Manual Multilevel Statistical Models (Wiley Series in Probability and Statistics)

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I really enjoyed reading this book, and am sure that others will have a similar pleasurable experience. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Skip to Main Content. First published: 29 October About this book Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood.

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Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models.

Key Features: Provides a clear introduction and a comprehensive account of multilevel models.

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New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts. Visible learning: a synthesis of over meta-analyses relating to performance. New York, NY: Routledge. Hox, J. Multilevel analysis: Techniques and applications 2th ed. Kim, J.

Multilevel Statistical Models : Harvey Goldstein :

Multilevel analysis of assessment data. Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis , Lamb, S. The stratification of learning opportunities and achievement in Tasmanian secondary schools. Australian Journal of Education, 45 2 , Marks, G. Explaining socioeconomic inequalities in student achievement: The role of home and school factors. Educational Research and Evaluation, 12 2 , Martins, L.

Economics of Education Review, 29 6 , — Martin, M. TIMSS international results in science. McCoach, D. Instrument development in the affective domain. New York, NY: Springer. Ministry of Education, MOE. Malaysian education blueprint — preschool to postsecondary education. Putrajaya: MOE.

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Mullis, I. TIMSS international results in mathematics. OECD PISA results: excellence through equity—giving every student the chance to succeed Vol. Prensel, M. Raudenbush, S.

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Hierarchical linear models: applications and data analysis methods 2nd ed. Thousand Oaks, CA: Sage. Stankov, L. Noncognitive predictors of intelligence and academic achievement: An important role of confidence.

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Personality and Individual Differences, 55 7 , — Steele, F. Module 5 concepts : Introduction to multilevel modelling. Centre for Multilevel Modelling. Bristol, England: University of Bristol.

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Spiegelhalter, D. The problems with PISA statistical methods. Tabachnick, B. Using multivatiate statistics , 6th ed. Boston, MA: Pearson Education. Taylor, G.