Course Objectives
This course section explores missing data methods in applied statistics, data science, and psychometrics; emphasizing techniques such as multiple imputation, Bayesian methods, and maximum likelihood to handle and analyze incomplete data sets effectively.
Current Assignments
Read Enders (2022) Chapter 2 |
4 Feb |
Complete Reading Assessment 2 (in ICON) |
4 Feb |
Read Enders (2022) Chapter 3 |
11 Feb |
Complete Reading Assessment 3 (in ICON) |
11 Feb |
Homework #1 (Available in ICON) |
14 Feb |
Lecture and Class Files and Links
Date |
Topic |
Materials |
22 Jan |
Introduction to class |
|
29 Jan |
Introduction to Missing Data |
|
05 Feb |
Technical Prerequisites |
|
Remaining Schedule (Tentative)
2 |
29 Jan |
Introduction to Missing Data |
Chapter 1 |
3 |
5 Feb |
Technical Prerequisites |
Chapter 2 |
4 |
12 Feb |
Maximum Likelihood Estimation |
Chapter 3 |
5 |
19 Feb |
lavaan and Maximum Likelihood Estimation with Missing Data |
|
6 |
26 Feb |
No Class: Follow Introduction to JAGS activity |
Chapter 4 |
7 |
5 Mar |
Bayesian Estimation with Missing Data |
Chapter 5 |
8 |
12 Mar |
Bayesian Estimation for Categorical Variables |
Chapter 6 |
9 |
19 Mar |
Spring Break: No Class |
No Readings |
10 |
26 Mar |
Multiple Imputation |
Chapter 7 |
11 |
2 Apr |
Multilevel Missing Data |
Chapter 8 |
12 |
9 Apr |
Missing Not at Random Processes |
Chapter 9 |
13 |
16 Apr |
Special Topics and Applications/Guidance for Working with Missing Data |
Chapters 10 and 11 |
14 |
23 Apr |
Missing Data in Psychometric Models: Latent Variable Scores |
Mislevy et al. (1992) |
15 |
30 Apr |
No Class: AERA/NCME Conference |
|
16 |
7 May |
Missing Data in Psychometric Models: Implications for CAT |
Doebler et al. (2013); Jewsbury et al. (2024); |