Data Collection ; methods ; Metabolomics ; methods

With the development of high-throughput technologies, biomedical data has been increasing exponentially in an explosive manner. This brings enormous opportunities and challenges to biomedical researchers on how to effectively utilize big data. Big data is different from traditional data in many ways, described as 3Vs - volume, variety and velocity. From the perspective of biomedical research, here I introduced the characteristics of big data, such as its messiness, re-usage and openness. Focusing on microbiome research of meta-analysis, the author discussed the prospective principles in data collection, challenges of privacy protection in data management, and the scalable tools in data analysis with examples from real life.
Biomedical Research ; Confidentiality ; Data Collection ; methods ; Microbiota

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

Implementation of information technology in clinical research has resulted in revolutionary changes in drug development. Based on the good clinical practice (GCP) requirements on data, processes and documentations, and the era of fast growth in clinical studies using up-to-date information technology, we explore an integrated e-clinical solution in clinical studies in China.
China ; Clinical Trials as Topic ; Data Collection ; methods ; Medical Informatics ; methods

PURPOSE: This study was done to verify the validity and reliability of a Psychiatric Nurses' Image Scale (PSYNIS) for Korean psychiatric and mental health nurses. METHODS: A methodological study design was used with an exploratory factor analysis, Pearson's correlation, and a fitness of the modified mode for validity. Cronbach's alpha coefficients and an alternative-form method for reliability were used. Psychiatric Nurses' Image Scale (PSYNIS) was tested with 345 psychiatric and mental health nurses, residing in G city, J city, and J province. Data were collected from Jun. 1 to Aug. 2, 2012. Responses were obtained from respondents through self reports method and each item had a possible score of 5. Collected data were analyzed using the SPSS 20.0 and LISREL 8.54 programs. RESULTS: The 28 items making up the instrument were classified into the following 4 factors: 'Professionalism', 'Activism', 'Coordination competence', and 'Personalism'. These factors explained 63.2% of the total variance. Fitness of the modified mode was good (chi2= 1052.30, RMSEA=.05, GFI=.90, AGFI=.86, NFI=.97, and CFI=.98). The reliability of the PSYNIS was .95 (Cronbach's alpha). CONCLUSION: Results of the present study suggest that the PSYNIS is useful for efficiently evaluation of the image of psychiatric nurses.
Data Collection ; Mental Health ; Methods ; Reproducibility of Results* ; Self Report

PURPOSE: The aimof this phenomenological study was to qualitatively classify the experience of spouses caring for patients with schizophrenia. METHODS: Data were gathered using 2 hour in-depth, unstructured interviews with nine spouses of patients with schizophrenia. The data collection period was 2012 to 2013. Colaizzi's phenomenological method was used to analyze the resulting data. RESULTS: Five clusters of themes were identified. These were termed according to the experiences described by the spouses, as: suffering fromfalling into the abyss of despair; deepening heartbreak, clouds of misery hanging over one's family; possibly of losing the bond between familymembers; getting over one's hurt and stepping forward to the future. Participants experienced many burdens while caring for their spouse, however, they showed the ability to overcome difficulties positively and actively. CONCLUSION: The results of this study indicate that the spouse of a patient with schizophrenia experiences multiple sources of distress, and suggests a process to overcoming them. Recommendations include helping nurses be aware of the sufferings of both the patient and the spouse and to plan and provide for psychological interventions, such as stressmanagement programs and informational support on social welfare programs.
Data Collection ; Humans ; Methods ; Qualitative Research ; Schizophrenia* ; Social Welfare ; Spouses*

Air pollution epidemiologic studies are intrinsically difficult because the expected effect size at general environmental levels is small, exposure and misclassification of exposure are common, and exposure is not selective to a specific pollutant. In this review paper, epidemiologic study designs and analytic methods are described, and two nationwide projects on air pollution epidemiology are introduced. This paper also demonstrates that possible confounding issues in time-series analysis can be resolved and the impact on the use of data from ambient monitoring stations may not be critical. In this paper we provide a basic understanding of the types of air pollution epidemiologic study designs that be subdivided by the mode of air pollution effects on human health (acute or chronic). With the improvements in the area of air pollution epidemiologic studies, we should emphasize that elaborate models and statistical techniques cannot compensate for inadequate study design or poor data collection.
Air Pollution* ; Data Collection ; Epidemiologic Methods* ; Epidemiologic Studies ; Epidemiology ; Humans

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

Data Collection ; methods ; Humans ; Occupational Exposure ; prevention & control ; Workplace

Content validity is the degree to which an instrument has an appropriate sample of items for the construct being measured and is an important procedure in scale development. Content validity index (CVI) is the most widely used index in quantitative evaluation. There are 2 kinds of CVI: I-CVI and S-CVI. A method to compute a modified kappa statistic (K*) can be used to adjust I-CVI for chance agreement. S-CVI/UA and S-CVI/Ave are both scale level CVI with different formulas. Researchers recommend that a scale with excellent content validity should be composed of I-CVIs of 0.78 or higher and S-CVI/UA and S-CVI/Ave of 0.8 and 0.9 or higher, respectively. The characteristics and qualifications of the experts, process and main results of content validity evaluation should be reported in scale-related manuscript.
Data Collection ; Humans ; Psychometrics ; methods ; Reproducibility of Results ; Surveys and Questionnaires ; standards