Contents

- 1 What is nominal ordinal and scale variables in SPSS?
- 2 What is the difference between scale and ordinal in SPSS?
- 3 Is age a scale variable?
- 4 What is an example of a nominal scale?
- 5 Is age ordinal or scale in SPSS?
- 6 Is weight nominal or ordinal?
- 7 What does the meter scale mean in SPSS?
- 8 Is the interval and ratio scale the same in SPSS?

## What is nominal ordinal and scale variables in SPSS?

In summary, nominal variables are used to “name,” or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one.

## What is the difference between scale and ordinal in SPSS?

Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as, a specific interval between each of its variable options.

**What are nominal ordinal and scale variables?**

Summary. In summary, nominal variables are used to “name,” or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one.

### Is age a scale variable?

Scale . A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

### What is an example of a nominal scale?

Nominal scale is qualitative in nature, which means numbers are used here only to categorize or identify objects. For example, football fans will be really excited, as the football world cup is around the corner!

**What is an example of ordinal measurement?**

Examples of ordinal variables include: socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”), satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”).

## Is age ordinal or scale in SPSS?

A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years, and income in thousands of Rupees, or score of a student in GRE exam.

## Is weight nominal or ordinal?

4. Nominal Ordinal Interval Ratio. Weight is measured on the ratio scale.

**How are categorical and scale variables treated in SPSS?**

Some procedures in SPSS treat categorical and scale variables differently. By default, variables with numeric responses are automatically detected as “Scale” variables. If the numeric responses actually represent categories, you must change the specified measurement level to the appropriate setting.

### What does the meter scale mean in SPSS?

In SPSS, for all practical purposes, it combines the Interval and Ratio scale into one and called Scale variable. We can see the meter scale kind of symbol for the scale variable, so it is showing that it’s a quantitative variable. However, the quantitative variables are either interval variables or ratio variables.

### Is the interval and ratio scale the same in SPSS?

In SPSS, for all practical purposes, it combines the Interval and Ratio scale into one and called Scale variable. We can see the meter scale kind of symbol for the scale variable, so it is showing that it’s a quantitative variable.

**Can a variable be ranked or quantified in SPSS?**

While some can be ranked as well as can be quantified. Upon importing the data for any variable into the SPSS input file, it takes it as a scale variable by default since the data essentially contains numeric values. It is important to change it to either nominal or ordinal or keep it as scale depending on the variable the data represents.