Missing Data Analysis

Starts Dec 4, 2025

SHORT COURSE DESCRIPTION

Missing data is a common challenge in empirical research across different fields of research. Missing data can lead to biased results, reduced statistical power, and invalid inferences if they are not handled properly. This short course introduces modern methods for handling missing data, including full information maximum likelihood (FIML), Bayesian estimation, and multiple imputation. Participants will be introduced to the theoretical concepts, as well as learn how to apply these methods to real data. This short course consists of lectures and hands-on software demonstrations. Examples and syntax will be provided in R, Mplus, and Blimp. Additional topics will also be introduced, such as planned missing data designs and nonparametric missing data handling in machine learning models.

TOPICS

Introduction to R and Mplus

Exploring missing data

Missing data mechanisms

Auxiliary variables

Maximum likelihood estimation

Full information maximum likelihood

Bayesian estimation

Multiple imputation

Non-parametric missing data methods in machine learning

Planned missing data designs

REQUISITE KNOWLEDGE

Participants don’t need advanced statistical knowledge or programming skills to take this short course, although a good understanding of basic statistical concepts like regression and conditional distributions, along with some comfort using command-line software, will be helpful. The workshop is beginner-friendly and starts with the fundamentals before moving on to more advanced methods for handling missing data.

SOFTWARE

Empirical examples and hands-on exercises will be demonstrated using R, Mplus, and Blimp. Participants are encouraged to have the latest versions of R and Blimp installed on their computers prior to the short course.

DATES AND TIMES

Dec 4-5, 2025 (Thurs-Fri)

10am-5pm Eastern Time (UTC-4)

The instructor will determine timing of lunch break, as well as morning and afternoon breaks.

COURSE FEES

Professional: $375

Full-time student*: $195

*Full-time students need to submit student status proof at CILVR-STUDENT to request a discount code prior to registration.

*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: CILVR--HDQM Affiliate.

HOW TO REGISTER

Prior to registration, participants not affiliated with UMD need to get a valid UMD associate account in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details about the UMD associate account, please click here.

For UMD affiliated participants, you may register using your existing UMD directory ID.

To request the promotional code prior to registration:

- Full-time students can submit the student status proof at CILVR-STUDENT to request a student discount code prior to registration. Note that it may take 2-3 business days for your request to be processed.

- HDQM department registrants can request the HDQM discount code by submitting the following form: CILVR--HDQM Affiliate. Note that it may take 2-3 business days for your request to be processed.

TARGET AUDIENCE

Graduate students, faculty, research professionals who encounter missing data in their work and who are interested in learning modern missing data methods.

LOCATION AND PLATFORM

· The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/).

· This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).

· Typically within 24 hours, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.

IMPORTANT COURSE DETAILS

Platform: Participants who are not affiliated with UMD need to get a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.

Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.

Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.

Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.

Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.

THE INSTRUCTOR

Dr. Yi Feng is an Assistant Professor of Quantitative Psychology at the University of California, Los Angeles (UCLA). Her research focuses on developing and applying advanced quantitative methods to better address the research needs in psychology and education research. Her interests include structural equation modeling (SEM), latent growth models, modeling of random variability, power analysis and sample size determination, and causal graphical models. Yi Feng’s work has appeared in leading methodological journals such as Psychological Methods and Multivariate Behavioral Research, and she regularly presents her research at national and international conferences. She has extensive experience in teaching introductory and advanced statistical methods courses to graduate students, applied researchers, faculty, and professionals. She may be reached at yi.feng@ucla.edu.

REFUND POLICY

Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

CONTACT

For any further questions, please contact missing.cilvr@gmail.com.

To request a copy of the payment receipt, please contact the OES office at oes-finance@umd.edu.

CILVR Short Course Series

Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)

CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Measurement, Statistics and Evaluation (EDMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. EDMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.

Full course description