Medical research is a critical field that aims to advance our understanding of various diseases, their causes, and potential treatments.^[1] With the increasing complexity of medical data, researchers have turned to advanced statistical methods to extract meaningful insights from the data. These methods have proven to be essential in identifying trends, patterns, and relationships in medical data that would otherwise go unnoticed.^[2,3] Advanced statistical methods in medical research have revolutionized the way we approach health care, from personalized medicine to precision diagnostics.^[4,5]

Dental research is a constantly evolving field that involves the study of the anatomy, physiology, pathology, and treatment of teeth and the oral cavity. Researchers in this field employ a wide range of scientific methods, including experimental, observational, and epidemiological studies, to investigate various aspects of dental health. They explore topics such as the etiology and pathogenesis of dental diseases, the efficacy and safety of dental treatments, and the development of new materials and technologies for dental care.^[6] Dental research plays a crucial role in advancing our understanding of oral health and in developing effective strategies for the prevention, diagnosis, and treatment of dental diseases.^[7] Moreover, dental research has significant implications for overall health, as oral health is closely linked to a range of systemic diseases, including diabetes, cardiovascular disease, and cancer. Therefore, continued investment in dental research is essential for improving oral health and enhancing overall health outcomes.^[8,9]

Structure equation modeling (SEM) is a statistical technique that has become increasingly popular in dental research. It is a powerful tool for analyzing complex relationships among multiple variables and can be used to test theoretical models, identify important predictors, and estimate causal effects. This review article aims to provide an overview of SEM, its applications in dental research, and the key considerations for conducting SEM studies.^[4]

SEM is a statistical technique that allows researchers to test complex theoretical models. It is a combination of factor analysis and regression analysis, and it is used to determine the relationships among variables. SEM is a multivariate statistical technique that allows researchers to examine how variables are related to one another and how they contribute to a particular outcome or dependent variable. SEM is a flexible technique that can be used to analyze data from a wide range of research designs, including cross-sectional, longitudinal, and experimental studies.^[8] The SEM process involves specifying a theoretical model, testing the model with data, and evaluating the model fit. The theoretical model is based on a priori assumptions about the relationships among variables, and it is typically represented graphically as a path diagram. The path diagram consists of variables represented as boxes and arrows representing the hypothesized relationships between the variables.^[8] The data used to test the theoretical model are collected from a sample of participants. The data are then analyzed using SEM software, which estimates the relationships between the variables and the fit of the model to the data. The model fit is assessed using a variety of fit indices, which provide information about how well the model fits the data.^[10,11]

SEM has been used in dental research to investigate a wide range of topics, including oral health-related quality of life (OHRQoL), dental anxiety, oral health behavior, and dental caries. The following sections provide an overview of some of the key applications of SEM in dental research.^[9]

When conducting an SEM study, there are several key considerations that researchers should keep in mind. It is important to carefully specify the theoretical model, including the hypothesized relationships among variables and the direction of those relationships. Researchers should also consider the appropriate measurement models for each variable, such as confirmatory factor analysis, and should use appropriate statistical tests to assess the validity and reliability of the measures. Researchers should carefully consider the sample size and power of their study. SEM requires a relatively large sample size, as well as adequate power to detect meaningful relationships among variables. Researchers should also consider the distributional properties of their data and use appropriate techniques to address issues such as non-normality and missing data. Researchers should carefully evaluate the fit of their model to the data. There are a variety of fit indices that can be used to assess model fit, including the Chi-square test, the comparative fit index, and the root mean square error of approximation. Researchers should also consider the potential for model misspecification and should use techniques such as sensitivity analysis to assess the robustness of their findings.

Finally, researchers should carefully interpret their findings and consider the limitations of their study. SEM can provide valuable insights into the relationships among variables, but it is important to remember that the results are based on statistical models and may not necessarily reflect causal relationships or generalizable findings. Researchers should carefully consider the implications of their findings and the potential for future research to build on their work.

There are several advantages to using SEM in dental research. SEM allows researchers to test complex theoretical models that are difficult to analyze using traditional statistical techniques. This can help researchers to better understand the complex relationships among variables and to identify important predictors of dental health outcomes.^[19-21] It provides a comprehensive assessment of the measurement models and the structural models and allows researchers to evaluate the fit of the overall model to the data. This can help to ensure that the results are robust and reliable.^[9] It can be used to test competing models and to compare the relative strengths of different hypotheses. This can help to refine theoretical models and to identify areas for future research.^[22]

Despite its many advantages, SEM also has several limitations that should be considered when interpreting the results of SEM studies. SEM requires a relatively large sample size and may not be appropriate for smaller studies or studies with limited power. SEM relies on assumptions about the measurement models and the structural models, and violations of these assumptions can lead to biased or unreliable results. Researchers should carefully evaluate the assumptions underlying their model and use appropriate statistical techniques to address issues such as nonnormality or missing data. SEM can be computationally intensive and requires specialized software and training. Researchers should be familiar with the software and the underlying statistical principles to ensure that their results are accurate and reliable.^[25]

SEM is a powerful statistical technique that has become increasingly popular in dental research. It allows researchers to test complex theoretical models, identify important predictors of dental health outcomes, and refine theoretical models for future research. However, SEM also has several limitations that should be carefully considered when interpreting the results of SEM studies. Researchers should carefully specify their theoretical models, consider the appropriate measurement models and statistical tests, evaluate the fit of the model to the data, and carefully interpret their findings. Overall, SEM is a valuable tool for dental researchers and can provide valuable insights into the complex relationships among variables that contribute to oral health outcomes.