Throughout your project, you will need ways of discovering and exploring patterns, testing hunches and creating and validating theories. You may want to discover words in the text that indicate patterns or themes and to discover and test relationships between the categories you have been coding at.
The nature of your question determines which of the different query types to use:
If you would like to see all occurrences of a particular word or phrase in your documents, consider creating a Text Search Query.
If you would like to see source content coded by a specific combination of nodes, or combination of nodes and attributes, consider creating a Coding Query.
If you would like to see patterns in the source content coded by one group of nodes by another group of nodes, or one group of nodes by a number of attribute values, consider creating a Matrix Coding Query.
If you would like to see source content that has been coded by a specific nodes and also has specific text, consider creating a Compound Query.
If you would like to compare the coding of two researchers or research teams, consider creating a Coding Comparison Query.
If you want to search only specific groups of items, you can achieve this by selecting these items as the query's scope. This can help you ask more targeted questions such as "Where is this word used in these documents?" or "Of the content in these sources, what is coded at the free node Motivation and the node Sense of Achievement (in the Personal Goal tree).
As you work with your data, you may find that particular words or phrases are being used in different ways. So, you might want to see all of the instances of these word or phrases to see how often and in what contexts they appear. A Text Search Query allows you to do this.
You could also use Text Search Queries to:
Explore the different use of words or phrases and their meanings in different contexts
Gather and code material early in your project. Later, when meanings become clearer, you can review and code on to finer categories.
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Be aware if you are using text search code that it may miss out some useful references (e.g. if the specific words searched for have not been used) and that it may gather references not needed (e.g. where the researcher rather than the respondent used the word). Nodes created by text search are not a substitute for nodes created and coded to by yourself. |
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In the Volunteering Sample Project 
In the Volunteering Sample Project, one of the questions that came up in reading through the data was "What motivates people to volunteer?".
The Text Search Query Motivation or Reason was created to gather all of the instances of the words 'motivation' or 'reason' and the paragraph surrounding them and code them to the free node motivation.
Not all of the instances of these words are relevant to our question of "What motivates people to volunteer?" Some were used in the wording of interview or focus group questions, and as such were not relevant and uncoded from the Motivations free node.
Once you have created nodes to represent the themes and categories in your data and coded the content of your sources to those nodes, you are likely to want to see if there are patterns in your coding. You can achieve this using Coding Queries.
Coding Queries can also be used to:
Gather material coded in combinations of categories to see what new meanings emerge, then rethink and recode them
Clarify a concept by asking where coding at this concept overlaps with coding at another or what is coded at some concepts but not others
Sort through your coding by groups defined by your attributes
In the Volunteering Sample Project
One of the questions of interest is "Do older volunteers have a personal goal of social interaction?".
This opinion was expressed by participants who saw an image of an older volunteer and assumed she volunteers for social interaction, but is this demonstrated in the responses from older participants themselves?
The Coding Query Greater than 50 yrs and Social Interaction gathers all content coded by cases where the attribute Age Range has a value greater than50-59 yrs and coded by the node Social Interaction (in the Personal Goal tree).
Matrix Coding Queries are a way of asking a wide range of questions about patterns in the data and gaining access to the content that shows those patterns. Matrix Coding Queries allow you to 'break down' one grouping of project items by another grouping of project items.
You can also use Matrix Coding Queries to:
Discover and explore patterns early in your project
Establish the strength of a pattern later in your project and analyze in detail the content of each cell
Pursue themes broken down by significant factors. Read first what the men say, then what the women say—can you discern noticeable differences?
In the Volunteering Sample Project
One of the questions that emerged through the interviews was "Do the images of volunteers differ with the context of their volunteering?".
Given that the relevant passages were coded at the nodes in the trees Images of volunteering and Contexts where appropriate, content coded at these nodes can be retrieved using a Matrix Coding Query.
The Matrix Coding Query Images of Volunteers and Contexts was created to see the different images of volunteers and the different contexts and the content coded by both.
Compound Queries combine the functionality of Text Search Queries and Coding Queries. They allow you to find source content that has been coded by a specific nodes and also has specific text. They also allow you to find particular text which has a particular proximity to other text.
Compound Queries allow you to:
Explore hunches about the use of particular words or phrases in content which is coded at nodes representing particular categories or concepts
Rethink and refine a category within your project based on the different use of words or phrases within them
Gather material where particular words have been used close to other words to see if there is a pattern emerging around their use
In the Volunteering Sample Project
Some of the images of volunteers depicted people volunteering in foreign countries. One of the questions that emerged was "Do the young people see volunteering in foreign countries as exciting?".
Given that the statements were coded at the Foreign countries node (in the tree node Contexts).
The Compound Query Foreign countries and excite gathers all content coded by cases where the attribute Age Range has a value equal to 20-29 yrs and coded by the context tree node 'foreign countries' and containing words beginning with 'excit'.
A coding comparison query enables you to compare coding done by 2 users or 2 groups of users.
It provides two ways of measuring inter-rater reliability or the degree of agreement between the users: through the calculation of the percentage agreement and Kappa coefficient.
In the Volunteering Sample Project
Two researchers - identified by their initials ST and MWO - have coded the sources in the Compare Coding set.
A Coding Comparison query has been created to check the coding consistency of these two team members - it is called Coding Consistency for ST and MWO.
The query compares all the coding that ST and MWO have done in the set of sources.
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Coding queries only search the coded content in your project. If you have only sparsely coded your sources or used nodes inconsistently, your coding query may not return the results you expect. This may not mean there is no association between these concepts in your data, just in their coding. |
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You have a number of options when choosing what to do with your query's results. The options you choose are very much driven by the reason you ran the query.
If you wanted to gather together material containing specific text or combinations of coding at different nodes, consider saving the results as a node.
If you wanted to locate items based on a specific criteria so that you can further analyze them as a whole, consider saving the results as a set.
You can then use the node or set you created from these results as the scope of another query. So you are building more subtle enquiry on the results of your first question.
