Lithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high
energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics …

Although educational research and evaluation generally occur in multilevel settings, many
analyses ignore cluster effects. Neglecting the nature of data from educational settings …

Neural networks are a contending data mining procedure to estimate propensity scores due
to its robustness to non-normal residual distributions, ability to detect complex nonlinear …

The integration of machine learning in educational data analysis presents challenges
regarding the availability of sufficient training data, especially in the context of high missing …

Background: The generalized propensity score (GPS) addresses selection bias due to
observed confounding variables and provides a means to demonstrate causality of …

This article introduces researchers in the science concerned with developing and studying
research methods, measurement, and evaluation (RMME) to the educational data mining …

Asymmetric Likert-type items in research studies can present several challenges in data
analysis, particularly concerning missing data. These items are often characterized by a …

Research methodologists typically use descriptive statistics and plots to report the findings of
Monte Carlo experiments. But previous literature suggests that Monte Carlo results deserve …

Abstract Machine learning has become a common approach for estimating propensity
scores for quasi-experimental research using matching, weighting, or stratification on the …

Background Finite mixture models cluster individuals into latent subgroups based on
observed traits. However, inaccurate enumeration of clusters can have lasting implications …