Methodological and Technological Expertise

Representative Representative A sample is said to be representative when it has been generated via a representative draw, meaning that each individual in the population had a non-zero probability of being selected. Representativeness relates to the sample and not to a unit of the population. It is not synonymous with a proportional sample or a "reduced model" of the population studied. In survey theory, it has been demonstrated that an optimal sampling strategy consists of organising the sample according to the variables that are most correlated to the subject of the study. Find out more samples make it possible to produce reliable data data Digital data Find out more by reflecting a reference population population The universe of a survey composed of basic statistical units. These units may be physical persons, households, companies, municipalities, etc. The population serves as a sampling frame for selecting a sample, and as a basis for calculation of the extrapolations from the sample. Find out more . This page explains the evolution of the concept of representativeness, probabilistic probabilistic The term probabilistic data refers to a process of identifying an individual based on a probabilistic model rather than on an identifier considered to be “infallible” thanks to its uniqueness. Find out more and non-probabilistic methods, and the conditions necessary to minimize bias bias The bias of a statistical result is the difference between the result obtained and the exact value that one is seeking. There are three distinct types of bias: sampling bias (for example: using an unsuitable sampling frame), observational bias (for example: poor wording of a question or answer grid), and estimate bias (for example: not fully taking into account the categories for drawing the sample). Find out more .

Data Data Digital data Find out more collection through self-reporting relies on questionnaires designed to elicit actionable responses. The wording of the questions, the structure of the survey survey A statistical method that aims to produce information about a population by interviewing part of the population (sample). This word is generally used to mean a survey conducted on a sample that have been interviewed using a questionnaire or else systematically observed. Find out more process, and the choice of formats all contribute to the quality of the data produced as part of a survey.

Automatic data data Digital data Find out more collection is at the heart of modern measurement systems. This section presents the methods, technologies, and equipment used to collect data reliably, continuously, and on a large scale, in order to better understand behaviors, usage patterns, and audiences.

Data Data Digital data Find out more processing processing Means any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction Find out more involves enriching, consolidating, and ensuring the reliability of data to produce robust results. From sample sample A subset of the population studied, selected in accordance with a sampling plan, and subject to the collection of information. Find out more adjustment adjustment A statistical processing that aims to correct structural discrepancies on one or more variables between the sample and the entire population studied (based on a known structure, such as a census or a framing study). This processing involves the calculation of a so-called adjustment coefficient for each individual surveyed. The coefficient is then combined with the extrapolation to arrive at its weight and thus align the weighted sample's structure to the structure of the population. Find out more to the merging and hybridization of data sources, this expertise helps improve the quality, representativeness, and depth of analyses.