Likert Scale Analysis Service: Scores, Reliability and Group Comparisons
Likert data are everywhere and often analysed badly. We help you decide how to score, summarise and test rating-scale responses so the results hold up to review.
What this service covers
- Scoring of items, reverse-coded items and scale totals
- Frequencies, medians, means and standard deviations for items
- Reliability checks with Cronbach’s alpha and item-total statistics
- Comparison of groups with Mann-Whitney, Kruskal-Wallis or t-test and ANOVA
- Correlations between scales, such as Spearman’s rho
- Factor analysis to check that items group as expected
- Diverging stacked bar charts and clear summary tables
Items versus scales
A single Likert item is ordinal: the gap between “agree” and “strongly agree” is not necessarily the same as between “neutral” and “agree”. A scale made of several items, summed or averaged, is often treated as approximately continuous. We explain which treatment suits your data, and why, so the choice is documented in your methods.
Reliability and validity
If items are meant to measure one construct, they should hang together. We calculate Cronbach’s alpha and review items that lower it, and when you need stronger evidence we can run factor analysis and discuss reliability and validity testing. Reverse-coded items are a common source of error, so we check them carefully.
Choosing tests
For item-level comparisons we usually use non-parametric tests. For scale scores with reasonable distributions, t-tests, ANOVA and regression are common. We examine distributions, report effect sizes and avoid over-reading small differences. See also survey data analysis.
Presenting Likert results
Diverging stacked bars show the balance of agreement and disagreement better than pie charts. We also provide summary tables with n, percentages and means. Before analysis, you can sanity-check text responses with our free word frequency counter if your survey includes comments.
How the work runs
- You send the data and the item list with scale direction.
- We score the data, check reliability and agree the tests.
- We run the analysis and build charts and tables.
- You receive results, interpretation and methods text.
What you receive
- Scored dataset with scale totals
- Reliability tables with alpha and item statistics
- Test results with effect sizes
- Charts such as diverging stacked bars
- A written summary of findings
Frequently asked questions
Can I use the mean for Likert data?
For single items it is debated; many researchers report medians and percentages. For multi-item scales, means are common. We explain the choice in your methods.
What is a good Cronbach’s alpha?
Values around 0.70 or higher are often considered acceptable, but the right level depends on the purpose and the number of items.
Which test compares two groups on a Likert item?
The Mann-Whitney U test is usually appropriate for ordinal data. For scale scores that are approximately normal, a t-test may be used.
How do I handle reverse-coded items?
They must be recoded before scoring. We check and document the recoding.
Should I use a 5-point or 7-point scale?
It should be decided at design stage. See our questionnaire design service for advice.
Can you analyse several scales together?
Yes. We can correlate scales, compare them across groups, and model them with regression.
Related survey & mixed-methods analysis services
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Clear questions, valid scales and sound sampling before you collect data.
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Survey & Mixed-Methods Analysis Services
See every survey and mixed-methods service we offer.
Also related: Cronbach’s alpha calculator