How to Analyse Secondary Data for a Dissertation
Secondary data is data that has already been obtained by another researcher and is no longer being used. Secondary data, whether qualitative or quantitative, can be a highly useful source of information for researchers (and students!) who are constrained in their time and financial resources. Furthermore, with technological advancements and the availability of peer-reviewed publications and studies through the internet, it is becoming increasingly popular as a method of data collection for researchers. The question that usually arises among students, however, is: how should secondary data be analyzed to the greatest extent possible?
In secondary research, the process of data analysis is known as
Secondarily, secondary analysis (i.e., the use of previously collected data) is a systematic methodological technique that follows a set of clearly defined processes in order to be effective. In layman’s terms, there are three steps to follow:
Step one is the formulation of research questions.
Identification of the dataset (second step)
Step Three: Analyze and evaluate the dataset.
Let’s take a closer look at each of these in turn:
Step one is the formulation of research questions.
To answer research questions with secondary data, you must apply theoretical knowledge and conceptual abilities, which means you must apply theoretical knowledge and conceptual skills to the dataset. It follows from this that, in order to find the most appropriate secondary data for your research, you must first properly define and develop your research questions so that you know what areas of interest you need to investigate in order to find it.
Identification of the Dataset (second step)
To begin, it is necessary to investigate the subject area in order to determine what is currently known about it and where there are gaps in knowledge, as well as what data is available to fill in those gaps. Secondary datasets can be derived from academic studies that have employed quantitative or qualitative data in the past, which can then be compiled and aggregated to create a new secondary dataset. In addition, additional more informal or “grey” literature, such as consumer reports, commercial research, or other comparable materials, might be used into the project. When conducting secondary research, one of the benefits is that original survey works often do not use all of the data acquired, which implies that this unused knowledge can be applied to various contexts or viewpoints.
Important point: Making effective use of secondary data requires determining how the data can be used to provide meaningful and relevant responses to the research topics at hand. In other words, the data used is a suitable fit for the study and the research issues being investigated.
Step Three: Evaluate the dataset to determine its usefulness and fit.
When evaluating data, it is recommended that you take a reflective approach. In other words, it is prudent to identify the objective of the work, the credentials of the authors, and the sources of secondary data for each item of secondary data that will be used (i.e., credibility, what data is provided in the original work and how long ago it was collected). In addition, the methodologies employed and the level of consistency that exists when compared to other works are taken into consideration. Because understanding the primary technique of data collection will have an impact on the total review and analysis when it is used as a secondary source, it is critical to comprehend it. In essence, if there is a lack of knowledge of the coding that is utilized in qualitative data analysis to identify significant themes, there will be a mismatch between interpretations when the data is used for secondary research. Furthermore, having several sources that reach similar findings provides a better level of validity than relying solely on one or two secondary sources, which is less reliable.
In order to make decisions, a useful framework provides a flow chart, as seen in the image below.
Secondary Data Should Be Analyzed
Maintaining a systematic approach ensures that only the works most relevant to your research questions are included in the final dataset, and it also communicates to your readers that you have been meticulous in your selection of appropriate works to employ.
Preparing a Report on the Analysis
Once you have your dataset, the process you take to write up the analysis will determine how you write up the results. If the information is qualitative in nature, you should follow the steps outlined below.
Pre-Planning
Read and re-read all materials, making notes on first observations, correlations, and relationships between topics, as well as how they relate to your research objectives and how they might be applied.
It is advisable to investigate further and identify sub-themes that follow on from the core themes and correlations in the dataset, as this encourages the identification of new insights and increases the originality of your own work. Once initial themes have been identified, it is advisable to investigate further and identify sub-themes that follow on from the core themes and correlations in the dataset.
Introduction to the Analysis Presentation: Structure and Content
A comprehensive summary of all of your sources should be included in the introduction. Organizing things in a table and listing them chronologically is a smart technique to ensure that your work has an ordered and consistent progression. In addition, a brief (2-3 sentences) description of the important outcomes and findings found should be included in the opening paragraph.
Text in the body
Regardless of whether quantitative or qualitative data is employed, the body text for secondary data should be divided into sub-sections for each argument or theme that is provided. In the case of qualitative data, depending on whether content, story, or discourse analysis is employed, this entails presenting the most important publications in the field, their conclusions, and how these answer, or do not answer, the research questions you have posed. Ensure that each source is properly attributed and referenced at the conclusion of the work. For qualitative data, any figures or tables should be replicated with the appropriate citations to the original source, unless otherwise stated. Providing a major heading for a significant topic, as well as sub-headings for each of the sub-themes identified during the analysis, is considered best practice in both situations.
Direct quotations from secondary data should not be used unless they are:
proper citations, and they are essential in emphasizing a certain point or conclusion that you have formed from the facts.
It is important that all results sections, regardless of whether primary or secondary data was used, refer back to the research questions and previous studies. This is because, regardless of whether the results corroborate or contradict earlier research, adding past works demonstrates a deeper degree of reading and comprehension of the topic under investigation and allows you to go into more depth with your own study.
a summary of the findings
The summary of the findings portion of a secondary data dissertation should provide a concise review of the most important findings, as well as, if appropriate, a conceptual framework that clearly shows the findings of the work in question. This demonstrates that you have comprehended the secondary data, how it has responded to your research objectives, and, more importantly, that your interpretation has resulted in some concrete conclusions.
