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Coding and Further Analysis in NVivo.
Coding in Nvivo is not as hard as we think. Have we wondered why most companies or institutions conduct interviews or why some countries ask a politician for a debate?
However, stakeholders collect candidates' information in different formats, and qualitative analysis is conducted to select the candidate that best identifies and understands the problems from the collected spoken or written word which can be done with NVivo.
Framework matrices can help us understand our data and handle massive numbers of interview data by breaking it down into more manageable portions.
Secondly, framework matrices are made up of a grid with columns for codes and rows for cases, such as the individuals we interviewed. Each grid cell symbolises the point where a case and a topic cross; by entering text into the cell, we can produce a summary of the information pertinent to that case and theme.
Figure 1: Framework Matrices in Row and column
When no summary has been made at the point where the case and code overlap, the cells remain blank, all the cells were empty when we initially constructed a framework matrix in our project. we will gradually complete the cells as we go through the player information or interviews to gather data.
What is coding
Well, coding is like assigning specific labels to some particular extract in the text. However, the data analyst is to decide what the labels are or should be and also decide the text to extract, but generally, coding helps open up the data, which helps to generate an understanding of the data without having to read through every text in the data to extract meaning.
It is recommended to build up a code structure in List View before coding because this is helpful since we already know the subjects we want to explore.
As we browse our files in Detail View, we can also create codes "on the fly", Additionally, NVivo offers auto coding methods and queries to assist us in creating codes based on the information in our files.
Since the cases in NVivo are units of our analysis, we will be coding based on keeping in mind that coding is also a reflection of our analytical and structural thinking. Let us get started.
Coding queries by Overlapping and attribute
Using coding queries, we can test concepts, look for trends, and draw the dots between our project's themes, subjects, people, and locations.
They are unique tools that make us go beyond just summarising code, viewing them and charting. Often, they are the essence of analysis because they enable us to explore complex code relationships. Eg. To see whether codes overlap and to compare the value between male footballers and female footballers and even their age range
Therefore, to create coding queries, we need to go to the top of the menu option and Click on Queries, then on coding as shown below in figure 1.
Figure 2: Using Queries in NVivo
After clicking on coding, the windows below are displayed as shown in figure 2; So; therefore, label [1] is where the code has been created or can be created; label [2] is where we can select items, folders, files or attribute we want to query in the data and then label [3] is where the condition is selected based on what we want to get or see and label [4] is where we choose the code to select.
Finally, label [5] is where we can view our overlapping code or text extract to see the summary, reference and text resulting from the query. However, from figure 2, code strips are used to find the overlapping keyword in every query
Figure 3: Using conditions to query qualitative data
Coding queries by others
The specialised text search feature and the Query Wizard provide the same choices in a slightly different presentation. We can execute text searches from either one.
To list the terms that appear most frequently in our files, use word frequency queries. Using a word frequency search, we can search for a particular phrase in the file we want from the project, and potential themes can be considered.
As shown in the figure, word frequency queries are used to investigate a particular demographic's vocabulary in a given folder, extract or node, as shown below in figure 4.
Figure 4: Word frequency Search in NVivo.
Visualising our coding
Visualisations assist us at every level of the research process and complement the iterative nature of our qualitative research.
In order to plot the histogram to represent Male and Female footballers, we have to select the chart option in the menu and select the case-by-attribute value; then, the histogram is shown, but the chart can be displayed in a 3-dimensional format.
This visualisation can also be exported out of NVivo however, the sample image shown below can have more bars depending on the numbers of code and file classes contained, as shown in figure 5.
Figure 5: Histogram of male and female footballers
Charts, hierarchy charts, comparison and explore diagrams, cluster analysis diagrams, and sociograms are graphical representations of chosen data from projects. Maps are more dynamic and let us see our ideas differently.
The Explore diagram shows all the words or phrases connected to a given item or word, which enables us to investigate a connection or association in the word or extract, as shown in figure 6.
Figure 6: using explore diagram in NVivo
A cluster analysis diagram in NVivo is an exploratory tool that groups files, codes, or instances with similar terms, attribute values, or coding to help us find trends in our data.
It generates diagrams that use colour (to indicate "clusters") and location (similar items are near together and different things apart) to graphically depict the similarity or dissimilarity of the objects we are comparing.
These can be seen after we have generated or run the query of our word frequency, so the cluster diagram can be seen by selecting the bottom left edge in the NVivo interface, as shown in figure 7.
Figure 7: Cluster analysis diagram in NVivo
We utilise a word frequency inquiry to determine our study participants' topics. From the Explore tab, launch the search as shown in figure 8.
Choose the files to run the query on, the number of words to count, and whether or not we want to treat related terms as synonymous. The query can be seen as a word cloud once it has been executed.
Figure 8: Word Cloud for Male and Female footballers
A report comprises details taken from the project's underlying data. Depending on the "view" of the data we select, we may be able to include different fields in our report. For instance, the majority of the attributes related to codes, such as code name, description, or originator, are included in the Code view.
Text report: we may also base our report on the standards from a text report that is already part of our project; several basic text reports are available in NVivo, which we can use but can also create our own, but the report is only created based on the query we have run.
However, this report can be created by selecting share on the Nvivo menu bar, then selecting either a newly formatted text report or text report that can be customised and exported to any format needed; figure 8 below is the sample report.
Figure 9: Report on footballer's code summary.
The transparency of our study findings is further improved by using NVivo to organise and analyse our data. We used framework matrices and notes to show how our ideas can be developed and recorded our early prejudices and assumptions to show how they have been identified and challenged.
When coding, it is necessary to consider our findings after studying and coding a file then, run a report to check which codes have been used most frequently or display coding stripes to examine our code.
Finally, we can find illustrated quotations easily by constantly going back to the context in which our coded material was intended. The queries and visualisations that assisted us in reaching the primary purpose of our findings should be saved and reviewed or viewed from different angles.
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