Textbook Examples
Multilevel Analysis: An introduction to basic and advanced multilevel modeling
Tom Snijders and Roel Bosker

This is one of the books available for loan from Academic Technology Services (see Statistics Books for Loan for other such books, and details about borrowing). See Where to buy books for tips on different places you can buy these books.

You can obtain more information about the book, including access to the data files from the web site for the book.

• Data Files
  
  • HLM - mlbook1.SSM,
    
  • MLwiN - mlbook1.ws , ch12.ws
    
  • Mplus - mlbook1.dat, mlbook1-a.dat, ch14.dat
    
  • SAS - mlbook1.sas7bdat , ch12.sas7bdat
    
  • Stata - mlbook1.dta , ch12.dta
    
  • SPSS - mlbook1.sav , ch12.sav

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Centre for Multilevel Modelling

Multilevel Data Analysis

Don Hedeker

Multilevel Data

Reading material: Hedeker, D., Gibbons, R.D., & Flay, B.R. (1994). Random-effects regression models for clustered data with an example from smoking prevention research. Journal of Consulting and Clinical Psychology, 62, 757-765. (pdf file)

Overheads: Multilevel Analysis: An Applied Introduction (pdf file)

Example using SAS PROC MIXED:
TVSFPMIX.SAS - ASCII file with SAS code from analysis of TVSFP dataset using a few different MIXED models. Also includes individual-level and aggregate-level analyses.
TVSFP2B.DAT - ASCII datafile for example above.

Longitudinal Data

Reading material: Hedeker, D. (2004). An introduction to growth modeling. In D. Kaplan (Ed.), Quantitative Methodology for the Social Sciences. Thousand Oaks CA: Sage Publications. (pdf file)

Overheads: Mixed Models for Longitudinal Data: An Applied Introduction (pdf file)

Example using SAS PROC MIXED:
RIESBYM.SAS - ASCII file with SAS code from analysis of Riesby dataset using a few different MIXED models. Includes grouping variable and curvilinear effect of time.
RIESBY.DAT - ASCII datafile for example above.

Missing Values in Longitudinal Data

Reading material: Hedeker, D., & Gibbons, R.D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78. (pdf file)

Overheads: Mixed Pattern-Mixture Models for Missing Data (pdf file)

Example using SAS PROC MIXED:
schizpm2.sas - ASCII file with SAS code from analysis of NIMH Schizophrenia dataset to perform a pattern-mixture analysis. Includes IML code to do the mixing over the pattern results.
SCHIZREP.DAT - ASCII datafile for example above.

Longitudinal Dichotomous Data

Reading material: Hedeker, D. and Gibbons, R.D. "Longitudinal Data Analysis" (in progress).
Chapter 9: Mixed-effects regression models for binary outcomes. (pdf file)

Overheads: Mixed Models for Longitudinal Dichotomous Data (pdf file)

Example using SAS PROC NLMIXED:
schzbnl.sas - SAS code for mixed-effects logistic regression analysis of NIMH Schizophrenia data.
SCHIZX1.DAT - ASCII datafile for example above.

Longitudinal Ordinal Data

Reading material: Hedeker, D. and Gibbons, R.D. "Longitudinal Data Analysis" (in progress).
Chapter 10: Mixed-effects regression models for ordinal outcomes. (pdf file)

Overheads: Mixed Models for Longitudinal Ordinal Data (pdf file)

Example using SAS PROC NLMIXED:
schzonl.sas - SAS code for mixed-effects ordinal logistic regression analysis of NIMH Schizophrenia data.

Sample Size Estimation for Longitudinal Studies

Reading material: Hedeker, D., Gibbons, R.D., & Waternaux, C. (1999). Sample size estimation for longitudinal designs with attrition: comparing time-related contrasts between two groups. Journal of Educational and Behavioral Statistics, 24:70-93. (pdf file)

Overheads: (pdf file)

RMASS2.EXE contains:
- executable program for sample size determination based on this paper.
RMASS2.PDF contains:
- PDF version of program guide

More information and materials:

Don's short course on Longitudinal Data Analysis

Don's 15-week course on Longitudinal Data Analysis

The MIX website

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Software Reviews of Multilevel Analysis Packages

Textbook Examples:Multilevel Models

Joop Hox

This is one of the books available for loan from Academic Technology Services (see Statistics Books for Loan for other such books, and details about borrowing). See Where to buy books for tips on different places you can buy these books. You can find more information about this book, including the data files, table of contents and sample chapters at the web site for the book.

