Multilevel regression analyses to investigate the relationship between two variables over time: examining the longitudinal association between intrusion and avoidance.
Multilevel modeling is a powerful and flexible framework for analyzing nested data structures (e.g., repeated measures or longitudinal designs). The authors illustrate a series of multilevel regression procedures that can be used to elucidate the nature of the relationship between two variables across time. The goal is to help trauma researchers become more aware of the utility of multilevel modeling as a tool for increasing the field's understanding of posttraumatic adaptation. These procedures are demonstrated by examining the relationship between two posttraumatic symptoms, intrusion and avoidance, across five assessment points in a sample of rape and robbery survivors (n = 286). Results revealed that changes in intrusion were highly correlated with changes in avoidance over the 18-month posttrauma period.
Suvak
Michael K
MK
National Center for Posttraumatic Stress Disorder, VA Boston Healthcare System, Boston, MA 02130, USA. Michael.Suvak@VA.gov
Walling
Sherry M
SM
Iverson
Katherine M
KM
Taft
Casey T
CT
Resick
Patricia A
PA
D000223:Adaptation, Psychological; D003674:Defense Mechanisms; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D055361:Multilevel Analysis / Q000379:methods*; D015999:Multivariate Analysis; D010555:Personality Inventory / Q000706:statistics & numerical data; D011594:Psychometrics / Q000706:statistics & numerical data; D011902:Rape / Q000523:psychology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D013313:Stress Disorders, Post-Traumatic / Q000175:diagnosis / Q000523:psychology*; D017741:Survivors / Q000523:psychology*; D013800:Theft / Q000523:psychology*