The Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models Academic Article uri icon


  • In longitudinal multilevel studies, especially in educational settings, it is fairly common that participants change their group memberships over time (e.g., students switch to different schools). Participants mobility changes the multilevel data structure from a purely hierarchical structure with repeated measures nested within individuals and individuals nested within clusters to a cross-classified structure with repeated measures cross-classified by both individuals and clusters. If researchers fail to consider the cross-classified data structure and simply use the hierarchical linear models (HLMs) instead of the more appropriate cross-classified random-effects models (CCREMs) to analyze the data, there will be biases in the estimates of variance components and inaccurate statistical inference regarding the fixed effects. In addition, the impact of such model misspecification depends on factors including the rate of mobility and the pattern of mobility.

published proceedings

  • Journal of Educational and Behavioral Statistics

altmetric score

  • 0.5

author list (cited authors)

  • Luo, W., & Kwok, O.

citation count

  • 41

complete list of authors

  • Luo, Wen||Kwok, Oi-man

publication date

  • February 2012