Premium
Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross‐Classified Random Effects Models
Author(s) -
Moreno Mario,
Harwell Michael,
Guzey S. Selcen,
Phillips Alison,
J. Moore Tamara
Publication year - 2016
Publication title -
school science and mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.135
H-Index - 2
eISSN - 1949-8594
pISSN - 0036-6803
DOI - 10.1111/ssm.12191
Subject(s) - multilevel model , mathematics education , random effects model , hierarchical database model , mathematics , statistics , computer science , data mining , meta analysis , medicine
Hierarchical linear models have become a familiar method for accounting for a hierarchical data structure in studies of science and mathematics achievement. This paper illustrates the use of cross‐classified random effects models (CCREMs), which are likely less familiar. The defining characteristic of CCREMs is a hierarchical data structure defined by multiple random factors at higher levels. We illustrate CCREMs using data for approximately 10,000 students from more than 250 high schools who attended one of 27 four‐year postsecondary institutions. The appropriate use of CCREMs helps to ensure unbiased estimates of effects and credible inferences.