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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the consequence of ignoring the changes in cluster memberships in multilevel mediation analysis. Results show that the proposed method, Cross-Classified Multilevel Mediation Model (CC-M3), outperforms the conventional multilevel model with substantially smaller relative biases in parameter estimates (about 50% less) and a more consistent and higher coverage rate. Findings of this simulation study inform the empirical researchers that the changes in cluster-membership needs to be appropriately taken into consideration in mediation analysis. We demonstrate the use of CC-M3 in the applied example.

Using a Cross-Classified Multilevel Mediation Model (CC-M3) with Longitudinal Data Having Changes in Cluster Membership