what is analysis in social science research

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Examining Data Analysis Techniques in Social Research: Qualitative vs. Quantitative

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Data analysis provides social researchers with the tool to unlock insights and understand complex social phenomena. You can interpret the data, and uncover relationships and patterns to address human behavior and social experiences. 

Social research, as we all know, focuses on expanding our knowledge on social dynamics. Data analysis in social science research provides you with empirical evidence to dig deeper and explore human experience, attitudes, interactions, and social structures. Social data analysis enables you to assess the effectiveness of policies and programs, helping you make informed decisions and design effective interventions. 

In this blog we focus on exploring quantitative and qualitative data analysis in social science research. 

What is data analysis in research?

In research, data analysis refers to employing statistical and logical techniques to evaluate and synthesize the data collected. It allows researchers to extract meaningful insights from an unstructured mass of data. 

Extracting insights and meaning from data gives us a better understanding of the world and different phenomena and empowers improved decision-making. 

Different data will need to be analyzed 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 analyze 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, color , 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 number of possible values. Some examples of categorical data are race, gender, age group, etc.

Key objectives of data analysis in social research

The followings are the primary objectives of data analysis in social research.

• Data analysis techniques help you describe and summarize the social phenomenon you are studying. It provides you with statistical values such as means, medians, frequencies, and standard deviation, giving you a snapshot of the collected data. 
• It helps facilitate exploratory analysis allowing you to uncover previously unknown insights. The analysis provides a foundation for further research by identifying patterns and relationships for hypothesis generation. 
• Data analysis in social science research enables you to make inferences and draw meaningful conclusions about the target population based on the research sample. By gathering empirical evidence, you can generalize the research results to the larger population, ensuring the external validity. 

Related read: Importance of social research.

Challenges in social research data analysis

While data analysis is central to social research and offers multiple benefits, it is not without its challenges. Here are some common obstacles you may encounter when performing data analysis in social science research. 

• Data quality – It is important to ensure that you remove any missing or inconsistent data to main data integrity and validity. 
• Selecting the proper data analysis technique – You must have a good understanding of various analysis techniques to select the one that is appropriate for the research. 
• Interpreting complex results – You need to communicate the findings effectively and provide a clear explanation of the implication of your research result. 

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Data analysis in social research using a qualitative approach

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. 

Data preparation for qualitative data analysis – 

Before you dive into analyzing your qualitative social research data, you need to prepare the data to make sense of the rich information. 

Step 1: Data familiarization: 

You need to start by getting familiar with the qualitative or textual data you have gathered. Take the time to read and re-read the interviews or feedback to gain a holistic understanding of the content. 

Step 2: Coding and categorization: 

This step involves assigning codes or labels to segments of data. Coding helps you identify themes, concepts, and patterns within your data. Organize your codes into categories (grouping related codes together) and themes (overarching ideas that arise from the data). 

Step 3: Theme and pattern identification: 

Once you have assigned codes, you can start identifying common themes. Look for recurring responses to questions, or identify shared experiences. You can now identify similarities and differences across the data and participants. 

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. 

6 Data analysis methods in qualitative social research -

There are six main analysis methods in quantitative research that you can use in data analysis for social research. Let’s look at these six methods. 

• Narrative analysis. 
• Qualitative content analysis. 
• Grounded theory. 
• Discourse analysis.
• Thematic analysis.
• Interpretive phenomenological analysis. 

Let’s explore these six qualitative data analysis methods. 

1. Narrative Analysis: 

Narrative analysis, or narrative inquiry, is a qualitative research method where researchers interpret texts or visual data in a storied form. There are different approaches to narrative analysis, including; functional, thematic, structural, and dialogic.

2. 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.

3. 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 focusing on social processes or actions. 

4. 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 explains how these sentences function in a social context. 

5. Thematic Analysis: 

The thematic analysis involves looking for patterns by taking large bodies of data and grouping them based on shared themes or similarities to answer the research question being addressed. This method of qualitative data analysis is widely used in the field of psychology. 

