PPT-Best Practices for Handling Missing Data

Author : olivia-moreira | Published Date : 2018-09-22

David R Johnson Professor of Sociology Demography and Family Studies Pennsylvania State University Outline What are missing data and why do we need to do something

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Best Practices for Handling Missing Data: Transcript

David R Johnson Professor of Sociology Demography and Family Studies Pennsylvania State University Outline What are missing data and why do we need to do something about them Classic Approaches and their problems. Estie Hudes. Tor . Neilands. UCSF . Center for AIDS Prevention . Studies. Part 2. January 18, 2013. 1. Contents. 1. Summary of Part 1. 2. EM Algorithm . 3. Multiple Imputation (MI) for normal data. 4. Multiple Imputation (MI) for mixed data. longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . Validating and Preparing your data. Lyytinen & Gaskin . Data Screening. Data screening . (also known for us as “data screaming”) ensures . your data is . “clean” . and ready to go before you conduct . Katherine Lee. Murdoch Children’s Research Institute &. University of Melbourne. Missing data in epidemiology & clinical research. Widespread problem, especially in long-term follow-up studies. Adapting to missing data. Sources of Missing Data. People refuse to answer a question. Responses are indistinct or ambiguous. Numeric data are obviously wrong. Broken objects cannot be measured. Equipment failure or malfunction. Troubleshooting. Log Files. Save in text format for readability:. l. og using ps1. .log. , replace. o. r:. log using ps1, text. Handling Missing Values. By default, . Stata. excludes all observations marked with a period (.) from subsequent statistical analysis. . C. ontacting researchers. Algebraic recalculations, conversions and approximations. Imputation method (substituting missing data). Imputation Method . - When recalculations not possible. -e.g. no standard deviation for a study. Statistical Modeling of Adolescent Fertility. . Dudley . L. Poston, Jr.. Texas A&M . University. &. Eugenia . Conde. Rutgers University. Missing Data. Missing data are a pervasive challenge in. longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . Data cleaning. Data . d. iscretization. Binning. Clustering. Binarization. Data integration. Aggregation. Smoothing. DATA PREPARATION: Preprocessing. Data . reduction. Sampling. Dimensionality reduction. Sources of Missing Data. People refuse to answer a question. Responses are indistinct or ambiguous. Numeric data are obviously wrong. Broken objects cannot be measured. Equipment failure or malfunction. Tor . Neilands. UCSF . Center for AIDS Prevention . Studies. Part 2. January 18, 2013. 1. Contents. 1. Summary of Part 1. 2. EM Algorithm . 3. Multiple Imputation (MI) for normal data. 4. Multiple Imputation (MI) for mixed data. Part . I – Dichotomous . outcomes. Elie Akl, . Shanil. . Ebrahim. , . Bradley . Johnston, Pablo Alonso, Matthias . Briel. , Gordon . Guyatt. Disclosures. No conflicts of interest to disclose. This work was funded by the Cochrane Methods Innovation Fund. in longitudinal clinical studies:. Critical skills and important habits for statisticians. Craig Mallinckrodt. PSF Forum. June 4, 2015. . 2. Background. Influence and change . Application in Missing data .

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