Various kinds of rating scales have been developed to measure attitudes directly (i.e. the person knows their attitude is being studied). The most widely used is the Likert Scale.
Likert (1932) developed the principle of measuring attitudes by asking people to respond to a series of statements about a topic, in terms of the extent to which they agree with them, and so tapping into the cognitive and affective components of attitudes.
A series of statements is prepared, relevant to the particular attitude to be measured, with half the statements being favourable and half unfavourable. Statements are presented in random order and participants rate each statement on a five-point (sometimes seven- point) scale, to which numerical values are attached.
In it final form, the Likert Scale is a five (or seven) point scale which is used to allow the individual to express how much they agree or disagree with a particular statement.
For example:
I believe that ecological questions are the most important issues facing human beings today.
Strongly agree / agree / don’t know / disagree / strongly disagree
Each of the five (or seven) reponses would have a numerical value which would be used to measure the attitude under investigation.
Likert Scales have the advantage that they do not expect a simple yes / no answer from the respondent, but rather allow for degrees of opinion, and even no opinion at all. Therefore quantitative data is obtained, which means that the data can be analysed with relative ease.
However, like all suveys, the validity of Likert Scale attitude measurement can be compromised due the social desirability. This means that individuals may lie to put themselves in a positive light. For example, if a likert scale was measuring discrimination, who would admit to being racist?
Agreement |
Frequency |
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Importance |
Likelihood |
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• Summarise using a median or a mode (not a mean); the mode is probably the most suitable for easy interpretation.
• Display the distribution of observations in a bar chart (it can’t be a histogram, because the data is not continuous).