Export Data Dictionary 

How to read this page

Every variable you define in your protocol is exported under its variable name from the codebook. In CSV files, most variable types become a single column named after the variable; categorical and layout variables expand into several columns, as described below. In GraphML files, each variable is declared with a <key> element whose attr.name is the variable name and whose attr.type tells analysis software how to parse the values.

For the overall file structure—which files an export produces and how they link together—see Data Export. This page focuses on how individual values are encoded.

Variable types at a glance

The sections below give the full details for each type, including how missing values appear.

Per-type details

Text

A text variable becomes one CSV column named after the variable, containing the participant's response as entered. Values containing a comma, a quotation mark, or a line break are wrapped in double quotes (with internal quotes doubled), following standard CSV rules. As a safeguard against spreadsheet formula injection, any value beginning with =, +, -, @, or a tab character is prefixed with a single quote (') so spreadsheet applications treat it as literal text. In GraphML, text values are exported with attr.type="string".

Number

A number variable becomes one CSV column containing the numeric value as entered, with no rounding or reformatting. In GraphML, the attr.type is detected from the values in your data: int when every value is a whole number, double when any value has a decimal part, and string when the values are mixed or absent.

Boolean

A boolean variable becomes one CSV column containing true or false (lowercase). In GraphML, it is declared with attr.type="boolean" and each value is written as true or false. A question the participant never answered produces an empty CSV cell, and no value at all in GraphML.

Ordinal

An ordinal variable stores the value of the selected option (not its label), which may be a string or a number depending on how you defined the options in Architect. The CSV column contains that value directly. In GraphML, the attr.type is detected from the data in the same way as for number variables: all-integer option values produce int, decimal values produce double, and string option values produce string.

Categorical

Because participants can select multiple options, a categorical variable is expanded into one column per option, named variable_name_optionValue—for example, a support_type variable with options emotional and financial produces support_type_emotional and support_type_financial. Each column contains true if that option was selected and false otherwise. If the participant never answered the question, every option column contains false in CSV.

In GraphML, the same expansion happens: one <key> per option with attr.type="boolean" and attr.name set to variable_name_optionValue. The machine-readable id of each key is the variable's internal identifier followed by a hash of the option value (key ids must be XML NMTOKENs, which arbitrary option values are not). If the participant never answered, no values are written for any of the option keys.

Scalar

A scalar variable (collected with the Visual Analog Scale) stores a number on a normalized 0–1 scale. The CSV column contains that number, and GraphML declares it with attr.type="float".

Date

A date variable stores an ISO-formatted date string whose precision matches the variable's resolution setting: 1989-07-21 (full), 1989-07 (month), or 1989 (year). The CSV column contains this string as-is, and GraphML declares it with attr.type="string".

Layout

A layout variable stores a node's position on a Sociogram canvas as normalized coordinates between 0 and 1, with the origin at the top-left. It is split into two columns in CSV, named with lowercase suffixes:

• variable_name_x — horizontal position (0 = left edge, 1 = right edge)
• variable_name_y — vertical position (0 = top edge, 1 = bottom edge)

If the use screen layout coordinates export option is enabled, the export keeps these normalized columns and adds two more, named with camelCase suffixes: variable_name_screenSpaceX and variable_name_screenSpaceY. These contain pixel values rounded to two decimal places. Screen-space X is the normalized x multiplied by the screen width; screen-space Y is inverted— calculated as (1 − y) × screen height—so it is measured from the bottom of the screen, matching the convention of most graph visualization software.

In GraphML, the same split happens but with uppercase suffixes on the human-readable names: two keys with attr.name values variable_name_X and variable_name_Y, both declared as attr.type="double" (plus variable_name_screenSpaceX and variable_name_screenSpaceY when screen layout coordinates are enabled).

A node that was never positioned produces empty cells in CSV and no values in GraphML.

Location

A location variable (collected with the Geospatial interface) stores a text identifier for the map area the participant selected, taken from the property of the GeoJSON shapefile that the stage is configured to record (or the special value outside-selectable-areas when a selection was outside the selectable regions). It becomes one CSV column containing that string, and GraphML declares it with attr.type="string".

Identifier and metadata columns

Alongside your variables, each CSV file contains identifier columns that link the files together:

• The ego file begins with networkCanvasEgoUUID, networkCanvasCaseID, networkCanvasSessionID, networkCanvasProtocolName, sessionStart, sessionFinish, sessionExported, APP_VERSION, and COMMIT_HASH, followed by your ego variables. Session timestamps are ISO 8601 strings.
• Alter files begin with nodeID (a sequential number unique within the export), networkCanvasEgoUUID (the join key to the ego file), and networkCanvasUUID (the alter's permanent identifier).
• Edge files begin with edgeID, from, to (which reference nodeID values in the alter files), networkCanvasEgoUUID, networkCanvasUUID, and the permanent endpoint identifiers networkCanvasSourceUUID and networkCanvasTargetUUID.

Missing values

An unanswered question appears as an empty cell in CSV and as an omitted value in GraphML (the <key> declaration may still be present, but no <data> element is written for that entity). The one exception is categorical variables in CSV, where an unanswered question produces an explicit false in every option column—so treat an all-false row with care if the question was optional.

Encrypted variables

If your protocol uses the experimental Anonymisation interface, encrypted variables are never exported in readable form: every exported value for an encrypted variable is the literal string ENCRYPTED, in both CSV and GraphML.

Variable types in analysis

Different variable types call for different analytical approaches once your data is loaded:

• Categorical and ordinal variables suit frequency tables and cross-tabs, and support chi-square tests and logistic regression. Remember that exports contain option values, so recode them to labeled factors or categories in your analysis software if you want readable output.
• Boolean variables (including the expanded categorical option columns) can be treated as categorical with two levels, or converted to 0/1 for use in regression models.
• Number and scalar variables support descriptive statistics (mean, median, standard deviation) as well as correlation, regression, and t-tests. Scalar values are already normalized to 0–1, which simplifies comparison across scales.
• Text variables are best suited to qualitative approaches such as content analysis and coding.

Related pages

• Data Export — file formats, export options, and loading data into R and Python
• Variables — defining variables when designing your protocol