Course Title:
Missing Data Analysis
Instructor:
Dr. Fan Jia (https://sites.google.com/view/fjiaquant)
Short Course Description:
This course is intended to provide an overview of the logic and implementations of modern missing data handling techniques in social sciences. Topics covered include: missing data patterns and mechanisms, traditional missing data techniques, maximum likelihood estimation, expectation–maximization algorithm, multiple imputation, missing data in research designs, techniques for handling “non-ignorable” missing data.
This course will help participants gain an understanding of missing data challenges in substantive research and be able to identify appropriate solutions in different scenarios. By completing the hands-on practice activities, participants will learn how to use the R software program to summarize and visualize missing data, and to implement a variety of missing data handling techniques. Both scripts and data will be provided for all examples and exercise problems.
The participants are assumed to be familiar with basic statistical concepts, such as descriptive statistics, probability distributions, correlation and multiple regression. Experiences with statistical software (e.g. SPSS, SAS, Stata, or R) will be helpful.
Target Audience:
Graduate students, emerging researchers, continuing researchers
Requisite Knowledge:
Participants should have a basic understanding of basic statistical approaches up through multiple regression. Previous experience with R or more advanced models is a plus, but it is not required.
Software:
All examples and exercises will be done using R. Participants will be provided all resources to perform these exercises on their own, and we will provide instructions on how to download free versions of the software prior to the course.
Dates and Times:
[Times provided in Pacific Standard Time]
January 14, 2022
[Breaks will be provided according to the Instructor’s schedule]
9:00am – 9:15am: Check-in and Installation of R and RStudio
9:15am – 12:00pm: Morning Session
12:00am – 1:00pm: Lunch Break
1:00pm – 3:00pm: Afternoon Session
Online Details:
Participants will receive a code to use on their own computer to access a live-stream of the workshop. Electronic workshop materials will be provided ahead of time.
Instructions for software downloads (etc.) will be shared in advance. Support for such activities will be limited, given the online nature of the course.
The workshop will NOT be recorded.
How to Register:
Contact for Questions:
Dr. Fan Jia (fjia3@ucmerced.edu)
The Instructor:
Dr. Fan Jia is an Assistant Professor of Quantitative Methods, Measurement, and Statistics in the Psychological Science Department at the University of California, Merced. Her research interests revolve around multilevel modeling, structural equation modeling, longitudinal data analysis, and mediation and moderation analysis, with an emphasis on methodological issues related to missing data and non-normal data. She teaches both undergraduate and graduate level courses including Analysis of Psychological Data, Advanced Psychological Statistics I, and Missing Data Analysis.
