PURPOSE: This article explored issues arising from secondary analysis of qualitative data and analyzed the results of qualitative secondary analysis studies published in English, focusing on the methodological aspects. METHOD: A total of 29 studies were identified as qualitative secondary analysis studies, retrieving from the CINAHL database from 1982 to 3rd week of April 2003. These studies were analyzed by publication year, research method, and type of approach to secondary analysis. RESULT: The year that the qualitative secondary analysis study first published was 1992 and the number of the studies using secondary analysis has increased after the middle of 1990s. Grounded theory was the one the most frequently used(n=11, 37.9%) and phenomenological study the second most(n=6, 20.7%). In terms of types of approach, fifteen studies(51.7%) focused on the specific concepts that were not explored in the primary studies. Nine(31.0%) focused on the specific types of participants. Six were aimed to integrate contexts or perspectives to generate more general and abstract analysis of qualitative data. CONCLUSION: The results of this article will stimulate methodological discussion of qualitative secondary analysis and activate qualitative studies using secondary analysis.
*Data Interpretation, Statistical ; *Research

Analysis on data from epidemiological studies is the sequencing process of applying statistical methods to collected data from different angles, interpreting intermediate results, drawing statistical conclusions and forming scientific findings based on existing knowledge. This is also called the 'process of converting data to evidence'. Final results from the analysis are expressed through scientific papers. Process of an accurate, clear and comprehensive data analysis is critical to form a convincing conclusion on a paper. This article discusses how to form the analytical thoughts for conducting a thorough data analysis in order to draw a convincing evidence from epidemiological data.
Data Interpretation, Statistical ; Epidemiologic Studies

In most medical research the P value is commonly used to describe test results. Because the power of statistical test is influenced by sample size, the null hypothesis can be rejected (P<0.05) in most cases if the sample size is tremendously big even if the real difference (or relationship) is extremly small. To overcome the weakness of using the P value, effect size can be used in the statistical analysis. Effect size can be defined as the "degree to which the phenomenon (difference or relationship) is present in the population". The effect size is used in sample size calculation, data interpretation and conducting meta-analysis. This manuscript describes limitations in using the P value and further introduces the concept of effect size.
Data Interpretation, Statistical ; Research Design ; Sample Size

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.
Data Interpretation, Statistical ; Investigative Techniques ; Statistics, Nonparametric

The maximum likelihood estimator is the point estimator of the top priority in statistical data analysis because of its optimum properties for large sample size. While the maximum likelihood estimator is widely used, it has been an abstruse subject for pharmacometricians without statitics bagkround because of high dimensional calculus and asymptotic theories. This tutorial provides a general and brief introduction to the maximum likelihood estimator and its related caluculus for non-statisticians.
Calculi ; Data Interpretation, Statistical ; Sample Size

PURPOSE: The purposes of this study were to assess the trend of use of statistical methods including parametric and nonparametric methods and to evaluate the use of complex statistical methodology in recent periodontal studies. METHODS: This study analyzed 123 articles published in the Journal of Periodontal & Implant Science (JPIS) between 2010 and 2014. Frequencies and percentages were calculated according to the number of statistical methods used, the type of statistical method applied, and the type of statistical software used. RESULTS: Most of the published articles considered (64.4%) used statistical methods. Since 2011, the percentage of JPIS articles using statistics has increased. On the basis of multiple counting, we found that the percentage of studies in JPIS using parametric methods was 61.1%. Further, complex statistical methods were applied in only 6 of the published studies (5.0%), and nonparametric statistical methods were applied in 77 of the published studies (38.9% of a total of 198 studies considered). CONCLUSIONS: We found an increasing trend towards the application of statistical methods and nonparametric methods in recent periodontal studies and thus, concluded that increased use of complex statistical methodology might be preferred by the researchers in the fields of study covered by JPIS.
Data Interpretation, Statistical ; Periodontal Diseases ; Statistics, Nonparametric

The purpose of this research is to analysis the general job of 76 dietitian on common management of school food service in Kyoung-buk area. In this research we asked them some general aspects, and made use of three variants(job performing time, the degree of major recognition, and the degree of difficulty) each question after classifying their jobs into 13 standard jobs. Statistical data analysis was completed using SPSS package program. The results of this survey showed the following : 1. The types of common management are as in the following : of the whole 76, 37 on the rotative trip to one single school, 8 to two schools, 1 to three schools, 28 on the trip to one single school plus central food production and 1 on the trip to two schools plus central food production. 2. The average job performing time in his or her school is 2813 minutes(8.52 hours) per week. 3. The factor of the evaluation and study of school foodservice has the highest level in every variant, but there were no standard job which needed the high-level difficulty and the longer job performing time as it needed the low degree of major recognition.
Data Interpretation, Statistical ; Food Services ; Humans ; Nutritionists*

OBJECTIVETo explore the differences between the Bayesian interim analysis and the classical interim analysis.

