Data collection/analysis types

The log section of fieldnotes includes the factual information regarding the date and site of data collection, the type of data collection (covert participant observation, focus group interview, document analysis of student evaluations, etc.), the names of both the research subjects and the researchers collecting the data, and a statement of the purpose of this data collection activity. 

As used here, project information spans all aspects of data collection, analysis, and storage throughout R D. The information ranges from screening data at the basic research level to adverse experience reports in Regulatory Affairs. The requirements at this level include the integration of data of various types (i.e., chemical, biological, text. 

The following experiments may he used to introduce the statistical analysis of data in the analytical chemistry laboratory. Each experiment is annotated with a brief description of the data collected and the type of statistical analysis used in evaluating the data. 

The types of data required for incident reporting and root cause analysis systems are specified. Data Collection practices in the CPI are described, and a detailed specification of the types of information needed for causal analyses is provided. 

Analysis type As discussed previously, data-collection analyzers incorporate analysis parameter sets that allow the user to control the data-gathering process. APSs provide the option of selecting either frequency analysis for fixed-speed machinery or orders analysis for variable-speed machinery. 

Also the a-ester sulfonates are less important today. In the Federal Republic of Germany, for example, the total production of surfactants was about 700,000 t/a in 1993. For a more detailed analysis of different types of surfactants, use must be made of data collected before the unification of Germany. In 1988 the consumption of surfactants in detergents was about 227,500 t/a, the consumption of anionic surfactants was about 116,000 t/a and less than 1000 t/a of a-sulfo fatty acid esters [5] (the values refer to German Detergent Law). 

Although it is possible to identify and test every scenario of consistency checks, a business decision has to be made as to the depth of the checks. However, it is essential that all serious scenarios that could affect data analysis be tested. This is true for any type of data collection system. However, for a system to be one hundred percent reliable, every scenario must be tested and dealt with appropriately. A scenario that cannot be predicted at the time of development may be incorporated into the system or handled as an exception and dealt with accordingly when it occurs. 

In practice, the GC conditions should be designed to give the shortest analysis time while still providing the necessary selectivity (i.e., separation of both analyt-analyte and matrix-analyte). Selective detectors often have fast data collection rates and improved matrix-analyte selectivity, but analyte-analyte selectivity must be addressed solely by the GC separation. MS can improve both types of selectivity and, by reducing the reliance on the GC separation, faster analysis times can often be achieved in complicated mixtures. 

Audits of each phase of the study should include personnel training, preparation of collection forms, application calibration, each sample collection procedure, sample transport, each type of chemical analysis, data recording, data entry, data verification and data storage. Data collection in the field is often tedious if automated logging devices are not in place. To ensure data integrity, the paper and ink used for field studies should be waterproof. Each data collection form should contain appropriate locations for information detailing the time and location of sample collection, sample transport and sample analysis. Data collection forms should be stored in an orderly fashion in a secure location immediately upon return of field teams from the field at the end of each day. It is also important for data quality for studies to collect necessary field data seven days per week when required. In our experience, poor study quality is likely when field sample and data collection do not proceed on weekends. 

To provide useful data, the latter requires change over time. The limitation of this type of analysis is that, as yet, there is no systematic collection of organic market data, which makes it difficult to generalise across countries. The situation in Europe is being addressed by the activities of EISfOM. See for example, Recke el al. (2004). The current chapter therefore draws on a number of different studies and data collections in order to illustrate the arguments put forward. 

Statistical analysis should be appropriate to the types of outcome data collected and the number of genotypes nsed in the analysis. The handling of missing data should be clearly stated. Corrections for multiple comparisons (e.g., controlling for false discovery rates 36) should be performed if multiple statistical tests are carried out. 

Finally, the determination of methodology for cell staining must be evaluated based on the type of tissue or cells being examined. It is absolutely critical that the sample be a viable, single-cell suspension. Not only is this important during the staining and data collection, but it is also important in the analysis of the specimen as representative of the pathologic sample. 

