Aggregation of Multi-Item (Likert) Scales

If your survey includes multiple items to measure a construct – such as satisfaction, trust, or stress – at some point, you’ll face the question:

How do I combine the individual responses into an overall score?

This article is all about that. You’ll learn:

  • Which aggregation methods exist (e.g., mean, median, sum)
  • Their respective advantages and disadvantages
  • And when each method is appropriate

So you’ll get not just a theoretical overview but also practical guidance for your own survey and analysis.

Aggregation of Likert scales

In this article:

Which aggregation methods exist?

To determine the overall score of a latent construct (e.g., “satisfaction”), each associated item is evaluated individually and then combined using an aggregation rule. The most common methods are:

  • Mean calculation
  • Median calculation
  • Simple sum
  • Weighted sum
  • Factor scores (based on factor analysis)

Which method is appropriate depends on your research goals, data structure, and intended interpretation.

Mean and median calculation

The advantage of these two methods is that the aggregated value remains on the original scale level. For example, if your Likert scale ranges from “1 = strongly agree” to “5 = strongly disagree,” the aggregated value will also lie within this range. This makes the overall score easy to interpret without requiring further transformation.

Mean:
From a statistical point of view, the mean represents the most likely rating assuming no additional information is available. It’s particularly suitable when the items on the scale are relatively equal and no extreme distortions are expected.

Median:
The median is robust against outliers and always reflects the value that at least half of the respondents achieved or exceeded. It emphasizes the “typical” response.

When to use what?
Whether to use the mean or median often comes down to preference or the data distribution:

  • Mean: Suitable for symmetrically distributed data, low variability, few outliers.
  • Median: Preferable with skewed distributions or when outliers are expected.

Simple and weighted sum

With summation (simple or weighted), the individual item scores are added together. This naturally results in a new scale, with the range depending on the number of items evaluated.

Simple sum:
All items are treated equally in the aggregation. The main drawback is that the resulting scale must be reinterpreted. The maximum depends on the number of items:

  • With five items on a 1–5 scale, the total score ranges from 5 to 25.

Weighted sum:
Here, certain items are given more weight than others – for example, when some aspects of a construct are considered more important. This is useful when you know that specific items correlate more strongly with the overall construct or are theoretically more relevant.

A downside of summation is that mid-range values can be hard to interpret. The same total score can result from very different response patterns (e.g., a mix of extremes vs. all mid-range ratings).

Factor scores (Advanced method)

Another more advanced method for aggregating multi-item scales is factor scores, which are derived from a factor analysis (e.g., principal component analysis, PCA). This method weights items automatically based on their empirical importance.

This method is especially suitable when:

  • The construct being measured is complex
  • You are working on scale reduction, validation, or scale optimization
  • You need precise, data-driven overall scores (e.g., for academic research or sophisticated models)

Note: This method requires statistical expertise and suitable software (e.g., SPSS, R, Python).

Conclusion on aggregation

  • Mean or median: Ideal for standardized scales where clarity and comparability are key.
  • Simple or weighted sum: Useful with many items and for quick calculations – but requires redefining the scale.
  • Factor scores: Best choice for complex models or scientifically validated constructs – but more methodologically demanding.

Which method is best depends on your specific research question, your data, and the depth of your analysis. There is no one-size-fits-all solution – but an informed decision leads to more reliable results.

Date: 10.04.2025

Author: Dr. Paul Marx

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