Qualitative inquiry growth

Qualitative data analysis methods

Numerous data analysis methods exist. The choice of method must fit the research design, for example analysis of the transcribed text for meaning with the view of finding themes or the interpretation of actions to derive meaning. Other methods could focus on the discovery of patterns, or typologies, or categories through constant comparison, matrices or charting. Further methods include reflective or metaphorical analysis. Don Ratcliff provides an oversimplified (in his opinion) listing of 15 methods (Download 15methods his descriptors):

Typology	Classification system derived from patterns, themes, or other kinds of groups of data. Categories should be mutually exclusive.
Content analysis (considered a specific form of typology)	Analysis of documents, text, or speech for emerging themes and find how themes relate. Start by reading through for holistic view and then specify rules. Determine categories and frequency to find latent emphasis.
Discourse analysis	It is a linguistic analysis of several people conversing. Find patterns of interaction, domination by who, duration and how.
Narrative Analysis	Study an individual's speech, the story a person shares about self. Consider the core plot in the story, the context-situation and basic actions. Could involve study of diaries or folklore or literature.
Phenomenology	A heuristic analysis of how individuals experience the world. It emphasizes idiosyncratic meaning to individuals. Bracket self out and enter into the other person's perspective and experience
Hermeneutical Analysis (hermeneutics = making sense of a written text)	Not looking for objective meaning of text, but meaning of text for people in situation— use their words. Use context—time and place of writing, the cultural and historical context—to understand.
Semiotics	Determine the meanings of signs and symbols, such as body language, also social construction from influence of others (symbolic interactionism).
Metaphorical Analysis	Try various metaphors and see how well they fit and check validity of metaphor with participants or ask participant for metaphors.
Quasi-statistics	Use enumeration (count) to provide evidence for categories created, based on the frequency something is mentioned in field notes.
Event Analysis/Microanalysis	Finding precise beginnings and endings of events—the boundaries, then find phases in event by repeated viewing.
Logical/Matrix Analysis	An outline of generalized causation by using flow charts, diagrams, etc. to pictorially represent.
Analytic Induction	Consider an event and develop a hypothetical statement of what happened. Look for a similar event and see if it fits the hypothesis. If it doesn't, revise hypothesis. Look for exceptions to hypothesis, when find it, revise hypothesis to fit all examples.
Domain Analysis	Analysis of language of people in a context and description of social situation and meaning of cultural patterns within it—semantic relationships. Folk, mixed and analytic domains. Worksheet and samples from field notes, formulate questions about relationships.
Taxonomy	Often used with Domain Analysis in developing a taxonomy from a single domain. Sophisticated typology of super-ordinate and subordinate categories
Grounded Theory	Constant Comparison—look for indicators of categories in events and behaviour, name them and code them on document. Compare codes to find consistencies and differences. Consistencies between codes—similar meanings or pointing to a basic idea—reveals categories, eventually category saturates. Memo on the comparisons and emerging categories. Certain categories become more central focus—axial categories.

Research designs

An outline of research designs:

Qualitative designs

• Single case study
• Multiple case studies
• Participatory action research (PAR)
• Content analysis
• Textual analysis
• Discourse analysis
• Narrative analysis
• Life history/ies

Quantitative designs

• Cross-sectional surveys
• Cross-cultural or cross-national studies
• Experimental designs
• Quasi-experimental (field) designs
• Predictive (statistical) modelling
• Secondary data analysis

Evaluation research

• Implementation evaluation
• Experimental outcome studies
• Qualitative evaluation

Theoretical (non-empirical) studies

• Conceptual analysis
• Theory / model building
• Philosophical analysis
• Literature reviews

Source: Mouton, J. 2001. How to succeed in you master's and doctoral studies. Pretoria: Van Schaiks

Taxonomy of research

The practice and science of classification is called taxonomy—the term originates from Greek τάξις, taxis (meaning 'order', 'arrangement') and νόμος, nomos ('law' or 'science'). A taxonomy or taxonomic scheme comprises units, known as taxa (singular taxon), which are arranged in a hierarchical structure of super-type (see the dimensions column on the left below) and subtype (see column on the right below) relationships—also called generalisation-specialisation relationships, or less formally, parent-child relationships. The subtype by definition has the same properties, behaviours, and constraints as the super-type plus one or more additional properties, behaviours, or constraints.

Dimension	Type
Goal	Explanatory — ideographic Descriptive — nomothetic Predictive
Target	Individuals Groups/teams Organisations Communities/societies
Topic	Conditions Orientations — overt Actions (behaviour) — covert
Time	Cross-sectional (diachronic) Longitudinal (synchronic) Cyclical
Control	Experimental Correctional Observational
Application	Basic research Applied research Action research Contract research
Data generating	Non-empirical (theoretical) Empirical (qualitative and/or quantitative)

Source: University of Johannesburg, Department of Industrial Psychology and People Management — I recently had the privilege of attending a study leaders' workshop.