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    Multilevel Analysis

    A Pratical Introduction

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    Download Url(s)
    https://books.openedition.org/ined/17717
    Author(s)
    Bringé, Arnaud
    Golaz, Valérie
    Contributor(s)
    Reeve, Paul (other)
    Language
    English
    Show full item record
    Abstract
    Demographers describe and analyse individual events at multiple levels of observation that range from the individuals themselves to the overall population of interest. In quantitative population studies, one way to streamline investigation is to perform a multilevel statistical analysis using a single model, which improves the accuracy of the estimates and therefore of the results. To that end, this book guides the reader through the first stages of multilevel analysis, from design to implementation, with step-by-step explanations on how to navigate the three most common statistical software environments (Stata®, SAS®, and R). Concrete examples based on census data are provided using an analysis of school enrolment in rural Kenya. Intended for all statistical database users seeking to develop or expand their knowledge of multilevel analysis, this manual details and illustrates the procedures for creating multilevel models and discusses their prerequisites, advantages, and limitations. Suggestions for further reading are also provided.
    URI
    https://directory.doabooks.org/handle/20.500.12854/85502
    Keywords
    demography; quantitative data; study of populations; computing; statistical methodology; multilevel
    DOI
    10.4000/books.ined.17717
    Webshop link
    https://www.7switch.com/fr/ebo ...
    ISBN
    9782733290538
    Publisher
    Ined Éditions
    Publisher website
    https://books.openedition.org/ined
    Publication date and place
    Paris, 2022
    Series
    Méthodes et Savoirs,
    Classification
    Population & demography
    Rights
    https://www.openedition.org/6540?lang=en
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    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

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    Credits


    • logo Investir l'avenirInvestir l'avenir
    • logo MESRIMESRI
    • logo EUEuropean Union
      This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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