• Data Files
  
  • Description of Files
    
  • HLM (Use all the SPSS files)
    
  • MLwiN data files
    
  • Mplus
    
  • SAS data files
    
  • Stata data files
    
  • SPSS data files

[此贴子已经被作者于2006-5-2 12:23:59编辑过]

• Advanced Structural Equation Modeling: Issues and Techniques Edited by George A. Marcoulides and Randall E. Schumaker 376 pages. 1996.
  
• Basics of Structural Equation Modeling Geoffrey M. Maruyama. 328 pages. 1997.
  
• A Beginner's Guide to Structural Equation Modeling Randall E. Schumacker and Richard G. Lomax. 498 pages. 2004.
  
• Contemporary Psychometrics Edited by: Albert Maydeu-Olivares and John J. McArdle. 600 pages. 2005.
  
• A First Course in Structural Equation Modeling Tenko Raykov, George A. Marcoulides. 216 pages. 2000.
  
• Hierarchical Linear Models: Applications and Data Analysis Methods - Second Edition Stephen W. Raudenbush. 485 pages. 2002.
  
  
• HLM 6: Hierarchical Linear and Nonlinear Modeling Stephen W. Raudenbush, Anthony S. Bryk, Yuk Fai Cheong and Richard Congdon. 306 pages. 2004.
  
• Interaction and Nonlinear Effects in Structural Equation Modeling Edited by Randall E. Schumacker, George A. Marcoulides. 320 pages. 1998.
  
• Interactive LISREL User's Guide Mathilda Du Toit, Stephen Du Toit, with a contribution by Douglas Hawkins. 500 pages. 2001.
  
• Introducing Lisrel Adamantios Diamantopoulos and Judy A. Siguaw. 171 pages. 2000.
  
• Introducing Multilevel Modeling Ita G. G. Kreft, Jan de Leeuw. 160 pages. 1998.
  
  
• An Introduction to Multilevel Modeling Techniques Ronald H. Heck, Scott L. Thomas. 224 pages. 2000.
  
• Introduction to Latent Variable Growth Curve Modeling Terry E. Duncan, Susan C. Duncan, Lisa A. Strycker, Fuzhong Li, Anthony Albert. 208 pages. 1999.
  
• Latent Variable and Latent Structure Models Edited by George A. Marcoulides and Irini Moustaki. 280 Pages. 2002
  
• Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis John C. Loehlin. 317 pages. 2004.
  
• LISREL 8 New Statistical Features Karl G. Jöreskog, Dag Sörbom, Stephen Du Toit and Mathilda Du Toit. 248 pages. 2001.
  
  
• LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language Karl G. Jöreskog and Dag Sörbom. 226 pages. 1996.
  
• LISREL 8 User's Reference Guide Karl G. Jöreskog and Dag Sörbom. 378 pages. 1996.
  
• Modeling Intraindividual Variability With Repeated Measures Data Edited by D. S. Moskowitz and Scott L. Hershberger. 276 Pages. 2002
  
• Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples Edited by Todd D. Little, Kai U. Schnabel, and Jürgen Baumert. 308 pages. 2000.
  
• Multilevel Analysis: An introduction to basic and advanced multilevel modeling (Hardcover) Tom Snijders and Roel J. Bosker. 272 pages. 1999.
  
  
• Multilevel Analysis: Techniques and Applications Joop Hox. 304 pages. 2002
  
• Multilevel Modeling: Methodological Advanced, Issues, and Applications Edited by Steven P. Reise and Naihua Duan. 314 Pages. 2003
  
• New Developments and Techniques in Structural Equation Modeling Edited by George A. Marcoulides and Randall E. Schumacker. 333 Pages. 2001
  
• PRELIS 2 User's Reference Guide Karl G. Jöreskog and Dag Sörbom. 224 pages. 1996.
  
