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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

This article reports a meta-analysis of 45 studies that explicitly compare the response obtained using a mail, telephone or face-to-face survey. The data analysis uses a generalized hierarchical linear model. Sampling procedure (e.g., local convenience sample, random general sample), saliency of topic, and research organization (university, government versus market research) had an effect on the response. On the average, the face-to-face condition achieved the highest completion rate (70.3%), the telephone survey the next highest (67.2%), and the mail survey the lowest (61.3%). There is a significant interaction with the year of publication: The response to face-to-face and telephone surveys is going down in the period covered by this analysis (1947 to 1992), but the response to mail surveys is going up slightly. We attribute this to the large amount of research on nonresponse problems with mail surveys, and recommend more research and development in this direction for face-to-face and telephone methods.

/pdf/a-comparison-of-nonresponse-in-mail-telephone-and-face-to-hlj4z9389i.pdf

A comparison of nonresponse in mail, telephone, and face-to-face surveys

In this quasi-experimental study among staff of 29 oncology wards, the authors evaluated the effects of a team-based burnout intervention program combining a staff support group with a participatory action research approach. Nine wards were randomly selected to participate in the program. Before the program started (Time 1), directly after the program ended (Time 2), and 6 months later (Time 3), study participants filled out a questionnaire on their work situation and well-being. Results of multilevel analyses showed that staff in the experimental wards experienced significantly less emotional exhaustion at both Time 2 and Time 3 and less depersonalization at Time 2, compared with the control wards. Moreover, changes in burnout levels were significantly related to changes in the perception of job characteristics over time.

https://www.wilmarschaufeli.nl/publications/Schaufeli/263.pdf

Take Care! The Evaluation of a Team-Based Burnout Intervention Program for Oncology Care Providers

Social researchers increasingly survey children and young adolescents. They are con- vinced that information about perspectives, attitudes, and behaviors of children should be collected from the children themselves. Methodological expertise on surveying children is still scarce, and researchers rely on ad-hoc knowledge from fields such as child psychiatry and educational testing, or on methodological knowledge on surveying adults. Regarding adults, empirical evidence shows that respondent characteristics (cognitive abilities) as well as question characteristics (question difficulty) affect response quality. This study reports on a methodological survey experiment on the effect of negatively formu- lated questions, the number of response options and offering a neutral midpoint as response option question characteristics on the reliability of the responses, using children and young adolescents as respondents. The study shows no effects of negatively formulated questions on the reliability measures, although children respond consistently differently on negatively formulated questions than on pos- itively formulated questions. Taking all results on the effects of number of response options and offering a neutral midpoints on the different reliability measures into consideration; it would appear that offering about four response options is optimal with children as respondents.

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Response effects in surveys on children and adolescents: the effect of number of response options, negative wording, and neutral mid-point

Part 1. Introduction. J. Hox, J.K. Roberts, Multilevel Analysis: Where We Were and Where We Are. Part 2. Multilevel Latent Variable Modeling (LVM). B. Muthen, T. Asparouhov, Beyond Multilevel Regression Modeling: Multilevel Analysis in a General Latent Variable Framework. A. Kamata, B. Vaughn, Multilevel IRT Modeling. J. Vermunt, Mixture Models for Multilevel Data Sets. Part 3. Multilevel Models for Longitudinal Data. J. Hox, Panel Modeling: Random Coefficients and Covariance Structures. R.D. Stoel, F.G. Garre, Growth Curve Analysis using Multilevel Regression and Structural Equation Modeling. Part 4. Special Estimation Problems. D. Hedeker, R.J. Mermelstein, Multilevel Analysis of Ordinal Outcomes Related to Survival Data. E.L. Hamaker, I. Klugkist, Bayesian Estimation of Multilevel Models. H. Goldstein, Bootstrapping in Multilevel Models. S. van Buuren, Multiple Imputation of Multilevel Data. J. Kim, C.M. Swoboda, Handling Omitted Variable Bias in Multilevel Models: Model Specification Tests and Robust Estimation. J.K. Roberts, J.P. Monaco, H. Stovall, V. Foster, Explained Variance in Multilevel Models. E.L. Hamaker, P. van Hattum, R.M. Kuiper, H. Hoijtink, Model Selection Based on Information Criteria in Multilevel Modeling. M. Moerbeek, S. Teerenstra, Optimal Design in Multilevel Experiments. Part 5. Specific Statistical Issues. J. Algina, H. Swaminathan, Centering in Two-Level Nested Designs. S.N. Beretvas, Cross-Classified and Multiple Membership Models. D.A. Kenny, D.A. Kashy, Dyadic Data Analysis using Multilevel Modeling.

Handbook of Advanced Multilevel Analysis

Multilevel structural equation modeling (multilevel SEM) has become an established method to analyze multilevel multivariate data. The first useful estimation method was the pseudobalanced method. This method is approximate because it assumes that all groups have the same size, and ignores unbalance when it exists. In addition, full information maximum likelihood (ML) estimation is now available, which is often combined with robust chi-squares and standard errors to accommodate unmodeled heterogeneity (MLR). In addition, diagonally weighted least squares (DWLS) methods have become available as estimation methods. This article compares the pseudobalanced estimation method, ML(R), and two DWLS methods by simulating a multilevel factor model with unbalanced data. The simulations included different sample sizes at the individual and group levels and different intraclass correlation (ICC). The within-group part of the model posed no problems. In the between part of the model, the different ICC sizes had no effect. There is a clear interaction effect between number of groups and estimation method. ML reaches unbiasedness fastest, then the two DWLS methods, then MLR, and then the pseudobalanced method (which needs more than 200 groups). We conclude that both ML(R) and DWLS are genuine improvements on the pseudobalanced approximation. With small sample sizes, the robust methods are not recommended.

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Joop J. Hox

Papers

A comparison of nonresponse in mail, telephone, and face-to-face surveys

Take Care! The Evaluation of a Team-Based Burnout Intervention Program for Oncology Care Providers

Response effects in surveys on children and adolescents: the effect of number of response options, negative wording, and neutral mid-point

Handbook of Advanced Multilevel Analysis

The effect of estimation method and sample size in multilevel structural equation modeling