OBJECTIVETo explore several numerical methods of ordinal variable in one-way ordinal contingency table and their interrelationship, and to compare corresponding statistical analysis methods such as Ridit analysis and rank sum test.

METHODSFormula deduction was based on five simplified grading approaches including rank_r(i), ridit_r(i), ridit_r(ci), ridit_r(mi), and table scores. Practical data set was verified by SAS8.2 in clinical practice (to test the effect of Shiwei solution in treatment for chronic tracheitis).

RESULTSBecause of the linear relationship of rank_r(i) = N ridit_r(i) + 1/2 = N ridit_r(ci) = (N + 1) ridit_r(mi), the exact chi2 values in Ridit analysis based on ridit_r(i), ridit_r(ci), and ridit_r(mi), were completely the same, and they were equivalent to the Kruskal-Wallis H test. Traditional Ridit analysis was based on ridit_r(i), and its corresponding chi2 value calculated with an approximate variance (1/12) was conservative. The exact chi2 test of Ridit analysis should be used when comparing multiple groups in the clinical researches because of its special merits such as distribution of mean ridit value on (0,1) and clear graph expression. The exact chi2 test of Ridit analysis can be output directly by proc freq of SAS8.2 with ridit and modridit option (SCORES =).

CONCLUSIONThe exact chi2 test of Ridit analysis is equivalent to the Kruskal-Wallis H test, and should be used when comparing multiple groups in the clinical researches.

In data analysis, given that various statistical methods assume that the distribution of the population data is normal distribution, it is essential to check and test whether or not the data satisfy the normality requirement. Although the analytical methods vary depending on whether or not the normality is satisfied, inconsistent results might be obtained depending on the analysis method used. In many clinical research papers, the results are presented and interpreted without checking or testing normality. According to the central limit theorem, the distribution of the sample mean satisfies the normal distribution when the number of samples is above 30. However, in many clinical studies, due to cost and time restrictions during data collection, the number of samples is frequently lower than 30. In this case, a proper statistical analysis method is required to determine whether or not the normality is satisfied by performing a normality test. In this regard, this paper discusses the normality check, several methods of normality test, and several statistical analysis methods with or without normality checks.
Data Collection ; Methods ; Normal Distribution ; Statistics as Topic

Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.
Methods ; Sample Size ; Statistics as Topic ; Survival Analysis*

Meta-analysis is a statistical method that combines and synthesizes multiple studies and integrates their results. Meta-analysis increases the sample size, and in turn, the power to study the effects of interest by combining primary studies and providing a precise estimate of the effects. Data synthesized from meta-analyses are usually more beneficial than the results of narrative reviews. In a meta-analysis, the decisions are transparent, and statistical analysis yields an objective measure of the integrated quantitative evidence. The biases of narrative reviews can be limited or overcome by conducting a meta-analysis. The systematic approach and transparency in meta-analysis help to resolve conflicts and uncertainties between studies, while leading to significant conclusions. However, this method is controversial and may not always be the best tool. Moreover, meta-analysis has several shortcomings, and in some cases, it may not be appropriate. Although meta-analysis has been criticized due to its limitations, there are solutions to such problems. The aim of this review is to describe and discuss the strengths and weaknesses of meta-analysis.
Bias (Epidemiology) ; Methods ; Sample Size ; Statistics as Topic

OBJECTIVETo evaluate the research design and the use of statistical methods in Chinese Journal of Cardiology.

METHODPeer through the research design and statistical methods in all of the original papers in Chinese Journal of Cardiology from December 2007 to November 2008.

RESULTThe most frequently used research designs are cross-sectional design (34%), prospective design (21%) and experimental design (25%). In all of the articles, 49 (25%) use wrong statistical methods, 29 (15%) lack some sort of statistic analysis, 23 (12%) have inconsistencies in description of methods. There are significant differences between different statistical methods (P < 0.001). The correction rates of multifactor analysis were low and repeated measurement datas were not used repeated measurement analysis.

CONCLUSIONMany problems exist in Chinese Journal of Cardiology. Better research design and correct use of statistical methods are still needed. More strict review by statistician and epidemiologist is also required to improve the literature qualities.

OBJECTIVETo re-evaluate and compare the research design and the use of statistical methods in Chinese Journal of Cardiology.

METHODSummary the research design and statistical methods in all of the original papers in Chinese Journal of Cardiology all over the year of 2011, and compared the result with the evaluation of 2008.

