Likert scales power most survey research in management and social science — and generate more viva arguments than any other data type, because of one old dispute: are Likert responses ordinal or interval? Here's the honest position and a defensible SPSS workflow, so your analysis survives both your data and your examiners.
The distinction that resolves the debate
A single Likert item (one 1–5 question) is ordinal — the distance between 'agree' and 'strongly agree' isn't guaranteed equal. But a summated Likert scale (the mean of several items measuring one construct, e.g. five job-satisfaction items) behaves approximately continuously, and treating it with parametric statistics is standard, defensible practice across the literature. Most thesis analysis operates on summated scales — which is why means, t-tests and regression are everywhere and mostly fine.
The SPSS workflow, in order
- 1Screen the data — missing values, straight-lining respondents, out-of-range codes.
- 2Reverse-score negatively worded items (Transform → Recode into Different Variables) — forgetting this silently wrecks reliability.
- 3Test reliability: Cronbach's alpha per construct (Analyze → Scale → Reliability Analysis); 0.70+ is conventionally acceptable.
- 4Compute summated scales (Transform → Compute Variable, mean of items).
- 5Describe: means and SDs for scales; frequencies/medians for single items.
- 6Check distributions (skewness/kurtosis) to justify test choices.
- 7Run inferential tests matched to your questions (below).
Which test for which question
- Compare two groups on a scale: independent-samples t-test (Mann-Whitney U if badly skewed).
- Compare 3+ groups: ANOVA (Kruskal-Wallis as the non-parametric fallback).
- Association between scales: Pearson correlation (Spearman for single items).
- Predict an outcome from several constructs: multiple regression — or SEM in AMOS/SmartPLS when constructs are latent.
- Single items: stick to frequencies, medians, chi-square — don't average one item and call it a construct.
(1) 'You used parametric tests on ordinal data' — answer: analysis was on summated multi-item scales with acceptable reliability, per standard practice. (2) 'Your scale isn't reliable' — answer with your Cronbach's alpha table. Report both proactively and the objections never land.
If you're staring at SPSS output unsure what's defensible, our data analysis help pairs you with a statistician-mentor who works through your actual dataset with you — see also SPSS mentoring and the SPSS thesis guide.
Frequently asked
Can I use a t-test on Likert data?+
On summated multi-item scales, yes — it's standard practice. On a single Likert item, prefer non-parametric alternatives like Mann-Whitney U, or report medians and frequencies.
What Cronbach's alpha is acceptable?+
0.70+ is the usual threshold, 0.80+ comfortable. Very high values (0.95+) can signal redundant items. Report alpha for every construct before using its summated scale.
Should I analyse a 5-point and 7-point Likert scale differently?+
The workflow is identical. Seven points can capture slightly more variance; what matters far more is item quality, reliability and matching tests to your questions.
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