Likert scales, frequently used in survey research, measure respondents' attitudes by asking how strongly they agree or disagree with a set of questions or statements. A typical scale might consist of the following response categories: strongly agree, agree, undecided, disagree, and strongly disagree. Numerical coding of the responses allows researchers to analyze Likert scale data with descriptive and inferential statistical methods.

Analyzing Likert Scale Data

Code the responses by assigning a numerical value to each. Suppose, for example, we ask respondents how much they agree or disagree with ten actions taken by Congress. The response categories are strongly agree, agree, unsure/undecided, disagree, and strongly disagree. We might code the responses as follows: strongly agree = 1, agree = 2, undecided = 3, disagree = 4, and strongly disagree = 5. You can enter these data on an Excel spreadsheet for analysis.

Depict the range of responses visually with bar charts that display the number and percentage of respondents who expressed agreement, disagreement, etc., with each position covered in your survey.

Summarize your Likert scale data using descriptive statistics. Exercise caution in this step. A common mistake is to calculate a numerical average, or mean value of the coded responses. This is not a valid method for analyzing Likert scale data, which are ordinal in nature. As an alternative, summarize your Likert scale data with the mode, or the most frequent response. For example, if “agree” was the most frequent response to an item, the mode would be the numerical value assigned to that response.

Explore the data further with inferential statistical techniques. Many such techniques exist, and the most appropriate one will depend on the exact nature of your study. Analysis of variance is one approach. For the example in Step 1, you could analyze responses with the respondent’s gender as an independent variable, examining the difference in responses between male and female survey participants. Factor analysis, which tries to explain responses as a function of underlying factors, is also used frequently.