Constant comparative method

Words are the way that most people come to understand their situations; we create our world with words; we explain ourselves with words; we defend ourselves with words. The task of the researcher is to find patterns in these words and to present those patterns for others to inspect while at the same time staying as close to the construction of the world as the participants originally experiences it. (Maykut & Morehouse, 1994:18)

Data analysis for this research thesis followed Glaser & Strauss’ (1967) constant comparative method which incorporates the data analysis procedures outlined above, although in a unique manner (see samples in appendices O, P, Q, R, AB and AC). The constant comparative method (Fig 3.6) is concerned with reconstructing data into a “recognisable reality” along with the researcher’s own interpretations (Strauss & Corbin, 1990:22). To achieve this, responses are not grouped according to pre-defined categories or schematics; rather the first stage in the process is to gather salient categories and relationships between categories as they emerge from the data itself, through a process of inductive reasoning. The method offers the researcher a process that allows the interrogation of participants’ own words in a manner that facilitates the structured explanation of social situations. Following analysis and interpretation of data, categories are labelled using propositional statements which are statements designed to capture the essence of the category they represent, using the language of the participants themselves. This unique approach of using propositional statements in the

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language of the participants stays most true to the action research ethos of allowing the voices of participants to come through the data.

Figure 3.6 - Constant comparative method

The constant comparative method of data analysis and interpretation focuses not only on the analysis but also on the recording of the process, the creation of what Lincoln & Guba (1985) call an ‘audit trail’ and visual representation of the process:

The visual record of your work contributes to the audit trail available to you and others who are interested in tracing the path from your initial ideas to your research outcomes. (p. 135)

The process is as follows:

Unitising the data

This first step involves identifying chunks or units of meaning in the data such as comments from questionnaires and ascribing a word or short phrase which indicates the essence of the unit’s meaning (Fig. 3.7).

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Figure 3.7 - Unitising the data

This process of labelling units of data makes for easier identification and retrieval at a later stage. When this process is complete and all of the data has been labelled and described as a specific unit, the job of the researcher is to carefully re-read the data gathered and look for emerging themes or patterns in the data. Maykut & Morehouse (1994) liken this discovery process to the accordion:

The word accordion is derived from German and French words meaning

agreement and harmony. The accordion is a portable musical instrument with a small keyboard and free metal rods, that sound when air is forced passed by them by pleated bellows operated by the musician. The action of playing an accordion is one of pulling these bellows apart with both hands, while pressing the appropriate keys, and then squeezing the bellows together to create the harmonic sound. In qualitative data analysis, the discovery step metaphorically pulls apart the bellows just a bit, widening the array of potentially salient aspects of the phenomenon under study. (p. 132)

A key ingredient of the constant comparative method is inductive category coding.

Inductive category coding

Following initial review of the data and creation of units of information, units are analysed for meaning and grouped with those of similar meaning or used to create a new category altogether. As stated by Glaser & Strauss (1967):

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The constant comparative method of analysing qualitative data combines inductive category coding with a simultaneous comparison of all units of meaning obtained. (p. 103)

This categorisation process seeks to develop a set of categories that provides a reasonable reconstruction of the data that has been collected, and to present this in a way that allows the exploration in sufficient detail of the issues surrounding the study, such as the impact of specific features of the VRS. As the process unfolds, the data begins to take shape under meaningful categories (Fig. 3.8).

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Maykut & Morehouse (1994) illustrate this process in the following way:

The expansive process of categorising data is analogous to fully pull apart the folds of the accordion, which is necessary for the eventual harmonic synthesis to occur. Like an accordionist, the researcher methodically pulls apart the meaning contained in the data, enabling him or her to eventually reconstruct the

important melodies contained in the phenomenon being studied. (p. 138)

It is important to understand that some units of data may fit into more than one category, especially when dealing with longer pieces of qualitative data. Towards the end of data analysis, there will be a small number of data units that belong to no category as they may touch on issues that are outside the scope of the study. The final stage of the constant comparative method is writing the refining categories through rules of inclusion.

Rules of inclusion

Rules of inclusion are used to distil the meaning of a cluster of units so that a basis for including or excluding units of data can be justified. It is here that the development of propositional statements begins, where a statement is made about the content of a category and the learning that can be drawn from it. Maykut & Morehouse (1994) state:

A propositional statement is one that conveys the meaning that is contained in the data cards gathered together under a category name. Rules for inclusion stated as propositions, begin to reveal what you are learning about the

phenomenon you are studying and are a critical step in arriving at your research outcomes. (p. 139)

Once rules for inclusions have been developed and final adjustments have been made to data categories, the final stage in the constant comparative method of the data analysis process is exploration of relationships and patterns across categories.

Relationships and patterns

By this stage in the process several propositional statements exist with numerous units of data surrounding each. The final stage of the process is to synthesise these

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propositional statements together into a meaningful whole, to tell the whole picture that has emerged from the data. Maykut & Morehouse (1994) state:

It is time to carefully and systematically squeeze the bellows (the data) together to create a sight and sound somewhat different but accurately reflective of the data with which you started. (p. 143)

The goal is to identify the propositions which were significant enough to stand alone, and those that require connections with other propositions in order to fully tell the story. This process of identifying key and interconnected propositions forms the basis for discussing and outlining the findings of the research and re-appropriating participant data in a meaningful manner, interwoven with the researcher’s own thoughts and conclusions. Maykut & Morehouse (1994) state:

The last step in data analysis is writing about what you have heard, seen, and now understand, to create the harmonic sound of data coming together in narrative form to make sense of the phenomenon you have studied. (p. 145) The constant comparative method provided me with a systematic process for analysing participant data for common themes and reconstructing these into a recognisable reality, along with my own interpretations of what this meant in the context of my research. This process was beneficial in two ways: first, it facilitated the description of the participants’ experiences in the words they used and second, it assisted me in developing insights into the area under study, as stated by Lincoln & Guba (1985) “the process of constant comparison stimulates thought and leads to both descriptive and explanatory categories” (p. 341). The systematic nature allowed concepts to be developed and refined, priorities and relationships to be explored and finally integrated into a coherent explanatory whole.

Adopting the constant comparative method of data analysis provided an audit trail of the process through which categories and themes were arrived at. This process ensured the reliability and validity of the data analysis process by ensuring that categories could

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be traced back to original data sources and procedures could be validated by a third party. Additionally, findings were presented to students at the end of each cycle to confirm their agreement on themes which emerged.