• The Sage Handbook of Quantitative Methodology for the Social Sciences Edited by David Kaplan. 511 pages. 2004.
  
  
• Structural Equation Modeling: Concepts, Issues, and Applications Edited by Rick H. Hoyle. 290 pages. 1995.
  
• Structural Equation Modeling: Foundations and Extensions David Kaplan. 215 pages. 2000.
  
• Structural Equation Modeling: Present and Future Edited by Stephen DuToit, Robert Cudeck, and Dag Sörbom. 620 pages. 2001.
• Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming Barbara M. Byrne. 338 pages. 2001
  
• Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming Barbara M. Byrne. 424 pages. 1998.
  
• Testing Structural Equation Models Edited by Kenneth A. Bollen, J. Scott Long. 328 pages. 1993.
  
• Using LISREL for Structural Equation Modeling: A Researcher's Guide E. Kevin Kelloway. 164 pages. 1998.

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Wolfgang Ludwig-Mayerhofer:The Multilevel Modeling Page

Multilevel modeling (MM) is a family of statistical procedures that try to come to terms with influences that are located on different, well, levels. So naturally the question arises what is meant by "level".

One way to think about it is as follows: People do not live entirely on their own, but rather embedded in social units. Even though today, in a globalized world, we may say that people have relationships with other people all over the world, most people have some relationships that are more special than others. People who are linked together via special relationships frequently communicate among each other, and thus the possibility rises that the people you are linked to influence your views. So we may think about the individuals as one (the lowest) level and their network (whether it consists of people that are met in person or of people communication with whom may take place only via artifical media) as a next (higher) level.
(Note that " low" and " high" are just names; we may well think about things the other way round. "High" just means something like "aggregate"; that is, several individuals -- entities on the "low" level -- are seen as agglutinated).

A second way: Opportunities structure the behaviour of individuals, and as many people select their opportunities by local proximity, the region in which a person lives may enhance or restrict his or her opportunity. For instance, if a person lives in a region with high unemployment, this may influence his or her behaviour about acceptable wage levels when looking for a new job.

A third way: Often people, be it voluntarily or not, are subject to common external influences. Take, for instance, a university professor. All the students that come to her or him are subject to her or his way of teaching. Could be that this way of teaching influences these students (even though this certainly -- if sometimes fortunately -- happens less frequently than we professors might desire). Therefore, again we may think of a multitude of professors as the "higher" level units and of their many students as the "lower" level units.

For a variety of reasons, data referring to more than one level often cannot be analyzed by conventional statistical models. For instance, classical OLS regression analysis requires that residuals from individual observations are not correlated. This requirement becomes doubtful if these individual observations are subject to the same influences or are related to each other in other ways.

After so many words, this page -- at the moment -- does very little: It provides a few links to MM related pages, and it also provides selected references to the literature, with short comments.

Links

• The Centre for Multilevel Modeling at Bristol – the follow-up of the former Multilevel Models Project at London
• Software Review – a subsite of the Centre for Multilevel Modeling of tremendous help for users, therefore mentioned specifically here.
• The Multilevel Modelling Portal at the UCLA
• Note specifically this very helpful paper by Judith Singer. The paper is about how to use SAS to estimate multilevel models, and this page also demonstrates how this can be achieved with specialized (HLM or MLwiN) as well as multi-purpose (SPSS, S-Plus) software.
• Tom Snijders' Page
• Wolfgang Langer's Multilevel Page

Literature: The Multilevel Modeling

• Bressoux, Pascal, Coustère, Paul & Leroy-Audouin, Christine (1997): Les modèles multiniveau dans l'analyse écologique: le case de la recherche en éducation, in: Revue française de sociologie, 38, 67-96.
  Some like it French: An introductory paper on multilevel analysis of educational achievement, with good data. No maths.
  
  
• Bryk, A. S. & Raudenbush, S. W. (1992): Hierarchical Linear Models. Applications and Data Analysis Methods. Newbury Park, CA: Sage.
  Very good for those who wish to arrive at an advanced understanding.
  
