Datasets

If you auto code this dataset, you must select the columns (or values) that you want to use to create your nodes.

You cannot change the analysis type (codable or classifying) of a column after import, so you should decide how you want to use your data before you import a dataset.

Open-ended questions that contain data you want to analyze are created and coded to nodes (codable)—for example, responses to open-ended survey questions such as How do you think we can reduce our carbon emissions?

Close-ended questions that describe your data (metadata) are created as attributes (classifying)—for example, the ID number, Age, Gender and Annual Income of your survey respondents. Values in classifying fields:

• Provide context when you view coded dataset content in a node.
• Can be used to build case node structures that group your codable content—for example, by Age or Sex.
• Can be used to create and classify nodes that represent the subjects (cases) of your research. For example, if you create a 'person' node for a survey respondent, you can use the classifying field values Age or Sex as attribute values on the node.

If you want to create nodes that represent the subjects (cases) in your research—you will need a unique identifier for each subject in your data. If your data does not contain a unique identifier that you can use as a node name, you could add a unique ID number for each subject. This identifier should be imported as a classifying field.

You cannot add additional rows or columns to a dataset after import, so it is important to gather your data before you start the import operation.

For each dataset that you want to create, your data must be gathered into a Microsoft Excel spreadsheet (.xlsx or .xls).

You cannot select multiple worksheets (within a spreadsheet), so the data you want to import must be gathered into a single worksheet.

The maximum amount of data that can be imported into a single dataset is 256 fields (columns) and 100,000 records (rows).

1. Open the spreadsheet file that contains your data in Microsoft Excel.
2. Ensure that the data is gathered into a single worksheet.
3. Review your data as described below:

   Element	Description
   Blank rows	Remove any blank rows within the data.
   Extraneous content	Your file should contain only the rows you want to import, and (optionally) a 1st row containing field labels. Any other content should be removed.
   Data types	Make sure every value in a field contains the same type of data. During the import operation, NVivo scans the first 25 rows of your file to determine what data types are appropriate for each field. If you choose to import a field using the date, time, date/time, Boolean, integer or decimal data types, all the rows in your text file must contain valid data for that data type. If a row after the 25th line, contains invalid data, the import operation will terminate with errors.
   Merged cells	Merged cells can cause errors during the import operation. We recommend that you do not import spreadsheets containing merged cells.
   Calculated values	If a cell displays a calculated value, the displayed value (not the formula) is imported into the dataset.
   Error values	If any of the cells in your worksheet display error values such as #NUM or #DIV, you should correct them before you start the import operation. Columns containing error values can only be imported as text fields.
   Buttons and checkboxes	If any cells contain controls such as buttons or checkboxes the spreadsheet cannot be imported. You should remove these controls before import.

4. Close the spreadsheet. Keeping the file open may cause errors during the import operation.

NVivo provides an assistant that guides you through the import process. The assistant examines the data you are importing and helps you to ensure that the data is imported the way that you want.

1. On the Data tab, in the Import group, click Dataset.
2. Select the spreadsheet file that contains the data you want to import.
3. Follow the instructions in the Import Dataset Assistant.

You can double-click a dataset in List View to open it in Detail View.

When you open a dataset, it opens in Table View (below)—the records and fields are displayed in a grid:

1 Classifying fields—contain information about your data—for example, the age and sex of survey respondents. Classifying fields have a gray background.

2Codable fields—contain the information you want to analyze—for example, responses to open-ended survey questions. Codable fields have a white background.

When the dataset is open in Detail View, you can switch between Table View (view all records) and Form View (view one record at a time).

Click the Expand button (at the top right of Detail View) to make more room for working with your data. Customize the workspace

Each row in a dataset has a unique record ID, based on the order in which it is imported. The ID is the first column in the Table pane.

Datasets of more than 100 records are divided into pages. Each page can contain up to 100 records. You can navigate from page to page by using the navigation buttons—you can move to the first, previous, next or last page.

To navigate within a page, you can use scroll bars to move up and down, or left and right

When working in a dataset file you can:

• Code or query the text in codable columns
• Auto code to organize the data—for example, you could gather the survey responses for each participant in a case node

After importing a dataset youcan use a number of techniques to analyze the data—for example, you could:

• Code the content—select the text in a codable field and code at new or existing nodes.
• Run a Word Frequency query to get a feel for what respondents are saying.
• Auto code the dataset to make a node for each survey question and gather all the answers—a useful starting point for further exploration. Then auto code the dataset to make a case node for each respondent and gather all of a respondent's answers.

When you import data from surveys, spreadsheets or text files, you choose identify closed-ended and open-ended questions. NVivo uses this information to identify the responses as 'codable' or 'classifying'.

You cannot change the analysis type (codable or classifying) of a response after import, so you should decide how you want to use your data before you import a dataset.

Fields that contain data that you intend to code and analyze should be stored as codable fields—for example, responses to open-ended survey questions, such as How do you think we can reduce our carbon emissions?

Fields that describe your data (metadata) should be stored as classifying fields—for example, the ID number, Age, Sex and Annual Income of your survey respondents. Values in classifying fields:

• Provide context when you view coded dataset content in a node.
• Can be used to build case node structures that group your codable content—for example, by Age or Sex
• Can be used to create and classify nodes that represent the subjects (cases) of your research. For example, if you create a 'person' node for a survey respondent, you can use the classifying field values Age or Sex as attribute values on the node.