000			03817nam a2200361 a 4500
001			8906
003			OSt
005			20170404080515.0
008			160613t2004 xxuad||| |||| 00| 0 eng d
020			_a97808857029164
040			_aOSt _bcat
041			_aeng
080			_a311
080			_a519.237
100	1		_aLuke, Douglas A. _94830
245	1	0	_aMultilevel modeling in plain language / _cKaren Robson & David Pevalin
260			_aLondon : _bSage Publications, _c2004
300			_avii, 146 p. : _bgràf., quadres estadístics ; _c24 cm
490			_a(A Sage University Papers Series. Quantitative Applications in the Social Sciences; 143)
504			_aInclou bibliografia i índex
505			_aChapter 1: What Is Multilevel Modeling and Why Should I Use It? Mixing levels of analysis Theoretical reasons for multilevel modeling What are the advantages of using multilevel models? Statistical reasons for multilevel modeling Assumptions of OLS Software How this book is organized Chapter 2: Random Intercept Models: When intercepts vary A review of single-level regression Nesting structures in our data Getting starting with random intercept models What do our findings mean so far? Changing the grouping to schools Adding Level 1 explanatory variables Adding Level 2 explanatory variables Group mean centring Interactions Model fit What about R-squared? R-squared? A further assumption and a short note on random and fixed effects Chapter 3: Random Coefficient Models: When intercepts and coefficients vary Getting started with random coefficient models Trying a different random coefficient Shrinkage Fanning in and fanning out Examining the variances A dichotomous variable as a random coefficient More than one random coefficient A note on parsimony and fitting a model with multiple random coefficients A model with one random and one fixed coefficient Adding Level 2 variables Residual diagnostics First steps in model-building Some tasters of further extensions to our basic models Where to next? Chapter 4: Communicating Results to a Wider Audience Creating journal-formatted tables The fixed part of the model The importance of the null model Centring variables Stata commands to make table-making easier What do you talk about? Models with random coefficients What about graphs? Cross-level interactions Parting words
520			_aHave 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.
650		0	_aCorrelació múltiple (Estadística) _912344
650	0	4	_aModels matemàtics _97962 _2lemac _xMètodes estadístics
655		7	_91949 _aANÁLISIS MULTINIVEL _fMULTILEVEL ANALYSIS _iANÀLISI MULTINIVELL _2popin
655		7	_9181 _aANÁLISIS MULTIVARIADO _fMULTIVARIATE ANALYSIS _iANÀLISI MULTIVARIADA _2popin
655		7	_9589 _aESTADÍSTICA _fSTATISTICS _iESTADÍSTICA _2popin
655		7	_999 _aMETODOLOGÍA _fMETHODOLOGY _iMETODOLOGIA _2popin
650		0	_98282 _aAnàlisi multivariable
655		7	_998 _aMODELOS _fMODELS _iMODELS _2popin
901			_aRevisat
942			_2udc _cMO
999			_c5898 _d5898