RESULTS(1) There is no difference in the distribution of the design of researches of between the two volumes. Compared with the early volume, the use of survival regression and non-parameter test are increased, while decreased in the proportion of articles with no statistical analysis. (2) The proportions of articles in the later volume are significant lower than the former, such as 6(4%) with flaws in designs, 5(3%) with flaws in the expressions, 9(5%) with the incomplete of analysis. (3) The rate of correction of variance analysis has been increased, so as the multi-group comparisons and the test of normality. The error rate of usage has been decreased form 17% to 25% without significance in statistics due to the ignorance of the test of homogeneity of variance.

CONCLUSIONMany improvements showed in Chinese Journal of Cardiology such as the regulation of the design and statistics. The homogeneity of variance should be paid more attention in the further application.

Scoping reviews are a useful approach to synthesizing research evidence although the objectives and methods are different to that of systematic reviews, yet some confusion persists around how to plan and prepare so that a completed scoping review complies with best practice in methods and meets international standards for reporting criteria. This paper describes how to use available guidance to ensure a scoping review project meets global standards, has transparency of methods and promotes readability though the use of innovative approaches to data analysis and presentation. We address some of the common issues such as which projects are more suited to systematic reviews, how to avoid an inadequate search and/or poorly reported search strategy, poorly described methods and lack of transparency, and the issue of how to plan and present results that are clear, visually compelling and accessible to readers. Effective pre-planning, adhering to protocol and detailed consideration of how the results data will be communicated to the readership are critical. The aim of this article is to provide clarity about what is meant by conceptual clarity and how pre-planning enables review authors to produce scoping reviews which are of high quality, reliability and readily publishable.
Comprehension ; Evidence-Based Practice ; Methods ; Practice Guidelines as Topic ; Review Literature as Topic ; Statistics as Topic

PURPOSE: The present study aims at researching the subjective satisfaction of patients who have experienced both conventional impression taking and digital impression taking to measure the possibility of wide clinical application of digital impression. MATERIALS AND METHODS: The study surveyed 170 adult patients over the age of 20, between October 2015 and April 2016, who voluntarily consented to participation and who experienced both conventional impression and digital impression at five dental hospitals that use intraoral digital impression. A total of 128 surveys were used for data analysis, involving frequency analysis, multiple response frequency analysis, descriptive statistics, and contingency table analysis, with the significance level set at 0.05. RESULTS: Responses on the reason for taking impressions using the digital method appeared in the order of 'for implant treatment' (43.8%), 'for crown treatment' (30.5%), and 'for inlay treatment' (15.6%). Patients satisfaction was higher for digital impression taking than conventional impression taking (P<.05). As the preferred choice of impression, digital impression (60.2%) was higher than conventional impression (11.7%). Responses on the reason for choosing digital impression taking appeared in the order of 'no vomiting reflex' (35.1%), 'reliability of 3D digital scanning' (33.8%), and 'short time' (33.8%). CONCLUSION: The patients preferred digital impression taking to conventional impression taking in terms of satisfaction.
Adult ; Crowns ; Humans ; Inlays ; Methods ; Patient Satisfaction* ; Statistics as Topic ; Vomiting

Qualitative research is very important in educational research as it addresses the “how” and “why” research questions and enables deeper understanding of experiences, phenomena and context. Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience. Getting at the everyday realities of some social phenomenon and studying important questions as they are really practiced helps extend knowledge and understanding. To do so, you need to understand the philosophical stance of qualitative research and work from this to develop the research question, study design, data collection methods and data analysis. In this article, I provide an overview of the assumptions underlying qualitative research and the role of the researcher in the qualitative process. I then go on to discuss the type of research objectives which are common in qualitative research, then introduce the main qualitative designs, data collection tools, and finally the basics of qualitative analysis. I introduce the criteria by which you can judge the quality of qualitative research. Many classic references are cited in this article, and I urge you to seek out some of these further reading to inform your qualitative research program.
Data Collection ; Education, Medical* ; Humans ; Methods ; Qualitative Research ; Statistics as Topic

WHY: Calculating the sample size is essential to reduce the cost of a study and to prove the hypothesis effectively. HOW: Referring to pilot studies and previous research studies, we can choose a proper hypothesis and simplify the studies by using a website or Microsoft Excel sheet that contains formulas for calculating sample size in the beginning stage of the study. MORE: There are numerous formulas for calculating the sample size for complicated statistics and studies, but most studies can use basic calculating methods for sample size calculation.
Chi-Square Distribution ; *Research Design ; *Sample Size ; Statistics as Topic/*methods