Imputation of missing values in survey data (Version 1.0)

Survey data often includes missing values. An approach to deal with missing values is imputation in order to obtain a complete dataset. However, the process of imputation requires researchers to make various decisions regarding the imputation method to be applied, the number of values to be imputed for each missing value, the selection of predictor variables, the treatment of multivariate nonresponse and the conduct of variance estimation. This survey guideline provides an overview of imputation procedures for missing values. It aims to support the reader with respect to aforementioned decisions when imputing missing values in survey data.Survey Daten enthalten häufig fehlende Werte. Eine Methode mit fehlenden Werten umzugehen ist die Imputation, welche darauf abzielt, einen vollständigen Datensatz zu erhalten. Im Zuge der Anwendung der Imputation müssen jedoch verschiedene Entscheidungen getroffen werden. Zum Beispiel muss festgelegt werden, welche Imputationsmethode verwendet werden soll, wie viele Werte für einen fehlenden Wert imputiert werden sollen, welche Variablen als Prädiktoren verwendet werden und wie mit multivariatem Nonresponse umzugehen ist und wie die Varianzschätzung durchgeführt werden soll. Diese Survey Guideline gibt einen Überblick über die Imputation fehlender Werte. Das Ziel ist es, den Leser bezüglich der zuvor genannten Fragestellungen bei der Imputation fehlender Werte in Survey Daten zu unterstützen

Prior Choice for the Variance Parameter in the Multilevel Regression and Poststratification Approach for Highly Selective Data: A Monte Carlo Simulation Study

The multilevel and poststratification approach is commonly used to draw valid inference from (non-probabilistic) surveys. This Bayesian approach includes varying regression coefficients for which prior distributions of their variance parameter must be specified. The choice of the distribution is far from being trivial and many contradicting recommendations exist in the literature. The prior choice may be even more challenging when data results from a highly selective inclusion mechanism, such as applied by volunteer panels. We conduct a Monte Carlo simulation study to evaluate the effect of different distribution choices on bias in the estimation of a proportion based on a sample that is subject to a highly selective inclusion mechanism.Die Multilevel Regression and Poststratifikationsmethode (MrP) wird häufig verwendet, um Schätzungen, die auf (nicht-probabilistischen) Befragungen basieren, zu verbessern. Für dieses Bayesianische Verfahren müssen Verteilungen für Varianzparameter geeignet festgelegt werden, wofür in der Literatur keine einheitliche Empfehlungen bestehen. Insbesondere für Befragungen mit hoch-selektiver Teilnahme stellt die Wahl der Verteilung eine große Herausforderung dar. Im Rahmen dieser Studie wurde eine Monte Carlo Simulation durchgeführt, um den Effekt verschiedener Verteilungen auf den (Monte Carlo) Bias der Schätzung basierend auf Stichproben mit hochselektivem Inklusionsmechanismus zu evaluieren

Sample Size Calculation For Complex Sampling Designs (Version 1.0)

Before conducting a survey, researchers frequently ask themselves how large the resulting sample of respondents needs to be to answer their research questions. In this guideline, we discuss how sample size calculation is affected by the sampling design. We give practical advice on how to conduct sample size calculation for complex samples.Bevor eine Umfrage durchgeführt wird, stellen sich Forscher häufig die Frage, wie groß die Stichprobe der Befragten sein muss, um ihre Forschungsfragen zu beantworten. In diesem Leitfaden wird erörtert, wie die Berechnung des Stichprobenumfangs durch das Stichprobendesign beeinflusst wird. Wir geben praktische Ratschläge, wie der Stichprobenumfang für komplexe Stichproben berechnet werden kann

General-purpose imputation of planned missing data in social surveys: Different strategies and their effect on correlations

Planned missing survey data, for example stemming from split questionnaire designs are becoming increasingly common in survey research, making imputation indispensable to obtain reasonably analyzable data. However, these data can be difficult to impute due to low correlations, many predictors, and limited sample sizes to support imputation models. This paper presents findings from a Monte Carlo simulation, in which we investigate the accuracy of correlations after multiple imputation using different imputation methods and predictor set specifications based on data from the German Internet Panel (GIP). The results show that strategies that simplify the imputation exercise (such as predictive mean matching with dimensionality reduction or restricted predictor sets, linear regression models, or the multivariate normal model without transformation) perform well, while especially generalized linear models for categorical data, classification trees, and imputation models with many predictor variables lead to strong biases.Geplant fehlende Werte in sozialwissenschaftlichen Befragungen, beispielsweise infolge eines Split Questionnaire Designs, treten in der Umfrageforschung immer häufiger auf. Um hinlänglich analysierbare Daten zu erhalten, ist hierbei oftmals eine Imputation erforderlich. Die statistische Modellierung bei der Imputation solcher Daten kann jedoch aufgrund niedriger Korrelationen, einer Großzahl möglicher Prädiktoren und begrenzter Stichprobengrößen mit enormen Herausforderungen verbunden sein. Der vorliegende Beitrag stellt Ergebnisse aus einer Monte-Carlo-Simulation vor, in der basierend auf Daten des German Internet Panels (GIP) die Validität von Korrelationsschätzungen in einem Split Questionnaire Design unter Verwendung verschiedener Imputationsstrategien untersucht wird. Dabei zeigt sich, dass Ansätze, die die Imputation vereinfachen, zu guten Ergebnissen führen können (z.B. Predictive Mean Matching mit Dimensionsreduktion oder wenigen Prädiktorvariablen). Demgegenüber können insbesondere Generalisierte Lineare Modelle für kategoriale Daten, Klassifikationsbäume (CART) und Imputationsmodelle mit vielen Prädiktorvariablen starke Verzerrungen zur Folge haben

