Halfway point.
My colleague Amy wrote about our journey this week and reminded me that we are halfway through this DBA journey. That doesn’t seem possible. Four more months of classes, and then one year to complete our dissertations.
This gave me pause for reflection. What do I wish I would have known and done differently at the inception of this journey? One word.
Statistics.
I feel foolish saying it, but it’s a given. Research requires a perfunctory use and understanding of statistics. That was likely in the small print somewhere that I should have read. Likely, I was still mastering addition without using my fingers. But this halfway point reminds me that while there is still so much to be done, there is a lot I have mastered.
I never had a statistics course in my undergraduate or graduate school endeavors. Even saying statistics gives me chest pains (See previous blog entries.) But, as you look at this opportunity, here’s some insight into the “What” and “Why” about different methodologies and their applications. I wish I had taken a bit more time in advance to have a rudimentary understanding, so that is my gift to you. Pull up a seat, find some YouTube videos, and start brushing up on introductory textbooks earlier in this adventure.
Quantitative and qualitative methods of statistical analysis. You’ll likely understand one better than the other. I appreciate and understand the education of qualitative analysis more than quantitative analysis because of my background in marketing. It’s not just analysis through words but the concept of analyzing and looking at the data differently.
Qualitative analysis involves analyzing non-numerical data, such as interviews, observations, and textual analysis, to uncover patterns and themes. This method gives a deeper understanding of complex phenomena and is well-suited for research questions requiring exploration and interpretation. On the other hand, quantitative analysis focuses more on numerical data, statistical tests, and measurements to draw conclusions and make predictions. This approach is more structured for research questions that seek to establish relationships and patterns through statistical evidence.
The benefits of qualitative analysis lie in its ability to capture the richness and complexity of human experiences. Psychology, education, and technology fields may find deeper insights in this analytical method. Conversely, quantitative analysis offers the advantages of objectivity and generalizability, making it suitable for studies requiring large-scale data collection and statistical validation. When to use which method? Just like we’ve said before and you’ve heard on the podcast, it depends…and it then it depends on what.
What is your takeaway? There are robust learnings available on both qualitative and quantitative approaches. Your DBA experiences will educate you on the nuances of both methodologies and the analytical tools and processes for both. Whether you love it or detest it, you will have a relationship with statistics during this journey. Dust off those books, and don’t be afraid to jump in.
