Glossary

Data that has been assigned a label or category, typically as the result of a manual or human labelling process, to indicate the correct classification or category for each data point. Labelled data is used in supervised learning tasks where the machine learning model builds a correlation between the features of each data point and the assigned labels. Examples include an image of a pastry labelled as a ‘pain au chocolat’ or of an animal as a ‘sloth’, an email labelled as ‘spam’ or as ‘not spam’ or some piece of text labelled as ‘positive’, ‘negative’, ‘neutral’ or ‘mixed’. See also Machine learning, Supervised learning, Semi-supervised learning and Unlabelled data.

These sophisticated AI systems are developed using extensive datasets, enabling them to understand, summarize, generate, and predict content in human language with remarkable accuracy. By recognizing intricate patterns and contextual cues in text, LLMs deliver highly relevant responses tailored to the user’s needs. For learners and researchers, LLMs serve as powerful tools for answering questions, drafting and refining text, translating languages, and analysing documents. Their use accelerates research, fosters independent learning, and encourages deeper exploration of complex topics. Notable examples include Gemini, Claude, and GPT.

Refers to the kind of information that the values of numeric variables represent and the relationships between them. It determines what kind of statistical analyses are appropriate. There are traditionally four type: nominal, ordinal, interval, and ratio.

A collection of pre-written code and functions that can be reused to perform specific tasks by a program. For example, in Python, the pandas library is commonly used to work with and analyse data tables.

A document or workflow that progresses in a fixed sequence from start to finish, where each step follows directly from the previous one. In practice, this means a script or notebook moves from data import through cleaning, analysis, and output in a single, unbroken order, making it easier for others to follow, audit, and reproduce the analysis.

The study of correct reasoning, including formal and informal types. Formal logic focuses on deductively valid inferences and how conclusions follow from premises based on argument structure, regardless of topic. Informal logic deals with fallacies, critical thinking, and argumentation theory.

In long format data, each row represents a single observation or measurement for a subject, often resulting in multiple rows for each subject. For example, in a longitudinal survey tracking student performance over several years, each student will have multiple rows corresponding to different years, with each row recording their performance for that particular year. Contrast with wide-format data.

A longitudinal design is one that measures the characteristics of the same individuals on at least two, but ideally more, occasions over time. Its purpose is to directly address the study of individual change and variation. Longitudinal studies are expensive in terms of both time and money, but they provide many significant advantages relative to cross-sectional studies.

Source: SAGE Research Methods.