If you've arrived here, your program may emphasize a series of courses dedicated to quantitative (i.e., statistics) analysis methods and tools at some point in the curriculum.
You must learn (unless you already know it) new analysis software such as SPSS or R (in addition to Excel), and you'll need to know how to interpret the results correctly.
This undertaking might be the most difficult part of the journey for anyone like me that is NOT a numbers person. Here's how my program designed this part of the analytical learning voyage:
Year 2 Classes:
Research Methods – Quantitative
SPSS – Research Data Analysis
Applied Research
Analytics for Decision Making
Strategic Management in Global Organizations
Consulting Learning Seminar
Let's begin with a little background context. My education and independent training have involved data processing and analysis throughout. Still, as a non-numerical thinker, this always meant that I had to work longer and harder to make sense of it. It makes sense that a third-time business major needs to be accounting, finance, and statistics literate. Still, the depth of that literacy was about to be expanded in one of those professional arenas. It was time to swim in the data; I hoped there were no sharks in these waters.
One Toe in the Water
It all began with the Quantitative Research course under a professor with an incredible reputation for helping students understand the magic of quantitative data analysis. The first class was held during lunch, and I was among the few students to attend the live session in our small but mighty cohort. The course consisted again of weekly discussions with research integrated into the responses and data analysis practicums. Thankfully, we started in familiar territory (ye olde Excel) and only needed to generate a frequency table for two given data sets. We then needed to provide feedback on a peer's analysis. "Okay, not too bad," I thought. Maybe I'll be okay.
The lecture content first covered a review of the different categorical types of data, a review of the measures of central tendency, and basic probability computations. One surprising challenge was learning to format equations in Microsoft Word to submit the assignments. Having been in school long enough to submit written mathematical HW assignments or Excel spreadsheets, having to type work in Word proved interesting. Still, after some head-scratching and a tutorial, I got it.
From week to week, we worked across the statistical testing methods. We then swiftly worked our way up to testable hypothesis formation based on the potential dissertation topics we considered. Looking back on my assignments, I was interested in examining grit scores concerning the stages of job burnout as a prospective modifying factor and proposed using a one-way analysis of variance to test it.
Here are the research questions:
RQ1: What effect, if any, does grit have on an individual's susceptibility to job burnout onset?
RQ2: If an individual is already experiencing job burnout, what effect, if any, does grit have on job burnout stage transgression?
Here's the model I built to frame the line of analysis:
A working Quantitative Model to test Grit as a modifying agent of Job Burnout (Note: needs revision to be testable see point in text.)
Sexy, right? Well, in the world of data, this is a supermodel level of hotness, or at least what I gathered from the feedback given on the assignment, plus a slight metaphorical exaggeration. Still, this read should be fun, IMO.
In hindsight, this model incorrectly treated the burnout stages as progressive. More recent studies have shown that those afflicted with Burnout can experience any, all, or some combination of the three burnout variables at any point. I digress. At the time, I was reading Dr. Angela Duckworth's Grit and became enamored with the research surrounding grit and resiliency. Maslach's work on Burnout also found its way to me, and it seemed like a beautiful crossing of the fates to explore these phenomena in tangent.
We didn't segue into an analysis software beyond Excel at this stage; that would be the next class (SPSS), but remember when I said that life doesn't stop just because you're pursuing a Doctorate? Allow me to present the image (badly drawn by yours truly) that comes to my mind when "Life" comes your way; queue the line he says while plotting: "Excellent…."
A poorly drawn Mr. Burns by Dr.G
Welp, the same was true for me. Life decided to come at me, and it was not 'excellent.' You see, I had been managing an issue over the years that now demanded a response. It was time for me to take care of it, which would force me to take a term off to recover. We planned a move while this was all going down to add more fun to the situation. Didn't I promise you this was going to get interesting?
Herein lay the dilemma, the way these courses usually work, I would have to either pick up with the next course my cohort was on and make up the course I missed later, or I would have to wait two terms and move to another cohort delaying my completion date by six months. Why does this matter? TBF, it wouldn't have made a vast difference from a progress standpoint, but it was important to me to stay with my cohort and not lose progress. If I moved to a new cohort, I would also be in a group where in-person residency requirements would be expected going forward, which was not feasible.
Accordingly, I chose the first option (which was only an option due to the support and grace of our department chair and advisor). This choice meant I would go from a smooth, progressive submersion into analytical thinking to a 'sink or swim' plunge into the deep end. The class I would pick back up in would be Applied Research, where it is presumed that I would know how to use SPSS, a program that, at the time, I had never opened. I took the term off and, to the best of my ability, learned SPSS basics on my own in preparation for returning. As it happened, I would come to take the SPSS course one year later in addition to my first dissertation class. Knowing that analytical thinking was not my preferred course of study, the stakes couldn't seem higher.
After eight weeks, I returned with a determination not to let this instance hinder my progress, but I also knew that the transition back into the flow would not be easy. Applied Research was the name of the game, and, thankfully, a familiar professor teaching the course. He focused the class on formulating the foundation of our dissertations. While analysis methods were discussed as part of the methods building, the deep data was delayed to the next class (thank goodness), giving me more time to acclimate to the SPSS platform. The final project in this course was producing a more robust 30-page literature review concerning our then-research interests.