One approach is to mesh all investigation and root cause analysis activities under one management system for investigation. Such a system must address all four business drivers (1) process and personnel safety, (2) environmental responsibility, (3) quality, and (4) profitability. This approach works well since techniques used for data collection, causal factor analysis, and root cause analysis can be the same regardless of the type of incident. Many companies realize that root causes of a quality or reliability incident may become the root cause of a safety or process safety incident in the future and vice versa. 

General Analysis [indude Filas UbrariesXstimuKis Options Data Collection ] Probe Wndow Analysis type... 

Central Analysis include Files Libraries Stimulus Options Data Collection Probe Window i Analysis type ... 

Samples submitted for chemical and physical analyses are collected for a variety of reasons, but the collection of each sample should always conform to certain guidelines. The application of precise techniques in sample collection helps to ensure that data from each analysis performed on the samples will be useful. For interpretations and comparisons of elemental compositions of coal beds to be valid, the samples must be collected so that they are comparably representative of the coal bed. Such interpretations and comparisons should never be based on data from different types of samples (Swanson and Huffman, 1976 Golightly and Simon, 1989). 

No analytical method is perfect. Spectral interpretation is still difficult, and standard spectra databases are scarce. The issues of quantification, comparison with data collected by other methods, and scale up are important, especially in spectromi-croscopy studies. Radiation damage and sectioning artifacts can make analysis of susceptible samples difficult. The biggest obstacle to widespread use of NEXAFS spectroscopy, microscopy, and spectromicroscopy in environmental studies remains the extremely limited number of such instruments. Typically, each beamline allocation committee receives 2 or 3 times as many requests for time as is available. Studies, when granted, are usually for 2-5 days every 4-6 months. Thus, scientists have to be very selective about the types of questions and samples that they choose to examine using these techniques. Continued pressure and education from the scientific community will be needed to increase the number of beamlines suitable for NOM studies in the future, even as new synchrotron facilities are planned or built. 

There are several sample digestion procedures used in elemental analysis. All of them use strong oxidizers (nitric acid, hydrochloric acid, and hydrogen peroxide) to solubilize environmentally available metals. The following distinctions between different types of elemental analysis digestion procedures are important for the planning of data collection and in data interpretation. 

The EPA developed a document titled Guidance for Data Quality Assessment, Practical Methods for Data Analysis, EPA QA/G-9 (EPA, 1997a) as a tool for project teams for assessing the type, quality, and quantity of data collected for projects under the EPA oversight. This document summarizes a variety of statistical analysis techniques and is used primarily by statisticians. DQA, however, is not just a statistical evaluation of the collected data. It is a broad assessment of the data in the context of the project DQOs and the intended use of the data, which requires a... 

Hypothesis tests may compare the collected data to an action level or compare two sets of data to each other. A statistician will select a statistical analysis test that is appropriate for the intended use of the data and the type of the collected data distribution and will identify assumptions underlying the test. The probabilities for false rejection decision... 

In the fixed sample clinical trial approach, one analysis is performed once all of the data have been collected. The chosen nominal significance level (the Type I error rate) will have been stated in the study protocol and/or the statistical analysis plan. This value is likely to be 0.05 As we have seen, declaring a finding statistically significant is typically done at the 5% p-level. In a group sequential clinical trial, the plan is to conduct at least one interim analysis and possibly several of them. This procedure will also be discussed in the trial s study protocol and/or the statistical analysis plan. For example, suppose the plan is to perform a maximum of five analyses (the fifth would have been the only analysis conducted had the trial adopted a fixed sample approach), and it is planned to enroll 1,000 subjects in the trial. The first interim analysis would be conducted after data had been collected for the first fifth of the total sample size, i.e., after 200 subjects. If this analysis provided compelling evidence to terminate the trial, it would be terminated at that point. If compelling evidence to terminate the trial was not obtained, the trial would proceed to the point where two-fifths of the total sample size had been recruited, at which point the second interim analysis would be conducted. All of the accumulated data collected to this point, i.e., the data from all 400 subjects, would be used in this analysis. 

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