Ordinal Scale Development
Also known as: Likert scale development, ordinal measurement scale construction, ordinal item development, polytomous scale construction
Ordinal scale development is the systematic construction and validation of multi-item measurement instruments whose response options form an ordered but not necessarily equal-interval sequence — most commonly Likert-type formats (e.g., 1 = Strongly Disagree to 5 = Strongly Agree). It applies psychometric techniques that respect the ordinal nature of items rather than treating them as continuous.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
Use ordinal scale development whenever your items carry a ranked response format (Likert, semantic differential, or ordered categories) and you cannot justify treating the intervals as equal. It is especially important when response options are coarse (fewer than 5–6 categories), when distributions are visibly skewed, or when the research context demands rigorous measurement. Avoid generic continuous-data procedures (Pearson correlations, standard CFA with maximum likelihood, plain Cronbach's alpha computed from raw scores) in these situations — they can inflate or deflate reliability estimates and produce misleading factor solutions. Ordinal methods are not needed when items are truly continuous (e.g., reaction times, physiological measures).
Strengths & limitations
- Correctly models the categorical, ordered nature of Likert and similar items rather than forcing an interval-scale assumption.
- Polychoric-based factor analysis recovers latent structure more accurately than Pearson-based approaches when data are ordinal.
- WLSMV and DWLS estimators are robust to non-normality and perform well with as few as 200 respondents.
- McDonald's omega provides a theoretically sounder reliability estimate than Cronbach's alpha for multidimensional and ordinal scales.
- Explicit content validity procedures (expert panels, CVI) link scale scores to the intended construct domain.
- Polychoric correlations require assumptions about bivariate normality of the underlying continuous latent responses; severe violation can bias estimates.
- WLSMV estimation requires a larger sample than standard ML estimation for stable results; recommendations typically start at n = 200.
- The pipeline involves more decision points (number of response categories, choice of estimator, rotation) than simpler continuous-data approaches, increasing the complexity of reporting.
- Ordinal alpha and omega require specialised software (e.g., R packages lavaan, psych, or FACTOR) and are less familiar to some reviewers.
Frequently asked
Can I just treat my Likert items as continuous and use standard EFA?
It is common but not recommended. When items have fewer than 5–6 ordered categories or are skewed, Pearson correlations underestimate inter-item relationships and maximum-likelihood EFA can distort the factor solution. Using polychoric correlations and an ordinal estimator (WLSMV, DWLS) yields more accurate results with little extra effort in modern software.
How many response categories should I use?
Research generally supports 5 to 7 categories as balancing reliability and respondent ease. Fewer than 4 categories reduce score variance and may inflate ceiling or floor effects; more than 9 usually add no practical precision. The choice should be guided by the construct and the population.
Is McDonald's omega always better than Cronbach's alpha for ordinal scales?
Omega is theoretically preferable because it does not require tau-equivalence (equal factor loadings) and handles multidimensionality better. However, when a scale is essentially unidimensional and loadings are roughly equal, alpha and omega will be very similar. For ordinal items specifically, ordinal alpha (from the polychoric matrix) is preferable to conventional alpha computed from raw scores.
What sample size do I need?
A minimum of about 200 participants is a widely cited lower bound for WLSMV estimation. More complex scales (more items, more factors) require larger samples. Subject-to-item ratios of 5–10 are a common heuristic, but absolute sample size matters more than the ratio.
When should I test measurement invariance?
Whenever scale scores will be compared across groups (e.g., gender, language, culture, or clinical vs. non-clinical samples), measurement invariance testing with multi-group ordinal CFA is needed to confirm that the items function in the same way across groups. Without this step, observed group differences may reflect measurement artifacts rather than true construct differences.
Sources
- DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). SAGE Publications. ISBN: 978-1506341569
- Finney, S. J. & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269–314). Information Age Publishing. link ↗
How to cite this page
ScholarGate. (2026, June 3). Ordinal Scale Development. ScholarGate. https://scholargate.app/en/psychometrics/ordinal-scale-development
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Item Response TheoryPsychometrics↔ compare
- Ordinal CFAPsychometrics↔ compare
- Ordinal EFAPsychometrics↔ compare
- Ordinal Reliability AnalysisPsychometrics↔ compare
- Scale developmentPsychometrics↔ compare