How does switching a Probability-Based Online Panel to a Smartphone-Optimized Design Affect

In recent years, an increasing number of online panel participants respond to surveys on smartphones. As a result, survey practitioners are faced with a difficult decision: Either they hold the questionnaire design constant over time and thus stay with the original desktop-optimized design; or they switch to a smartphone-optimized format and thus accommodate respondents who prefer participating on their smartphone. Even though this decision is all but trivial, little research thus far has been conducted on the effect of such an adjustment on panel members’ survey participation and device use. We report on the switch to a smartphone-optimized design in the German Internet Panel (GIP), an ongoing probability-based online panel that started in 2012 with a desktop-optimized design. We investigate whether the introduction of a smartphone-optimized design affected overall response rates and smartphone use in the GIP. Moreover, we examine the effect of different ways of announcing the introduction of the smartphone-optimized design in the invitation email on survey participation using a smartphone

General-purpose imputation of planned missing data in social surveys: different strategies and their effect on correlations

Planned missing survey data, for example stemming from split questionnaire designs are becoming increasingly common in survey research, making imputation indispensable to obtain reasonably analyzable data. However, these data can be difficult to impute due to low correlations, many predictors, and limited sample sizes to support imputation models. This paper presents findings from a Monte Carlo simulation, in which we investigate the accuracy of correlations after multiple imputation using different imputation methods and predictor set specifications based on data from the German Internet Panel (GIP). The results show that strategies that simplify the imputation exercise (such as predictive mean matching with dimensionality reduction or restricted predictor sets, linear regression models, or the multivariate normal model without transformation) perform well, while especially generalized linear models for categorical data, classification trees, and imputation models with many predictor variables lead to strong biases.Geplant fehlende Werte in sozialwissenschaftlichen Befragungen, beispielsweise infolge eines Split Questionnaire Designs, treten in der Umfrageforschung immer häufiger auf. Um hinlänglich analysierbare Daten zu erhalten, ist hierbei oftmals eine Imputation erforderlich. Die statistische Modellierung bei der Imputation solcher Daten kann jedoch aufgrund niedriger Korrelationen, einer Großzahl möglicher Prädiktoren und begrenzter Stichprobengrößen mit enormen Herausforderungen verbunden sein. Der vorliegende Beitrag stellt Ergebnisse aus einer Monte-Carlo-Simulation vor, in der basierend auf Daten des German Internet Panels (GIP) die Validität von Korrelationsschätzungen in einem Split Questionnaire Design unter Verwendung verschiedener Imputationsstrategien untersucht wird. Dabei zeigt sich, dass Ansätze, die die Imputation vereinfachen, zu guten Ergebnissen führen können (z.B. Predictive Mean Matching mit Dimensionsreduktion oder wenigen Prädiktorvariablen). Demgegenüber können insbesondere Generalisierte Lineare Modelle für kategoriale Daten, Klassifikationsbäume (CART) und Imputationsmodelle mit vielen Prädiktorvariablen starke Verzerrungen zur Folge haben

Indicadores de sostenibilidad y la herramienta IDEA: Un estudio de caso en una propiedad rural familiar de Uruguaiana, Rio Grande do Sul

