Likert-Scale Questionnaires: From Design to SPSS Analysis for Your Dissertation (2026)
Most survey dissertations in the UAE and GCC use Likert scales, and most analysis problems start at the design stage. This guide covers how to build the questionnaire, code it in SPSS, check reliability and validity, and choose the right tests.
Design the questionnaire for the analysis you will run
- Measure constructs, not single questions. Each concept in your framework (for example job satisfaction or perceived usefulness) should be measured by several items, ideally adapted from a published, validated scale that you cite.
- Keep the response format consistent. Use the same number of points and the same anchors (for example 1 = Strongly disagree to 5 = Strongly agree) across a construct.
- Plan reverse-worded items deliberately. They can reduce acquiescence bias, but they must be reverse-coded before analysis.
- Write one idea per item. “My manager is supportive and fair” asks two things; split it.
- Pilot it. A small pilot shows unclear wording and gives an early reliability check.
- Translate carefully. For Arabic and English versions, use translation and back-translation so both versions measure the same thing.
- Get ethics approval first. Most universities require approval before data collection; build consent and anonymity into the first page.
How many respondents do you need?
It depends on the analysis. A power analysis (for example with G*Power) gives a defensible minimum for t-tests, ANOVA, correlation and regression. If you plan factor analysis or structural equation modelling, sample size guidance is usually larger: rules of thumb such as several respondents per item, or a few hundred in total, are commonly cited, but your supervisor’s and your field’s expectations take priority. State the basis for your sample size in the methodology chapter.
Coding and cleaning the data in SPSS
- Variable View: give each item a short name (JS1, JS2…), a label with the full wording, and value labels (1 = Strongly disagree…). Set the measure to Ordinal for items.
- Reverse-code negatively worded items with Transform > Recode into Different Variables (on a 5-point scale, 1→5, 2→4, 3→3, 4→2, 5→1).
- Screen the data: check frequencies for out-of-range values, look for straight-lining (the same answer to every item) and decide how you will handle missing data, then report that decision.
- Compute composite scores for each construct (Transform > Compute Variable, usually the mean of its items) only after reliability is confirmed.
Check the scale before testing hypotheses
Reliability: run Analyze > Scale > Reliability Analysis for each construct. Cronbach’s alpha of .70 or above is the common convention; check “Cronbach’s Alpha if Item Deleted” for weak items.
Validity: exploratory factor analysis (EFA) in SPSS shows whether items group as intended; check the KMO measure, Bartlett’s test and the factor loadings. If your model uses established constructs, confirmatory factor analysis (CFA) in AMOS, or a measurement model in SmartPLS, tests fit, convergent validity (loadings, AVE, composite reliability) and discriminant validity. Which one fits depends on your framework and your supervisor’s preference.
Which test for Likert data?
Single Likert items are ordinal, so report them with medians and frequencies, and compare groups with non-parametric tests (Mann-Whitney U for two groups, Kruskal-Wallis for three or more). Composite scale scores, built from several items, are widely analysed as approximately interval data, which allows t-tests, ANOVA, Pearson correlation and regression when assumptions hold. Whatever you choose, justify it in your methodology chapter.
| Your question | Composite scores | Single items |
|---|---|---|
| Do two groups differ? | Independent t-test | Mann-Whitney U |
| Do three or more groups differ? | One-way ANOVA | Kruskal-Wallis |
| Are two constructs related? | Pearson r | Spearman rho |
| Do several factors predict an outcome? | Multiple regression, or SEM in AMOS/SmartPLS | Ordinal regression |
To read and report each test’s output, use our table-by-table guide to SPSS output and APA 7 reporting, and see how to choose the right statistical test for other designs.
Stuck on the analysis? Our data analysis support covers SPSS, AMOS, SmartPLS and NVivo: we check your coding, reliability and validity, run or review the tests, and explain every output so you can defend it. Ask Labeeb AI about your dataset.
Likert questionnaire questions
Is Likert data ordinal or interval?+
A single Likert item is ordinal. Composite scores averaged across several items are widely treated as approximately interval, which is why t-tests, ANOVA and regression are commonly used on them. Justify your choice in the methodology chapter.
Should I use a 5-point or 7-point Likert scale?+
Both are common. Keep the format of the validated scale you are adapting where possible, and use the same format within each construct.
Do I need CFA if I use an existing validated scale?+
Often yes. A scale validated elsewhere still needs to be shown to work in your sample, especially after translation or adaptation. Many supervisors expect at least reliability and a factor analysis; SEM-based studies usually require CFA or a PLS measurement model.
How do I reverse-code items in SPSS?+
Use Transform, Recode into Different Variables, and map each value to its opposite (on a 5-point scale: 1 to 5, 2 to 4, 3 to 3, 4 to 2, 5 to 1). Recode before running reliability or computing composite scores.
Want your questionnaire analysis checked?
We review questionnaire design, coding, reliability and validity, and run or check SPSS, AMOS and SmartPLS analyses so you can explain every result yourself.
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