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December Member Training: Missing Data

by TAF Support

Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.

In this overview, you’ll learn the big picture of the concepts, challenges, and solutions of missing data.

  • The goals of a good missing data approach
  • The three missing data mechanisms and why they’re so important
  • Common situations where missing data arises and their specific challenges
  • Related data issues that share some of the same challenges and solutions
  • The four main approaches to dealing with missing data and when each one does and doesn’t work
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Not a Member? Join!

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Not a Member Yet?

It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking. You'll get access to this training webinar and 85+ other stats trainings — plus the expert guidance you need to progress with live Q&A sessions and an ask-a-mentor forum.

Tagged With: data issues, listwise deletion, mean imputation, Missing Data, Multiple Imputation

Related Posts

  • Member Training: Elements of Experimental Design
  • Member Training: Hierarchical Regressions
  • Member Training: Multiple Imputation for Missing Data
  • Linear Mixed Models for Missing Data in Pre-Post Studies

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This Month’s Statistically Speaking Live Training

  • January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

Read Our Book



Data Analysis with SPSS
(4th Edition)

by Stephen Sweet and
Karen Grace-Martin

Statistical Resources by Topic

  • Fundamental Statistics
  • Effect Size Statistics, Power, and Sample Size Calculations
  • Analysis of Variance and Covariance
  • Linear Regression
  • Complex Surveys & Sampling
  • Count Regression Models
  • Logistic Regression
  • Missing Data
  • Mixed and Multilevel Models
  • Principal Component Analysis and Factor Analysis
  • Structural Equation Modeling
  • Survival Analysis and Event History Analysis
  • Data Analysis Practice and Skills
  • R
  • SPSS
  • Stata

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