A practical guide to using multilevel modeling for communication assessment: Assessing student growth over time
Date:
Researchers throughout the social sciences utilize multilevel modeling (MLM) to investigate the ecological conditions influencing social interactions. However, communication researchers and administrators – particularly those who collect data from learners within classrooms – have not yet embraced MLM as a statistical tool that meets contemporary research needs. Instead, these researchers often (a) eliminate correlated error terms in their data by asking students to explicitly think of instructors from different classes when responding to survey items (i.e., Plax et al., 1986) or (b) aggregate data across varying levels of abstraction. The purpose of this article is to clarify the 6 major steps involved in conducting a multilevel analysis so that communication researchers and program administrators can more accurately and precisely assess students’ classroom experiences. Theoretically framed by elements from Walberg’s (1981, 1984) model of educational productivity, we illustrate the model building process with both a longitudinal and cross-sectional example using assessment data collected from [institution redacted for blind review]. The results assist researchers in both classroom communication and communication-program assessment by offering assistance that makes conducting, interpreting, and reporting multilevel analyses more accessible.