Glossary

Machine learning – a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

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.

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.