  
• DiPrete, Thomas A. & Forristal, Jerry D. (1994): Multilevel Models: Methods and Substance, in: Annual Review of Sociology, 20, 331-357.
  A good overview of the basic ideas and of applications, with most emphasis on random-coefficient models.
  
  
• Ditton, Hartmut (1998): Mehrebenenanalyse. Grundlagen und Anwendungen des Hierarchisch Linearen Modells. Weinheim und München: Juventa.
  This book is specifically useful for those who want to analyze school data (and have to resort to books in German). However, readers of chapter 3 (dealing largely with centering) should consult chapter 5.2 in Kreft/de Leeuw 1998 and the paper by Kreft/de Leeuw/Aiken 1995.
  
  
• Engel, Uwe (1998): Einführung in die Mehrebenenanalyse. Opladen: Westdeutscher Verlag (WV Studium 182).
  The wide coverage of this book has much to recommend it - for users who already have acquired an elementary understanding.
  
  
• Goldstein, Harvey (1995): Multilevel Statistical Models. London: Arnold.
  A more advanced introduction by one of the " fathers" of multilevel modeling, now (2003) in its third ediation. An earlier version of the book can be downloaded here.
  
  
• Hox, Joop (1995): Multilevel Analysis. Techniques and Applications. Mahwah, NJ: Erlbaum.
  This book is the expanded and updated version of an earlier book, Multilevel Analysis, Amsterdam: TT Publishers, 1995, which is downloadable for free (attention, this is a PDF file with a size of several MB).
  
  
• Hox, Joop J. & Kreft, Ita G. G. (1994): Multilevel Analysis Methods, in: Sociological Methods & Research, 22, 283-299.
  This introduction to a special issue of SMR gives a brief overview of problems resulting from using traditional approaches for multilevel data, of the basic structure of the random coefficient model, of available software, and of current problems and possible future developments.
  
  
• Kreft, Ita G. G. (1991): Using Hierarchically Linear Models to Analyse Multilevel Data. In: ZUMA-Nachrichten, 29, 44-56.
  A brief introduction that should be found in most German sociology libraries, but (naturally) cannot replace a textbook.
  
  
• Kreft, Ita & de Leeuw, Jan (1998): Introducing Multilevel Modeling, London: Sage.
  This is certainly the most helpful textbook for those with a strong dislike of maths, formal derivations and the like. The reader is carefully guided through a number of examples. However, one should be aware that there are many advanced topics that are not dealt with in this book.
  
  
• Longford, N. (1993): Random coefficient models. Oxford: Oxford University Press.
  In this book, statistical reasoning is paramount, but it is also applied to several datasets with helpful discussions of the results.
  
  
• Ohr, Dieter (1999): Modellierung von Kontexteffekten: Voraussetzungen, Verfahren und eine empirische Anwendung am Beispiel des politischen Informationsverhaltens, in: ZA- Information 44, 39-63.
  A fine brief introduction, but I feel that it is somewhat infortunate that MM is introduced via an example where the gains from using a MM approach are almost nil (statistically speaking, i.e. the MM analysis barely differs from OLS regression results).
  
  
• Snijders, Tom & Bosker, Roel (1999): Multilevel Analysis. An introduction to basic and advanced multilevel modeling. London, Thousand Oaks: SAGE.
  This is a very thorough introduction that requires quite some effort on part of the student - but it pays. Especially chapters 8 and 9 should be studied carefully, as I have found no comparable discussion of heteroscedascity and the basic assumptions (and how to check them) of multilevel modeling.
  

