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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

Multilevel Models for Meta-Analysis

Self-administered questionnaires have many advantages, especially when sensitive questions are asked. However, self-administered paper questionnaires have a serious drawback: Only relatively simple questionnaires can be used. Computer-assisted self-interviewing (CASI) can overcome these problems. CASI can be part of a personal interview in which the interviewer hands the computer over to the respondent for specific questions. It also can be a computerized version of the disk-by-mail survey. The authors have used both forms with very special populations (primary school children, visually impaired young adults, and parents and children from multiproblem families, in which professional guidance for the family was sought). This article introduces CASI and reviews its advantages and disadvantages, giving special attention to data quality.

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Computer-Assisted Self-Interviewing Tailored for Special Populations and Topics

Surveys increasingly use mixed mode data collection (e.g., combining face-to-face and web) because this controls costs and helps to maintain good response rates. However, a combination of different survey modes in one study, be it cross-sectional or longitudinal, can lead to different kinds of measurement errors. For example, respondents in a face-to-face survey or a web survey may interpret the same question differently, and might give a different answer, just because of the way the question is presented. This effect of survey mode on the question-answer process is called measurement mode effect. This study develops methodological and statistical tools to identify the existence and size of mode effects in a mixed mode survey. In addition, it assesses the size and importance of mode effects in measurement instruments using a specific mixed mode panel survey (Netherlands Kinship Panel Study). Most measurement instruments in the NKPS are multi-item scales, therefore confirmatory factor analysis (CFA) will be used as the main analysis tool, using propensity score methods to correct for selection effects. The results show that the NKPS scales by and large have measurement equivalence, but in most cases only partial measurement equivalence. Controlling for respondent differences on demographic variables, and on scale scores from the previous uni-mode measurement occasion, tends to improve measurement equivalence, but not for all scales. The discussion ends with a review of the implications of our results for analyses employing these scales.

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Measurement equivalence in mixed mode surveys

Background: This meta-analysis examined differences in health-related quality of life (HRQoL) between seekers of surgical and non-surgical treatment, and non-treatment seekers, over and above differences that are explained by weight, age, and gender. Methods: Our literature search focused on the ‘Impact of Weight on Quality of Life-Lite’ (IWQOL-Lite) and the 'Short Form-36' (SF-36) questionnaires. Included were studies published between 1980 and April 2006 providing pre-treatment descriptive statistics of adult overweight, obese or morbidly obese persons. Excluded were elderly and ill patient groups. Results: 54 articles, with a total number of nearly 100,000 participants, met the inclusion criteria. Persons seeking surgical treatment demonstrated the most severely reduced HRQoL. IWQOL-Lite scores showed larger differences between populations than SF-36 scores. After adjustment for weight, the population differences on the IWQOL disappeared. In contrast, the differences on the SF-36 between the surgical treatment seeking population and the other populations were maintained after adjustment for weight. Conclusion: The IWQOL-Lite questionnaire predominantly reflects weight-related HRQoL, whereas the SF-36 mostly reflects generic HRQoL that is determined by both weight and other factors. Our metaanalysis provides reference values that are useful when explaining or evaluating obesity-specific (IWQOL-Lite) or generic (SF-36) HRQoL, weight, and demographic characteristics of obese persons seeking or not seeking surgical or non-surgical treatment.

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Joop J. Hox

Papers

Multilevel Models for Meta-Analysis

Computer-Assisted Self-Interviewing Tailored for Special Populations and Topics

Measurement equivalence in mixed mode surveys

The Health-Related Quality of Life of Obese Persons Seeking or Not Seeking Surgical or Non-surgical Treatment: a Meta-analysis

Mixed Mode Surveys: When and Why