METHODSTo compare the means of two independent samples between control and treatment, superior hypothesis test was established. In line with the data requirements for group sequential design, Type Iota error of Bayesian interim analysis based on various prior distributions, Power, Average Sample Size and Average Stage were estimated in the interim analysis.

RESULTSIn the Pocock and O' Brien & Fleming designs, the Type Iota errors in the Bayesian interim analysis based on the skeptical prior distribution and the handicap prior distribution were controlled at around 0.05. When the powers of these two classical designs were both 80%, Bayesian powers of the skeptical prior distribution and the handicap prior distribution were markedly lower. The powers of the non-informative prior distribution and the enthusiastic prior distribution were distinctly higher than 80%.

CONCLUSIONIn the Bayesian interim analysis based on the skeptical prior distribution and the handicap Prior distribution, the Type Iota errors can be well controlled. Bayesian interim analyses using these two prior distributions, compared with the analysis adopting the O' Brien & Fleming method, can markedly increase the possibility of ending the clinical trials ahead of time. The Bayesian interim analyses based on these two distributions do not have practical value for group sequential design of the Pocock method.

The purposes of this research were to assess the quality of worklife of university foodservice managers and workers by operation type, and to investigate the characteristics of demographic variables in university foodservice employees. A questionnaire was administered to 27 managers and 180 personnels who are working in 9 university foodservice facilities. And 21 managers and 160 workers were responded with a response rate of 78% and 89%, respectively. Statistical data analysis was completed using the SPSS programs for descriptive analysis, ANOVA, T-test and SNK test. The results of this study can be summarized as follows : 1. Almost all respondents were female(87%), 40.5 percent of the respondents were between 40 to 49 years of age, 42.9 percent of the respondents had been in their current job between 2 to 5 years, and 55.2 percent of the respondents eamed pays between 500,000won to 800,000won per month, Only 42.6% percent of the respondents were full-time employee. 2. The mean scores for the quality of worklife was 3.07 on a 5-point scales of 1=disagree very much and 5 = agree very much. 3. Factors receiving the higher ratings included "optimum levels of work variety"(3.83), "positive attitude toward work"(4.14), and "cooperative relationship with coworkers"(4.22). But respondents were least satisfied with "promotion"(2.07), "temperature of workplace"(2.17) "rest time"(2.25), and "pay"(2.28) factors. 4. There was a significant difference in the perception of the quality of worklife according to the operation type(self-operated, contracted, and rented management), but no difference was noted by position(managers vs workers) Results can be user to develop intervention and training strategies for enhancing positive attitude and the quality of work of employees.
Surveys and Questionnaires ; Data Interpretation, Statistical ; Weights and Measures

The purpose of the study was to assess customer satisfaction concerning service quality characteristics of university foodservice by using a developed DINESERV model. In particular, it was intended to develop a tool to assess the difference between customer judgements on importance and customers perceptions with actual service delivery by university foodservices. Quenstionnaires were distributed to 1,000 university students. A total at 820 university students responded with a usable response rate of 77.7%. A statistical data analysis was completed using SAS programs for descriptive analysis; a t-test, chi-square test and Dunan's multiple range test. The results of the study are as follows; 1) The mean number of students visiting university foodservices per week for males was larger than that of females. The students' first choice depended on distance when they selected foodservices. They answered their preference as the first factor when they order a particular menu items in foodservices. The first complaint factor concerning university foodservices was the price of the food. 2) Customers was not satisfied with the quality of the service of university foodservices. The important mean score of the service quality was 3.63 out of 5, but the perception mean score of the service quality was 2.87. Therefore, there was a gap(0.76) between the importance score and perception score. 3) Customers' satisfaction with the service quality by dimensions wee int he follow order: assurance>reliability>responsiveness>tangibles>empathy. Customers were more satisfied with the service quality of contracted management than that of self-operated facilities.
Data Interpretation, Statistical ; Female ; Humans ; Male