Analysis likert scale data in stata forex

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Surveys are consistently used to measure quality. For example, surveys might be used to gauge customer perception of product quality or quality performance in service delivery. 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. Nominal data: The weakest level of measurement representing categories without numerical representation.

Ordinal data: Data in which an ordering or ranking of responses is possible but no measure of distance is possible. Interval data: Generally integer data in which ordering and distance measurement are possible. Ratio data: Data in which meaningful ordering, distance, decimals and fractions between variables are possible. Data analyses using nominal, interval and ratio data are generally straightforward and transparent. Analyses of ordinal data, particularly as it relates to Likert or other scales in surveys, are not. This is not a new issue.

The adequacy of treating ordinal data as interval data continues to be controversial in survey analyses in a variety of applied fields. Also, conclusions and interpretations of parametric tests might be considered easier to interpret and provide more information than nonparametric alternatives. 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. Basics of Likert Scales Likert scales were developed in 1932 as the familiar five-point bipolar response that most people are familiar with today.

3 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. There’s really no wrong way to build a Likert scale. The most important consideration is to include at least five response categories. The seven-point scale has been shown to reach the upper limits of the scale’s reliability. 4 As a general rule, Likert and others recommend that it is best to use as wide a scale as possible.

You can always collapse the responses into condensed categories, if appropriate, for analysis. Rensis Likert’s original paper clearly identifies there might be an underlying continuous variable whose value characterizes the respondents’ opinions or attitudes and this underlying variable is interval level, at best. Analysis, Generalization To Continuous Indexes As a general rule, mean and standard deviation are invalid parameters for descriptive statistics whenever data are on ordinal scales, as are any parametric analyses based on the normal distribution. Nonparametric procedures—based on the rank, median or range—are appropriate for analyzing these data, as are distribution free methods such as tabulations, frequencies, contingency tables and chi-squared statistics. Kruskall-Wallis models can provide the same type of results as an analysis of variance, but based on the ranks and not the means of the responses. Given these scales are representative of an underlying continuous measure, one recommendation is to analyze them as interval data as a pilot prior to gathering the continuous measure.

Table 2 includes an example of misleading conclusions, showing the results from the annual Alfred P. Sloan Foundation survey of the quality and extent of online learning in the United States. Respondents used a Likert scale to evaluate the quality of online learning compared to face-to-face learning. If these data were analyzed using means, with a scale from 1 to 5 from inferior to superior, this separation would be lost, giving means of 2. 7 for these three years, respectively.

Under what circumstances might Likert scales be used with interval procedures? Also, the scale item should be at least five and preferably seven categories. Another example of analyzing Likert scales as interval values is when the sets of Likert items can be combined to form indexes. However, there is a strong caveat to this approach: Most researchers insist such combinations of scales pass the Cronbach’s alpha or the Kappa test of intercorrelation and validity. Also, the combination of scales to form an interval level index assumes this combination forms an underlying characteristic or variable. Alternative Continuous Measures for Scales Alternatives to using a formal Likert scale can be the use of a continuous line or track bar.

For pain measurement, a 100 mm line can be used on a paper survey to measure from worst ever to best ever, yielding a continuous interval measure. In the advent of many online surveys, this can be done with track bars similar to those illustrated in Figure 1. The respondents here can calibrate their responses to continuous intervals that can be captured by survey software as continuous values. Conclusion Your initial analysis of Likert scalar data should not involve parametric statistics but should rely on the ordinal nature of the data. While Likert scale variables usually represent an underlying continuous measure, analysis of individual items should use parametric procedures only as a pilot analysis. Combining Likert scales into indexes adds values and variability to the data. If the assumptions of normality are met, analysis with parametric procedure can be followed.