Survey of the use of statistical methods in Journal of the Korean Association of Oral and Maxillofacial Surgeons
10.5125/jkaoms.2018.44.1.25
- Author:
Yong Geun CHOI
1
Author Information
1. Epidemiology & Biostatistics, Korea University Graduate School of Clinical Dentistry, Seoul, Korea. ebdent@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Statistics;
Normal distribution;
Confounding factors
- MeSH:
Confounding Factors (Epidemiology);
Logistic Models;
Methods;
Normal Distribution;
Oral and Maxillofacial Surgeons;
Proportional Hazards Models
- From:Journal of the Korean Association of Oral and Maxillofacial Surgeons
2018;44(1):25-28
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: This study aimed to describe recent patterns in the types of statistical test used in original articles that were published in Journal of the Korean Association of Oral and Maxillofacial Surgeons. MATERIALS AND METHODS: Thirty-six original articles published in the Journal in 2015 and 2016 were ascertained. The type of statistical test was identified by one researcher. Descriptive statistics, such as frequency, rank, and proportion, were calculated. Graphical statistics, such as a histogram, were constructed to reveal the overall utilization pattern of statistical test types. RESULTS: Twenty-two types of statistical test were used. Statistical test type was not reported in four original articles and classified as unclear in 5%. The four most frequently used statistical tests constituted 47% of the total tests and these were the chi-square test, Student's t-test, Fisher's exact test, and Mann-Whitney test in descending order. Regression models, such as the Cox proportional hazard model and multiple logistic regression to adjust for potential confounding variables, were used in only 6% of the studies. Normality tests, including the Kolmogorov-Smirnov test, Levene test, Shapiro-Wilk test, and Scheffé's test, were used diversely but in only 10% of the studies. CONCLUSION: A total of 22 statistical tests were identified, with four tests occupying almost half of the results. Adoption of a nonparametric test is recommended when the status of normality is vague. Adjustment for confounding variables should be pursued using a multiple regression model when the number of potential confounding variables is numerous.