I could get back into the swing and felt pumped about returning strong. The next course would undoubtedly put those feelings to the test. As an aside, our cohort was down to four, self-included. We were one-third the size from the beginning of this adventure, and a little over half of the journey left. I wanted to avoid becoming another one lost along the way.
The Plunge
The time had come; it was sink or swim. I would either understand this and succeed or succumb to my hesitations.
Analytics for Decision Making and Strategic Management had arrived.
At this stage, a phrase that a mentor used to say often came to mind. He would tell the story about conversations he had with his father after he earned a degree. After earning his Bachelor, he exclaimed, "Look what I did!" His father responded, "That shows me you know how to think." After his Masters, he did the same thing. His father replied, "That shows me you know how to learn." Missing the admiration or pride he was hoping for and thinking that the next degree, his father's response would be different, he showed his dad his Ph.D. His father responded, "That shows me you know how to teach."
Why am I mentioning this now? You should be prepared to teach yourself at some point in your degree. This point was my moment of recognizing reality.
TBF, it makes sense. You are likely pursuing this degree to become a professor at some point in your career; one stands to reason that you'll need to learn how to teach. What better way than learning to teach yourself first?
The course hosted a series of modules with analytical assessment work to be performed in SPSS. This point is where I offer you, dear reader, a HUGE (pronounced as "Yyyuuuuuuggggggeeeee…") tip concerning the SPSS platform:
You are far better served to use the PC version in a Windows virtual machine than to try to 'Mac attack' your way through this one. The books are designed around a Windows experience; the lessons are designed around the Windows version, and your professors are equally likely not to be familiar with the nuances of the Mac version. Refrain from adding to the burden of learning a new tool in an unfamiliar environment. If you're so inclined, learn it later.
With that said, let's go back to our regularly scheduled program. M'kay? Coolsies!
The class maintained the cadence of work established in prior courses, weekly modules, and analysis of HW problems. This time, I would be using SPSS the entire way, which prompts me to highlight the small arsenal of textbooks on SPSS and statistical analysis methods (and steps) that I would cross-reference and tab (insert a non-paid advertisement for the self-adhesive tabs for book pages here, as they helped a lot) frequently:
Albright, S. C. & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Mason, OH: South-Western Cengage Learning. ISBN 978-1-305-94754-2
Cronk, B. (2016). How to use SPSS: A step-by-step guide to analysis and interpretation (9th ed.). Glendale, CA: Pyrczak Publishing. ISBN: 978-1-936523-44-3.
George, D., & Mallery, P. (2020). IBM SPSS statistics 26 step by step: A simple guide and reference.
Reinard, J. C. (2006). Communication research statistics. SAGE Publications.
In addition to these times, I had a fantastic peer who would soon become my Partner in my Dissertation or PID. More on the importance of a PID in a later post. She helped me understand concepts I missed in my absence (Thank you, Dr. Kempink) and worked with me to produce a stellar final assignment. Words cannot express how much of a blessing she was and is in my life.
The final assignment for this course infused all we've learned (including the lessons from the SPSS course) into one business analysis report and presentation using provided dummy data as the basis for the interpretations. We contextualized our analysis around a hypothetical California university under fire for pay inequity accusations. The report wove research on the variables with the analysis and interpretations of the supporting "university" survey data. We examined reported pay rates against gender while controlling for education level, using this model and research question with supporting hypothesis:
RQ1: What is the nature of gender across different educational levels when determining appropriate compensation?
H0: There is no difference in pay for different educational levels between genders.
H1: There is a difference in pay for different educational levels between genders.
To find an answer, we ran some statistical analyses, including:
Descriptive Statistics
Multiple Linear Regression Analysis (multiple)
An Analysis of Variance (ANOVA)
Paired t-Tests
After running the tests, we were able to interpret the results and determined that we "failed to reject the null hypothesis." More importantly, I finally overcame my analytical reservations by the end of this leg of the journey. I could understand quantitative research. I recognized the magic quantitative analysis produces and the value of the thinking supporting it. This win was no small victory. (Insert celebratory happy dances in your moments of triumph.). The next course was the Strategic Management course which had the same tone and vibe as the one just discussed, so we'll move past this one.
The final course of year two was one that I was most looking forward to, a consulting duo leading into the third year. We would learn consulting practices and then use them in the following course for our first client. While not tied to the dissertation experience, this course was one that I was excited to use in the future. More on that at a later time, in another series.
The class was hosted by the same professor possessing a great wealth of knowledge in HR. He also worked as a consultant, bringing practical advice and guidance to consulting theory and application in both courses. For this first course, our challenge was establishing a business plan for our new consulting company. We created a company called Divergent Business Solutions, only to discover that someone had grabbed the name in our state. C'est la vie.
By the end of this year, we were down to three Cohort 17 members. In the next post, I'll talk about the last year, including a term where I had to double up to stay on track. Til' next time!