Methodology Chapter Template
Research
philosophy
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Research philosophy refers to the set of beliefs,
assumptions, and principles that will guide your approach to conducting your
research. There are several research philosophies to choose from, including positivism, interpretivism and pragmatism: Positivism emphasises the use of scientific methods and seeks to uncover
universal laws and generalisable knowledge. It assumes an objective reality
that can be studied through empirical observation and measurement.
Positivists aim for objectivity, reliability, and replicability in their
research. Interpretivism, on the other end of the spectrum, focuses on understanding and
interpreting human behaviour and social phenomena through the lens of
subjective meanings and social contexts. It recognises the importance of
individual experiences, values, and interpretations. Interpretivists often
use qualitative methods, such as interviews, observations, and textual
analysis, to explore the richness and complexity of social phenomena. Pragmatism sits somewhere in the middle and takes a practical and
problem-solving approach to research. It emphasises the use of mixed methods
and acknowledges the value of both quantitative and qualitative data.
Pragmatists are concerned with finding effective solutions and generating
useful knowledge that can be applied to real-world situations. It's worth noting that research philosophies are not
necessarily mutually exclusive - researchers sometimes combine elements from
different philosophies based on their research aim and questions.
Importantly, your choice of research philosophy should align with and support
your research aims, objectives and questions. So, in this section, be sure
to detail both what philosophy
you’ll be adopting and why you’ve
chosen to do so. |
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Next up, you’ll typically discuss your research approach
- in other words, qualitative, quantitative or mixed methods. Qualitative research is focused on understanding and interpreting the meaning,
context, and subjective experiences of individuals or groups. It typically
draws on text-based data and aims
to explore complex social phenomena, often using open-ended questions,
observations, interviews, focus groups, or analysis of textual or visual
data. Quantitative research involves the systematic collection and analysis of numerical data to test hypotheses,
examine patterns, and establish relationships between variables. It aims to
quantify and generalise findings to a larger population. This method uses
structured data collection instruments such as surveys, experiments, or
existing datasets. Lastly, mixed
methods research combines elements of both qualitative and quantitative
approaches. It involves collecting and analysing both qualitative and
quantitative data in a single study or across multiple phases of research.
The purpose is to gain a more comprehensive understanding of a research
problem by integrating different types of data. In this section, once again, you’ll need to clearly state
which approach you’ve chosen and why you’ve made that choice specifically.
Importantly, your choice should align
with your research philosophy (the previous section). For example, if you
adopted an interpretivist philosophy, you’d likely take a qualitative
approach as this naturally supports interpretivist enquiry. |
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Next up is the research strategy, also known as the
research design. The research design refers to the overall plan, structure or strategy that guides a research
project, from its conception to the final data analysis. There are many
potential options here, but for the sake of simplicity, we’ll list the most
common ones for both qualitative and quantitative studies: Common research designs for qualitative studies: ●
Phenomenological design ●
Grounded theory ●
Ethnographic ●
Case study Common research designs for quantitative studies: ●
Descriptive ●
Correlational ●
Experimental ●
Quasi-experimental Again, make sure that your choice here aligns with your
previous choices (philosophy and approach), as well as your overall research
aims and research questions. |
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Your sampling strategy refers to the process you’ll adopt
in terms of selecting a subset of
participants from a larger group of interest. For example, if your
research involved assessing US consumers’ perceptions about a particular
brand of laundry detergent, you wouldn’t be able to collect data from every
single person that uses laundry detergent – but you could potentially collect
data from a smaller subset of this group. There are two overarching approaches to sampling under
which all sampling methods can be classified: probability and
non-probability. Probability
sampling - focuses on achieving a random sample that
is representative of the population of interest. Popular sampling methods
within this category include: ●
Simple random sampling ●
Stratified random sampling ●
Cluster sampling ●
Systematic sampling Non-probability
sampling - is less concerned with achieving a random
or representative sample. Popular sampling methods within this category
include: ●
Purposive sampling ●
Convenience sampling ●
Snowball sampling As with all methodological choices, your sampling
strategy needs to firmly align with
your broader research aims. For example, if you want to be able to
generalise your findings to the broader population, you’ll have to adopt one
of the probability-based sampling methods. |
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Next, you’ll discuss how you’ll go about collecting the
data required for your study. In this section, it’s best to provide as much detail as possible to
demonstrate that you’ve thought through the practical aspects of your study.
You’ll also need to state whether you’ll be taking a cross-sectional or longitudinal approach. For qualitative studies, data collection methods could
include: ●
Interviews ●
Focus groups ●
Observations ●
Document analysis On the quantitative side, collection methods could
include: ●
Surveys ●
Measurements ●
Data from lab equipment ●
Existing datasets As always, it’s essential that you explain both the what and the why - i.e., how you’ll be collecting data and why you chose to
take that approach. |
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Last but not least, you’ll need to discuss how you’ll
analyse your data. Commonly, you’ll use only one analysis method (mono-method), but in some cases, it may make
sense to take a multi-method approach.
As usual, you’ll need to state your approach and justify each choice you make
here. On the qualitative side, common analysis methods include: - Content analysis - Thematic analysis - Discourse analysis - Narrative analysis On the quantitative side, you’ll almost always need to
start with some descriptive statistics. Then, depending on your research aims
and questions, you may also make use of various inferential statistical
tests, such as: ●
T-tests ●
ANOVA ●
Correlation ●
Regression |
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In this section, you should aim to concisely summarise
what you’ve presented in the chapter in a paragraph or two maximum. Be
careful to include only what you’ve already discussed in your chapter (i.e.,
don’t add any new information). Here’s an example of what this might look like in
practice: This chapter
commenced by restating the research objective and question, thereafter
presenting a hypothesis-driven theoretical framework in response to the
research question. A quantitative methodological approach was argued for
based on the correlational nature of the study, access to data, and
contextual appropriateness. The next chapter will apply the chosen
methodology to analyse the data and test the hypotheses. |
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