Back to the multilevel page of Tom Snijders

The second edition of the textbook, Multilevel Analysis: An introduction to basic and advanced multilevel modeling, written by Tom A.B. Snijders and Roel J. Bosker, appeared November 2011 at Sage Publishers. The Sage announcement of this book is here, and here is the table of contents. The book was totally updated, with new chapters on missing data, multilevel analysis and survey weights, and miscellaneous methods (Bayesian estimation, sandwich standard errors, latent class models). Each chapter (from 2 to 17) ends with a glommary, which is a combination of a glossary and a summary, giving the main terms and an overview of the chapter.

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Data Sets

• The main data set, used in Chapters 4-10 (where missings are excluded):
  • mlbook2_r_dat.zip, zipped file containing the file named mlbook2_r.dat, an ascii file with 11 variables, top line gives the variable names, may be read into R by a command such as

    > mlbook_red <- read.table("mlbook2_r.dat", header=TRUE)

    For reading this file into other software, delete the top line but note that it contains the variable names.
  • mlbook2_r_obe.zip, zipped file containing the file named mlbook2_r.obe, the same, but formatted as a macro file to be read directly by MLwiN:

    obey mlbook2_r.obe

  • mlbook2_mm.zip, zipped file containing the file named mlbook2_mm.dat, an ascii file with 23 variables, top line gives the variable names. This is the data file used in Example 9.3, containing some more cases that have missing values on the dependent variable langPOST. For the construction of this file and meaning of the variable names, consult script mlbook2_prepare_data.r contained in the zipped file mlbook2_data_preparations.zip which is also mentioned below.
    After unzipping, this file may be read into R by a command such as

    > mlbook_mm <- read.table("mlbook2_mm.dat", header=TRUE)

    For reading this file into other software, delete the top line but note that it contains the variable names.
  • The original data set and an r script transforming it to the derived data sets used here, is in the zipped file mlbook2_data_preparations.zip.
    This is not directly needed, but is included just to show the data transformations that were carried out.

• Data set used in example 3.7 in Chapter 3:
  • mlbook2_bb_obe.zip, zipped file containing the file named mlbook2_bb.obe; a data set with a few more cases than mlbook2_r (obtained by also including those that had a missing value on aritPOST but not on any other variables) formatted as a macro file to be read directly by MLwiN:

    obey mlbook2_bb.obe

    If you wish to use this data file in other software, just delete the top and bottom text lines that contain the information for MLwiN.

• Data set used in Chapter 14:
  • DataPISA.zip, zipped file containing the file combineusa-999_c.sav.
    
  • pisacc.zip, an excerpt from this file in ascii format, as written in script pisa_b.r below.
  • For how to use this: see the MLwiN and r scripts below.

• Data set used in Sections 17.2-3:
  • rel_level12.zip, zipped file containing the files named rel_level1.txt and rel_level2.txt, two ascii files for both of which the top line gives the variable names. These files may be read into R by a command such as

    > level2 <- read.table("rel_level2.txt", header=TRUE)

    For reading this file into other software, delete the top line but note that it contains the variable names.

• Data set used in Sections 17.3.4-5:
  • LOOPDIC.zip, zipped file containing the file named LOOPDIC.DAT, an ascii file with 10 variables.

Macros / scripts

MlwiN

• hetsced1.obe, a MLwiN macro for testing level-one heteroscedasticity using test (10.5) of Snijders and Bosker (2011), which is the same as test (9.6) in Bryk and Raudenbush (2002). Used in Chapter 10.
• smooth_2.obe, a MLwiN macro for smoothing Y as a function of X, using moving averages. Used in Chapter 10.
• table_2.obe, a MLwiN macro for averaging Y as a function of variable X that should have a limited number of discrete values. Used in Chapter 10.
• dinfl_2.obe, a MLwiN macro for calculating deletion influence statistics for level-two units. It is based on Snijders and Berkhof (2008, Section 3), and implements the methods also explained in Section 10.7 of Snijders and Bosker (2011). Used in Chapter 10.
• explvar.mac, a MLwiN macro for computing the explained variance in a two-level random intercept logistic regression model, according to formula (17.22).

R

• ml2_impute_b.R, a collection of R functions for multiple imputation by two-level random intercept models for continuous and binary variables.
• nlme_utils.R, a couple of nlme utilities used in the R script for Chapter 9.

Stata

• predict_margins.do, a do file to plot marginal effects and predicted probabilities from multilevel logistic regression models, contributed by Tim Mueller.

Software Setups

Note that you can download the files below in many browsers by right-clicking on the file, and choosing something like "save as".

	HLM	MLwiN	R	Mplus	SAS	Stata	Chapter Title
Chapter 3		CH3ex7.obe					Statistical Treatment of Clustered Data
Chapter 4		CH4568.obe	ch45.r			chap4.do	The Random Intercept Model
Chapter 5		CH4568.obe	ch45.r			chap5.do	The Hierarchical Linear Model
Chapter 6		CH4568.obe	ch6.r			chap6.do	Testing and Model Specification
Chapter 7							How much does the model explain?
Chapter 8		CH4568.obe	ch8.r			chap8.do	Heteroscedasticity
Chapter 9			ch9.r			chap9.do	Missing Data
Chapter 10		ch10.obe ch10_infl.obe	ch10.r			chap10.do	Assumptions of the Hierarchical Linear Model
Chapter 11							Designing Multilevel Studies
Chapter 12						chap12.do	Other Models and Methods
Chapter 13						chap13.do	Imperfect Hierarchies
Chapter 14		PISA.obe	pisa_b.R			chap14.do	Survey Weights
Chapter 15							Longitudinal Data
Chapter 16							Multivariate Multilevel Models
Chapter 17			ch17.r ch17_ex6.r			chap17.do	Discrete Dependent Variables

Materials Oxford Spring School, April 2012