[Pdf/ePub] Multilevel Thinking: Discovering

Multilevel Thinking: Discovering Variation, Universals, and Particulars in Cross-Cultural Research by Andrew Grogan-Kaylor

• Multilevel Thinking: Discovering Variation, Universals, and Particulars in Cross-Cultural Research
• Andrew Grogan-Kaylor
• Page: 208
• Format: pdf, ePub, mobi, fb2
• ISBN: 9780197806463
• Publisher: Oxford University Press

Multilevel Thinking: Discovering Variation, Universals, and Particulars in Cross-Cultural Research

Android books download free Multilevel Thinking: Discovering Variation, Universals, and Particulars in Cross-Cultural Research

Multilevel models have become very common in social research, however, there are aspects of these models that are explored only infrequently. Those that rarely make their way into courses and training materials are published articles that appear in academic journals. The author's view is that multilevel modeling offers powerful tools for understanding the multilevel data that social researchers often confront. For example, researchers are often interested in studying outcomes for diverse groups of children in different schools, residents of diverse and different neighborhoods, or individuals or families living in diverse and different countries. Such inherently multilevel data lead to analytic complexities, some of which might be well understood, while others seem to be much less often appreciated. This book offers an accessible tutorial for applied researchers, especially those who see their research having some advocacy based component. The approach of this book, while offering up some equations, is less mathematically rigorous than some of the existing texts, and written with the intent of providing a clear and practically focused guide for the applied researcher who is attempting to carry out better research with diverse populations.

Andrew Grogan-Kaylor Writes Book on Multilevel Thinking
The book provides an introduction to multilevel modeling, which allows researchers to explore the variation and diversity across a data set.
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