ARTICLE 93: Research Methods for Ph. D. and Master’s Degree Studies: Data Analysis: Part 6 of 7 Parts

Written by Dr. Hannes Nel

In academic research we need to think inductively and deductively.

Inductive thinking is used to develop a new theory.

Therefore, it is what you would mostly use when writing a dissertation for a doctoral degree.

And you should use inductive thematic analysis to analyse the data that you collect.

Deductive thinking is used to test existing theory.

Therefore, it is what you would mostly use when writing a thesis for a master’s degree.

And you should use retrospective analysis to analyse the data that you collect.

Narrative analysis uses both inductive and deductive thinking more or less equally.

That is why both a dissertation and a thesis can be written in a narrative format.

I will discuss the nature of inductive thematic analysis, narrative analysis and retrospective analysis in this article.

Inductive thematic analysis (ITA)

Inductive thematic analysis draws on inductive analytic methods. It involves reading through textual data and identifying and coding emergent themes within the data.

ITA requires the generation of free-flow data. The most common data collection techniques associated with ITA are in-depth interviews and focus groups. You can also analyse notes from participant observation activities with ITA, but interview and focus group data are better. ITA is often used in qualitative inquiry, and non-numerical computer software, specifically designed for qualitative research, is often used to code and group data.

Paradigmatic approaches that fit well with ITA include post-structuralism, rationalism, symbolic interactionism, and transformative research.

Narrative analysis

The word “narrative” is generally associated with terms such as “tale”, or “story”. Such stories are mostly told in the first person, although somebody else might also tell the story about a different character, that is in the second or third person. First person will apply if an interview is held. Every person has his or her own story, and you can design your research project to collect and analyse the stories of participants, for example when you study the lived experiences of somebody who is a member of a gang on the Cape Flats.

There are different kinds of narrative research studies ranging from personal experiences to oral historical narratives. Therefore, narrative analysis refers to a variety of procedures for interpreting the narratives obtained through interviews, questionnaires by email or post, perhaps even focus groups. Narrative analysis includes formal and structural means of analysis. One can, for example, relate the information obtained from a gang member in terms of circumstances and reasons why he or she became a gang member, growth into gang activities, the consequences of criminal activities for his or her personal life, career, etc. One can also do a functional analysis looking at gang activities and customs (crime, gang fights, recruiting new members, punishment for transgression of gang rules, etc.)

In the analysis of narrative, you will track sequences, chronology, stories or processes in the data, keeping in mind that most narratives have a backwards and forwards nature that needs to be unravelled in the process of analysing the data.

Like many other data collection approaches, narrative analysis, also sometimes called ‘narrative inquiry’, is based on the study and textual representation of discourse, or the analysis of words. The type of discourse or text used in narrative analysis is, as the name indicates, narratives.

The sequence of events can be generated and recorded during the data collection process, such as through in-depth interviews or focus groups; they can be incidentally captured during participant observation; or, they can be embedded in written forms, including diaries, letters, the internet, or literary works. Narratives are analysed in numerous ways and narrative analysis can be used in research within a substantial variety of social sciences and academic fields, such as sociology, management, labour relations, literature, psychology, etc.

Narrative analysis can be used for a wide range of purposes. Some of the more common usages include formative research for a subsequent study, comparative analysis between groups, understanding social or historical phenomena, or diagnosing psychological or medical conditions. The underlying principle of a narrative inquiry is that narratives are the source of data used, and their analysis opens a gateway to better understanding of a given research topic.

In most narratives meaning is conveyed at different levels, for example informational content level that is suitable for content analysis; textual level that is suitable for hermeneutic or discourse analysis, etc.

Narrative analysis has its own methodology. In narrative analysis you will analyse data in search of narrative strings (present commonalities running through and across texts), narrative threads (major emerging themes) and temporal/spatial themes (past, present and future contexts).

Retrospective analysis

Retrospective analysis is sometimes also called ‘retrospective studies’ or ‘trend analysis’ or ‘trend studies’. Retrospective analysis usually looks back in time to determine what kind of changes have taken place. For example, if you were to trace the development of computers over the past three decades, you would see some remarkable changes and improvements.

Retrospective analysis focuses on changes in the environment rather than in people, although changes in the fashions, cultures, habits, values, jobs, etc. are also often analysed. Each stage in a chronological development is represented by a sample and each sample is compared with the others against certain criteria.

Retrospective analysis examines recorded data to establish patterns of change that have already occurred in the hope of predicting what will probably happen in the future. Predicting the future, however, is not simple and often not accurate. The reason for this is that, as the environment changes, so do the variables that determine or govern the change. It, therefore, stands to reason that, the longer ahead one tries to predict the future, the more inaccurate will your predictions probably be.

Retrospective analysis does not include the same respondents over time, so the possibility exists for variation in data due to the different respondents rather than the change in trends.


Inductive thematic analysis, or ITA:

  1. Draws on inductive analytical methods.
  2. Involves reading textual data.
  3. Identifies and codes emergent themes within the data.
  4. Requires the generation of free-flow data.
  5. Favours in-depth interviews and focus groups.
  6. Can also use participant observation.
  7. Fits well with qualitative research and critical or interpretive paradigms.

Narrative analysis:

  1. Tells stories related by people.
  2. Ranges from personal experiences to historical narratives.
  3. Can use a wide range of data collection methods.
  4. Includes formal, structural and functional analysis.
  5. Tracks sequences, chronology, stories or processes in data.
  6. Is based on the textual representation of discourse, or the analysis of words.
  7. Is used by a substantial variety of social sciences.
  8. Can be used for a wide range of purposes.
  9. Conveys meaning on different levels.
  10. Has its own methodology.

Retrospective analysis:

  1. Looks back in time to identify change.
  2. Focuses on change in the environment.
  3. Represents and compares change in samples.
  4. Sometimes tries to predict the future.
  5. Does not include the same respondents over time.


It is a good idea to mention and explain how you analysed the data that you collected in your thesis or dissertation.

Ph. D. students will already do so in their research proposal.

That is why you need to know which data analysis methods are available and what they mean.

It will also help to ensure that you use the data that you collect efficiently and effectively to achieve the purpose of your research.

Enjoy your studies.

Thank you.

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