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Intensive Longitudinal Methods (Methodology in the Social Sciences) (Englisch) Gebundene Ausgabe – 27. März 2013


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Pressestimmen

"Because they allow researchers to understand within-person processes in natural settings, intensive longitudinal methods are essential tools for building a science of the individual. Bolger and Laurenceau do a superb job of taking readers through the mechanics of research design, data analysis, and interpretation, enabling readers to tackle important questions about 'who we are' in depth and detail. I highly recommend Bolger and Laurenceau's book for those wishing to learn and use these powerful methods."--Walter Mischel, PhD, Niven Professor of Humane Letters in Psychology, Columbia University"In a remarkably short period of time, intensive longitudinal designs have become a staple of the behavioral scientist's toolbox, yet researchers do not always know how to make the most of their data. This is the book we have been waiting for. Bolger and Laurenceau have written a complete, authoritative, and highly accessible volume that is sure to set the standard for years to come. Researchers and students will find this landmark volume to be an essential resource."--Harry T. Reis, PhD, Department of Clinical and Social Sciences in Psychology, University of Rochester"Bolger and Laurenceau have put together a fantastic primer. The well-crafted and clearly explained background, examples, datasets, and programming codes make this a go-to book for learning how to prepare, analyze, and make the most of intensive longitudinal data. Both instructors and students will appreciate the straightforward, highly readable format. This book's pages are sure to get well worn."--Nilam Ram, PhD, Department of Human Development and Family Studies, The Pennsylvania State University"A valuable and immensely practical resource from two of the world's intensive longitudinal masters. This book is a 'must read' for researchers. Each chapter provides detailed, step-by step guidance on basic to advanced analytic techniques, including exemplar data sets, visual imagery, complete statistical code, and sample write-ups. From spaghetti plots to power analysis, with Bolger and Laurenceau as expert guides, researchers will learn what to do, how to do it, and how to write it up."--Tamlin S. Conner, PhD, Department of Psychology, University of Otago, New Zealand"This book details exactly how to analyze data gathered from diary and experience sampling studies. In clear language and with real data, it explains how to use multilevel modeling to answer common types of research questions. To make the presentation complete, syntax for SPSS, SAS, and Mplus is provided. Researchers and graduate students conducting studies of daily life will find this book indispensable. I would use it as a text in a graduate-level research methods class and as a resource when designing and conducting my own analyses."--Joel M. Hektner, PhD, Department of Human Development and Family Science, North Dakota State University"This book couldn't have come at a better time. All too often, investigators are not sure how to deal with the vast amounts of data they collect using diary methods, and do not fully appreciate the strengths and limitations of their data. This book, in my opinion, is the cure."--Howard Tennen, PhD, Department of Community Medicine and Health Care, University of Connecticut Health Center"Intensive longitudinal research yields uniquely rich data, but the analyses quickly get complex. Bolger and Laurenceau give researchers the necessary tools and knowledge to conduct and analyze their own intensive longitudinal studies. Using to-the-point explanations and helpful, realistic examples, the book goes step by step through everything there is to know, from the very basics to advanced data-analytic issues--and does so in a delightfully engaging manner."--Matthias R. Mehl, PhD, Department of Psychology, University of Arizona"The book addresses cutting-edge quantitative approaches to within-subject causal modeling of change while illustrating how researchers can combine rich qualitative data with powerful mixed-modeling approaches. I will definitely use this book in extending my own research related to these types of scenarios."--Larry R. Price, PhD, Director, Initiative for Interdisciplinary Research Design and Analysis, Texas State University

