the money. Ideally, your faculty sponsor will be able to fund your work after the first year with a continued tuition waiver. Even if you’re getting paid and receive a tuition waiver, there is still the lost opportunity cost of what you could be earning by starting your career. That lost opportunity cost only gets more significant as the time to degree grows. Few people get rich after earning a Ph.D., and fewer doctoral graduates are finding tenure-line positions that offer lifetime job security. So do the math and figure out if the financial commitments and personal rewards are balanced.

If you’re applying to a master’s or professional program (e.g., health, MBA, professional science master’s), you will most likely be paying tuition—just like an undergraduate program. Unfortunately, unlike undergraduate studies, there are fewer opportunities for financial aid. One bright spot is that some employers have tuition reimbursement programs as part of their often underused benefits. I recognize that this assumes you are working in a closely related field, but this is an option to consider if the stars align in your favor.

Does it ever make sense to pay for a master’s or professional degree? If the total debt is manageable and the salary increases authentic, there should be a reasonable return on investment, but you have to do the math.

Before you apply, find out if you need a faculty sponsor (Ph.D. programs).

I’ll admit, this is not a requirement I’m familiar with as it’s not common in my field; however, I understand it is becoming more prevalent for smaller STEM programs and non-STEM areas. It’s frustrating to have a stellar GPA, excellent GRE scores, and solid letters of recommendation only to find out you weren’t accepted into a program because you didn’t nail down a sponsor during the application process. If the program you’re interested in doesn’t clearly state this requirement on their website, send an email to the program advisor and ask the question. 

Before you commit to a program, find out what their graduates are doing.

If you’re starting down the Ph.D. path, you probably have visions of being a professor. Unfortunately, the number of tenure-line faculty positions has collapsed as colleges and universities have decided to follow the cost-saving practice of hiring non-tenure line (aka, adjunct) faculty. Perhaps you’re doing better work than graduates from the nation’s top research institutions, but don’t ignore the inherent bias towards prestigious universities built into the system. Understanding what graduates do after completing their degree is a reasonable way to gauge your possible paths after graduation. Tracking graduate outcomes is hard, and most departments are just learning how to do this, so you might want to do this on your own. Successful faculty mentors will often advertise where their alumni go; however, some diligent work using LinkedIn might provide enough insights to guide your decision. 

Choose your advisor carefully.

If you’re looking at a traditional, research-based graduate program, you will spend most of that time working with a single faculty advisor. Yes, you may be required to rotate through several groups during your first year or “interview” multiple faculty as potential advisors, but this can be a ceremonial dance for many. Regardless, at the end of the courtship, it will come down to choosing one person to be your boss for the next four-plus years. If everything goes great, they will be a mentor and eventually a colleague, but at the beginning, they’re your boss. If you can’t identify who you’ll work for before starting a program, you should determine several faculty with whom you’d be willing to work—make sure you have options.

Focus on the process.

Each program should document the expected milestones for its program. Are placement exams required? How many courses do you need to complete, and by when? How are qualifying exams managed? When will you choose an advisor (if you weren’t required to do so before starting the program)? When will you form an advisory committee and present your dissertation proposal? What resources are available to students. And finally, when do you get to defend your work and graduate?

Be wary if a program hasn’t documented its process.

When you start your research, begin with the end in mind. 

(With apologies to the late Steven Covey)

When I started graduate school and joined a research group, several members were finishing—each was kind enough to give me a “comb” bound copy of their thesis. I didn’t realize this at the time, but this gave me a clear vision of what I needed to do to graduate. 

Go to the library (or open up a web browser) and download several recent dissertations from previous students in your group or department. Don’t look at how long it took the authors to complete the work—ask yourself, “can I complete a similar project in under five years?” Better yet, ask the question, “what do I need to complete a similar project in under five years?”

Don’t rely on luck.

I lucked into a good graduate experience; unfortunately, I’ve known people who struggled. Many don’t have a mentor with whom they can work as they contemplate graduate school, so I hope the thoughts above are helpful.

In some circles, “Statistics” has a bad reputation—primarily because most of us had limited training though the techniques are applied in numerous fields. The pharmaceutical industry uses statistical tests to determine if new drugs are effective (or harmful), manufacturing industries implement statistical process control to maintain ever higher quality standards, politicians have increased the use of polling to drive policy decisions, and the list could go on.

But in the current discussions on STEM education, I have yet to see an argument where statistics is elevated in the curriculum the way “computer programming” has been promoted.

Is it the way we teach math in general—heavy on theory and light on applications? For many, the applications are what makes the work interesting.

The discussion on college debt seems to focus on extreme cases, and Six Figures in Debt for a Master’s Degree from Inside HigherEd pointed out several of these. While they did break down the averages by degree. It’s helpful to look at the type of institutions students attend and the programs of study. I’m personally interested in this analysis and started to explore the dataset and thought I’d share some of the first impressions.

Looking at the data from 2016–2017, the mean debt is following a log-normal distribution, so some care will need to be taken in making comparisons between the groups. Including proprietary schools may not be useful given the low number of programs, but the spread is similar to private institutions.

For all master’s degrees with reported data, the mean debt values are $52k, $44k, and $39k for private, proprietary, and public institutions, respectively. Looking at various programs, the number with student debt over $100,000 is low.

