Abstract : Structural Equation Modeling (SEM) is a prominent research methodology widely employed in various fields. Before diving into data analysis, it is essential to outline the core principles, criteria, assumptions, and concepts of SEM. In this study, SEM is utilized to assess assumptions related to normality, missing data, and the measurement of sampling errors. Confirmatory Factor Analysis (CFA) is a key step in evaluating the model's fit with the data. CFA begins with a hypothesized model that specifies the expected number of latent factors, and the indicator variables associated with each factor. Firstly, the Normality Test involves assessing the "Skewness and Kurtosis" scores of the CFA model, which should fall between -2 and +2. The study's independent variables include Self-Efficacy (SE), Perceived Benefits (PB), and Behavioral Beliefs (BB), while the Mediating Variable is Consumer Innovativeness (CI) and the Dependent Variable is Health Protective Behaviors (HPB). The sample consists of 400 private healthcare customers, confirming the normal distribution of the data, which aligns with SEM's normality assumptions. Secondly, missing data is addressed to ensure robust statistical analysis. Questionnaires with over 30% missing data are excluded to prevent bias. Thirdly, to minimize sampling errors, an appropriate sample size is determined using the "10-times rule" method. This method suggests that the sample size should exceed ten times the maximum number of links pointing to any latent variable. Lastly, the study model is evaluated against various fit indices, all of which are satisfactorily met, indicating a good model fit.