Question

8. Whe data. Why is it important to define this type of data? Also, how does the use of blanks assist you understanding qualified data? What types of blanks are best suited for data quality assurance/quality control? (15 pts.) n examining analytical data, it is possible that you will receive qualified or coded

Answer 1

Answer

The idea of analysis implies some kind of transformation. It is started with some collection of qualitative data and then processing it through analytical procedures into clear, understandable, trustworthy original analysis.

Researchers use thematic analysis as a means to gain insight and knowledge from data gathered. The method enable.s researchers to develop a deeper appreciation for the group or situation they are researching.

Using their findings, researchers can apply a statistical analysis to validate themes. In practice, depending on the context of the research study, thematic analysis could include a bit of grounded theory, positivism, interpretivism and phenomenology.

Process

1) Collect data – Data is gathered from sources that can include:

• field diaries
• observational data,etc.

2) Coding data – Researchers code the data by hand or through a software program. Typically, the researcher will be coding every two or three lines of text with handles that identify key words, concepts, images and reflections. Coding is an explicit and iterative process in which the researcher will alter and modify the analysis as reflected by the data and as ideas emerge. Coding skills improve with experience. A “good code” is one that captures the qualitative richness of the phenomenon. A code should be clear and concise, clearly stating what it is, its boundaries and how to know it when it occurs. Codes become the foundation for the themes that are going to be used by the researcher.

3) Defining Code – To ensure the integrity of the codes—that is, that they have not been misinterpreted and are free of researcher bias—they should be developed and reviewed by more than one person. The researcher(s) read and re-read the data, double-checking the codes for consistency and validation. The integration of the codes from the data becomes the codebook from which themes emerge. Coding is the process of organizing and sorting your data. Codes serve as a way to label, compile and organize your data. One of the keys in coding your data, and in conducting a qualitative analysis more generally, is developing a storyline. Our coding process will thus enable us to show the richness, complexities and contradictions of the social milieu we are evaluating, which is the basis of qualitative methods.

Refining Your Codes

It is important to note that as your data are coded, the coding scheme will be refined. Meaning, you will add, collapse, expand and revise the coding categories. This is especially true of the pre-set codes. Oftentimes, what one expects to find in the data is not there. It happens. Moreover, some codes simply do not work or conflate other ideas from different codes. Alternatively, sometimes codes flourish in a way that there is too much data. In this case, the code needs to be broken down into sub-codes in order to better organize the data. The rule of thumb for coding is to make the codes fit the data, rather than trying to make your data fit your codes.

4) Themes/frameworks identification – From the codebook, the researcher identifies themes and sub-themes: patterns that have emerged from the coded data. Themes can emerge from patterns, such as conversation topics and vocabulary. Other factors could include the frequency of occurrence, occurrence only when certain factors are present, and time of the day, week or month. The researcher needs to be able to define each theme sufficiently so that it is clear to others exactly what the theme is.

5) Information consolidation, finalize theme names – The researcher finalizes the name of each theme, writes its description and illustrates it with a few quotations from the original text to help communicate its meaning to the reader.

Use of blanks in understanding qualified data

The primary purpose of blanks is to trace sources of artificially introduced contamination. The diagram below shows how comparison of different blank sample results can be used to identify and isolate the source of contamination introduced in the field or the laboratory.

A blank or blank determination is an analysis of a sample without the analyte or attribute, or an analysis without a sample, i.e. going through all steps of the procedure with the reagents only. The latter type is the most common as samples without the analyte or attribute are often not available or do not exist.

Another type of blank is the one used for calibration of instruments as discussed in the previous sections. Thus, we may have two types of blank within one analytical method or system:

- a blank for the whole method or system and
- a blank for analytical subprocedures (measurements) as part of the whole procedure or system.

For instance, in the cation exchange capacity (CEC) determination of soils with the percolation method, two method or system blanks are included in each batch: two percolation tubes with cotton wool or filter pulp and sand or celite, but without sample. For the determination of the index cation (NH₄ by colorimetry or Na by flame emission spectroscopy) a blank is included in the determination of the calibration graph. If NH₄ is determined by distillation and subsequent titration, a blank titration is carried out for correction of test sample readings.

The proper analysis of blanks is very important because:

• In many analyses sample results are calculated by subtracting blank readings from sample readings.
• Blank readings can be excellent monitors in quality control of reagents, analytical processes, and proficiency.
• They can be used to estimate several types of method detection limits.

For blanks the same rule applies as for replicate analyses: the larger the number, the greater the confidence in the mean. The widely accepted rule in routine analysis is that each batch should include at least two blanks. For special studies where individual results are critical, more blanks per batch may be required.

Types of blanks for Quality Control

For quality control, Control Charts are made of blank readings identically to those of control samples. The between-batch variability of the blank is expressed by the standard deviation calculated from the Control Chart of the Mean of Blanks, the precision can be estimated from the Control Chart of the Range of Duplicates of Blanks.

Data generated from quality-control (QC) samples are a requisite for evaluating the quality of the sampling (environmental samples) and processing techniques, as well as data from the actual samples themselves. Without QC data, environmental-sample data can not be adequately interpreted because the errors associated with the sample data are unknown.

BLANK SAMPLES- Blank samples are collected and analyzed to ensue that environmental samples have not been contaminated during the data-collection process. The blank solution used to develop specific types of blank samples is a solution that is free of the analytes of interest. Any measured-value signal in a blank sample for an analyte (a specific component measured in a chemical analysis) that was absent in the blank solution is believed to be due to contamination. Many types of blank samples are possible, each designed to segregate a different part of the data-collection process. The types of blank samples are:

Source solution blank - a blank solution that is transferred to a sample bottle in an area of the office laboratory in a clean and protected atmosphere with respect to target analytes.

Ambient blank - a blank solution that is put in the same type of bottle used for an environmental sample, kept with the set of sample bottles before sample collection, and opened at the site and exposed to the ambient conditions.

Field blank - a blank solution that is subjected to all aspects of sample collection, field-processing preservation, transportation, and laboratory handling as an environmental sample.

Trip blank - a blank solution that is put in the same type of bottle used for an environmental sample and kept with the set of sample bottles before and after sample collection.

Equipment blank - a blank solution that is processed through some or all equipment used for collecting and processing an environmental sample (similar to a field blank but normally done in the more controlled conditions of the office than the field).

Sampler blank - a blank solution that is poured or pumped through the same field sampler used for collecting an environmental sample.

Filter blank - a blank solution that is filtered in the same manner and through the same filter apparatus used for an environmental sample.

Splitter blank - a blank solution that is mixed and separated using a field splitter in the same manner and through the same apparatus used for an environmental sample.

Preservation blank - a blank solution that is treated with the sampler preservatives used for an environmental sample.