What follows is a step-by-step “demo” about how this would work.  This is created using synthetic data files for the purpose of showcasing the steps.  

Turning on the person case node selection.  To use the “person” feature on case nodes, a user first has to select in this feature.  Go to the Create tab of the NVivo ribbon.  In the classifications section, click on Node Classification.  The “New Classification” window will open.  

Select the second option:  “Add one or more predefined classifications to the project.”  Select “Person.”  Click "OK".  

The project is now ready for the uses of case nodes that are classified as “Person” ones.  

In the NVivo project, create a folder in the Sources -> Internals to house the interviews.  For our purposes, we used “Synthetic Interviews.”  In the NVivo ribbon, click on the External Data tab.  Click Documents in the Import area.  Browse to the desired files.  Highlight them all.  

Now that the interviews are ingested as “documents,” it is time to change them to “case nodes.”  (A "case node" is a special type of node which may be categorized as a type. This typing of the node means that it is a category or grouping of node.  As such, each "case node" then will include a set of descriptive information related to that case type.  The classification fields are specific to the node type.  For each node, though, the data in each field will be potentially quite unique.)  

In the Detail View, highlight all the interviews.  Right click for the dropdown menu.  Create As -> Create as Case Nodes  

To add or revise attributes to the “person” node classification, changes have to be made at the global level (so that the changes apply across the project).  Go to the bottom left menu in the Navigation View and click "Classifications".  Select the "Node Classifications" folder at the upper left area.  The attributes may be viewed in the drop-down under the Person node classification in the Details area. Right-click on any attribute to revise it.  

A person may code the data this way.  The other way to code this data is through the Classification -> Node Classification area of the Navigation View.  Click the Person node in the Detail View to activate it.  Right-click to open the Classification Sheet.

 

To use the classification sheet to code values, a researcher needs to first set the ranges of attribute values.  In the Navigation View, go to Classifications -> Node Classifications -> the selected Node Classification (Person).  Click on the plus sign to reveal the attributes for the respective node classification.  

Right-click on the attribute, and go to "Attribute Properties".  

The "Attribute Properties" window opens.  It has two tabs:  general (with the name and a description of the attribute and a dropdown menu of its data type: text, integer, decimal, date/time, date, time, or Boolean), or values (which enables pre-sets for types of values which may be applied to attributes).  

Click "Apply".  Click "OK".   (What is grayed out is not editable by the user.  Once "immutable" attribute data is ingested into NVivo, it is not editable--so as to protect the data against human manipulation.  If attribute data is brought in as "mutable," then the related data cells will be editable.  "Immutable" means unchangeable, and "mutable" means changeable.)  

Now, in the classifications sheet, the dropdown for each cell contains the four choices.  

There is one other way to both input the attributes (demographic variables) and their values simultaneously (to nodes coded with the person) by using an .xl or .xls file. To proceed, go to Classifications in the Navigation View.  Generally, it helps to use the data structure already understandable to the software tool.  In that spirit, it is a good idea to download the built-in Classification Sheet.  A screenshot of it follows.  

Notice that the first column contains what NVivo calls “hierarchical source names.”  These include the folder structure linking to the source.  

The cells in the above sheet may be populated with data.  

In the Detail View window, right-click and select "Import Classification Sheets".  

Re-import it.  Such sheets should be properly structured.  The nodes should run down the left column, and the variables should run across the top row.   

Make sure to indicate that the data worksheet is being ingested as having “hierarchical node names” (or hierarchical source names, whichever applies).  

Another approach involves automatically ingesting data with each record (individual) treated as a node and attribute columns about them included with the node.  To achieve this, go to the Create tab.  In the Classifications grouping, go to "Classify Nodes from Dataset."  The Wizard will open and enable the classifying of dataset information as individual records.  This is especially useful for online survey data, for example, which tends to offer structured data.  The pink highlight shows the rows which will be treated as single nodes, and the green highlight shows the different attributes.  

In the next window, researchers are asked to set the location for the new nodes, whether this is to an existing node or new node, an existing folder or new folder.  Then, users have to decide the type of node classification for these nodes (such as "person" or "organization"). The classification types for the nodes have to be made ahead of time and cannot be made on-the-fly in this sequence.  

In the next step, the user needs to define the column which contains the names (usually A1).  If researchers want, he / she / they may choose to create a node hierarchy based on the attribute data describing the nodes.  

Then, in the fourth step, researchers may select the columns with attribute values describing the individual nodes.  They will be able to see a preview of the resulting node classifications.  The screenshot below was created without using an actual dataset, so this is not as representative as it could be.  

For more information on “Import (or export) classification sheets,” please link to this source.  QSR International also has a strong source describing how to organize demographic data.  

As one more side note, some of the categories may be used as "dummy variables."  A binary categorical variable (such as "male" or "female"; present or absent; specific experience or absence of that experience) may be used in a Boolean way to look for comparative differences. These will not be used in the same way as in a linear regression, but they are still helpful to group respondents in order to look for similarities and differences in responses and other patterning.