I have always been interested in learning about notable Mizzou alumni (e.g., Sam Walton, Jon Hamm, etc.). One famous graduate, Margaret Weis (BA β70 Creative Writing and Literature) has authored numerous books to have appeared on the New York Times bestseller list (over 30 according to Gemini; that is a LOT!). Best known for her work on the Dragonlance books in the 80s (with Tracy Hickman), her books have sold over 25 million copies and have been translated into multiple languages.
Including random slopes (when warranted) in multilevel models is necessary to avoid making Type I errors as a result of underestimated standard errors. We typically investigate the presence of random slopes (RS) by using a likelihood ratio test and comparing two competing models. Often, random slopes are included when we have cross level interactions. If a random intercept (RI) model is used when a random slope model is warranted, the regression coefficients will be the same though the standard errors will be incorrect.
At a conference, a former student (who had moved on to a tenure track assistant professor position at an R1 university) asked what I do to manage the stress of being an academic. If youβve been following the news, you will know that 2025 has been a challenging year for scientists (in general) in the US!
In a prior career as a strategy consultant, I had the chance to speak with Dado Banatao (in 2004), who was a well-known entrepreneur from the Philippines. He passed away on December 25, 2025.
I had written before about using the CR2 standard error variant and how they can be used to account for clustering when using basic OLS (or GLM) regression. Some articles on the topic:
List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of September 2025.
Postings for (2025-2026):
As of 2025.11
As of 2025.10
List of Research, Statistics, and Evaluation job postings (that I’ve seen) as of July 2025.
Postings for (2024-2025):
As of 2025.07
As of 2025.06
Although the mixPV function was introduced as a way to analyze large
scale assessments using multiple plausible values (PV), the function
only works if the plausible values are used as the outcome (i.e., it is
the Y variable or on the left hand side [LHS] of the equation).
However, there are times when the PV is the predictor of interest. This
still has to be analyzed properly (i.e., just donβt average all the
values).
Statistics students are taught that correlation does not equal causation. Just because two variables (e.g., x and y) are related to each other does not necessarily mean that one causes the other (e.g., x causes y). The correlation coefficients (i.e., Ο) for Ο(x,βy) and Ο(y,βx) are the same and does not provide information on the directionality of the effect (e.g., xβββy or xβββy). It could also be that the variables are related due to a third variable z which causes both (i.e., a confounder).
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