Measurement Levels
SPSS recognizes three primary levels of measurement. Each determines what statistical tests you can perform.
Nominal
Categorical data without any intrinsic order or ranking.
Examples
- Gender
- Region
- ID Codes
Ordinal
Ordered categories, but the distance (interval) between them is not uniform.
Examples
- Satisfaction
- Rankings
- Likert Scales
Scale
Continuous data with equal intervals. Includes Interval and Ratio data.
Examples
- Age
- Test Scores
- Income
Note: SPSS combines Interval and Ratio into this single category.
🔠Variable Types in SPSS
Numeric
Used for numbers and calculations.
score = 89
String
Used for text characters.
name = "Alice"
Date
Represents calendar dates/times.
2025-10-01
Variable View Properties
Defining your dataset structure in the Variable View interface.
| Property | Description |
|---|---|
| Name |
Short identifier, no spaces. e.g., age, q1_satisfaction |
| Type | Numeric, String, Date, etc. |
| Label | Full description of the variable. "Student satisfaction with course" |
| Values | Codes assigned to categories. 1 = Male, 2 = Female |
| Measure | Nominal, Ordinal, or Scale. |
Why It Matters
Using the correct data type and measurement level is not just a formality:
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Ensures accurate statistical testing
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Prevents critical errors in analysis
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Allows SPSS to automatically suggest the correct procedures