This is the syntax for accounting for missing data/imputing data with large scale assessments (without plausible values). This is Appendix A and accompanies the article: Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9
Apr 17, 2025
This is the syntax for accounting for missing data/imputing data with large scale assessments (with plausible values). This accompanies the article: Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9
Apr 17, 2025
The article is open access. Additional syntax can also be seen here. An updated, corrected version of the article can be accessed here.
Apr 16, 2025
Using FIML in R with Multilevel Data (Part 3) A recurring question that I get asked is how to use full information maximum likelihood (FIML) when performing a multiple regression analysis BUT this time, accounting for nesting or clustered data structure. For this example, I use the the leadership dataset in the mitml package (Grund et al., 2021). We’ll also use lavaan (Roseel, 2012) to estimate the two-level model. The chapter of Grund et al. (2019) is available here. We’ll replicate the Mplus FIML results in Table 16.3 in the chapter and is shown below:
Jun 1, 2022
Missing data
Feb 27, 2020