Applied multilevel analysis : a practical guide

By J. Twisk. 2006

652 citations recorded by the source corpus.

Preface 1. Introduction 2. Basic principles behind multilevel analysis 3. What do we gain by applying multilevel analysis? 4. Multilevel analysis with different outcome variables 5. Multilevel modelling 6. Multilevel analysis in longitudinal studies 7. Multivariate multilevel analysis 8. Sample size calculations in multilevel studies 9. Software for multilevel analysis References Index.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6f7129856e4763a687bdc1efdaf206bfebc7c56e

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In Sociology and Political Science

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Citations in the corpus

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera (2013, methodology): “For an evaluation of different software used for logistic multilevel analysis, see Twisk (2010).”
Is the ideal mother a sensitive mother? Beliefs about early childhood parenting in mothers across the globe (2016, methodology): “In the modeling process, random effects for each of the centered sociodemographic and attitude variables were added to the null model (Twisk, 2011), and none of these random effects were significant.”
Results of international standardised beekeeper surveys of colony losses for winter 2012–2013: analysis of winter loss rates and mixed effects modelling of risk factors for winter loss (2014, methodology): “Further model fitting was done using mixed effect regression models (also known as multilevel models), which incorporate both fixed and random effects (Zuur et al., 2009; Twisk, 2010) and which allow for the nature of the data, i.e. colonies belonging to beekeepers within regions.”