What is Phenomenography

Phenomenography is a research method developed for analysing qualitative data sets to answer questions about thinking and learning. There are two ways to deal with questions regarding learning/education...

1. You can study what happens. (E.g. First years find programming difficult, lets have a look at that)
2. You can study how people experience the event (Michelle knew she would fail from back in February, how?, why?)

Phenomenography attempts to shed some light on the second approach. Here is my explanation of the method, from start to end. Phenomenography involves the separation of qualitative data into distinct categories, each with their own motives/reasoning. It can often be a useful method to employ for qualitative data, Here is my take on how you do it...

Assumptions of Phenomonography

Phenomenography has the following the underlying assumptions, if they do not hold true, do not proceed with the method.

• It assumes that the subjects examined think about their experiences. That is, it presumes that an individuals conceptions are a product of the individual, the experience and the surrounding environment.
• It assumes that a persons conceptions are accessible, either through language or other methods.
• It assumes that there is a limited number of ways a subject can experience a given phenomena.

Methodology for Phenomenography

The methodology employed for phenomonography is typically an open deep interview. I have heard of cases where by subjects write essays in responses to questions but I have my doubts how rigorous these methods are. If you're not used to gathering qualitative data, the two operative words here are open and deep.

Open: Open means that you talk about pretty much anything during the interview. The interviewer cannot restrict the topics in anyway. If the student wants to complain about the lecturers handwriting, it must be allowed, for all you know the next 4 students after might also have plenty to say about it

Deep: Deep means you must talk about every topic until the candidate has literally nothing more to say. This is very important, just because you have spent 10 minutes talking about a topic, doesn't mean that you've heard enough.

The purpose of the interviews are to encourage the participant to reflect on their experiences and explain them as best they can. These reflections should be a result of the interview, they should not exist in advance.

Analysis

During the data analysis phase the researcher will identify categories for describing each person experience. One of the assumptions of phenomenography is that there is a limited number of these categories. The categories can be hierarchical, the only restrictions is that they are internally consistent, and that there should be as few as necessary. (As Einstein said, make everything as simple as possible, but no simpler).

The researcher must make multiple passes of the data to ensure that the choice of categories is minimal and consistent. Once the researcher is finished, the final group of categories is called the "outcome space"

Results

At the end of all this you will be left with the outcome space, which can then be analysed looking for underlying meanings of each category. A good example of this work is the study by Ray Lister on 2^nd Year Data Structures teaching. (The debate is "Do we teach students how to write data structures, or how to use data structures (e.g. STL, JDK etc..). Doing both is pretty difficult. Here is a phenomonographical study by Lister. The dimensions of variation in the teaching of data structures

Criticisms of Phenomenography

Here are some popular criticisms of phenomongraphic studies.

• Phenomonography tends to equate students experience with their account of their experience, this is most certainly a problem.
• It is often claimed that Phenomonographers do not look at the external environmental influences during the interviews. (This can be solved by being scientific and rigorous in your approach)
• It is impossible to be neutral when analysing data. Therefore your data analysis can be seen as flawed if you do not provide an explicit background of who you are and what areas you work in.
• It is possible that two different researchers could find 2 different category sets, given the same data. I personally do not find this to be a problem. Lister addressed an issue similar to in his paper I mentioned earlier...

If phenomenographers do not necessarily identify a unique set of categories from the same data, is phenomenographic work therefore not repeatable? (And therefore not science?) Phenomenographic work is repeatable in the following sense. If two people were given some categories, and some quotes from data, those people would usually place the quotes into the same categories.

Much of this text was written directly after reading a piece by a researcher in Chemistry Education named MaryKay Orgill. The text is no longer available on the web, other than in google cache which mauls it. With MaryKays Permission I have re-published it, and you can find it here:
Phenomenography by MaryKay Orgill

So, where do you do phenomenography? Well, It's at stage 3 of 6 in the Principles of Scientific Education Research.

1. Ask Questions that can be investigated empirically
2. Link your research to appropriate theory
3. Use Methods that permit investigation of the question
4. Provide a coherent and explicit chain of reasoning
5. Replicate and Generalize
6. Disclose all research to encourage professional scrutiny and critique.

If your research is in Education, and you are not following this line, then you better start quickly. (Note: It has been said to me many times that these steps abstract away to be pretty much a guide to a PhD in general)

And that's all I have to say on this subject. If you're still reading, thanks for your patience!