Qual Image Analysis – how?

24th October, 2013

snapshot_share_pic_xFollowing on from our recent blog concerning images entering research, we will now briefly explore communication and analysis methods for dealing with the vast amounts of additional data that could be received. Visual data is not a new phenomenon. Indeed, the presentation and collation of visual images as part of qualitative research has existed for centuries as means to provide an additional communicative mode in research presentation. The use of imagery now transcends previous uses through the use of mass-media input and output; researchers can potentially request visual data on myriad subjects where previously unavailable, extending their contextual understanding of their chosen subject.

The addition of visual data comes with its own challenges. The provision of subjective imagery asks the question of analysis: how? At a simplistic level, visual data (primarily photographic – video presents its own challenges) can be broken down into key categories to enable analysis of their content:

–          Similarities and absence: the combinations of images that contain similarities or the absence of specific aspects should, similarly to traditional text based qual-research, initiate contextual analysis.

–          Focus and Perspective: the portrayal of focus and perspective in provided imagery can provide identification and individual aspirational values toward the given subject. This can signal a range of particulars: brand knowledge, brand intimacy, passion-points, item preferences and so on. By seeking to understand the focus of the image researchers can discern the relationship between individual and object potentially obtaining an insight into participant moods (where moods can be related to specific notions such as wealth, sociability, nature, nurture etc.)

–          Abstract: there is the potential that images received will contain cryptic viewpoints that are not easily discerned. In this case researchers should apply a framework in relation to their field of study that implements an identification model that allows for classification of images outside the mean for a particular project.

Even when we can successfully apply a methodology for analysing the data provided, digital recognition software still hasn’t successfully caught up with the widespread use of images. Digital processes are still exactly this: a series of numbers, devoid of human meaning. Unfortunately this does mean that consistent human interaction needed to decisively contextualise each image. However this does have its advantages. The knowledge that each image contains describes a period of time, a space, a passage. The cognitive patterns of the human mind allow for a deeper insight into the human interactions, the functions of memory, the associative designs between meaning and representation. So in this sense, at least to the point where technology successfully begins to contextualise semantic fields found in mass imagery the bulk of the work will be carried out by rote.

Whilst it is certain that this format of technology will exist in the near future, there is no certain date. We can see through large social sites such as Facebook, with their tagging technology developments how reactive image processing may work, but for research purposes, commercially marketed software may take longer to reach us.

Qualitative image research is nothing entirely new and even with data heavy time constrains, the additional data provided could prove vital to ethnographic studies now and in the future.