Understanding different research perspectives

# 8 Research methodology

The most important methodological choice researchers make is based on the distinction between qualitative and quantitative data. As mentioned previously, qualitative data takes the form of descriptions based on language or images, while quantitative data takes the form of numbers.

Qualitative data is richer and is generally grounded in a subjective and interpretivist perspective. However, while this is generally the case, it is not always so. Qualitative research supports an in-depth understanding of the situation investigated and, due to time constraints, it generally involves a small sample of participants. For this reason the findings are limited to the sample studied and cannot be generalised to other contexts or to the wider population. Popular methods based on qualitative data include semi-structured or unstructured interviews, participant observations and document analysis. Qualitative analysis is generally more time-consuming than quantitative analysis.

Quantitative data, on the other hand, might be easier to collect and analyse and it is based on a large sample of participants. Quantitative methods are based on data that can be ‘objectively’ measured with numbers. The data is analysed through numerical comparisons and statistical analysis. For this reason it appears more ‘scientific’ and may appeal to people who seek clear answers to specific causal questions. Quantitative analysis is often quicker to carry out as it involves the use of software. Owing to the large number of respondents it allows generalisation to a wider group than the research sample. Popular methods based on quantitative data include questionnaires and organisational statistical records among others.

The choice of which methodology to use will depend on your research questions, the formulation of which is consequently informed by your research perspective. Generally, unstructured or semi-structured interviews produce qualitative data and questionnaires produce quantitative data, but such a distinction is not always applicable. In fact, language-based data can often be translated into numbers; for example, by reporting the frequency of certain key words. Questionnaires can produce quantitative as well as qualitative data; for example, multiple choice questions produce quantitative data, while open questions produce qualitative data.

## Activity 8

About 90 minutes

Go back to the two papers you started reading in Activity 1 and read the methodology sections (they may be called methods or something similar) of both papers.

Now answer the following questions.

- What type of method(s) have the author/s in article 1 used to collect data?
- What method of analysis have these author/s used?
- What type of method(s) have the author/s in article 2 used to collect data?
- What methods of analysis have these author/s used?
- How do you think the methods used in both papers address the initial research aims or questions?
- Why do you think the methods used in both papers are appropriate to address these initial research aims or questions?

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## Discussion

If you have chosen two papers based on different methodologies, you should reflect on the link between the ways in which the purposes of the studies were developed and the specific methods that the authors chose to address those questions. In the articles there should be a fit between research questions and methodology for collecting and analysing the data. The activity should have helped you to familiarise yourself with processes of planning a research methodology that fits the research question.

This course so far has given you an overview of the research strategy, design and possible methodologies for collecting data. What you have learned should be enough for you to have developed a clear idea of the general research strategy you want to adopt before you move on to develop your methodology for collecting data and review the methods in detail so that you are clear about the benefits and limitations of each before you collect your data.

OpenLearn - Understanding different research perspectives
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