Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the results and degrades the efficiency of the data. Outliers significantly affect the process of estimating statistics (e.g., the average and standard deviation of a sample), resulting in overestimated or underestimated values. Therefore, the results of data analysis are considerably dependent on the ways in which the missing values and outliers are processed. In this regard, this review discusses the types of missing values, ways of identifying outliers, and dealing with the two.
Bias (Epidemiology) ; Data Collection ; Statistics as Topic

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

Many problems have been stated in the surveillance system of notifiable acute communicable diseases in Korea. Lack of objective tools for detecting epidemic is one of the most fundamental. We propose a statistical standard for detecting epidemic of those diseases that could be easily and promptly applicable to the existing data. Suggested standard measure is computed from the median and the spread of upper and lower hinge(spr(H)) which is robust to the assumption of normal distribution, so frequently used in exploratory data analysis as a measure of variation, and the results are compared with those of existing method using recent 3 years from January 1994 to December 1996 of monthly data of 8 notifiable acute communicable diseases in Korea. Monthly pattern of statistical epidemic between the proposed (median) and existing(mean) methods is similar. Therefore, we propose that the statistical epidemic should be defined when the current occurrence exeeds the standards of both methods. When the data collection is made weekly than monthly, the proposed method of determining the time of epidemic will be much helpful for the management of notifiable diseases.
Communicable Diseases* ; Data Collection ; Korea* ; Statistics as Topic

China ; Data Collection ; Disabled Persons ; statistics & numerical data ; Humans

For the integrated leprosy control, including the elimination of leprosy and the leprosy related activity, the collection and analysis of the accurate data is essential. In Korea, we have applied the computerized information system of the leprosy control. This system was designed for data collection as well as data analysis. The major functions of this system include data entry, data transfer, from local level to central level, data sum-up, and production of information, including epidemiologial and clinical information and information for decision-making. Firstly we used this system for treatment workload only. Because the basic concept of our system's data structure is more flexible than other's, we try that we can use it more widely, for the leprosy related activity now. We think that this system has been supported effectively our integrated leprosy control work in Korea. We therefore present the computerized leprosy information system of Korea.
Data Collection ; Information Systems* ; Korea ; Leprosy* ; Statistics as Topic

PURPOSE: The purpose of this study was to investigate the relationship between depression and life satisfaction of the middle aged man. METHOD: The study was designed as a descriptive correlation study. Data were collected using a structured questionnaire which included general characteristics, depression, and life satisfaction. Data collection was done between Oct. 28 and Nov. 28 on the 145 middle aged man. RESULT: The degree of depression of the subjects was 8.98 and life satisfaction was 10.52 on the average. About 8% of the subjects was included in the depression group. There was a negative correlation between depression and life satisfaction of the subjects and it was statistically significant. CONCLUSION: Therefore, it should be developed the nursing program for qualitative life of middle aged man.
Data Collection ; Depression* ; Humans ; Middle Aged ; Nursing ; Statistics as Topic

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

PURPOSE: This study is aimed to search the lived experiences of the participants and to analyze the contents so that we can be assured about what the elderly wish and find the appropriate nursing intervention for them in real life. METHOD: This is based on a phenomenological approach. Participants of this study consist of people older than 65 years old. Data was collected from May to August in 2001. Data collection was done through in-depth interviews and observations. The time used in each interview was from 50 minutes to 2 hours. Each participant was interviewed three to five times. Giorgi's phenomenological analysis was used in data analysis. RESULT: The elder's wishes are 1) a respected life, 2) a happy life, 3) a peaceful life, 4) a independent life, 5) a financially independent life, 6) a satisfactory life, 7) a mind to depend on someone, 8) a serving life, 9) a historic family, 10) a happy-ending life. CONCLUSION: The study will contribute for the nursing intervention to enhance the quality of life and successful aging of the elderly.
Aged* ; Aging ; Data Collection ; Humans ; Nursing ; Quality of Life ; Statistics as Topic

PURPOSE: The purpose of this study was to investigate the relationship between the level of mastery and depression in chronic arthritis patients. METHOD: The subjects for this study were 100 patients registered in S University Hospital, and the period of data collection was from June 20, 2006 to August 30, 2006. RESULTS: The cronbach's alpha of the research instruments were .70-.86. In data analysis, SPSSWIN 12.0 program was used for descriptive statistics. The results were as follows. 1) The range of total mastery scores was from 11 to 28 and the mean score of the depression in chronic arthritis patients was 17.88. 2) The range of total depression scores was from 20 to 72 and the mean score of the depression in chronic arthritis patients was 39.99. 3) The level of mastery was significant correlation with the level of depression in chronic arthritis patients(r=-.466, p<.01). CONCLUSION: Mastery had significant correlation with depression in patients who have chronic arthritis. Therefore, the strategy of nursing intervention which improve mastery must be developed for patients who have chronic arthritis.
Arthritis* ; Data Collection ; Depression* ; Humans ; Nursing ; Statistics as Topic

The purpose of this study was done to identify the relationship between the level of pain and depression in patients with rheumatoid arthritis. The subjects for this study were 222 patients registered in H University Hospital Rheumatoid Arthritis Center, and the period of data collection was from July 20, 2000 to August 30, 2000. The research instruments used in this study were the Graphic Rating Scale of Pain and the CES-D for depression. The cronbach's alpha of the CES-D scale was .89. Data analysis, was done by the SPSSWIN 10.0 program using descriptive statistics. The results are as follows. 1) The total pain score ranged from 0 to 147 with a mean score for pain in patients with rheumatoid arthritis of 72.64. 2) The total depression score ranged from 20 to 72 with a mean score of 39.86. 3) There was a significant difference in pain according to sex(F=5.26, p<0.05) and education level(F=3.59, p<0.05). 4) There was a significant difference in depression scores according to sex (F=7.76, p<0.05) and education level (F=3.02, p<0.05). 5) The level of pain had a significant correlation with the level of education level(r=-0.174, p<0.01). The level of depression was significant correlation with the level of pain (r=0.237, p<0.01).
Arthritis, Rheumatoid* ; Data Collection ; Depression* ; Education ; Humans ; Statistics as Topic