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Imputation Techniques in SAS(R)

Course Details
Code: BA2PB
Tuition (USD): $825.00 • Classroom (1 day)
Course Details
GSA (USD): $748.11 • Classroom (1 day)

Concentrating on the needs of those relatively new to the use of multiple imputation tools in SAS, this course provides a general introduction to using the MI and MIANALYZE procedures for multiple imputation and subsequent analyses with imputed data sets.

Skills Gained

  • recognize the type of missing data patterns that exist in your data sets
  • analyze imputed data sets using standard SAS procedures
  • use PROC MIANALYZE to correctly analyze output from imputed files and subsequent procedure output from standard SAS procedures
  • use real-world data sets in the virtual lab to obtain experience running SAS imputation procedures.

Who Can Benefit

  • Analysts, data managers, and other data professionals working with data sets with missing data

Prerequisites

  • Before attending this course, you should
  • have a solid understanding of the SAS DATA step, which can be gained by attending the SAS(R) Programming I: Essentials course or the SAS(R) Programming II: Manipulating Data with the DATA Step course, or have equivalent experience
  • have an intermediate knowledge of statistics, which can be gained by attending either the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course or the Statistics II: ANOVA and Regression course, or have equivalent academic training.

Course Details

Missing Data Issues

  • types of missing data and how to identify sources and patterns of missing data
  • why missing data occurs, and what to do about it

Introduction to Multiple Imputation Using SAS

  • comparison of simple and multiple imputation approaches
  • discussion of why multiple imputation is a preferred approach
  • PROC MI and PROC MIANALYZE

Overview of Three-Step Process

  • multiple imputation using PROC MI
  • analysis of imputed data sets using standard SAS procedures
  • use of PROC MIANALYZE for accounting for variability introduced during multiple imputation and analysis of output from standard SAS procedures

Practical Examples of Multiple Imputation

  • common examples of multiple imputation and analysis of imputed data sets using public release data from the Longitudinal Survey of Aging (a complex sample survey data set)
  • examples that cover typical imputation needs and subsequent analysis of imputed data using descriptive and regression approaches
  • output from the imputation step and the analysis of imputed data sets
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