6. Interpretive Phenomenological Analysis (IPA): 

It is an approach to psychological qualitative research and has an ideographic focus. It provides 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. 

Leverage online survey tools that enable you to perform text analysis and sentiment analysis to extract insights from your qualitative research data. 

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Data analysis in social research using a quantitative approach

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

Data preparation for quantitative data analysis

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

Step 1: Data Validation: 

Data validation refers to 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 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 transforming and organizing gathered information into a set of meaningful and cohesive categories. 

2 data analysis methods in quantitative social research

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

• Descriptive analysis. 
• Inferential analysis. 

1. 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 covered before moving on to inferential statistics. 

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

2. Inferential Statistics: 

Inferential statistics differs 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. Leverage a data analysis tool that streamlines the entire process of quantitative data analysis and automates any manual work. 

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Data analysis encompasses both quantitative and qualitative methods. Quantitative methods in social science research allow objective insights with the help of statistical analysis. Qualitative methods in social science research provide exploratory insights with the help of textual analysis.   Through data analysis in social science research, you uncover patterns, establish correlations, and gain a deeper understanding of social systems. You can contribute to the discipline with evidence-based insights and generate knowledge that informs decision-making, policies, and interventions advancing our understanding of human behavior and social phenomenon. 

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2.1: Unit of Analysis

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• Anol Bhattacherjee
• University of South Florida via Global Text Project

One of the first decisions in any social science research is the unit of analysis of a scientific study. The unit of analysis refers to the person, collective, or object that is the target of the investigation. Typical unit of analysis include individuals, groups, organizations, countries, technologies, objects, and such. For instance, if we are interested in studying people’s shopping behavior, their learning outcomes, or their attitudes to new technologies, then the unit of analysis is the individual . If we want to study characteristics of street gangs or teamwork in organizations, then the unit of analysis is the group . If the goal of research is to understand how firms can improve profitability or make good executive decisions, then the unit of analysis is the firm . In this case, even though decisions are made by individuals in these firms, these individuals are presumed to represent their firm’s decision rather than their personal decisions. If research is directed at understanding differences in national cultures, then the unit of analysis becomes a country . Even inanimate objects can serve as units of analysis. For instance, if a researcher is interested in understanding how to make web pages more attractive to its users, then the unit of analysis is a web page (and not users). If we wish to study how knowledge transfer occurs between two firms, then our unit of analysis becomes the dyad (the combination of firms that is sending and receiving knowledge).

Understanding the units of analysis can sometimes be fairly complex. For instance, if we wish to study why certain neighborhoods have high crime rates, then our unit of analysis becomes the neighborhood , and not crimes or criminals committing such crimes. This is because the object of our inquiry is the neighborhood and not criminals. However, if we wish to compare different types of crimes in different neighborhoods, such as homicide, robbery, assault, and so forth, our unit of analysis becomes the crime . If we wish to study why criminals engage in illegal activities, then the unit of analysis becomes the individual (i.e., the criminal). Like, if we want to study why some innovations are more successful than others, then our unit of analysis is an innovation . However, if we wish to study how some organizations innovate more consistently than others, then the unit of analysis is the organization . Hence, two related research questions within the same research study may have two entirely different units of analysis.

Understanding the unit of analysis is important because it shapes what type of data you should collect for your study and who you collect it from. If your unit of analysis is a web page, you should be collecting data about web pages from actual web pages, and not surveying people about how they use web pages. If your unit of analysis is the organization, then you should be measuring organizational-level variables such as organizational size, revenues, hierarchy, or absorptive capacity. This data may come from a variety of sources such as financial records or surveys of Chief Executive Officers (CEO), who are presumed to be representing their organization (rather than themselves). Some variables such as CEO pay may seem like individual level variables, but in fact, it can also be an organizational level variable because each organization has only one CEO pay at any time. Sometimes, it is possible to collect data from a lower level of analysis and aggregate that data to a higher level of analysis. For instance, in order to study teamwork in organizations, you can survey individual team members in different organizational teams, and average their individual scores to create a composite team-level score for team-level variables like cohesion and conflict. We will examine the notion of “variables” in greater depth in the next section.