Measurement levels

The measurement level helps characterize the information represented by each data field, and may determine how a given field is used in rules, modeling, or other applications. The measurement level can be specified in the Project Data Model for a data source; for example, you may want to set the measurement level for an integer field with values of 0 and 1 to Flag, to indicate that 0 = False and 1 = True. Alternatively, you can change the level in the Data Source Editor dialog when you specify the input fields to be used. For more information, see Selecting input fields.

The following measurement levels are available:

Default Data whose storage type and values are unknown (for example, because they have not yet been read) are displayed as <Default>.
Continuous Used to describe numeric values, such as a range of 0–100 or 0.75–1.25. A continuous value can be an integer, real number, or date/time.
Categorical Used for string values when an exact number of distinct values is unknown. This is an uninstantiated data type, meaning that all possible information about the storage and usage of the data is not yet known. Once data have been read, the measurement level will be Flag, Nominal, or Typeless , depending on the maximum number of members for nominal fields specified in the Project Properties dialog box.
Flag Used for data with two distinct values that indicate the presence or absence of a trait, such as true and false, Yes and No or 0 and 1. The values used may vary, but one must always be designated as the "true" value, and the other as the "false" value. Data may be represented as text, integer, real number, date, time, or timestamp.
Nominal Used to describe data with multiple distinct values, each treated as a member of a set, such as small/medium/large. Nominal data can have any storage—numeric, string, or date/time. Note that setting the measurement level to Nominal does not automatically change the values to string storage. For information about setting the maximum members allowed for nominal fields, see Properties.
Ordinal Used to describe data with multiple distinct values that have an inherent order. For example, salary categories or satisfaction rankings can be typed as ordinal data. The order is defined by the natural sort order of the data elements. For example, 1, 3, 5 is the default sort order for a set of integers, while HIGH, LOW, NORMAL (ascending alphabetically) is the order for a set of strings. The ordinal measurement level enables you to define a set of categorical data as ordinal data for the purposes of visualization, model building, and export to other applications (such as IBM® SPSS® Statistics) that recognize ordinal data as a distinct type. You can use an ordinal field anywhere that a nominal field can be used. Additionally, fields of any storage type (real, integer, string, date, time, and so on) can be defined as ordinal.
Typeless Used for data that does not conform to any of the above types, for fields with a single value, or for nominal data where the set has more members than the defined maximum. It is also useful for cases in which the measurement level would otherwise be a set with many members (such as an account number). When you select Typeless for a field, the role is automatically set to None, with Record ID as the only alternative. The default maximum size for sets is 250 unique values. This number can be adjusted or disabled in the Project Properties dialog box, which can be accessed from the toolbar icon.
Collection Used to identify non-geospatial data that is recorded in a list. A collection is effectively a list field of zero depth, where the elements in that list have one of the other measurement levels.
Geospatial Used with the List storage type to identify geospatial data. Lists can be either List of Integer or List of Real fields with a list depth that is between zero and two, inclusive.