ReviewDental research journal2024
Structure equation modeling in oral health research: A review of applications and considerations.
Review in Dental research journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
This review provides an overview of structure equation modeling (SEM) and its applications in dental research. SEM is a statistical technique that allows researchers to examine the relationships between variables and is useful for analyzing data from a wide range of research designs, including cross-sectional, longitudinal, and experimental studies. The process involves specifying a theoretical model, testing the model with data, and evaluating the model fit. It has been used in dental research to investigate a wide range of topics, including dental diseases, oral health-related quality of life, and dental anxiety. SEM is particularly useful in modeling the relationships between various risk factors and dental diseases and also has the potential to provide a deeper understanding of the multifactorial nature of dental diseases such as periodontitis, dental caries, and oral cancer. Moreover, the insights provided can aid in the development of effective strategies for the prevention and treatment of dental diseases. It is a powerful statistical tool that can be used by dental researchers to gain a better understanding of the intricate interplay of factors that underlie dental diseases and other oral health-related outcomes.
Indexed as
Identifiers
39376262PMC11457984What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.