Modeling Heterogeneity of the Level-1 Error Covariance Matrix in Multilevel Models for Single-Case Data Academic Article uri icon

abstract

  • Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants. However, the level-1 error covariance matrix may differ across participants and ignoring these differences can have an impact on estimation and inferences. Despite the importance of this issue, the effects of modeling between-case variation in the level-1 error structure had not yet been systematically studied. The purpose of this simulation study was to identify the consequences of modeling and not modeling between-case variation in the level-1 error covariance matrices in single-case studies, using Bayesian estimation. The results of this study found that variance estimation was more sensitive to the method used to model the level-1 error structure than fixed effect estimation, with fixed effects only being impacted in the most extreme heterogeneity conditions. Implications for applied single-case researchers and methodologists are discussed.

published proceedings

  • METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES
  • Methodology

author list (cited authors)

  • Baek, E., & Ferron, J.

citation count

  • 4

complete list of authors

  • Baek, Eunkyeng||Ferron, John JM

publication date

  • June 2020