The Research Methodology chapter should normally be broken down sequentially into at least the following sections:
Below are brief explanations on what is expected from students for each of the above.
Set out your research paradigm, depending on the philosophy that underpins your research. The philosophical approach sets a framework of the study which provides the right answers to the research questions. For example, if you are using the research onion (Saunders et al., 2016), there are four different approaches of research philosophy, namely positivism, realism, interpretivism and pragmatism. Each philosophy is suitable for a different sort of study, and each involves different assumptions about the world (ontology), how we know that world (epistemology) and the nature of knowledge.
The positivism approach is mainly based on facts rather than impressions which reflect the notion of social reality, and is about formulating a research strategy for data collection and use existing theories to develop hypotheses. If your research reflects the philosophy of positivism, then you will probably adopt the philosophical stance of the natural scientist, i.e., collect data about an observable reality, and search for regularities and causal relationships in your data to create law-like generalisations like those produced by scientists (Gill and Johnson, 2010).
Realism relates mainly to scientific enquiries, and is about debating on whether objects exist independently of our knowledge of their existence. Realism is explained through direct and critical realism. The former states that you see what you get, while the latter argues that what we experience are mainly sensations in the real world which is far from being the actual direct things. Realism is a branch of epistemology, which is similar to positivism in that it assumes a scientific approach to the development of knowledge.
Interpretivism is about understanding differences between human and social factors. Emphasis is being made on differentiating research among people to that of objects. Crucial to the interpretivist philosophy is that the researcher has to adopt an empathetic stance. The challenge is to enter the social world of research subjects and understand their world from their point of view (Saunders et al., 2016).
Pragmatism is about adopting one position which is somewhat unrealistic in practice. This approach avoids the debates on reality or truth but rather to what the researcher perceives of what is of interests or value based on a personal value system. Pragmatists recognise that there are many different ways of interpreting the world and undertaking research, that no single point of view can ever give the entire picture and that there may be multiple realities (Saunders et al., 2016).
You will need to explain the context of your research, its limitations, as well as answer the questions How, Why, What Where and When? It is in this section that you will explain and justify your decision regarding the use of qualitative, quantitative or mixed methods. Remember that each method is associated with a different approach to gathering data.
As an example, you first need to decide whether you are going to work along broadly positivist, scientific lines, starting with one or more defined hypotheses and testing them against reality. If so, you are likely to be collecting numerical data in reasonably large quantities and running statistical tests on this data. In other words, you'll be using a quantitative approach. On the other hand, you might be more interested in exploring broad areas, probably to do with people's experiences of, perceptions of or emotional reactions to a subject, and looking in detail at these responses in all their richness. By looking at broad areas of interest, you are aiming to generate theories about the area you are investigating. If this is the case, you will be adopting a qualitative approach. Lastly, you might want to use a mix of both of the abovementioned methods, i.e., mixed methods research, which is becoming increasingly popular. It's particularly useful when you want to reflect different perspectives on a subject, or put quantitative information into a robust real-world context.
Research design provides an overall guidance for the collection and analysis of data. It is a blueprint that enables researchers to find answers to the research questions in a study. Research can be classified as descriptive, exploratory or explanatory in nature (Saunders et al., 2016).
According to Saunders et al. (2016), a descriptive approach is conducted with an attempt to portray an accurate profile of a situation. This may be an extension to a piece of exploratory research and/or explanatory research. The concept is useful and crucial when the researcher's achievement focuses on details and description of phenomena or when it should show prediction about specific findings.
Exploratory designs are used to explore an area where little is known or to investigate the possibilities of undertaking a particular research study. This type of research is particularly useful when the nature of the problem is unsure. As per Saunders et al. (2016), this concept is characterised as flexible and adaptable to daily changes as result of new information upcoming or new hypotheses appearing. Exploratory research design is the most suitable and useful for new studies giving significant contribution to understanding the findings of the main subject.
Explanatory designs are also known as causal designs, and are used for answering the how and why questions. According to Saunders et al. (2016), emphasis is made on studying a situation or a problem in order to explain the relationship between variables. In general, this concept makes use of quantitative ways of research to describe relationships expressing the cause and effect of a particular incident. However, explanatory research design also allows the use of a qualitative approach to answer the questions how and why, depending on the sources of the relationship between cause and effect.