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Multivariate Analysis

MIT Open Course Ware Computing and Data Analysis for Environmental Applications, Fall 2003

http://www.core.org.cn/OcwWeb/Civil-and-Environmental-Engineering/1-017Computing-and-Data-Analysis-for-Environmental-ApplicationsFall2003/LectureNotes/index.htm

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Lecture 1	Binary Choice Models The Linear Probability Model; binomial probit; binomial logit; assumptions; Maximum Likelihood estimation methods; interpretation of coefficients; constructing probabilities; restrictions and limitations; marginal effects; measuring goodness-of fit; testing parameter restrictions. Downloads: [lecture notes] [overheads] [exercise] [binary choice estimates]
Lecture 2	Multiple Discrete Choice Models Ordered probit/logit; sequential probit/logit; methods of estimation; multinomial logit (MNL); the Independence of Irrelevant Alternatives (IIA) assumption; bivariate and Multinomial Probit models; measuring goodness-of fit; testing assumptions. Downloads: [lecture notes] [overheads]
Lecture 3	Limited Dependent Variable Models 1I truncated and censored samples; sample selection bias; the truncated regression model; marginal effects; the Tobit model; interpretation of Tobit model coefficients; testing for normality; limitations of the Tobit model. Downloads: [lecture notes] [overheads]
Lecture 4	Limited Dependent Variable Models 2 bivariate generalisations of the Tobit model; the Selectivity(Heckit) model; two-step and full-information estimation methods; interpretation of model coefficients; diagnostic testing; the Double Hurdle (DH) model; the DH model with dependence; switching regressions; diagnostic testing. Downloads: [lecture notes] [overheads]
Lecture 5	Duration Models and Survival Functions the concept of duration and survival; parametric hazard and survival functions; duration dependence; methods of estimation; the proportional hazard models, introducing heterogeneity; time-invariant and time-varying covariates. Downloads: [lecture notes] [overheads]
Lecture 6	Panel Data Models general definitions; fixed effects and random effects panel data models; methods of estimation; random coefficients; discrete choice panel data models; diagnostic testing; dynamic and nonlinear panel data models. Downloads: [lecture notes] [overheads]
Lecture 7	Nonparametric and Semiparametric Estimation Methods general definitions; kernel density estimation; Nadaraya-Watson nonparametric regression function; bandwidth selection; average derivative estimation; bootstrap methods and confidence bands; semiparametric estimation methods; partially linear models Downloads: [survey paper]

[此贴子已经被作者于2006-5-3 1:51:04编辑过]

为了论坛的兴盛繁荣，建议降价促销

Diagnostic Checks i....Time Series[长]

[此贴子已经被作者于2006-5-9 5:52:25编辑过]

Evaluating GARCH models

Stefan Lundbergh and Timo Teräsvirta^,

Department of Economic Statistics, Stockholm School of Economics, P.O. Box 6501, SE-113 83, Stockholm, Sweden

Available online 12 June 2002.

51292.pdf
大小:(210.17 KB)

 马上下载

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[下载]非线性回归分析及其应用（现代国....丛，426s，pdf）[长]

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Stochastic Limit Theory:An Introduction for Econometricians

Publication date 1994 (this edition)
Print ISBN-10: 0-19-877403-6
Print ISBN-13: 978-0-19-877403-7
doi:10.1093/0198774036.001.0001

Abstract:

This book aims to introduce modern asymptotic theory to students and practitioners of econometrics. It falls broadly into two parts. The first half provides a handbook and reference for the underlying mathematics (Part I, Chapters 1-6), statistical theory (Part II, Chapters 7-11) and stochastic process theory (Part III, Chapters 12-17). The second half provides a treatment of the main convergence theorems used in analysing the large sample behaviour of econometric estimators and tests. These are the law of large numbers (Part IV, Chapters 18-21), the central limit theorem (Part V, Chapters 22-25) and the functional central limit theorem (Part VI, Chapters 26-30). The focus in this treatment is on the nonparametric approach to time series properties, covering topics such as nonstationarity, mixing, martingales, and near-epoch dependence. While the approach is not elementary, care is taken to keep the treatment self-contained. Proofs are provided for almost all the results.

一本国外的高级计量经济学教材

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[此贴子已经被作者于2006-5-9 5:45:47编辑过]

萧政教授推荐的高等概率论教材

[此贴子已经被作者于2006-5-9 9:35:08编辑过]

[推荐][下载]Multilevel Statistical Models

Applied multilevel analysis by J.J. Hox (PDF, free download)