In this post, I will explain how this special analysis works. Although this analysis option is specifically meant to support my goal of using the Q sort process within an instructional context, the idea behind it originated much earlier, and the analysis itself can be applied to other survey data types besides Q sorts. So, allow me to give a little background first.
Those in the field of instructional design like to think that one of its strongest elements is learner analysis. This is a major phase of instructional design and is well documented within all of the best known models. Even though we devote a lot of attention to learner analysis in the literature, my own opinion is that we don't practice it well. Yes, we are fond of giving pretests and surveys, but these usually only provide very superficial information about the learners for whom we are designing instructional materials. So, I feel that the theory of learner analysis falls far short of its reality and importance in practice. Perhaps I feel this way based on my formative years as a public school teacher. It was only after spending many months with my students for five or six hours per day did I begin to feel as if I really knew them. So, I tend to think we know very little about the people we are designing instruction for despite following the advice and procedures in our canons and even though we might feel comfortable in thinking that we do.
Doing a Better Job at Teaching Learning Analysis
We also don't teach learner analysis very well, at least, I don't think I do. I've long been trying to come up some innovative activities to help my instructional design students recognize both the importance of learner analysis and the difficulty in doing it well. At the very least, I want to instill in them the idea that we should be very cautious and skeptical about thinking that we really understand who our learners are, what they know, and what they want. So, I've been trying to design some class activities over the past few years that get at some of these deeper principles. I've yet to succeed. But a few years ago I came up with a class activity called "Are You Like Lloyd?" that seemed to hold some promise. I asked a series of questions to see how many students in the class had similar life experiences to me. For example, one question was "Do you have at least three siblings?" I thought this was a good question because coming from a family of five, I feel certain that growing up with a few brothers and sisters will make a big difference in how you see the world. After asking about 10 questions like this, a nice discussion usually ensued. I always had the sense that I was on the verge of designing an interesting game based on this activity, but I could never quite figure out what the game's goal would be. Is it to ask the least amount of questions before I demonstrated that everyone in the room was different than me in some way? Heck, that would be easy for me just with the question "Do you play an accordion?" Maybe the goal should be to ask questions to show how much people shared. That seemed easy to do too and not very interesting (e.g. Do you like music? Do you like ice cream?) In the end, just asking some questions that I thought reflected important influences on how I saw the world to find out how many in the room were like or different than me was interesting.
Analyzing a Q Sort: Person to Group Comparison
The "Are You Like Lloyd?" activity is the inspiration for my current work with using Q sorts as instructional activity. I've been playing around with the idea of comparing each person in the group to every other person in the group in terms of their individual Q sort responses. The best way to explain this is with an example. Let's consider a very short and simple Q sort that has only four statements. The Q sort board ranges from least to most favorite with column values of -1, 0, and +2 with two slots in the 0 column:
And, to keep the example as concrete as possible, let's imagine these ice cream flavors are the four statements of the sorting activity:
Automatizing the Difference Analyses Between All Group Members
What Does It All Mean?
Is it better to be similar to everyone else, or different from everyone else? Neither. I think it's important not to impose any value-laden interpretation on the results. That is, it is not good or bad to be similar or different, but only to recognize that there are similarities and differences and to explore why. My hope is that doing this analysis right after a group completes a Q sort will stimulate some lively discussion with a slightly better understanding about each other. And, if these are people studying to become instructional designers, maybe it will give them a deeper understanding and appreciation about learner analysis.
Here's an interesting anecdote based on some early trials. I've tried this activity out with one of my doctoral classes in the fall 2015. The course was required by our majors, but it was open to nonmajors too. We only had one nonmajor take the course. He brought it to my attention in a hallway conversation during a break that he noticed he tended to be the person at the bottom of each comparison group. We both found it interesting that the sorting activities "revealed" him to have a different view or perspective about the statements being sorted than the others in the class.
As I end this blog post, I want to reiterate that this analysis will work on any survey involving quantitative data, such as surveys based on the more familiar Likert scale. So, even if you are not interested in Q sorts, you might find the ideas behind this analysis intriguing and useful if you are someone who wants to know more about how a group of people tick.
Also, I really don't know if any of this work will yield anything particularly useful. In the end, I may simply be generating an overly-complicated way of conducting an icebreaker activity. Yet, there seems something inherently important and useful in it. I look forward to exploring this issue in my Q Sort research. So, we'll see.