The need to have a clear research strategy is very important for any research under study, which can broadly be categorised as either positivist or phenomenological. Research strategies can be used for descriptive, exploratory and explanatory research. There are different types of research strategies amongst which the commonly used are experiment, survey, case study, action research, grounded theory, ethnography and archival research (Saunders et al., 2016). In this section, you will therefore have to outline how you collected your data and explain your choice for using the methods you did, i.e., survey, interview, focus group discussion and so on.
The following important aspects must be covered:
We specify the aim and focus of your research and choose the most appropriate philosophical approach and design that will address your research objectives. After defining your research approach and strategy, we design your sampling strategy in terms of sampling method and sample size, following which, we select the best measuring instrument(s) and data collection method(s) for your research. We also determine the most effective data analysis techniques, while handling study limitations, bias and ethical considerations, with a view to obtaining accurate results and insightful findings.
Schedule Your FREE Consultationby phone or video call Today: +230 5499 9070According to Wiener et al. (2017), the reliability of a measure is the degree to which a measurement technique can be depended upon to secure consistent results upon repeated application. Reliability also measures the internal consistency of a measuring instrument by the amount of intercorrelation between a set of items. When there are statements measured on the Likert scale in a survey questionnaire, the Cronbach alpha coefficient is the most appropriate measure of internal consistency (Laerd Statistics, 2018).
Validity determines whether an instrument measures what it is supposed to measure (Wiener et al., 2017). During the piloting stage of the questionnaire, the face and content validity are normally verified by respondents' feedback. A more scientific and objective way of validating a measuring instrument is via construct validity which, determines whether the data contains underlying constructs, i.e., exhibits dimensionality (Ahmad and Sabri, 2013). One way of verifying construct validity is by way of factor validity (Nako and Barnard, 2012), which consists of subjecting the data to factor analysis and then observing the results for Bartlett's test of Sphericity. The instrument, and hence the data, is deemed to be valid if the p-value does not exceed 0.05 (Field, 2016), the usual default level of significance.
You need to discuss about the extent to which your results and findings are also true of other populations. Remember that not all studies are as generalisable as others, namely case studies. Bearing in mind the delimitations of your research and its external validity, you need to discuss how generalisable your results are likely to be, and why.
Specify and justify the use of the techniques and tests that you intend to analyse your data with. For example, if you have sampled texts, or have a lot of qualitative data, will you be using semiotics analysis, discourse analysis and so on? You may also mention which software (if any) that you have used, e.g. NVivo and so on and why you chose to use these particular software.
With regards to quantitative analysis, mention (with justification) which software you used, e.g. SPSS, MS Excel, Stata and so on. With hindsight, you will realise the importance of the chosen measurement scales in your survey questionnaire, as they are crucial for the implementation of statistical tests and advanced techniques. This is why it is important to have an idea of how you will analyse your data, prior to designing your questionnaire.
In quantitative data analysis, tests and techniques should be judiciously chosen in order to maximise the accuracy and reliability of your findings. Ensure that you check all the necessary assumptions before you use any test or apply any technique. As an example, normality testing via the Shapiro-Wilk test or the Kolmogorov-Smirnov test is essential before deciding whether to use parametric or non-parametric tests. There is a wide variety of techniques that may be used, just to name a few well-known ones:
Ahmad, NS and Sabri, A (2013) "Assessing the unidimensionality, reliability, validity and fitness of influential factors of 8th grades student's Mathematics achievement in Malaysia", International Journal of Advance Research, Vol. 1, No. 2, pp. 1-7.
Cooper, DR and Schindler, PS (2014) Business Research Methods (12th edn), McGraw-Hill, New York.
Gill, J and Johnson, P (2010) Research Methods for Managers (4th edn), Sage, London.
Laerd Statistics (2018) "Cronbach's Alpha (α) using SPSS Statistics" [online] Available from: https://statistics.laerd.com/spss-tutorials/cronbachs-alpha-using-spss-statistics.php
Malhotra, NK (2019) Marketing research: An applied orientation (7th edn), Pearson/Prentice Hall, Upper Saddle River, NJ.
Nako, Z and Barnard, A (2012) "Construct validity of competency dimensions in a leadership assessment and development centre", African Journal of Business Management, Vol. 6, No. 34, pp. 9730-9737.
Saunders, M, Lewis, P and Thornhill, A (2016) Research Methods for Business Students (7th edn), Pearson Education Limited, England.
Wiener, BJ, Lewis, CC, Stanick, C, Powell, BJ, Dorsey, CN, Clary, AS, Boynton, MH and Halko, H (2017) "Psychometric assessment of three newly developed implementation outcome measures", Implementation Science, Vol. 12, No. 1, pp. 1-12.
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