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26 11 Αίθουσα Α11 Ώρα 15:00	Introduction to missing data analysis Τσισμεντζόγλου Χρυσούλα Abstract: Missing data is a common issue in data analysis that occurs when some values in a dataset are absent or not recorded. The presence of missing data can significantly affect the validity and reliability of statistical analyses, leading to biased results if not properly addressed. Missing data can arise from various sources, including non-response in surveys, errors in data collection or lost information over time. Understanding the types of missingness and employing appropriate methods for handling it is crucial for ensuring robust statistical modeling and inference.In this presentation, we will explore the sources and types of missing data and discuss the fundamental methods for handling it. R code examples and their corresponding results will be highlighted throughout the presentation. Βιβλιογραφία Molenberghs, G., Fitzmaurice, G., Kenward, M., Tsiatis, A. & Verbeke, G. (2014), Handbook of Missing Data Methodology, Chapman & Hall/CRC Handbooks of Modern Statistical Methods, CRC Press https://books.google.gr/books?id=tqvMBQAAQBAJ. https://books.google.gr/books?id=bLmItgEACAAJ Little, R. J. A. (1995), ‘Modeling the drop-out mechanism in repeated measures studies’, Journal of the American Statistical Association 90(431), 1112–1121. https://www.jstor.org/stable/2246252 Carpenter, J. & Kenward, M. (2012), Multiple Imputation and its Appli cation, Statistics in Practice, Wiley. Tanner, M. A. & Wong, W. H. (1987), ‘The calculation of posterior dis tributions by data augmentation’, Journal of the American Statistical As sociation 82(398), 528–540.

26
11

Αίθουσα Α11

Ώρα 15:00

Introduction to missing data analysis

Τσισμεντζόγλου Χρυσούλα

Abstract: Missing data is a common issue in data analysis that occurs when some values in a dataset are absent or not recorded. The presence of missing data can significantly affect the validity and reliability of statistical analyses, leading to biased results if not properly addressed. Missing data can arise from various sources, including non-response in surveys, errors in data collection or lost information over time. Understanding the types of missingness and employing appropriate methods for handling it is crucial for ensuring robust statistical modeling and inference.In this presentation, we will explore the sources and types of missing data and discuss the fundamental methods for handling it. R code examples and their corresponding results will be highlighted throughout the presentation.

Βιβλιογραφία

Molenberghs, G., Fitzmaurice, G., Kenward, M., Tsiatis, A. & Verbeke, G. (2014), Handbook of Missing Data Methodology, Chapman & Hall/CRC Handbooks of Modern Statistical Methods, CRC Press
https://books.google.gr/books?id=tqvMBQAAQBAJ.
https://books.google.gr/books?id=bLmItgEACAAJ
Little, R. J. A. (1995), ‘Modeling the drop-out mechanism in repeated measures studies’, Journal of the American Statistical Association 90(431), 1112–1121.
https://www.jstor.org/stable/2246252
Carpenter, J. & Kenward, M. (2012), Multiple Imputation and its Appli cation, Statistics in Practice, Wiley.
Tanner, M. A. & Wong, W. H. (1987), ‘The calculation of posterior dis tributions by data augmentation’, Journal of the American Statistical As sociation 82(398), 528–540.