Latent Class Modelling (LCM) comprises a set of techniques aimed to model situations where different subgroups (or, more generally, classes) of entities (for example organizations or individuals) are present in data and group membership is not directly observable, but has an impact on phenomena of interest.
Spatial data analysis and spatial modelling are methodological approaches for analysing spatial phenomena. They are used for their descriptive characterisation (e.g. distribution of a variable of interest across cities, regions or countries), and for estimating relationships in spatial data, also referred to as spatial econometric models.
MULTILEVEL MODEL (soon available)
PANEL MODEL (soon available)
BENCHMARK MODEL (soon available)
Data quality is a central aspect for trusted and usable datasets, particularly when widening the circle of users beyond those who developed the dataset and are acquainted with its specificities. Therefore, RISIS2 includes a specific activity on data quality, which build on the experience made with the RISIS-ETER dataset and which is managed by the University La Sapienza in Rome.
Data is a complex and multidimensional concept, which includes aspects such as completeness, consistency and coherence. This activity will provide support to RISIS datasets in implementing data quality and offer training activities on basic and advanced data quality, including also imputation techniques in order to fill missing values in datasets.