OBJECTIVES: This paper used data from an ongoing Merck sponsored study evaluating the effectiveness of diabetes self management education (DSME): Journey for Control of Diabetes: the IDEA Study.  During enrollment visits, patients with sub-optimally controlled Type 2 diabetes (A1c < 7%) completed surveys with 19 attitudinal and behavioral scales; clinical measures were also obtained.  Although the primary outcome for DSME evaluation was A1c, we included the attitudinal/ behavioral survey to identify intervening variables.  Our goal was to identify a set of underlying factors to efficiently explain attitudes and clinical outcomes. METHODS:  A total of 623 patients were enrolled from 2 sites, Minnesota and New Mexico.  The baseline survey included the following instruments:  general health (SF-12); depression (PHQ-9); Diabetes Empowerment (DES-SF); diabetes attitudes (DCP, 5 scales); personality (TIPI); Problem Areas in Diabetes (PAID); diet (RFS); physical activity (BRFSS); Readiness to Change; and hypoglycemia and hyperglycemia events (self-report).  Several clinical measurements were also obtained (BMI, waist circumference, and A1c level)..  Data from the above sample were used to conduct a factor analysis.  RESULTS:   Factor analysis was performed using varimax orthogonal rotation using the survey and clinical variables.  Five factors were identified for the sample of 588 patients with measures on all variables.  The first factor related to “agreeable” personality characteristics from the TIPI; the second and fourth factors both related to poor physical health with the second factor including those items relating to empowerment and low A1c while the fourth factor included extroversion, low activity levels, and increased glycemic events.  Factor three was high weight and waist circumference as well as low activity levels, and factor five was primarily readiness to change.  Communality scores ranged from .317 to .829. CONCLUSIONS: Factor analysis can help explain underlying factors affecting patients with diabetes.  Future analyses will use these factor scores to predict the effectiveness of DSME.