Data Types and Measurement Scales

MIDAS automatically infers data types and measurement scales when loading data. Data type and measurement scale are independent concepts: data type describes how the value is represented (number, date, text, etc.), while measurement scale describes its analytical properties (nominal, ordinal, interval, ratio). Measurement scales filter which items appear in Basic Statistics — statistics that do not fit the scale are not displayed — so verify the scale is correct after loading.

See Data Preparation and Import for instructions on loading data and changing types.

Measurement Scales

Measurement scales classify "what operations are meaningful for a given data". Based on Stevens' (1946) four levels of measurement. The scales form a hierarchy — nominal < ordinal < interval < ratio — where higher scales support more operations and include all operations available to lower scales.

Nominal Scale

Data representing categories with no meaningful order. Only equality ( $=$ , $\neq$ ) is meaningful.

Examples: Gender (male/female), colors (red/blue/green), country names

Ordinal Scale

Categories with meaningful order, but no defined interval between values. Comparisons ( $<$ , $>$ ) are meaningful.

Examples: Satisfaction (low/medium/high), grade level (1st/2nd/3rd year), grades (A/B/C/D)

Interval Scale

Equally spaced numeric data where differences are meaningful, but ratios are not. The zero point is arbitrary.

Examples: Temperature (Celsius), year (AD)

The difference between 20°C and 10°C is a meaningful 10°C
However, 20°C is not "twice as warm" as 10°C

Ratio Scale

Equally spaced numeric data with a true zero point. Both differences and ratios are meaningful.

Examples: Height, weight, price, age

The difference between 20kg and 10kg is a meaningful 10kg
Furthermore, 20kg is "twice as heavy" as 10kg

Ratio scale is not assigned by auto-inference. Whether zero represents a true origin depends on the meaning of the data and cannot be determined from values alone. For data with a true zero point such as height or weight, right-click the column in the Data Table and set ratio scale from Edit Scale of Measurement.

Auto-Inference from Data Types to Measurement Scales

MIDAS determines data types on import and automatically assigns measurement scales. Auto-inference is only an initial value — after loading, data type and measurement scale can be changed independently.

Data Type	Inferred Scale	Reason
boolean	Nominal	true/false are unordered categories
int64	Interval	Whether zero represents a true origin depends on the data
float64	Interval	Same as integers
date	Interval	Date differences are meaningful, but ratios typically are not
datetime	Interval	Same as dates
string	Nominal	Text is treated as categories
enum	Nominal	Can be changed to ordinal when order is defined

Auto-inference may not match the actual meaning of the data. For example, postal codes and ID columns are loaded as numeric and assigned interval scale, but they are semantically nominal. For these columns, right-click the column in the Data Table and select Edit Scale of Measurement to change to the appropriate scale. Note that numeric strings with leading zeros (0060001, 001, etc.) are automatically loaded as string, so leading zeros are preserved.

For ordered categories stored as text, create an Enum definition and convert the column to Enum type. Defining the order in an Enum does not automatically make the column ordinal, so after conversion, change to ordinal from Edit Scale of Measurement.

References

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680. https://www.jstor.org/stable/1671815