Temporal Changes in Symptom-Behavior Dynamics: A Process-Focused Network Modeling Approach

The field of clinical psychology has emphasized the exploration and identification of change processes that bring about clinically relevant outcomes, and largely has pursued this goal via group-based, nomothetic designs. However, group-based analyses are not necessarily valid at the level of the individual, calling into question the degree to which these findings are applicable for any one specific client. Meanwhile, recent innovations in statistical modeling have yielded novel methods for capturing processes at the level of the individual. In the present study, data were collected on a sample of individuals experiencing distress, both at baseline and following a self-help intervention. Meta-analytic statistical tests were then used to demonstrate that group-level averages inappropriately characterize individual level data. Idiographic analyses were constructed using resampling procedures (i.e., the Boruta feature selection and regression tree algorithms) in order to more accurately capture processes of change at the individual level at both baseline and post-treatment. Next, group- and sub-group level processes were derived by identifying commonalities within individual networks using network modeling analyses. Finally, trends in group level network changes from baseline to post-intervention were identified.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2023

Author dc:creator

• Chin, Fredrick

Advisor dc:contributor.advisor

• Hayes, Steven C

Committee members dc:contributor.committeemember

• Hofmann, Stefan G
• Ciarrochi, Joseph
• Follette, William C
• Kohlenberg, Barbara S

Handle dc:identifier.uri

http://hdl.handle.net/11714/10521

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/10521