000 02181nam a22002657a 4500
003 ISI Library, Kolkata
005 20240902125943.0
020 _a9781466517158
040 _aISI Library
_bEnglish
082 0 4 _223rd.
_a005.753
_bF492
100 1 _eauthor
_aFinch, W. Holmes
245 1 0 _aMultilevel modeling using R/
_cW. Holmes Finch
260 _aBoca Raton:
_bCRC Press,
_c2014
300 _axiii, 216 pages;
_bdig.;
_c23.5 cm.
490 0 _aStatistics in the Social and Behavioral Sciences Series
504 _aIncludes references and index
505 0 _aLinear Models -- Introduction to Multilevel Data Structure -- Fitting Two- Level Models in R -- Models of Three and More Levels -- Longitudinal Data Analysis Using Multilevel Models -- Graphing Data in Multilevel Contexts -- Brief Introduction to Generalized Linear Models -- Multilevel Generalized Linear Models -- Bayesian Multilevel Models
520 _aA powerful tool for analyzing nested designs in a variety of fields, multilevel/hierarchical modeling allows researchers to account for data collected at multiple levels. Multilevel Modeling Using R provides you with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book. Through the R code and detailed explanations provided, this book gives you the tools to launch your own investigations in multilevel modeling and gain insight into your research.
650 4 _aMathematics
650 4 _aStatistical Method
650 4 _aMultivariate Analysis
700 1 _aBolin, Jocelyn E.
_eauthor
700 1 _aKelley, Ken
_eauthor
942 _2ddc
_cBK
999 _c435872
_d435872