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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

Analyzing Longitudinal Data

This study examines the effects of a family's and individual children's characteristics on the probability of having a divorce. Current research shows a clear indication of increased divorce risks if an individual's parents or siblings have experienced a divorce. Explanations include both shared family characteristics (including genetic effects) and common characteristics of the individual children involved. This study analyzes the effects of shared family background characteristics on the divorce risk of individuals. By analyzing siblings within families and including individual children's characteristics in the analysis, it is possible to separate individual-level and family-level effects.

In addition to employing a multi-level structure of individual siblings nested within families, the data cited here are censored. For all individuals, the length of the marriage and the divorce status are known, but the divorce status is interpreted differently for individuals who have or have not experienced divorce. For divorced individuals, the final divorce status is known; for individuals who have not experienced divorce, the final marriage status is unknown or censored. The proper analysis model for such data is event history (also called survival) analysis. This study therefore employs a multi-level event history model.

Our results show that there is a similarity in the divorce risks of siblings from the same family, which is not explained away by the available child and family characteristics. This finding suggests that shared genetic and social heritage play an important role in the intergenerational transmission of divorce risks.

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The Importance of the Common Family Background for the Similarity of Divorce Risks of Siblings: a Multi-level Event History Analysis

In face-to-face surveys interviewers play a crucial role in making contact with and gaining cooperation from sample units. While some analyses investigate the influence of interviewers on nonresponse, they are typically restricted to single-country studies. However, interviewer training, contacting and cooperation strategies as well as survey climates may differ across countries. Combining call-record data from the European Social Survey (ESS) with data from a detailed interviewer questionnaire on attitudes and doorstep behavior we find systematic country differences in nonresponse processes, which can in part be explained by differences in interviewer characteristics, such as contacting strategies and avowed doorstep behavior.

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Interviewer Effects on Nonresponse in the European Social Survey

Person fit analysis investigates whether a person's answering behavior deviates from that predicted by a measurement model, or from the answer patterns of the majority in the group to which that person belongs. For example, if a student answers eight out of ten questions correctly, one generally expects him/her to have missed the two most difficult questions. If the two easiest questions are answered incorrectly, the response pattern is completely unexpected. For persons detected as aberrant, the total test score does not adequately reflect the attribute that is being measured and this total score should not be used in substantial research or decision processes (e.g., educational research, school examinations). Further research is needed before firm conclusions can be drawn about their test performance. To investigate response patterns, data have to be gathered on a test or scale that consists of a number of questions about the same topic. Furthermore, the test or scale should have good psychometric properties; the reliability and the scalability must be high.

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Aberrant response patterns in elderly respondents: latent class analysis of respondent scalability

The psychometric literature contains many indices to detect aberrant respondents. A different, promising approach is using ordered latent class analysis with the goal to distinguish latent classes of respondents that are scalable, from latent classes of respondents that are not scalable (i.e., aberrant) according to the scaling model adopted. This article examines seven Latent Class models for a cumulative scale. A simulation study was performed to study the efficacy of different models for data that follow the scale model perfectly. A second simulation study was performed to study how well these models detect aberrant respondents.

/pdf/latent-class-analysis-of-respondent-scalability-4tislktxjm.pdf

Joop J. Hox

Papers

Analyzing Longitudinal Data

The Importance of the Common Family Background for the Similarity of Divorce Risks of Siblings: a Multi-level Event History Analysis

Interviewer Effects on Nonresponse in the European Social Survey

Aberrant response patterns in elderly respondents: latent class analysis of respondent scalability

Latent Class Analysis of Respondent Scalability