I am particularly interested in STEM fields associated with the Professional Science Master’s movement; however, a quick survey shows that many schools’ data is suppressed to maintain privacy. As a first pass, CIP descriptions aligned with keywords from the PSM programs of study were used to narrow the scope of the analysis, including:

• Agricultural Science, Food Science, Nutrition
• Biotechnology
• Computational Science, Analytics, Big Data, Statistics
• Environmental Science, Ocean Science, Sustainability, GIS
• Physical Sciences, Chemical Sciences

Business Administration, Management and Operations shows a wide range and is included for comparison. Dietetics and Clinical Nutrition Services also shows a wide range of mean debt, but the remaining programs have more modest ranges.

These are preliminary numbers but would indicate most students are managing their graduate education debt responsibly.

Whiteboard space is at a premium in my office, but I’m always willing to erase a section to brainstorm a potential solution to new problems—afterall if we can visually create a “product” that provides insight, we can work backward and determine what data we need to produce that work.

I read Steven Covey’s book The 7-Habits of Highly Effective People well over a decade ago; however, “Begin with the End in Mind” has stuck with me as I’ve worked on various projects since that first reading. Currently, I’m working on several data-driven activities that remind me why this habit is so important.

Data Analysis (and Data Science) are hot topics, but too often folks want to jump right in and start crunching numbers—”Let’s run an X test on the Y samples and calculate the Z results.” It is easy to create large datasets that miss the mark and become wasted effort. While it may be hard, we need to get everyone to step back and ask the simple questions: “What problem are we trying to solve?” “What will this look like when we are ‘done’?”

The 5 Whys is a simple technique for looking at problems—if you participate in community discussion groups, the issue of property taxes will undoubtedly come up. Here is my take on the problem applying a 5 Whys analysis:

Why are property taxes increasing?
Because home values are going up.

Why are home values going up?
Because more people are willing to pay more money to buy a home in our community.

Why are people willing to pay more money to by a home in our community?
Because the demand for homes in our community exceeds the supply.

Why is the demand for homes exceeding the supply?
Because we won’t allow new homes to be built.

Why won’t we allow new homes to be built?
Because people won’t support policies that allow for new development.

I’ve seen this play out in California, and the unintended consequences for the community will be devastating. Maybe not next week or next year, but eventually, the market price will force people rooted to the community out, and it’s not the “transplant” from the Bay Area or Los Angeles or Portland or Seattle or “name a major metropolitan area here” who will be at fault, it will be us. Our children will pay the price.

One of the main reasons I returned to Utah was simple economics. From a family point-of-view, I could not see my children being able to enjoy the same, high quality of life I had experienced if they chose to stay in California. The cost of living was too high, and this was driven primarily by the cost of housing. Why were housing prices so high? (See above)

The residents of Holliday, Utah unanimously rejected a new development on a vacant mall site that would have added a 775-unit high rise apartment tower and 210 single-family homes which would have included higher density townhomes. These would have been new places to live for young families and first homes for others. The development would have added office space for businesses as well as dining and entertainment options for the local community. But the residents didn’t want to change; they didn’t want something different in their city; they didn’t want to open up their community to others who can’t afford a $700,000 home.

I only hope that my city of Millcreek is less selfish and works to find solutions that allow our children (and newcomers) to live next to us along the Wasatch front.

Did my degree get me a job?
No, but it got me noticed.

Did I learn everything needed to do the job in school?
No, but I learned how to learn new things.

Did I know what I would be doing five, ten, fifteen years after graduating?
No, but there were always new problems to solve or a need to simply get stuff done.

Did every new job come with a raise?
No, but each new job allowed me to grow professionally.

Did I do it alone?
No, I had—and still have—support from others every step of the way.

As the resident household chemist, my college-age kids have routinely asked me to check chemistry problems for them—and I’m happy to do so. That said, sometimes I get stuck, and this is not fun for them or me, after all, I am expected to be the domain knowledge expert! Recently, we had a particularly challenging problem come up, and neither of us was able to get the correct answer.

When I was a student (many moons ago), there were answers at the back of the text for the odd exercises. If you were lucky, there was a solutions guide for all the practice problems. With online materials, every answer is available, and many have examples on how to solve the exercises, and for this particular case, when the solution was requested, we were presented with a single equation that incorporated all the information provided in the problem with the unknown variable, x, appearing on both sides of the equation. Below, the equation was the answer.

x = 0.217

?!?!

I looked at the equation and then looked at the answer—repeatedly.

I don’t get frustrated with first-year chemistry too often, but this exercise was presenting a challenge and then to have the solution be nothing more than a single equation with no explanation left me exasperated.

Ten-steps later!

I have no idea why the publisher didn’t include a real solution, after all, it’s not like they need to conserve trees, but summarizing a complex problem to a single equation without additional context was less than helpful. For my eventual solution, each step documented the progression from what was known, to the unknown, and when completed, we understood the scope of the problem and how this problem fit into the section.

Sometimes, all we need is the answer—0.217. Sometimes, we need more—or we need to provide more. Were all ten steps required for this problem? Maybe not as some steps may have been self-evident, but for complex issues, I would prefer to receive too much information as opposed to too little.