**Research Scales**

Types of scales used in measuring behavior

Nominal

Ordinal

Interval

Ratio

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Types of scales used in measuring behavior

The lowest measurement level you can use, from a statistical point of view, is a nominal scale.

A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure.

In research activities a YES/NO scale is nominal. It has no order and there is no distance between YES and NO.

The statistics which can be used with nominal scales are in the non-parametric group. The most likely ones would be:

Mode

cross tabulation - with chi-square

There are also highly sophisticated modeling techniques available for nominal data.

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**Ordinal**

An ordinal scale is next up the list in terms of power of measurement.

The simplest ordinal scale is a ranking. When a market researcher asks you to rank 5 types of ice creams from most flavorful to least flavorful, he/she is asking you to create an ordinal scale of preference.

There is no objective distance between any two points on your subjective scale. For you the top ice cream may be far superior to the second preferred ice cream but, to another respondent with the same top and second beer, the distance may be subjectively small.

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Ordinal data would use non-parametric statistics. These would include:

Median and mode

rank order correlation

non-parametric analysis of variance

rank order correlation

non-parametric analysis of variance

Modelling techniques can also be used with ordinal data.

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**Interval**

The standard survey rating scale is an interval scale.

When you are asked to rate your satisfaction with a piece of software on a 7 point scale, from Dissatisfied to Satisfied, you are using an interval scale.

It is an interval scale because it is assumed to have central points between each of the scale elements. This means that we can interpret differences in the distance along the scale. We contrast this to an ordinal scale where we can only talk about differences in order, not differences in the degree of order.

Interval scale data would use parametric statistical techniques:

Mean and standard deviation

Correlation - r

Regression

Analysis of variance

Factor analysis

Correlation - r

Regression

Analysis of variance

Factor analysis

Plus a whole range of advanced multivariate and modelling techniques

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**Ratio**

A ratio scale is the top level of measurement and is not often available in social research.

The factor which clearly defines a ratio scale is that it has a true zero point.

The simplest example of a ratio scale is the measurement of length (disregarding any philosophical points about defining how we can identify zero length).

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Likert scales are a commonly used way of measuring opinions and attitudes and apply to a range of disciplines. They measure the extent to which participants agree or disagree with a given statement, and typically range from 1 (strongly disagree) to 5 (strongly agree) with a neutral point in the middle (e.g. neither agree nor disagree). They are often analyzed under the assumption that they are interval in nature

Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement. Sometimes an even-point scale is used, where the middle option of "Neither agree nor disagree" is not available

The format of a typical five-level Likert item, for example, could be:

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