Statistical Analysis and Structural Equations on Influential Parameters in Health

Statistics, as an applied science for data analysis, plays a fundamental role in all sciences, especially in the field of health. By using statistical methods, researchers can quantify the extent to which each variable impacts health and identify the most significant factors. Structural equation modeling takes this a step further by allowing researchers to examine how different variables interact with each other and the pathways through which they operate. These approaches can help healthcare professionals develop more effective interventions for preventing and treating diseases, as well as informing public health policies. In this chapter, statistical analysis and structural equation modeling play a crucial role in understanding the complex interplay between various factors that influence health outcomes. In this section, after a brief description of statistics and statistical tests, a general classification of statistical tests and their assumptions are presented. Then, after examining various approaches to structural equation modeling (SEM), an empirical example is provided to demonstrate the application of SEM in health. The presented model investigates the effect of contextual factors on individuals' preventive behavior during the COVID-19 pandemic. In the proposed model, preventive behavior includes 3 variables of personal protection behavior, social distancing behavior and social responsibility awareness, contextual factors include three variables of health literacy, social norms and information sources. In addition, COVID-19 knowledge and risk perception are considered as mediator variables between contextual factors and preventive behaviors. Material status and education are also moderator variables. The model implemented based on the Smart PLS software and the results are described in details.