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Likert scales were developed in 1932 as the familiar five-point bipolar response that most people are familiar with today.These scales range from a group of categories—least to most—asking people to indicate how much they agree or disagree, approve or disapprove, or believe to be true or false.Some examples of category groups appear in Table 1. For example, surveys might be used to gauge customer perception of product quality or quality performance in service delivery. Seaman Surveys are consistently used to measure quality.An underlying reason for analyzing ordinal data as interval data might be the contention that parametric statistical tests (based on the central limit theorem) are more powerful than nonparametric alternatives.Also, conclusions and interpretations of parametric tests might be considered easier to interpret and provide more information than nonparametric alternatives.

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There’s really no wrong way to build a Likert scale.

The most important consideration is to include at least five response categories.

However, treating ordinal data as interval (or even ratio) data without examining the values of the dataset and the objectives of the analysis can both mislead and misrepresent the findings of a survey.

To examine the appropriate analyses of scalar data and when its preferable to treat ordinal data as interval data, we will concentrate on Likert scales.

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Likert scales are a common ratings format for surveys.Respondents rank quality from high to low or best to worst using five or seven levels.