English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Cross-level interactions among fixed effects in linear mixed models (alsoknown as multilevel models) are often complicated by the variances stemmingfrom random effects and residuals. When these variances change across clusters,tests of fixed effects (including cross-level interaction terms) are subject toinflated Type I or Type II error. While the impact of variancechange/heterogeneity has been noticed in the literature, few methods have beenproposed to detect this heterogeneity in a simple, systematic way. In addition,when heterogeneity among clusters is detected, researchers often wish to knowwhich clusters' variances differed from the others. In this study, we utilize arecently-proposed family of score-based tests to distinguish betweencross-level interactions and heterogeneity in variance components, alsoproviding information about specific clusters that exhibit heterogeneity. Thesescore-based tests only require estimation of the null model (when variancehomogeneity is assumed to hold), and they have been previously applied topsychometric models. We extend the tests to linear mixed models here, detailingtheir implementation and performance when the data generating model is known.We also include an empirical example illustrating the tests' use in practice.

Score-based Tests for Explaining Upper-Level Heterogeneity in Linear Mixed Models