Deciding How to Treat Likert Scale Data

Likert scales (e.g., 1 = strongly disagree to 5 = strongly agree) are fundamentally ordinal because responses represent ordered categories with unequal intervals between them—e.g., the "jump" from strongly disagree (1) to disagree (2) may not feel as large as from agree (4) to strongly agree (5). However, in practice, the choice between treating it as ordinal (via ordinal logistic regression) or continuous (via linear regression or OLS) depends on your research goals, sample size, data properties, and assumptions. Below, I'll outline the key considerations, pros/cons, and recommendations to help you decide.

1. Key Considerations for Your Decision

• Nature of the Data and Research Question:


• If your outcome is naturally categorical (e.g., attitudes or satisfaction levels where order matters but magnitudes are subjective), prioritize ordinal treatment to preserve the ranking without assuming equal spacing.


• If you're exploring trends, relationships, or predictions in a more continuous-like way (e.g., modeling average agreement levels across groups), and the scale behaves "continuously" in your data, linear regression can be a pragmatic approximation—especially for 5+ point scales where categories aren't too coarse.


• Ask: Does assuming equal intervals make theoretical sense? For short scales (3-4 points), ordinal is safer; for longer ones (7+ points), linear is often defensible.


• Sample Size and Distribution:


• Ordinal logistic: Works well with smaller samples (n > 50-100) and handles skewed or non-normal distributions naturally, as it doesn't assume normality.


• Linear regression: Requires larger samples (n > 200 ideally) to approximate normality via the Central Limit Theorem. Check your data's distribution (e.g., via histograms or Shapiro-Wilk test). If it's bell-shaped or symmetric, linear is fine; if heavily skewed or bimodal, ordinal is better.


• Tip: Compute summary stats (mean, median, mode) and visualize: If the mean and median are close and it looks roughly normal, lean toward linear.


• Assumptions and Validity:


• Ordinal logistic regression (e.g., proportional odds model in R's MASS::polr() or Python's statsmodels):


• Assumes the effect of predictors is consistent across category thresholds (proportional odds—test this with a Brant test).


• No normality assumption, but assumes independence of observations.


• Outputs odds ratios for moving up the scale (e.g., odds of being in a higher agreement category).


• Linear regression (treating scores as continuous):


• Assumes linearity, homoscedasticity, normality of residuals, and equal intervals (which Likert violates mildly).


• Outputs interpretable coefficients (e.g., a 1-unit predictor increase raises the score by X points).


• Potential issue: Violating ordinality can inflate Type I errors or bias estimates if categories are unevenly used.


• Number of Categories and Power:


• Fewer than 5 points? Strongly favor ordinal—treating as continuous loses information.


• 5-7 points? Either works, but test both and compare model fit.


• More categories? Linear becomes more valid as the scale approximates a continuum.


• Practical Trade-offs:


• Ordinal models are more statistically "correct" but can be harder to interpret and implement (e.g., handling ties or multicollinearity).


• Linear models are simpler, faster, and widely used in social sciences (e.g., psychology, education), despite debates. They're often robust to violations if your goal is prediction over inference.

2. Pros and Cons Summary

3. Step-by-Step Recommendation to Decide

• Examine Your Data:


• Calculate descriptives: Mean, SD, skewness/kurtosis. If skewness > |1| or the distribution is U/J-shaped (e.g., polarized responses), go ordinal.


• Plot histograms or boxplots by key predictors to check symmetry.


• Test Assumptions:


• For linear: Run a preliminary OLS and check residuals (Q-Q plots, Durbin-Watson for independence). If residuals are normal and homoscedastic, it's viable.


• For ordinal: Fit the model and test proportional odds (e.g., in R: brant() from brant package). If violated, consider partial proportional odds or multinomial logistic.


• Compare Models:


• Fit both (if sample size allows) and compare fit statistics:


• For ordinal: Pseudo-R² (e.g., McFadden's).


• For linear: R², AIC/BIC (lower is better; ordinal AIC isn't directly comparable but can guide).


• Use cross-validation or bootstrap to assess predictive performance.


• Simulate: Rescale your Likert to 0-1 or z-scores for linear to see if results align.


• Contextual Factors:


• Field conventions: In surveys (e.g., psychometrics), ordinal is preferred (e.g., via cumulative logit models). In economics or large-scale analytics, linear is common.


• If polychoric correlations or factor analysis are involved (for latent traits), ordinal methods like polycor in R align better.


• Ethical note: Be transparent in reporting—justify your choice (e.g., "Treated as continuous due to approximate normality").

4. Implementation Tips

• Software:


• R: Ordinal (MASS::polr() or ordinal package); Linear (lm()).


• Python: Ordinal (statsmodels.discrete.ordinal_model); Linear (statsmodels or sklearn).


• Stata/SPSS: Built-in ordinal logit; treat as continuous via regression.


• Example in R for ordinal:

library(MASS)
     model <- polr(your_likert ~ predictors, data = df, method = "logistic")
     summary(model)

• If undecided, start with ordinal for rigor, then sensitivity-check with linear. For 5-point scales with n > 100 and normal-ish data, linear is often "good enough" per simulation studies (e.g., Harpe, 2015).