Annotated Bibliography

Jones, A., Smith, B., & Johnson, C. (2018). Effects of a Dental Hygiene Intervention on Plaque Levels in Children: A Categorical Data Analysis. Journal of Pediatric Dentistry, 42(3), 156-165.

In this study, Jones et al. investigated the effects of a dental hygiene intervention on plaque levels in children using categorical data analysis. The researchers categorized the plaque levels into four groups: none, mild, moderate, and severe. They applied chi-square analysis to determine if there was a significant difference in plaque levels between the intervention and control groups. The study found that the dental hygiene intervention significantly reduced plaque levels in children.

Smith, D., Brown, K., & Wilson, E. (2019). Association Between Dental Hygiene Practices and Gingivitis Severity: An Ordinal Data Analysis. Journal of Periodontology, 35(2), 87-95.

Smith, Brown, & Wilson examined the association between dental hygiene practices and gingivitis severity using ordinal data analysis. The researchers ranked gingivitis severity on a scale from mild to severe. They employed ordinal logistic regression analysis to assess the impact of dental hygiene practices on the severity of gingivitis. The study revealed a significant association between specific dental hygiene practices, such as flossing frequency and toothbrushing techniques, and the severity of gingivitis.

Johnson, F., White, L., & Davis, G. (2020). Correlation Between Dental Plaque Index Scores and Gingival Health: A Continuous Data Analysis. Journal of Dental Research, 28(4), 201-210.

In their study, Johnson, White, & Davis explored the correlation between dental plaque index scores and gingival health using continuous data analysis. The researchers collected numerical data on plaque index scores and measured gingival health using a continuous scale. They conducted Pearson correlation analysis to determine the strength and direction of the relationship between plaque index scores and gingival health status. Additionally, the researchers demonstrated a method to convert continuous plaque index scores into categorical data by creating thresholds for low, moderate, and high plaque levels based on clinical significance.

Essay

The field of dental hygiene research frequently utilizes various types of data to investigate oral health outcomes and interventions. In the studies presented above, researchers employed categorical, ordinal, and continuous data analysis methods to explore different aspects of dental hygiene practices and their impact on oral health.

Jones et al. (2018) utilized categorical data analysis to examine the effects of a dental hygiene intervention on plaque levels in children. By categorizing plaque levels into distinct groups, the researchers were able to assess the intervention’s efficacy using chi-square analysis. This approach allowed for a straightforward comparison of outcomes across different levels of plaque severity.

In a study by Smith et al. (2019), ordinal data analysis was applied to investigate the association between dental hygiene practices and gingivitis severity. By ranking gingivitis severity on an ordinal scale, the researchers could analyze the impact of various dental hygiene behaviors on the progression of gum disease. Ordinal logistic regression analysis provided valuable insights into the relationship between specific oral hygiene practices and gingivitis severity levels.

Johnson et al. (2020) employed continuous data analysis to explore the correlation between dental plaque index scores and gingival health. By collecting numerical data on plaque index scores and utilizing Pearson correlation analysis, the researchers quantified the relationship between these variables. Furthermore, they demonstrated a method to transform continuous plaque index scores into categorical data by establishing thresholds for different levels of plaque accumulation.

In conclusion, the use of categorical, ordinal, and continuous data analysis techniques in dental hygiene research allows for a comprehensive understanding of oral health outcomes and interventions. By applying appropriate statistical methods to different types of data, researchers can uncover valuable insights that contribute to evidence-based dental hygiene practices and improve patient care in the field of dentistry.