Measurement levels (SPSS Modeler)
Measurement levels The measure, also referred to as measurement level, describes the usage of data fields in SPSS Modeler. You can specify the Measure in the node properties of an import node or a Type node. For example, you may want to set the measure for an integer field with values of 1 and 0 to Flag. This usually indicates that 1 = True and 0 = False. Storage versus measurement. Note that the measurement level of a field is different from its storage type, which indicates whether data is stored as a string, integer, real number, date, time, or timestamp. While you can modify data types at any point in a flow by using a Type node, storage must be determined at the source when reading data in (although you can subsequently change it using a conversion function). The following measurement levels are available: * Default. Data whose storage type and values are unknown (for example, because they haven't 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. After data is read, the measurement level will be Flag, Nominal, or Typeless, depending on the maximum number of members for nominal fields specified. * 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 doesn't automatically change the values to string storage. * 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 doesn't conform to any of the Default, Continuous, Categorical, Flag, Nominal, or Ordinal types, for fields with a single value, or for nominal data where the set has more members than the defined maximum. Typeless 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. * 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's between zero and two, inclusive. You can manually specify measurement levels, or you can allow the software to read the data and determine the measurement level based on the values it reads. Alternatively, where you have several continuous data fields that should be treated as categorical data, you can choose an option to convert them. See [Converting continuous data](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/type_convert.html).