Para promover uma análise sobre a sustentabilidade em um determinado sistema de produção, torna-se fundamental levar em consideração uma abordagem que considere, minimamente, indicadores sociais, ambientais e econômicos.Neste tipo de levantamento, os índices de sustentabilidade apresentam-se enquanto resultados gerados por instrumentos de diagnóstico, a fim de possibilitar a coleta e a interpretação de informações. Assim, o presente artigo tem como objetivo demonstrar a utilização da ferramenta de Indicadores de Sustentabilidade das Explorações Agrícolas (IDEA) em uma propriedade rural familiar. A ferramenta IDEA leva em consideração as dimensões agroambiental, sócio-territorial e econômica. A partir do diagnóstico foi possível evidenciar que apesar de a ferramenta apresentar lacunas, constitui-se enquanto importante instrumento de análise, evidenciando que a propriedade objeto deste estudo possui baixos índices de sustentabilidade, necessitando a intervenção e correção de alguns pontos cruciais para que alcance níveis mais satisfatórios de sustentabilidade.To promote an analysis of sustainability in a particular production system, it is essential to consider an approach that takes into account at least social, environmental, and economic indicators. In this type of survey, sustainability indices are presented as results generated by diagnostic instruments to enable the collection and interpretation of information. Thus, this article aims to demonstrate the use of the Sustainability Indicators for Agricultural Farms (IDEA) tool on a family farm. The IDEA tool takes into account the agro-environmental, socio-territorial, and economic dimensions. Based on the diagnosis, it was possible to show that despite the tool having some gaps, it constitutes an important analysis instrument, demonstrating that the property studied in this research has low sustainability indices and requires intervention and correction of some crucial points to achieve more satisfactory levels of sustainability.Para promover un análisis de sostenibilidad en un sistema de producción determinado, es fundamental considerar un enfoque que tenga en cuenta al menos indicadores sociales, ambientales y económicos. En este tipo de encuesta, los índices de sostenibilidad se presentan como resultados generados por instrumentos de diagnóstico para permitir la recopilación e interpretación de información. Por lo tanto, este artículo tiene como objetivo demostrar el uso de la herramienta Indicadores de Sostenibilidad para Explotaciones Agrícolas (IDEA) en una finca familiar. La herramienta IDEA tiene en cuenta las dimensiones agroambiental, socio-territorial y económica. A partir del diagnóstico, fue posible evidenciar que aunque la herramienta tiene algunas limitaciones, constituye un importante instrumento de análisis, demostrando que la propiedad estudiada en esta investigación tiene bajos índices de sostenibilidad y requiere intervención y corrección de algunos puntos cruciales para alcanzar niveles más satisfactorios de sostenibilidad

823-2 The ratio of early diastolic mitral flow velocity to early diastolic mitral annular velocity predicts prognosis in patients with chronic congestive heart failure

Arquitectes: Lluís Clotet, Òscar Tusquets Blanca, Carlos DíazProposta d'alçats i seccions del convent dels Àngels.Digitalitzat per Tecnodo

Surgery of secondary mitral insufficiency in patients with impaired left ventricular function

<p>Abstract</p> <p>Background</p> <p>Secondary mitral insufficiency (SMI) is an indicator of a poor prognosis in patients with ischemic and dilated cardiomyopathies. Numerous studies corroborated that mitral valve (MV) surgery improves survival and may be an alternative to heart transplantation in this group of patients.</p> <p>The aim of the study was to retrospectively analyze the early and mid-term clinical results after MV repair resp. replacement in patients with moderate-severe to severe SMI and left ventricular ejection fraction (LVEF) below 35%.</p> <p>Methods</p> <p>We investigated 40 patients with poor LVEF (mean, 28 ± 5%) and SMI who underwent MV repair (n = 26) resp. replacement (n = 14) at the University Hospital Muenster from January 1994 to December 2005. All patients were on maximized heart failure medication. 6 pts. had prior coronary artery bypass grafts (CABG). Twenty-seven patients were in New York Heart Association (NYHA) class III and 13 were in class IV. Eight patients were initially considered for transplantation. During the operation, 14 pts had CABG for incidental disease and 8 had tricuspid valve repair. Follow-up included echocardiography, ECG, and physician's examination and was completed in 90% among survivors. Additionally, the late results were compared with the survival after orthotope heart transplantation (oHTX) in adults with ischemic or dilated cardiomyopathies matched to the same age and time period (148 patients).</p> <p>Results</p> <p>Three operative deaths (7.5%) occurred as a result of left ventricular failure in one and multiorgan failure in two patients. There were 14 late deaths, 2 to 67 months after MV procedure. Progress of heart failure was the main cause of death. 18 patients who were still alive took part on the follow-up examination. At a mean follow-up of 50 ± 34 (2–112) months the NYHA class improved significantly from 3.2 ± 0.5 to 2.2 ± 0.4 (p < 0.001). The LVEF improved significantly from 29 ± 5% to 39 ± 16 (p < 0.05). There were no differences in survival after MV repair or replacement. The 1-, 3-, 5-year survival rates in the study group were 80%, 58% and 55% respectively. In the group of patients after oHTX the survival was accordingly 72%, 68%, 66% (p > 0.05).</p> <p>Conclusion</p> <p>High risk mitral valve surgery in patients with cardiomyopathy and SMI offers a real mid-term alternative method of treatment of patients in drug refractory heart failure with similar survival in comparison to heart transplantation.</p