Multilevel modeling of longitudinal data for psychotherapy researchers: II. The complexities.
The authors previously reviewed the basic elements and steps to building multilevel models (MLMs) for longitudinal data typically found in psychotherapy research. The objective of this article is to focus on complexities associated with the MLM for longitudinal data analysis in psychotherapy research, which may result in proper use or misuse of the modeling structure. To do so, the authors illustrate complex scenarios and discuss issues in the implementation and interpretation of the MLM: (a) impact of missing data in the MLM, (b) determination of the complexity of the covariance structure and its implication on model interpretation, (c) issues with centering, (d) model diagnostics for MLM, (e) model formation, including implementation dependent on the treatment of time and distribution of outcome, and (f) model estimation. The authors also present data from psychotherapy research settings as examples of these complex situations. Finally, they offer some caveats and advice for recognizing these complexities and proper procession to ensure accurate implementation of the MLM and interpretation of the results.
Gallop
Robert
R
Department of Mathematics, Applied Statistics program, West Chester University, West Chester, Pennsylvania 19383, USA. rgallop@wcupa.edu
Tasca
Giorgio A
GA
D006801:Humans; D001523:Mental Disorders / Q000628:therapy*; D008960:Models, Psychological; D011613:Psychotherapy / Q000379:methods*