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Using NVivo: An Unofficial and Unauthorized Primer

Shalin Hai-Jew, Author

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Data Query: Group Query (Advanced)

Group Query (Advanced)

The advanced “group query” in NVivo requires that the project have a range of data already ingested, coded, categorized, related (if relevant), and labeled.  The coding, classification, and labeling may be done by both the human researchers and by the machine (“NV” for "NVivo"). Some classification and annotation information may be ingested as part of the capture of metadata from third-party bibliographic tools integrated into NVivo.

The “group query” enables a wide range of ways to call up and view the data based on selected “slices”.  Items may be called up based on coding, attribute value, (defined) relationships, “see also links,” model items, and models.  The “group query” tool showcases NVivo’s powers of data management and its creative search-and-find functions.  (Remember that NVivo works like a database for semi-structured, structured, and "unstructured" data.) 

Some Practical Uses of the Group Query

There are a variety of practical uses of the “group query” feature. A few are listed below.

  • Seeing what is in the data.  In a general sense, the group query feature enables the creation of clarity with the collected data by enabling a researcher to assess the types of information that have been ingested into the internal and external source files folders.  It enables a researcher to call up particular interviews and to see what sorts of nodes were used to code them; this “group coding” feature also enables the converse, the analysis of what sources were tied to a particular node (or nodes).  This tool may help researchers get a sense of what is missing from the data—such as gaps in information not currently addressed. 
  • Grouping interview or survey or focus group data.  Attribute data may be used to break out respondents to a survey by demographic information (age, gender, education, socio-economic status (SES), and other variables), geography (places of birth, addresses, and other variables), and other captured features (assuming this data was ingested into the project as attributes).  
  • Identifying relationships between concepts.  Conceptual relationships between particular nodes may be identified and probed in more depth. 
  • Exploring machine- and manual-drawn models.  Models may be explicated in more depth by exploring the constituent sources that inform each model.  Models that have been created to address particular topics may be located and called up for analysis. 
  • Testing hypotheses and hunches.  Another usage may involve the testing of hypotheses and hunches by posing particular questions and then collating the data that may answer those questions.  

Conducting a Group Query (Advanced) in NVivo

To start a group query, go to the NVivo ribbon.  In the Create space, click on Group, the last of the advanced data queries functionalities in NVivo.  The Group Query window opens.  

A check-mark at the top left, “Add to project,” will enable the saving of macro for a re-run in the Queries area (in the “Queries” subfolder).   

In the Group Query Criteria tag, users have a range of choices of what to look for:  
  • Items Coding
  • Items Coded At
  • Items by Attribute Value
  • Relationships
  • See Also Links
  • Model Items (and) 
  • Models
What is selected will affect the types of information that needs to be input for the “Scope” and “Range.”  “Scope” items are more particular descriptors of the items defined in the Group Query, from something as granular as a document to something as general as All Sources in the project.  “Range” items define the items that have associations (connections such as through coding, attribute value, relationships, links, or models) with the “Scope” items.  These may be items selected from the entire project, parts of the project, particular folders, or particular selected items. 

Understanding the Results of a Group Query

When you run a Group query the results are displayed in Detail View. The Result tab is displayed by default.  The resulting table shows Scope items at the top level of the Summary view.  The sub-files below are the Range items.  The Finds column shows the number of Range items linked to the particular Scope item.  At the far right is the Connection Map text enables a data visualization view (a reductionist one) of the data. 

The following table was created from a group query with items coded by attribute values (source type), from All Nodes and Node Matrices, from All Sources, and Coded by Any User. 

An exported spreadsheet

The created summary matrix may be exported for clearer reading and further analysis.  Right-click anywhere in the Summary view.  Select Export List.  Then, save out the file as an .xlsx (Excel) file.  (Other queries may be conducted outside of NVivo to maximize the understanding of what may be learned from the information.)  

Saving the Group Query results

Researchers may save the query results by exporting and saving the file.  It seems that this is available in preview mode, and the results are not directly saveable.  

The connection map data visualization 

A “connection map” visualization is another way to view the findings.  This data visualization is a simple ring lattice graph.  In a ring lattice graph, the nodes or entities are listed on the circle, and there are lines (edges, arcs, or relationships) that connect particular nodes to other nodes.  

The “Scope” items are the items listed in the Group query, and these are the labeled nodes with multiple connections. In boldface are the labels for the types of connections, such as “attribute values,” “sources,” or others.  The “Range” items are those that have associations with the scope items.  Both the “Scope” and “Range” items are listed on the outer edge of the ring lattice graph as nodes.  The lines or “edges” are the connections between the “Scope” objects and the “Range” ones.  

Do enjoy the aesthetics of the data visualizations, too. 
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