Glossary

A field of artificial intelligence in which systems learn from data by identifying patterns and relationships, allowing them to make predictions or decisions without being explicitly programmed step by step.

The process of combining changes from one branch into another within a version control system (such as Git).

Metadata is a set of data that describes and gives information about other data. Information that describes significant aspects (e.g. content, context and structure of information) of a resource; metadata are created for the purposes of resource discovery, managing access and ensuring efficient preservation of resources.

Microdata are unit-level data obtained from sample surveys, censuses, and administrative systems. They provide information about characteristics of individual people or entities such as households, business enterprises, facilities, farms or even geographical areas such as villages or towns.

Source: The World Bank.

Some variables have values that are recorded as missing. These values may be missing unintentionally (due to data entry errors) or may stem from the survey design (e.g. if only part of the sample were asked a particular question). Sometimes non-substantive responses (such as ‘don’t know’) are also recorded as missing values. To draw accurate inferences about the data missing values need to be treated prior to the analyses, e.g. excluded.

M&S involves developing a computerised or mathematical model of a real-world system and conducting experiments (simulations) to analyse its behaviour, improve performance, or forecast outcomes.

A reusable block of code that stores shared functions and data, written once and called upon whenever needed across a project. This keeps code organised and avoids repetition, similar to saving a template you reuse rather than rewriting it from scratch each time.

Multivariate analysis encompasses all statistical techniques that are used to analyse more than two variables at once.

Sources: International Encyclopedia of the Social & Behavioral Sciences;

This method attempts to model mathematically or statistically data from two or more variables measured on the same observations. Multivariate statistical modelling often involves a dependent variable and multiple independent variables. Examples of multivariate analyses are factor analysis, latent class analysis, and multivariate regressions. In contrast, univariate method involves an analysis of a single variable.

Resources: Centre for Statistical Methodology; STATA; Science Direct; UCLA Institute for Digital Research & Education.