GROUNDED THEORY

- is a systematic qualitative research methodology n emphasizing generation of theory form data in the process of conducting research.- GT was developed by two sociologists. Barney Glaser and Anselm Strauss.

GROUNDED THEORY (DIFFERENCES)

GLASER STRAUSSGT emphasizes on

induction or emergence, and the individual creativity within a frame of stages.

Strauss is more interested in validation of criteria and a systematic approach.

KEY CONCEPTS OF GROUNDED THEORY• An approach to describe relationships where little is known or to provide a fresh take on existing data.•A method to systematically build integrated sets of concepts from systematically obtained empirical data.•A process of composing knowledge through intimate contact with subjects and events under study.•A theory that is shaped by data as well as by the researcher.

THREE BASIC ELEMENTS OF GT ACCORDING TO STRAUSS(Strauss and Corbin 1990)

Theoretical Sensitive Coding Theoretical Sampling The need to compare between

phenomena and contexts to make the theory strong.

THE FOUR STAGES OF ANALYSISSTAGE PURPOSE

CODES Identifying anchors that allow the key points of the data to be gathered.

CONCEPTS Collections of codes of similar content that allows the data to be grouped.

CATEGORIES Broad groups of similar concepts that are used to generate a theory.

THEORY A collection of explanations that explain the subject of the research.

THE SPECIFIC NATURE OF GROUNDED THEORY

A theory must be fit, be relevant and A theory must be fit, be relevant and adaptable.adaptable.

FITNESSFITNESS The categories (elements) of the theory must fit the

data Data should not be forced to fit pre-existing

categories Categories emerge from data and are modified by

dataRELEVANCE A theory should be able to explain what happened,

predictwhat will happen and interpret what is happening

ADAPTABILITY A theory must be modifiable, Adaptability based on

new data

GROUNDED THEORY SCHEMES

(Strauss and Corbin 1998) Open Coding or Substantive

Coding

Selective Coding

Axial Coding

OPEN CODING

According to Strauss (1987) there are four essential guidelines to follow in the data analysis process:

1. Ask the data a specific and consistent set of questions, keeping in mind the original objectives of the research study.

2.Analyse the data minutely, but also include many categories, examples and incidents as possible.

OPEN CODING

3. Frequently interrupt the coding to write a theoretical account.

4. Do not assume the analytical relevance of any traditional variable such as age, gender, social class, etc. until its relevance emerges from the data.

SELECTIVE CODING

THE SELECTIVE CODING PROCESS:

Finding a story line formulated around core categories

Relating sub-categories to core categories

Validating these relationships against the data

Filling in categories that need further refinement

AXIAL CODING

The process of relating categories to their subcategories, termed ‘axial’ because coding occurs around the axis of a category, linking categories at the level of properties and dimensions.

Paradigm: “An analytical tool devised to help analysts integrate structure with process.”

Axial Coding (cont.) Purposes

Axial coding relates categories at a dimensional level

Properties and dimensions of categories are derived and used to relate categories and subcategories.

“…rather than standing for the phenomenon itself, subcategories answer questions about the phenomenon such as when, where, why, who, how and with what consequences, thus giving the concept greater explanatory power.” (p. 125).

Subcategory information about who, when, where, why, how and with what consequences helps relate structure with process (structure: why; process: how)

Axial Coding (cont.)

Tasks in axial coding: 1. Laying out properties of a category

and their dimensions 2. Identifying conditions, action,

consequences associated with a phenomenon

3. Relating a category to its subcategories

4. Looking for clues in data that indicated category relationships

THE PROCESS OF GROUNDED THEORY BUILDINGData collectionData interpretationData codingValidation of the Model

THE GT PROCESS: Principles of Data Collection The researcher is clear about the

purposes of the study, the issues she wants to illuminate and the practices the study might influence.

The researcher is competent in the information or data gathering techniques and composing data documents.

The data are ‘triangulated’, e.g., collected from multiple relevant sources to provide different vantage points.

THE GT PROCESS: Principles of Data Collection Whenever possible, the researcher

avoids being too structured in her methods of collecting information

Data collection follows theoretical sampling, whereby consequent data sources are guided by the existing data and emerging theory

THEORETICAL SAMPLING

The practice of actively searching and ‘sampling’ data

Theoretical sampling directs data gathering efforts towards finding information that best supports development of the emerging model

GT assumes that the researcher cannot identify in advance what categories will emerge; thus, cannot direct all data gathering efforts

Whatever the unit of analysis is, a GT researcher always selects it according to the relevance of the developing model

THE GT PROCESS: Principles of Data Interpretation Collected data are analysed

simultaneously, by looking for all possible interpretations

The researcher develops concepts to account for perceived patterns in sets of data observations

The researcher uses memoing as a reflexive practice that helps capturing and articulating ideas about the data

THE GT PROCESS: Principles of Data Interpretation The researcher holds back existing

ways of thinking about the subject, to prevent from prematurely giving form to the data

Iteration (constant comparison) is a distinctive and critical feature of the method at all stages throughout the analysis

THE GT PROCESS: Data Coding Open coding: breaking down the data

into

distinct units of meaning Memoing: capturing an idea that has

been

sparked by an incident in the field Naming: conceptualising and interpreting

what is happening in a given data incident;

1st step in creating conceptual categories Comparing: looking at what is similar and

different across data incidents; 2nd step in

creating conceptual categories Integrating: organising the emerging

conceptual categories

Open Coding

Memoing

Naming

Comparing

Integrating

THE GT PROCESS: Validation of the model

A model is usually considered valid only if the researcher has reached the point of saturation

A valid model is pragmatically useful, e.g. understandable to those who operate in the kinds of environment studied

A valid model is credible, e.g., can demonstrate how, why and from where early concepts and categories derived

THE PLACE OF LITERATURE

Two important points to be made about literature: In an emergent study, you probably won’t know at the beginning which literature will turn out to be relevant.

The Literature is not given a position of privilege when compared to the data.

LITERATURE AS EMERGENT

In an emergent study you begin collecting data as soon as you have a research situation. You can then access literature as it becomes relevant.

LITERATURE AS DATA

The key issue is how you treat apparent disagreement between your emerging theory and the literature.

You seek to extend the theory so that it makes sense of both the data from your study and the data from the literature.

Common pitfalls when using Grounded Theory Too much emphasis on

identifying codes and categories without explaining how they relate to each other

Constant comparison is not maintained throughout the analysis

The researcher analyses data with preconceived framework

A brief summary of the process Grounded Theory searches for meaning and

understanding, to build an innovative model and not universal laws

Data collection and sources are directed by the findings of the analysed data, rather then by specification prior to collecting data

The process involves coding strategies; the data are initially broken and then clustered into descriptive categories

Categories are re-evaluated for their relationships and gradually submitted into higher order categories that describe the model

CAQDAS: Computer Aided Qualitative Data Analysis CAQDAS: Computer Aided

Qualitative Data Analysis

The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Figure 1. Qualitative data analysis flowchart

Figure 2. Project pad with documents tab selected

Figure 3. Document browser with coder and coding stripe activated.

Figure 4. Node explorer with a tree node highlighted

Figure 5. Browsing a node

Figure 6. Document attribute explorer

Figure 7. Model explorer showing the perceived risk factors of cervical cancer