3. Intro to data analysis and visualisation

Data vs Information

Data and information are closely related but distinct concepts in academic discourse.

Data visualisation refers to the graphical representation of data and information using visual elements such as charts, graphs, and maps. It provides an accessible way to identify patterns, trends, and outliers, enabling users to interpret complex datasets more effectively. In the context of growing volumes of data, visualisation has become essential for making sense of large datasets and supporting evidence-based decision-making. By transforming data into visual formats, it allows users to quickly grasp insights that may not be apparent in raw or tabular data.

Data refers to raw, unprocessed facts, figures, or observations that have not yet been interpreted or given meaning. These may include numbers, measurements, or recorded responses collected through research or observation.

Information, by contrast, emerges when data are organised, analysed, and contextualised in a way that renders them meaningful and useful for understanding or decision-making.

flowchart LR
    A[Raw Data] --> B[Processing]
    B --> C[Organisation]
    C --> D[Contextualisation]
    D --> E[Information]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#FFF5E1,color:#000
    style E fill:#F3E6FF,color:#000

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Types of Data

Data can be classified into several distinct types, each reflecting different forms of representation and measurement.

At a broad level, data are divided into qualitative and quantitative forms.

Qualitative data capture descriptive, non-numerical information, such as opinions, experiences, or categories.
Quantitative data consist of numerical values that can be measured and analysed statistically.

Within quantitative data, a further distinction is made between discrete and continuous data:

• Discrete data refer to countable values, often expressed as whole numbers (e.g. number of students).
• Continuous data represent measurements that can take any value within a given range (e.g. height or temperature).

In addition, categorical data—closely related to qualitative data—organise information into defined groups or labels, enabling comparison across distinct categories.

Together, these data types provide a structured framework for collecting, analysing, and interpreting information in academic research.

Data can be qualitative, quantitative, or categorical. Quantitative data can be discrete or continuous.

• Qualitative (descriptive)
• Quantitative (numerical)
  • Discrete (countable)
  • Continuous (measurable)
• Categorical (grouped labels)

flowchart TD
    A[Data] --> B[Qualitative]
    A --> C[Quantitative]
    C --> D[Discrete]
    C --> E[Continuous]
    A --> F[Categorical]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#FFF5E1,color:#000
    style E fill:#F3E6FF,color:#000
    style F fill:#E0F7FA,color:#000

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Data Lifecycle

The data lifecycle refers to the series of stages through which data pass, from initial conception to eventual dissemination and preservation. It typically begins with planning, where research objectives are defined and data requirements are established, followed by collection, involving the systematic gathering of data through methods such as surveys, observations, or interviews. Once collected, data undergo processing, where they are cleaned, organised, and prepared for meaningful use, before moving into the analysis stage, where they are interpreted to identify patterns, relationships, or insights relevant to the research questions. Subsequently, data are stored securely to ensure integrity, accessibility, and compliance with ethical and legal standards, and finally shared, where findings are communicated to relevant stakeholders, contributing to knowledge dissemination and potential reuse. This cyclical process highlights that data management is not linear but iterative, with insights from later stages often informing new cycles of inquiry.

Stages:

• Planning
• Collection
• Processing
• Analysis
• Storage
• Sharing

flowchart LR
    A[Data] --> B[Information]
    B --> C[Knowledge]
    C --> D[Insight / Decision-making]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#F3E6FF,color:#000

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Key ideas

• Making data understandable: Visualisation simplifies complex data, enabling quicker interpretation and clearer communication of findings.

• Identifying patterns and insights: Visual formats help reveal trends, correlations, and anomalies that may be difficult to detect in spreadsheets.

• Supporting decision-making: By presenting information clearly, visualisation aids informed decision-making across disciplines.

• Combining analysis and storytelling: Effective visualisation is a balance between data accuracy and visual clarity, turning data into meaningful narratives.

Relevance across disciplines

Data visualisation is a key skill across sectors, including education, government, business, and research.

Research Design

A research design is a plan, structure and strategy of investigation so conceived as to obtain answers to research questions or problems. According to Selltiz, Deutsch and Cook (1962), “a research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.” Research designs therefore refer to the specific procedures involved in the research process, including data collection, data analysis, and report writing.

• Structured plan for collecting and analysing data

Types of Research Design

• Exploratory: Early-stage investigation
• Descriptive: Describes who, what, where, when, how

flowchart TD
    A[Research Question] --> B[Design]
    B --> C[Data Collection]
    C --> D[Analysis]
    D --> E[Reporting]
    E --> A[Iterate]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#FFF5E1,color:#000
    style E fill:#F3E6FF,color:#000

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Data Ethics and Governance

• GDPR (EU regulation for data protection)
• UK Data Protection Act (2018)

Key Ethical Principles

• Privacy
• Consent
• Transparency
• Fairness

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Critical Data Literacy

Critical data literacy involves the ability to analyse and question data-related practices and their implications. It requires that people understand not only how data are collected and used, but also that they are able to critically assess power dynamics and potential biases present in data-driven systems. Similarly, artificial intelligence literacy concerns understanding the principles and functioning of AI-based technologies, including the algorithms that power them and the ethical issues they raise. Both forms of literacy are essential for fostering a more informed citizenship capable of navigating an increasingly datafied world (Atenas et al., 2020; Long & Magerko, 2020).

• Ability to question data systems
• Awareness of bias and power structures

flowchart TD
    A[Data Collection] --> B[Bias Identification]
    B --> C[Power Analysis]
    C --> D[Ethical Reflection]
    D --> E[Responsible Use]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#FFF5E1,color:#000
    style E fill:#F3E6FF,color:#000

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Data Ethics & Justice

Concepts

• Data Ethics: Responsible data use
• Data Justice: Fairness and equity in data systems
• Data Feminism: Challenging bias and inequalities

flowchart LR
    A[Data Collection] --> B[Representation]
    B --> C[Equity]
    C --> D[Inclusion]
    D --> E[Justice Outcomes]

    style A fill:#FFE4E1,color:#000
    style B fill:#E6F2FF,color:#000
    style C fill:#E6FFE6,color:#000
    style D fill:#FFF5E1,color:#000
    style E fill:#F3E6FF,color:#000

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