Research news on Two-level models

Two-level models, also known as two-level hierarchical or multilevel models, are statistical techniques for analyzing data with a nested structure involving exactly two hierarchical levels, such as individuals within groups or repeated measures within subjects. They explicitly decompose variance into within-cluster (level-1) and between-cluster (level-2) components, typically via random intercepts and optionally random slopes. Estimation is commonly performed using maximum likelihood or restricted maximum likelihood within a linear mixed-effects framework, allowing correct standard errors, modeling of cross-level interactions, and improved inference when observations are correlated due to clustering or longitudinal dependence.

Quantum memory array brings us closer to a quantum RAM

The internet, social media, and digital technologies have completely transformed the way we establish commercial, personal and professional relationships. At its core, this society relies on the exchange of information that ...

Fundamental quantum model recreated from nanographenes

The smallest unit of information in a computer is the bit: on or off, 1 or 0. Today, the world's entire computing power is built on the combination and interconnection of countless ones and zeros. Quantum computers have their ...