ARTICLE 72: Research Methods for Ph. D. and Master’s Degree Studies: Data Collection

Written by Dr. Hannes Nel

Here is a hint that will save you lots of time, energy and money on the research that you will do towards your master’s degree or Ph. D.

In fact, your thesis or dissertation will probably not be accepted by the university if you do what I am advising you not to do.

The hint is simply this – avoid doing unnecessary work as far as you possibly can.

To achieve this, you must steer clear of three bad practices:

Do not pad.

Do not confuse volume with quality.

And do not confuse motion with action.

I introduce my series of videos on data collection for research purposes in this article.

Both qualitative and quantitative research covers a wide spectrum but share one important feature – the collection and organisation of research data to enable analysis. Most importantly, though, the data that you collect must satisfy the purpose of your research. Students sometimes complete a research report without producing any significant findings. 

When collecting and analysing data you need to interpret the data creatively to develop insights that will lead to new knowledge or at least add value to existing knowledge. In the case of especially action research your work should also produce new ways of doing things. To achieve this, you need to have the ability to analyse data, be sensitive to theoretical arguments and have sufficient writing abilities to write a professional report. All of this, however, would be worthless and perhaps even damaging to the current knowledge if you collect inaccurate, superficial, irrelevant or simply poor-quality data.

In research, questions of relevance, specificity and scope with which you will be able to cope, bearing the available time, cost and philosophical level in mind, are central to the process of subject analysis, offering strategies for effective information organisation and retrieval. The principles of data collection and organising are important for enhancing the thoroughness of research and any researcher should be aware of them.

In organising data for analysis, the ideal is to turn the raw data into a logical narrative, where emergent themes will be distinct and clearly identified, and will fit into an overall structure that makes sense, given the research questions. However, few research projects fit this ideal, and categories more commonly resemble a bag of puzzle pieces, with one or two pieces missing and a few others belonging to a different puzzle. Here, themes are identified, like the colours and shapes of the puzzles, but need to be picked and sorted carefully from the pile during the analysis process.

In a worst-case scenario, data are splattered all over like the colours when some of the puzzle pieces have been cut in two or three. Potential themes may be identifiable, but overall, the data gives little direction for rigorous analysis.

All research involves the collection and analysis of data, whether through reading, observation, measurement, questions, or a combination of these or other procedures. The data collected during and for research may, however, vary considerably in their characteristics. For example:

  1. Data may be numerical, or may consist of words, or may be a combination of the two.
  2. Data may be neither numbers nor words, but consist of, for example, pictures and artefacts.
  3. Data may be ‘original’, in the sense that you have collected information never before collected; or may be ‘secondary’, already put together by somebody else, but reused, probably in a different way, by you.
  4. Data may consist of responses to a questionnaire or interview transcriptions, notes or other records of observations or experiments, documents and material, or all of these things.

Collecting and using qualitative data are both important parts of qualitative research. Data collection methods tie up closely with the research approach that you choose, i.e. quantitative or qualitative.


All academic research requires the collection and analysis of data.

On doctoral level you will need to interpret the data that you collect in such a manner that it will lead to new knowledge or at least add to existing knowledge.

On master’s degree level you will need to show that you understand and can apply the data that you collected.

For the sake of efficient and effective research, the data that you collect must be relevant, specific and articulated to the scope for your research.

The ideal is to turn the raw data that you collected into a logical narrative.

You will probably collect many different types of data making use of different data collection methods.

Collecting and using data are equally important for the success of your research project.

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