Have you been told you need to do multilevel modeling, but you can't get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense?
Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated.
This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Karen Robson is Assistant Professor in the Department and Marketing and Hospitality at Central Michigan University. She holds a BSc (Honsd) in Psychology from Queen's University, and an MA in Psychology, an MBA and PhD from Simon Fraser University. Karen's research investigates consumer innovativeness, including how consumers repurpose or use market offerings in ways not intended by the manufacturer and the intellectual property law implications of this practice. A recipient of the Joseph-Armand Bombardier Doctoral Scholarship, her work has appeared in journals such as MIS Quarterly Executive, Business Horizons, Journal of Marketing Education, Journal of Advertising Research, and Journal of Public Affairs.
David Pevalin is Professor in the School of Health and Human Sciences and Dean of Postgraduate Research and Education at the University of Essex. He previously served in the Merchant Navy, the City of London Police and the Royal Hong Kong Police. He studied part time at the University of Hong Kong before graduate studies at the University of Calgary, Canada. He returned to the UK in 1999 as Senior Research Officer at the Institute for Social and Economic Research at the University of Essex and joined his current School in 2003 after obtaining his PhD. He co-authored (with Karen Robson) The Stata Survival Manual (Open University Press), co-edited (with David Rose) The Researcher's Guide to the National Statistics Socio-economic Classification (Sage), and authored research reports for the Department of Work and Pensions and the Health Development Agency. He has published papers in the Journal of Health and Social Behavior, British Journal of Sociology, Lancet, Public Health, and Housing Studies.
With a real focus on the practical, this book provides students with a step-by-step approach, plenty of real-life examples, and downloadable data and exercises on the accompanying study website to help take the fear and intimidation out of multilevel modeling
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Have you been told you need to do multilevel modeling, but you can't get past the forest of equations Do you need the techniques explained with words and practical examples so they make sense Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated. This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling. 160 pp. Englisch. N° de réf. du vendeur 9780857029164
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