The Likert scale is commonly used in survey research. It is often used to measure respondents' attitudes by asking the extent to which they agree or disagree with a particular question or statement. A typical scale might be "strongly agree, agree, not sure/undecided, disagree, strongly disagree." On the surface, survey data using the Likert scale may seem easy to analyse, but there are important issues for a data analyst to consider.
Get your data ready for analysis by coding the responses. For example, let's say you have a survey that asks respondents whether they agree or disagree with a set of positions in a political party's platform. Each position is one survey question, and the scale uses the following responses: Strongly agree, agree, neutral, disagree, strongly disagree. In this example, we'll code the responses accordingly: Strongly disagree = 1, disagree = 2, neutral = 3, agree = 4, strongly agree = 5.
Remember to differentiate between ordinal and interval data, as the two types require different analytical approaches. If the data are ordinal, we can say that one score is higher than another. We cannot say how much higher, as we can with interval data, which tell you the distance between two points. Here is the pitfall with the Likert scale: many researchers will treat it as an interval scale. This assumes that the differences between each response are equal in distance. The truth is that the Likert scale does not tell us that. In our example here, it only tells us that the people with higher-numbered responses are more in agreement with the party's positions than those with the lower-numbered responses.
Begin analysing your Likert scale data with descriptive statistics. Although it may be tempting, resist the urge to take the numeric responses and compute a mean. Adding a response of "strongly agree" (5) to two responses of "disagree" (2) would give us a mean of 4, but what is the significance of that number? Fortunately, there are other measures of central tendency we can use besides the mean. With Likert scale data, the best measure to use is the mode, or the most frequent response. This makes the survey results much easier for the analyst (not to mention the audience for your presentation or report) to interpret. You also can display the distribution of responses (percentages that agree, disagree, etc) in a graphic, such as a bar chart, with one bar for each response category.
Proceed next to inferential techniques, which test hypotheses posed by researchers. There are many approaches available, and the best one depends on the nature of your study and the questions you are trying to answer. A popular approach is to analyse responses using analysis of variance techniques, such as the Mann Whitney or Kruskal Wallis test. Suppose in our example we wanted to analyse responses to questions on foreign policy positions with ethnicity as the independent variable. Let's say our data includes responses from Anglo, African-American, and Hispanic respondents, so we could analyse responses among the three groups of respondents using the Kruskal Wallis test of variance.
Simplify your survey data further by combining the four response categories (e.g., strongly agree, agree, disagree, strongly disagree) into two nominal categories, such as agree/disagree, accept/reject, etc). This offers other analysis possibilities. The chi square test is one approach for analysing the data in this way.
Remember that there are many approaches to analysis. Consider your research questions when determining the best analytical approach for your study. Likert scales vary in the number of points in the scale. The five-point scale used here is the most common, but some Likert scales have 4-point response scales, eliminating the not sure/undecided category. Some even have 7-point response scales.