Handling missing data: analysis of a challenging data set using multiple imputation
نویسندگان
چکیده
منابع مشابه
کاربرد جای گذاری چندگانه در تحقیقات پزشکی و اپیدمیولوژی
Data missing, which occurs for different reasons, is an unavoidable problem in epidemiological studies. It is quite widespread and, therefore, it is considered as a challenge in research design and data analysis by many methodologists. Complete case analysis is often used in studies with missing data however, this approach may result in inaccurate estimates and inferences due to bias associated...
متن کاملچند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
Background and Objectives: A major challenge that affects the longitudinal studies is the problem of missing data. Missing in the data may result in the loss of part of the information which reduces the accuracy of the estimator and obtain the results will be biased and inaccurate. Therefore, it is necessary to evaluate the missing data mechanism from a longitudinal research and to consider thi...
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Most of the geochemical datasets include missing data with different portions and this may cause a significant problem in geostatistical modeling or multivariate analysis of the data. Therefore, it is common to impute the missing data in most of geochemical studies. In this study, three approaches called half detection (HD), multiple imputation (MI), and the cosimulation based on Markov model 2...
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THE ANALYSIS OF MISSING DATA IN PUBLIC USE SURVEY DATABASES: A SURVEY OF STATISTICAL METHODS Ping Xu November 20, 2004 Missing data is very common in survey research. However, currently few guidelines exist with regard to the diagnosis and remedy to missing data in survey research. The goal of the thesis was to investigate properties and effects of three selected missing data handling technique...
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