Glossary

A sample is a subset of a population.

The strategy used to determine how a sample will be selected from a target population. This usually involves defining the sampling frame, sampling method, and sample size.

The process of selecting a subset of a population or larger group of interest. Data is collected from this sample, and when suitable sampling methods are used, the sample can be used to make inferences about the wider population/group”?

A sampling frame is a comprehensive list of all the members of the population from which a probability sample will be selected.

Source: SAGE Research Methods.

The list or source which identifies the population from which a sample is drawn. Examples include electoral registers, address databases, and membership lists such as the Postcode Address File (PAF) and the Electoral Register.

Satellite images (also known as Earth observation imagery, spaceborne photography, or simply satellite photos) are images of Earth captured by satellites operated by governments and businesses worldwide.

The ability of a system, method, or workflow to efficiently handle increasing amounts of data, users, or processing demands without significant loss of performance or reliability.

A method used to train systems using a combination of labelled and unlabelled data as an input. In semi-supervised learning, the machine learning model learns to correlate the features of each labelled data point with the label assigned to it while also learning from the unlabelled data. For example, a small set of labelled emails combined with many unlabelled emails allows the system to learn more effectively whether a new email is spam or not than using only labelled data. See also Machine learning, Supervised learning, Unsupervised learning, Labelled data and Unlabelled data.

Data that requires additional protection because its disclosure could harm individuals, organisations, or communities.

A probability sampling method in which every member of a population has an equal chance of being selected. See also: Sampling.

Simulation in the context of synthetic data means creating artificial datasets by building virtual environments on a computer. These environments include agent-based models, digital twins, and more. Simulations allow researchers to generate data that is already labeled and ready for analysis. It is a popular way to create synthetic data when real-world data is unavailable.

A set of instructions written in a programming language (such as Python, R, JavaScript, or C++) that tells a computer how to perform specific tasks.  Think of it as the blueprint for a piece of software; it tells the computer exactly what to do and how the program should work. Source code is usually referred to simply as ‘code’ when referring to programming.  

SPSS is a commercial statistics package. Visit the website for more information.

Standard error measures the uncertainty or variability associated with a sample estimate when compared to the true population parameter. The standard error of a statistic (like a mean or percentage) indicates how much that statistic is expected to vary from the true population value.

The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error.

Source: Stat Trek Statistics Dictionary.

A uniform way of coding and organising common survey variables to ensure they are consistent between surveys and across time. Examples include the Standard Occupational Classification (SOC), and the National Statistics Socio-economic Classification (NS-SEC).

The process of using a mathematical model to describe, explain, or predict patterns and relationships in data.

A statistical model is a theoretical construction of the relationship of explanatory variables to variables of interest created to better understand these relationships.

They typically consist of a collection of probability distributions and are used to describe patterns of variability that data may display.

The statistical model is expressed as a function. For example, a researcher may model a linear relationship using the regression function below:

y = b₀ + b₁x₁ + b₂x₂ … + b_ix_i

In this model, y represents an outcome variable and x_i represents its corresponding predictor variables. The term b₀ is an intercept for the model. The term b_i is a regression coefficient and represents the numerical relationship between the predictor variables and the outcome for the ith term.

Statistical modelling is a major topic. Readers who want to know more will find extensive accounts of statistical models including linear regression and logistic regression in statistical texts and online.

Sources: Science Direct; Magoosh Statistics Blog.

Stratified random sampling refers to a sampling method in which the total population is divided into non-overlapping subgroups. Each of the subgroups is called a stratum, and two or more subgroups are called strata.

Sources: An Introduction to Statistical Methods and Data Analysis; Stat Trek Statistics Dictionary.

Distinct groups within a population that are defined using characteristics considered important to the research. In stratified sampling, the population is divided into these groups before a sample is selected from each one. See also: Stratified sampling.

A probability sampling method in which the population is divided into distinct groups, known as strata, and a sample is selected from each group. This is often used to improve the representation of important subgroups within the population.

How well a synthetic dataset preserves the structure or shape of the original dataset. This includes the number of columns or rows, the name of each column, the type of data in each, or even the way variables are coded. See also Fidelity and Substantive similarity.

A structured interview follows a strict protocol using a set of defined questions administered in the same order to all interviewees. It allows for a quick collection of focused data, however there are limited opportunities for probing and further exploration of topics. The interviews are usually conducted face to face or over the phone.

How well a synthetic dataset preserves the statistical properties, patterns, and relationships found in the original real-world data. This includes features like distribution, central tendency, correlations and more. See also Fidelity and Structural similarity.

A method used to train systems to make predictions or decisions that use labelled data as input. In supervised learning, the machine learning model learns to correlate the features of each data point with its assigned label. The trained model can then predict or categorize new data points. For example, emails labelled as “spam” or “not spam” allow the system to learn how to understand which features are likely to indicate whether a new email is spam or not. See also Machine learning, Unsupervised learning, Semi-supervised learning and Labelled data.

Survey design involves a series of methodological steps to create an effective survey, such as defining an objective, determining a target population, designing the questionnaire etc.

Survey design can also refer to the structure or format of the survey, such as a cross-sectional survey, longitudinal survey etc.

Survey nonresponse can occur at both an item and unit level.

Item nonresponse occurs when a sample member responds to the survey but fails to provide a valid response to a particular item (e.g. a question they refuse to answer).

Unit nonresponse occurs when eligible sample members either cannot be contacted, refuse to participate in the survey or do not provide sufficient information for their responses to be valid. Unit nonresponse can be a source of bias in survey estimates and reducing unit nonresponse is an important objective of good survey practice.

Source: SAGE Research Methods.

A programming language specific to SPSS. It is used to instruct SPSS by writing commands rather than using the point-and-click graphical user interface (GUI).

Data that is generated or created rather than directly collected from real-world observations. Synthetic data is often generated using real-world data or metadata or is designed to resemble real-world data. Such synthetic data must be evaluated for fidelity, safety and utility. Less commonly, synthetic data may be generated without using any real-world data or metadata and without any intention to resemble real world data. See also Observed data and Fidelity.

The hardware, software, and settings that determine how a computer system or project runs. For example, the operating system, installed software, and network settings.