Combining Multiple Likert Scale Questionnaire Items into a Single Variable: A Comprehensive Guide

When conducting surveys or research, one of the most common tools used is the Likert scale questionnaire. This system allows respondents to rate their agreement with a set of items on a scale, typically ranging from strongly agree to strongly disagree. While individual Likert scale items can provide valuable insights, it is often necessary to combine multiple items into a single variable for analysis. In this article, we will explore the method of taking the mean average as one of the ways to achieve this combination.

Understanding the Likert Scale

A Likert scale is a psychometric scale often used in survey research to measure attitudes, opinions, or beliefs. It is named after the psychologist Rensis Likert, who introduced the concept in 1932. The scale typically consists of a series of statements, and respondents rate their agreement or disagreement with each statement on a numerical scale, such as 1 (strongly disagree) to 5 (strongly agree).

The Importance of Combining Likert Scale Items

While individual Likert scale items provide useful information, combining multiple items can offer a more comprehensive and accurate representation of the underlying construct. For example, if you are measuring customer satisfaction, an individual item may only capture a small aspect of satisfaction. By combining several items that collectively address different facets of satisfaction, you can create a more robust and reliable measure.

Method: Taking the Mean Average

The most straightforward method for combining multiple Likert scale items into a single variable is to calculate the mean average. Here's how you can do it:

Sum the Scores: First, sum the scores of all the Likert scale items for each respondent. Count the Items: Count the number of Likert scale items being combined. Calculate the Average: Divide the sum of the scores by the number of items to obtain the mean average for each respondent.

For example, if you have four Likert scale items with scores of 4, 3, 5, and 3 for a particular respondent, the sum would be 15. Dividing 15 by 4 gives a mean average of 3.75.

Advantages of Using Mean Average

There are several advantages to using the mean average method:

Robustness: The mean average is a robust statistic that can handle a wide range of data distributions. Consistency: It provides a consistent measure across different respondents, making it easier to compare and analyze the data. Interpretability: The mean average is relatively easy to understand, making it a popular choice for both researchers and practitioners.

Considerations When Combining Likert Scale Items

While the mean average method is widely used, it is essential to consider a few factors:

Internal Consistency: Ensure that the items being combined are sufficiently related and measure the same construct. Cronbach's alpha or other reliability measures can be used to assess internal consistency. Distribution: Check the distribution of the scores to ensure that the mean average is an appropriate measure. Skewed distributions may require alternative methods. Weighting: In some cases, different items may be weighted differently based on their importance. This can be done using standardized coefficients or other statistical techniques.

Conclusion

Combining multiple Likert scale items into a single variable is a common practice in survey research. The method of taking the mean average is a simple and effective way to achieve this. By following the steps outlined above, researchers can create a robust and reliable measure that reflects the underlying construct accurately. Always consider the context and nature of the data when deciding whether the mean average is the most suitable method for your analysis.