Multicollinearity and misleading statistical results / 대한마취과학회지

Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (R(h)²) of a multiple regression model with one explanatory variable (X(h)) as the model’s response variable and the others (X(i) [i≠h] as its explanatory variables. The variance (σ(h)²) of the regression coefficients constituting the final regression model are proportional to the VIF(1/1−R(h)²). Hence, an increase in R(h)² (strong multicollinearity) increases σ(h)². The larger σ(h)² produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σ(h)² according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.

Nonparametric statistical tests for the continuous data: the basic concept and the practical use / 대한마취과학회지

Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results / 예방의학회지

OBJECTIVES: The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures. METHODS: This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010. RESULTS: Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses. CONCLUSIONS: Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.

An analysis of the articles published in the Journal of Korean Academy of Oral Health, from 1995 to 2012 / 대한구강보건학회지

OBJECTIVES: This study proposes to trace the development of the Journal of Korean Academy of Oral Health by analyzing its articles. METHODS: All of the articles published in the Journal of Korean Academy of Oral Health from 1995 to 2012 were assessed and analyzed with regard to the following: research design, MeSH database keywords, and statistical method. RESULTS: The total number of published articles was 830. This journal has conducted based on the relatively weak research designs and statistical analysis, and keyword does not matched with MeSH terms. The most frequently used research design was cross-sectional (53.1%). The statistical methods most often used were the F-test, t-test and contingency table. Only 34.3% of keywords matched MeSH terms. CONCLUSIONS: It was confirmed that the activities of the field of Journal of Korean Academy of Oral Health have become more prevalent over the past 18 years. In order to develop the quality of the journal, more systematic, refined study designs and methods are needed. It is also urgently essential that authors understand MeSH terms, and the Journal of Korean Academy of Oral Health should request that authors use accurate MeSH terms as their keywords when they submit articles.