Contact
How to shop
Delivery
Shopping guide
Multiple Comparisons for Mixed Models
Concepts, Theoretical Reasons, and Case Studies on Longitudinal and Repeated Measures Data Analysis
English
214 b
Up to 30 days for returns
Customers also purchased
/
Paperback
17 260
Ft
/
Paperback
6 034
Ft
/
Paperback
17 726
Ft
/
Paperback
5 932
Ft
The assumptions of constant variance and uncorrelated errors that are made in general linear models often do not hold for longitudinal data, repeated measures, and general mixed models. The traditional multiple comparison (MCP) methods based on the studentized range distribution and multivariate t distribution are exact only under the assumptions of linearity, normality, constant variance, and uncorrelated error. The element of MCPs is often to compare the means of two measures. For the two-sample t test, even a moderate correlation to observations will result in serious bias; this problem definitely carries over to multiplicity-adjusted inferences. The book briefly reviews the background of traditional procedures of MCPs then investigates some MCPs with longitudinal repeated measures. In these cases the sample errors are inflated/deflated because of the correlation among observations, and results in giving incorrect inferences. In this book, we suggest using different ways of dealing with this situation under various mixed models. This book includes full of case studies and gives detailed step by step theoretical explanations.
About the book
English
Categories
Give this book today
It's easy
1 Add to cart and choose Deliver as present at the checkout 2 We'll send you a voucher 3 The book will arrive at the recipient's addressYou might also be interested in
/
Paperback
6 708
Ft
Hi! I'm Libroamiko, your book advisor.
How can I help you?