Über den Autor und weitere Mitwirkende

Niall Bolger, PhD, is Professor and Chair of Psychology at Columbia University, USA. He is a Charter Member and Fellow of the Association for Psychological Science and a Fellow of the Society of Experimental Social Psychology and the Society for Personality and Social Psychology. Dr. Bolger studies adjustment processes in close relationships, using intensive longitudinal methods and laboratory-based studies of dyadic behavior, emotion, and physiology. He also studies personality processes as they are revealed in patterns of behavior, emotion, and physiology in daily life. Finally, he is interested in statistical methods for analyzing longitudinal and multilevel data. Jean-Philippe Laurenceau, PhD, is Professor of Psychology at the University of Delaware, USA. He is an appointed member of the Social, Personality, and Interpersonal Processes grant review panel of the National Institutes of Health. Dr. Laurenceau's research focuses on understanding the processes by which partners in marital and romantic relationships develop and maintain intimacy in the context of everyday life. His methodological interests include intensive longitudinal methods for studying close relationship processes and applications of modern methods for the analysis of change in individuals and dyads.

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1 von 1 Kunden fanden die folgende Rezension hilfreich
The Benchmark Book for Experience Sampling Methods 2. Oktober 2013
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
I've been doing experience-sampling research for 10 years, and I was more than a little curious to read this book, which has sparked a lot of buzz in methodological circles. As a young field, experience-sampling lacks a clear statement of "best practices" and "gold standards." How do you estimate power for these weird nested designs? What are good ways to estimate lagged effects? Should you include or ignore time when it isn't a variable of substantive interest?

Anyhow, this grizzled old dog learned some new tricks from this eye-opening book. The text lays out a rationale for good practices for statistical analysis, power analysis, and reporting results in papers. It makes clear much that was foggy, and it was exactly what I had hoped to find when I got started a decade ago. If you're planning a study or submitting a grant proposal that involves intensive assessment, you should read this book first.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Best Explanation of Multi-Level Models 26. März 2013
Von Dennis Fisher - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
This book is simply amazing. I got copies for myself and my staff and we could not put it down. We kept reading it and saying to each "this is a really good book!" It is an excellent, readable, and finally an explanation that I can follow and that makes sense of multilevel models and how to apply them to the type of primarily within-subject design that diary studies tend to be. There are very useful chapters on categorical data, measurement, and power analysis, in addition to clearly explained chapters using continuous variables. Each chapter has code in SAS, SPSS, and Mplus. Their webpage has more software packages. This is the book that should be in the toolkit of everyone doing event-level data analysis.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Great book! 29. März 2013
Von Jenny P - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
Very accessible introduction to generating mixed models. Niall Bolger and Jean-Phillipe Laurenceau did a great job with this book. I only wish it had code for R. Perhaps this will be added to the companion website soon.
Excellent resource for daily diary or other intensive longitudinal research! 10. Juli 2013
Von Jeremy Yorgason - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
I found this book to be an excellent resource! I had to learn this type of analysis by piecing together information from published papers, statistics books, software user's guides, and by asking others my questions. Now I have a great resource with the relevant info all in one place!

The authors provide a step-by-step approach to working with daily diary and intensive longitudinal data. I have worked quite a bit with such data, and yet as I read the author's explanations of why they took each step I gained new and interesting insights about the process. The authors do a great job of presenting info for those just starting to learn about this type of analysis, and yet also delve into complex aspects of the analysis that I found extremely helpful.

Sometimes statistics books can be overwhelming or difficult to grasp. I found this book's content to be accessible and well written. The data examples are clear and easy to follow. Syntax and graph examples are helpful.

I most highly recommend this book to any researchers or students working with daily diary or other intensive longitudinal data. It will be a foundational resource for years in this area!
Great book! Very useful and accessible 4. August 2013
Von Melissa Boone - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
I'm a graduate student who works with intensive longitudinal methods on a regular basis - including my dissertation data - and this book is a really great resource for this. I have it in the tiny bookcase on my desk because I am constantly pulling it as a reference. It's written in such an accessible manner; it's really easy to read through and understand not only the practice of ILM, but also the basic theoretical underpinnings of each model. This way, you understand what exactly it is you're doing, rather than just doing it blindly without background knowledge. And of course, the program codes are really useful!

If you haven't yet, visit the website: [...] It has very useful supplemental information, including sample data sets for practice and more code.
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