How to Plan Data Analysis of the Research Study

Plan Data Analysis
Data analysis is very easy if you know how you will do it. Collecting and analysing the data are very hectic jobs to do. But, what data analysis is? Data analysis is a process used to extract valuable information from raw data. Says LeCompte and Schensul. This process helps researchers break raw data into smaller fragments that make sense.

Why is data analysis important in a dissertation writing project? A research study relies heavily on data. There will be a question in the research. The respondents will answer that question. The answer to the question is, in fact, the data. If there are no questions, then you collect data yourself. The researchers call it “data mining.”

Data Analysis in Qualitative Research

There are two types of data, i.e. qualitative and quantitative. A qualitative consists of the words and descriptions. This is subjective and very hard to analyse in a research study. It explains taste, experience, texture, or an opinion. There will be no numbers in this data. So, it’s hard to make a comparison.

If it’s hard, then how will you analyse the data? A word-based method is a globally wide used method to analyse such data. The data analysis process is manual. The researcher reads the information available and finds repetitive words.

Methods for Analysing the Qualitative Data

The methods that researchers use to analyse qualitative data are as follows;

Content analysis

It is the most used method for qualitative data analysis. The researchers use this technique to examine data obtained from images, text, and sometimes from physical items. The use of this method depends on the research questions. The researcher analyses all the responses and information. After analysis, he then determines the presence of some words, themes or concepts. Those themes and concepts will be the results of the research study.

Narrative analysis

This method deals with analysing the data collected from surveys and personal interviews. The researcher finds answers to the research questions from the stories shared by the respondents. After all the questions have been answered, the researcher will analyse all the answers. After completing the analysis, he concludes and gives his argument.

Discourse analysis

It’s a similar technique to narrative analysis. Discourse analysis is used to analyse personal interactions. The major lies in the context of the interaction. The respondent and the researcher interacts with each other in a social context. The discourse analysis also focuses on the lifestyle and environment of the respondents.

Grounded theory

It is the best data analysis technique to explain the cause of happening of a phenomenon. The researchers use this method to investigate data from various similar cases that have occurred in various locations.

Data Analysis in the Quantitative Research

Any data presented in the form of numerical figures are quantitive data. A researcher can group, rank, calculate or categorise this data. It is objective data and very easy to run analysis on. The example questions of such data are age, rank, length, or weight. A researcher can present this type of data in graphical and chart form.

The first stage in analysing data is to make it ready for analysis. It will help in the conversion of nominal data into something meaningful. The preparation of data for analysis consists of the following three phases.
  • Data Validation: The researcher performs the data validation analysis to check whether the data is as per standard or not.
  • Data Editing: More often, research data comes loaded with many errors. Respondents sometimes miss some of the questions or fill them incorrectly. In the editing phase, the researchers ensure that data is free from any errors.
  • Data Coding: This is the most critical phase in analysing quantitative data. It associates with grouping and assigning some codes to the responses of the survey. In simple words, the researcher will set some filters and then analyse the data.

Methods for Analysing Quantitative Data

Primarily there are two methods for analysing quantitative data in academics.
  • Descriptive Statistics: The researchers use this method to describe the basic features of data. The pattern in the data makes sense to the readers.
  • Inferential Statistics: This technique is used to make predictions about many people after research. For example, you asked people outside the cinema some questions about the movies. You will analyse the answers using inferential statistics.


In conclusion, I would say there are many other data analysis plans that you can use. Plans of data analysis changes with the nature of your research study. Hence, you need to look for the best-suited method for your research.


  1. Navigating the complexities of data analysis, as noted by LeCompte and Schensul, underscores its inherent challenges. While unraveling the intricacies of raw data can be demanding, the process is indispensable for extracting meaningful insights. Much like tackling the complexities of data, seeking assistance, such as order essay online, can be a strategic choice when faced with intricate tasks. Both endeavors require a systematic approach to distill valuable information, emphasizing the importance of periodic processes in achieving clarity amidst the apparent chaos of raw data.

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