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06/01/2018    hoangthuphuong   PHD Journey

Defining elements and dimensions, refining categories and classifying data

Using Framework to define elements and constructs, refine categories and classify data involves the analyst in understanding 'what is happening' within a single subtopic - that is, within a column on a thematic chart. This entails the analyst reading down the particular column across cases to understand the range of data that exist. It is advisable to do this a number

of times but, once the analyst is familiar with the data within the chosen subtopic, he or she can then begin identifying different elements, constructs and categories that are emerging. 

The first step often involves using different coloured highlighter pens to label pieces of data in that particular column which suggest different representations of the phenomenon. 

Having done this, it is helpful to log and categorise different elements and categories on a separate sheet of paper, along with examples of each as presented within the data. While going about this task, it is important to question whether each piece of data provides a category, or is merely a characteristic or component of one already recorded. This is why extracting data from the thematic charts and summarising it on a separate sheet helps as similarities and differences become clearer. 

Throughout the process, the analyst should continually question the categorisation of each piece of data. This task is not complete until all of the data in that column or subtopic have been fully inspected and a decision made about where it belongs.

Sometimes everything in the column will be judged as relevant to the categorisation and thus the data within the column will have been exhausted.But other times there will be material there that does not belong in the conceptualisation

of the descriptive categories but which is nevertheless clearly linked. This may be dealt with in a separate categorisation or may be considered alongside other material in another column. Either way it should not be ignored unless a clear decision has been made that it is irrelevant.

Once the analyst has extracted all of the definitions, elements, constructs etc. summarised in the charts, it will then be possible to classify them by grouping them under one or more higher order labels. The aim of this task is to construct a coherent and logical structure within which to display the content of the descriptive elements.

This can take various forms since differently 
constructed categorisations can be derived from the same data, but would all encompass the same range of phenomena. However, with clarity about the elements and without over-interpretation of the data at this stage, the underlying conceptualisation of the categorisation should remain evident.

Source: Miles, M. B., Huberman, A. M., & Saldana, J. (2013). Qualitative data analysis. Sage.


Qualitative Data Analysis: Establishing typologies
Qualitative Data Analysis: Establishing typologies
To identify the relevant dimensions of a typology. Lazarsfield and Barton (1951) describe this as the 'dimensions which underlie the discrimination' made by the typology. For this, it is important for the analyst to have a strong familiarity with the data set and that tasks further down the analytic hierarchy, such as identifying the elements of a phenomenon and refining categories, have been completed.
How to... write a literature review
How to... write a literature review
To write a literature review, you must first decide what form the review will take – descriptive or a critical assessment. You need to look at the relationships between different views and draw out key themes, and you must structure it appropriately. See our 4 Step Guide to Writing a Literature Review to find out more.
Computer-assisted qualitative methods
Computer-assisted qualitative methods
Weitzman (2000) provides the most up-to-date categorisation of CAQDAS software, building upon earlier work with Miles (cited above), which categorises software into five types: