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South African Journal of Occupational Therapy

On-line version ISSN 2310-3833
Print version ISSN 0038-2337

S. Afr. j. occup. ther. vol.48 n.1 Pretoria Apr. 2018 




At an academic function last year, I overheard someone say: "Qualitative research is an invasive and alien species and we need to eradicate it from our world before it overruns us all." The most upsetting thing was, it came from a high-profile governmental decision and policymaker and his comment elicited quite a few smirks and nods from the people gathered around him.

An invasive and alien species?' What could have caused such a perception? I needed to know. So I turned to my computer for some answers. My conclusion? It's all about those numbers.

Numbers are so revered in quantitative research because they are seen to symbolise the notion of 'truth' and 'objectivity'. On the other hand, qualitative data are generated through dialogues or narratives; it is a dialectical process whereby individuals experience themselves in relation to others; it is a subjective process which places the qualitative researcher directly into the research, interpreting interpretations, making, unmaking, and remaking stories; and because of this, qualitative research is viewed as a more "feminine" process that causes positivists to question, and rightly so, the veracity of what are, in some measure, subjectively constructed conclusions, resulting in the de-valuing of qualitative data1.

The popularity of qualitative research is evident in our context as well. Currently, up to 88% of all submissions to the SAJOT follow this approach. However, there is also an erroneous perception that if the method of choice is qualitative, then 'numbers' somehow cannot or should not be used.

In order to mediate the subordinate positioning of qualitative data, it has become increasingly important to locate qualitative research within the "confines" of positivist frameworks1.

Surely, you may ask, qualitative research addresses questions that are NOT quantifiable (if they were, they would be addressed using quantitative methods), because the individual experience needs to be examined in itself, not as a reflection of a larger truth. There is an epistemological assumption about what research is attempting to learn when one assumes that a case represents a group, and that quantity identifies some specific characteristic of the group under investigation. If you are not operating from within that framework, the process used in quantitative research is nonsensical and by definition will lead to incorrect inferences2.

Some other arguments put forward by the positivists include that firstly, many qualitative studies conduct interviews or focus groups with five or six individuals and then contend that if they had shown rigour by employing a whole plethora of 'confirmabil-ity', 'transferability' and other strategies, the 'trustworthiness' of their findings is verified. Secondly, qualitative researchers may only cite those utterances that were most 'dramatic' in the fieldwork and then erroneously present them as being the most significant3whereas they should rather put forward those perceptions or utterances that occurred most frequently (those numbers again).

Can we meet them half-way? Are there ways and means of quantifying qualitative data and if so, shouldn't we investigate?

"Qualitative researchers may criticize [the] quantification of qualitative data, suggesting that such an inversion sublimates the very qualities that make qualitative data distinctive: narrative layering and textual meaning3". In addition, "until we know more about how and why and to what degree and under what circumstances certain types of qualitative research... can usefully or reliably be quantified, it is unlikely that program planners or policy makers will base decisions on studies generally regarded as 'qualitative3".

Quantifying qualitative data should not be confused with mixed method designs. The latter employs both qualitative and quantitative methods to gather data on, and explore, examine or interpret phenomena, whereas in qualitative designs, the design as well as the data gathered remain qualitative.

There are a number of computer-assisted qualitative data analysis software (QDAS) systems which could assist researchers with quantifying their data. Such programmes link code with text in order to perform complex model-building and help with data management. EZ-Text is just one of a number of such computer programmes, each of which is suitable to a particular type of research design. Atlasti is another sophisticated QDAS which is relatively easy to use, links code to text and quantifies the qualitative data by generating the frequencies with which each identified code occurs in the data, thereby guiding the researcher to confidently argue that:

only those codes with the highest frequencies warrant collapsing into categories and truly represent the views, perceptions or feelings of the majority of participants; and that

those codes with relatively low frequencies could be merged to increase their frequency, or discarded as representing only a small percentage of the participating individuals or records under study, no matter how 'dramatic' they are.

Frequencies could also indicate trends in the data, allow for statistical comparisons, correlations or associations (e.g. between the views or perceptions of experienced and less-experienced members of the group), or make possible comparisons between groups (e.g. viewpoints of staff versus clients).

Regardless of the study or the analysis procedure, it is always essential to clearly describe how the data were coded and interpreted, and it is important to quantify it in order to draw conclusions3.

I don't believe that quantifying qualitative data detracts from or minimises the richness, value and depth of narrative data. It can only add to it.

We have to start asking ourselves whether the inclusion of a mere one or two quotes (as restricted by word-count constraints imposed by scientific journals) in support of identified categories and themes, is sufficient evidence that they truly represent the perceptions, feelings or views of a particular group, and whether it shouldn't become policy that we also quantify our data to better support our findings. Maybe this will convince the policy and decision-makers of the veracity of our findings, and make them sit up and take note.


Blanche Pretorius

Editor in Chief




2. Dohan D and Sanchez-Jankowski M. (1998). Using computers to analyze ethnographic field data: Theoretical and Practical Considerations. Annu Rev. Socrol 1998. 24.477-98.         [ Links ]


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