Dissertation writing aims to groom a student by polishing a number of essential academic writing skills. For writing introductions and literature reviews, no technical skills are required. Concurrently, methodology structuring is one of the easiest tasks in dissertation writing. But the case is not the same for performing analysis in the dissertation, as mostly while conducting analysis, the researcher has to master some analytical skills. Thus, the article will be a complete guide for the cohort (a non-experimental and observational study design) analysis and its uses in academic research:
Cohort analysis – A brief introduction:
Cohort analysis is a data organising, summarising, and sorting technique that is based on categorizing it into different groups and sub-groups with common characteristics before analysis. The word ‘cohort’ simply means a group of people, and the term cohort analysis refers to studying people by categorizing them into different groups. To put it in another way, cohort analysis is a type of observational or non-experimental study design. In this study design, the researcher lacks the outcomes of interest to start the analysis. Thus, the exposure status of the individual is important in this type of analysis. Moreover, the outcomes of interest are noted over an extended period of time.
Uses of Cohort analysis in academic research:
- In academic research, cohort analysis is used to measure the risk and rate of occurrence of certain health outcomes over time.
- Cohort studies are ideal for studying rare exposures and investigating the multiple outcomes of a single but rare exposure.
- It creates a temporal relationship between the outcome and exposure. It is possible as the group of people under study is free from the effects of the outcome of interest, so exposure can directly precede the outcome.
- In business studies, cohort studies help the organisation in analysing, isolating, and detecting patterns per lifecycle of a user. That will be helpful in improving user retention and understanding the user’s behaviour in a cohort.
All in all, it helps the academic researcher, health care system, and companies in extracting useful inferences by grouping people based on their shared characteristics.
How to use cohort analysis in research?
In academic research, cohort analysis can be done by following five simple steps:
The form of the data depends on the type of study you are conducting. For business and marketing studies, the data must be in the form of customer behaviour and in medical sciences, it must be somewhat like the data of exposure to a disease and its outcome. You can use any database of your choice to collect data for conducting the analysis, such as PubMed and MySQL.
Select Cohort identifiers:
At this step, you must identify the cohort characteristics for effective grouping. For example, in business and management-related tasks, you can select the date of the first purchase of a product, and in health sciences, the data of a given disease exposure will be the cohort identifier.
Find the lifecycle stages:
To calculate the lifestyle stages, after grouping the patients and users, the events that happened to each user or patient over a selected period of time are calculated.
Create cohort tables and graphs:
Lastly, all the data is reported in the form of graphs and pivot tables that creates visual representations of the patient’s and users’ data for effective comparison. In this way, cohort analysis can help the researcher to use qualitative data to reach effective conclusions for solving a research problem.
Conducting cohort analysis is rather an easy task, but to attempt it for the first time, you must have to follow a step-by-step. Even when following a step-by-step guide, you still need to alter many steps as per the needs of your research project, which sometimes confuses researchers.
After all, is said and done, we must wrap up the discussion by stating that cohort analysis is an important method to suggest improvements in the existing policies and treatments in the light of behavioural and outcome analysis. It can be selected for finding long-term responses; thus, it is also important in maintaining sustainability. The article has discussed a brief guide to using this type of analysis in your research; however, you can also seek an expert’s help for the task.