Using Path Analysis To Test A Hypothesis On The Theory Of Change In Hemoglobin A1C (HbA1C) Among Clients In A Culturally Tailored Diabetes Intervention For African Americans And Latinos

Objective. To use path analysis to test a hypothesis about the theory of change in Hemoglobin A1c among clients in a culturally tailored diabetes intervention. The hypothesis was that changes in self-efficacy, diabetes-related distress, and knowledge would lead to change in self-management behavior, which would lead to reduction in HbA1c. Methods. Two cohorts were combined for a sample size of 326. Path analysis was chosen because it enables the analyst to test the order in which measurable variables affect each other. Mardia's multivariate kurtosis, along with univariate skewness and kurtosis, were used to check for multivariate normality. MAR (Missing at Random) was evaluated by testing if pre-intervention means differed significantly by whether post-intervention values were present. The structural equation model was estimated by FIML (Full Information Maximum Likelihood). Goodness of fit was evaluated with Joreskorg-Sorbom GFI for absolute fit, Bentler's CFI for comparative fit, RMSEA for parsimony, and SRMR for prediction. Results. All post-intervention measures were strongly correlated with pre-intervention values. Post-intervention HbA1c dropped by 0.5 per unit increase in self-management behavior. Program attendance in group, versus individual format, was associated with a 6 point drop in diabetes distress and significant increase in knowledge of diabetes management. Greater self-efficacy was associated with higher attendance. Based on GFI=.9928 and CFI=.935, the model explained 99.25% of the generalized covariance and was a 93.5% improvement over the null model. Conclusion. Path Analysis is an effective method to model the process of change in key outcome variables and to measure quantitative intervention effects.