Data Analysis using Qualitative and Quantitative Techniques1

Data Analysis for Social Research using Qualitative and Quantitative Techniques


Table of Contents

What is Data Analysis in Research?

In research, data analysis refers to the process of employing statistical and logical techniques to evaluate and synthesise the data collected. It allows researchers to extract meaningful insights from an unstructured mass of data. When we’re able to extract insights and meaning from data, it gives us a better understanding of the world and different phenomena and empowers improved decision-making in the process. 

Different data will need to be analysed using different techniques. Within this article, we will explore the different kinds of data in research, and the different methods of data analysis used to analyse them. 

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Types of Data in Research

There are three main types of data in research:

  • Qualitative Data: Qualitative data is used to describe qualities or characteristics and generally refers to the descriptive findings collected through different methods of research. It refers to data that is non-numerical in nature and is therefore not quantifiable. Some examples of qualitative data are blood type, ethnic group, colours, etc. 
  • Quantitative Data: The type of data whose value takes distinct figures or counts that are associated with a numerical value. It refers to quantifiable information that can be used to conduct statistical analysis and mathematical computations. Some examples of quantitative data are cost, age, and weight. 
  • Categorical Data: Categorical data refers to the types of data that can be divided into groups. Categorical variables can only take one of a limited, and usually fixed, a number of possible values. Some examples of categorical data are race, gender, age group, etc.

Data Analysis in Qualitative Research

Data Analysis using Qualitative and Quantitative Techniques2

Let’s take a look at how data analysis is conducted in qualitative research and the different methods that are commonly used to do so. 

How do we Identify Patterns in Qualitative Data

When analysing and looking for patterns in textual information, there are many different methods that can be used, including:

  • Word-based Method: The word-based method generally involves manually reading through the gathered data to find repetitive themes or commonly used words. 
  • Scrutiny-based Technique: The scrutiny-based technique is used to derive conclusions based solely on what is already known by the researcher. This is a popular method of text analysis for identifying correlations and patterns within textual information.   
  • Variable Partitioning: Variable partitioning, or dynamic partitioning, can be used to split variables so that more coherent descriptions and explanations can be extracted from vast vast data. 

Methods used for Data Analysis in Qualitative Research

There are six main methods of data analysis used in quantitative research:

  • Narrative Analysis: Narrative analysis, also known as narrative inquiry, is a qualitative research method where researchers interpret texts or visual data that have a storied form. There are different approaches to narrative analysis, including; functional, thematic, structural, and dialogic.
  • Qualitative Content Analysis: This is a straightforward method of qualitative research where patterns within a piece of content are evaluated. It can be used with different forms of content such as words, phrases, and/or images.
  • Grounded Theory: This method of qualitative analysis is used to create new theories using the data collected, by using a series of “tests” and “revisions”. Grounded theory (GT) follows a structured but flexible methodology and is generally focused on social processes or actions. 


  • Discourse Analysis: This method is used to study written, vocal, sign language, or any significant semiotic event, in relation to its social context. It allows researchers to examine a language beyond just sentences and provides an understanding of how these sentences function in a social context. 
  • Thematic Analysis: Thematic analysis involves looking for patterns by taking large bodies of data and then grouping them based on shared themes or similarities to provide an answer to the research question being addressed. This method of qualitative data analysis is widely used in the field of psychology. 
  • Interpretive Phenomenological Analysis (IPA): IPA is an approach to psychological qualitative research and has an ideographic focus. It is used to provide a detailed examination of a person and their lived experiences. The aim of IPA is to understand how participants make sense of their personal and social world. 

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Data Analysis in Quantitative Research

Data Analysis using Qualitative and Quantitative Techniques3

Let’s now delve into how data analysis is conducted in quantitative research and the different methods that are commonly used to do so. 

Data Preparation for Data Analysis of Quantitative Research

Before quantitative data can be analysed, it must first be prepared using the following three steps:

  • Step 1: Data Validation: Data validation refers to the process of comparing the gathered data against defined rules to ensure that it is within the required quality parameters, without any bias. It generally involves checking for the following; fraud, screening, procedure, and completeness. 
  • Step 2: Data Editing: Data editing refers to the process of reviewing and adjusting after checking for missing, invalid, or inconsistent entries within the data records. 
  • Step 3: Data Coding: As the name suggests, data coding involves deriving codes from observed data. It refers to the process of transforming organizing gathered information into a set of meaningful and cohesive categories. 

Methods used for Data Analysis in Quantitative Research

There are two main methods of data analysis used in quantitative research:

  • Descriptive Statistics: This quantitative method of data analysis is used to describe the basic features of data in a study and provides simple summaries about the measures and sample. It helps researchers understand the details of a sample group, and doesn’t aim to make assumptions or predictions about the entire population. Descriptive analysis generally includes the first set of statistics that are covered before moving on to inferential statistics. 

Some common statistical tests used in descriptive statistics are mean, median, mode, skewness, and standard deviation. 

  • Inferential Statistics: Inferential statistics is different from descriptive statistics as it aims to make inferences about the population, rather than about a specific data set or sample. It, therefore, allows researchers to make assumptions and predictions about an entire population. 

There are two main kinds of predictions made using inferential statistics, including predictions about the differences between groups within a population and predictions about the relationships between variables relevant to a population. 

Some common inferential methods used in quantitative data analysis are regression analysis, frequency tables, analysis of variance (ANOVA), cross-tabulation, and correlational research.

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FAQs on Data Analysis in Research

 As the name suggests, data analysis is the process of analysing data by utilizing logical and statistical techniques to evaluate the data collected through research. 

There are six methods used commonly to analyse qualitative data, and they are;

  • Narrative Analysis
  • Discourse Analysis
  • Thematic Analysis
  • Qualitative Content Analysis
  • Grounded Theory
  • Interpretive Phenomenological Analysis (IPA)

There are two key methods used in the analysis of quantitative data:

  • Descriptive Statistics
  • Inferential Statistics

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