How To Analyse Secondary Data For A Dissertation?

Secondary data analysis is a popular dissertation research method, allowing researchers to use existing data to answer research questions. Secondary data refers to data already collected by someone else and is available for use by other researchers. How to analyse secondary data for a dissertation is a crucial question for undergraduate students. 


Analysing secondary data can be a complex process, but careful planning and attention to detail can yield valuable insights into a wide range of research topics.

The Key Steps For Conducting Effective Secondary Data Analysis.

  1. Determine The Research Questions

The first step in analysing secondary data is to determine the research questions. It involves identifying the specific questions that the research aims to answer and understanding the scope of the research. 


Defining the research questions will help guide the selection of relevant data and ensure that the research stays focused on the desired outcomes.

  1. Identify Relevant Data Sources

It involves thoroughly searching existing literature, databases, and other sources of information related to the research topic. It is important to use various sources, including academic journals, books, reports, and other credible sources, to ensure the research is comprehensive and well-rounded.

  1. Evaluate The Quality Of Data

Evaluating the data quality used is important. It involves assessing each data source’s credibility, reliability, and relevance. 


Factors to consider when evaluating data sources include the reputation of the data source or publisher, the rigour of the research methods used, and the currency and relevance of the data presented.

  1. Clean And Prepare The Data

After relevant data sources have been identified and evaluated, the next step is to clean and prepare the data. It involves removing duplicate or irrelevant data and formatting it to make it easy to analyse and draw conclusions from. 

  1. Analyse The Data

Once the data has been cleaned and prepared, the next step is to analyse it. It involves reviewing the information gathered from each data source and identifying key themes, trends, and patterns. 


It is important to approach the data analysis in a systematic and structured manner to ensure that all relevant information is considered.

  1. Interpret The Results

After the data has been analysed, the next step is to interpret the results. It involves synthesising the findings from each data source and drawing overarching conclusions about the research topic. 


Considerations For Analysing Secondary Data

The following are several key considerations by Essays UK:

  1. Data Quality

It is important to carefully evaluate the data quality, as poor-quality data can lead to accurate or reliable results.

  1. Data Cleaning

Cleaning and preparing the data can be time-consuming, but ensuring that the data is accurate and usable for analysis is essential.

  1. Data Analysis Techniques

Many different data analysis techniques are available, and it is important to select the most appropriate method for the research questions and data being analysed.


  1. Reproducibility

It is important to document the data analysis process in a way that allows others to reproduce the findings and verify the accuracy of the information gathered.



Analysing secondary data is a valuable dissertation research method, allowing researchers to use existing data to answer research questions. To effectively analyse secondary data, it is important to determine the research questions, identify relevant data sources, evaluate the data quality, clean and prepare the data, analyse it, and interpret the results.