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Using Multivariate Statistics

The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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mice: Multivariate Imputation by Chained Equations in R

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Statistical Analysis with Missing Data

1. Introduction to Multilevel Analysis. 2. The Basic Two-Level Regression Model. 3. Estimation and Hypothesis Testing in Multilevel Regression. 4. Some Important Methodological and Statistical Issues. 5. Analyzing Longitudinal Data. 6. The Multilevel Generalized Linear Model for Dichotomous Data and Proportions. 7. The Multilevel Generalized Linear Model for Categorical and Count Data. 8. Multilevel Survival Analysis. 9. Cross-classified Multilevel Models. 10. Multivariate Multilevel Regression Models. 11. The Multilevel Approach to Meta-Analysis. 12. Sample Sizes and Power Analysis in Multilevel Regression. 13. Advanced Issues in Estimation and Testing. 14. Multilevel Factor Models. 15. Multilevel Path Models. 16. Latent Curve Models.

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Multilevel Analysis: Techniques and Applications

This practical introduction helps readers apply multilevel techniques to their research Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, and sociometric scores, to name a few The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files Readers are introduced to both the multilevel regression model and multilevel structural models
Highlights of the second edition include:

Two new chapters—one on multilevel models for ordinal and count data (Ch 7) and another on multilevel survival analysis (Ch 8)
Thoroughly updated chapters on multilevel structural equation modeling that reflect the enormous technical progress of the last few years
The addition of some simpler examples to help the novice, whilst the more complex examples that combine more than one problem have been retained
A new section on multivariate meta-analysis (Ch 11)
Expanded discussions of covariance structures across time and analyzing longitudinal data where no trend is expected
Expanded chapter on the logistic model for dichotomous data and proportions with new estimation methods
An updated website at http://wwwjoophoxnet/ with data sets for all the text examples and up-to-date screen shots and PowerPoint slides for instructors
Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including: psychology, education, sociology, the health sciences, and business The advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines A basic understanding of ANOVA and multiple regression is assumed The section on multilevel structural equation models assumes a basic understanding of SEM

Multilevel Analysis : Techniques and Applications, Second Edition

An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors. In addition, the influence of other factors, such as the lowest-level sample size and different variance distributions between the levels (different intraclass correlations), is examined. The results show that only a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second-level standard errors. In all of the other simulated conditions the estimates of the regression coefficients, the variance components, and the standard errors are unbiased and accurate.

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Sufficient Sample Sizes for Multilevel Modeling

Multilevel analysis: Techniques and applications, 2nd ed.

The analysis of measurement invariance of latent constructs is important in research across groups, or across time. By establishing whether factor loadings, intercepts and residual variances are equivalent in a factor model that measures a latent concept, we can assure that comparisons that are made on the latent variable are valid across groups or time. Establishing measurement invariance involves running a set of increasingly constrained structural equation models, and testing whether differences between these models are significant. This paper provides a step-by-step guide to analysing measurement invariance.

Joop J. Hox

Papers

Multilevel Analysis: Techniques and Applications

Multilevel Analysis : Techniques and Applications, Second Edition

Sufficient Sample Sizes for Multilevel Modeling

Multilevel analysis: Techniques and applications, 2nd ed.

A checklist for testing measurement invariance