When assessing a thesis or dissertation, one of the many issues examiners look for is consistency. They like to be able to follow a golden thread that stitches the disparate sections of your document together. One way in which you can do this is to ensure uniformity in how you design, execute and analyse your primary research.
Here are examples from three students Thesis Upgrade has worked with. They have taken different approaches to their data collection and analysis, but all have ensured robust consistency throughout the process.
John Shelly, an MBA student, adopted a quantitative approach. “My research design involved establishing if there was a relationship between investment in training in innovation techniques and the innovation outputs from small and medium-sized companies. I collected quantitative data through an online questionnaire. It was easy for business managers to complete it, as their answers were based on facts they could obtain from their own organisations. For example, spend on innovation training, quantities of new products developed, actual market demand, prices achieved by new products, and so on. The data I gathered were empirical and led me to test a range of hypotheses I had formulated using quantitative, mathematical techniques. Having a quantitative (mainly numerical) data set meant I used statistical functions and formula (totals, sums, averages, modal values) for my analysis. These analyses showed that there was a positive relationship between two variables. That is, the more money spent on training, the more likely it was for new products to be produced and sold. Using a quantitative method from research design, through to data collection and collation, and on to analysis and findings, helped me to demonstrate consistency in my approach”.
By contrast, Sonia Broadchurch, who was studying for a BSc in Human Resource Management, took a qualitative approach: “My research examined what some people view as a soft issue: how managers use performance appraisal meetings to motivate staff”, she said. “In my case, there were multiple realities to deal with. Each of our managers, and every worker, subjectively saw the purpose and outcomes of appraisal meetings differently. They construct their own viewpoints through ascribed meaning. It is easy to fall into the trap of gathering and analysing data that is unstructured and apparently meaningless. I therefore set out to collect data – words, phrases, sentences and exchanges – by being consistent in the questions I asked at interview. I continued to maintain this uniformity when I analysed the data thematically. The emerging themes helped me to substantiate the claim that appraisal meetings could be used effectively to motivate staff”.
Some students, like Charlie Reyners, conduct mixed methods research. “My study involved exploring the equalities, and inequalities, of internal promotions in government offices. My initial, quantitative research involved administering questionnaires to managers who interviewed internal candidates. The findings indicated that managers believed promotions occurred following a rigorous and objective interview process. Similar questionnaires administered to interviewees showed that few internal candidates felt the same. I followed up the contrasting data sets with two focus group discussions with the interviewers (managers) and interviewees (internal staff candidates). This approach allowed me to explore the issue qualitatively and in depth. Both interviewers and interviewees began to question how objective the decision making was. By confronting the ‘reality’ initially presented by the managers we began to uncover the hidden effects of different values, ideologies, biases and influences on the selection process. I had originally intended to conduct only quantitative analysis, but later added to that with critical interpretive qualitative analysis collected from the two focus groups”.
How to ensure consistency in your research is a key decision that is, ideally, made when designing your fieldwork or study. It involves choosing whether to carry out quantitative or qualitative research, or inductive versus deductive research, and making numerous decisions that might influence the data collection and analysis process. Sometimes researchers, such as Charlie, find it necessary to change their approach mid-process, as the information they have collated indicates they have insufficient data of the appropriate type to draw findings and a conclusion. In these cases, an additional data collection process should be undertaken, and an abductive form of analysis, which combines all the data sets, then follows.
Thesis Upgrade’s Analysing and Interpreting Your Data is a useful, downloadable, digital publication. It contains easy-to-understand information and straightforward explanations to help you interrogate, analyse and interpret your collected data. Buy now for immediate use.
