Implications of using Likert data in multiple regression analysis
Bibliographic record
Abstract
Many of the measures obtained in educational research are Likert-type responses on questionnaires. These Likert-type variables are sometimes used in ordinary least-squares regression analysis. However, among the key implications of the assumptions of regression is that the criterion is continuous. Little research has been done to examine how much information is lost and how inappropriate it is to use Likert variables in ordinary least-squares multiple regression. Therefore, this study examined the effect of Likert-type responses in the criterion variable and predictors for various scale points, on the accuracy of regression models using normal and skewed observed response patterns. This was done for the case of three predictors and one criterion. Similarly, eight levels of Likert-type categorization ranging from two to nine scale points were considered for both predictors and criterion variables. It was found that the largest bias in the estimation of the model R-squared, the relative Pratt Index, and Pearson correlation coefficient occurred for two or three-point Likert scales. The bias did not substantially reduce any further beyond the four-point Likert scale. Type of correlation matrix had no effect on the model fit. However, skewed response distribution resulted in large biases in both R² and Pearson correlation, but not in Relative Pratt index, which was not affected by the response distribution. Practical contribution and significance of the study is that it has provided information and insight on how much information is lost due to bias, and the extent to which accuracy is compromised in using Likert data in linear regression models in education and social science research. It is recommended that researchers and practitioners should recognize the extent of the bias in ordinary least-squares regression models with Likert data, resulting in substantial loss of information. For variable importance, the relative Pratt index should be used given that it is robust to Likert conditions and response distributions. Finally, when interpreting reported regression results in the research literature one should recognize that the reported R-squared values are underestimated and that the Pearson correlations are also typically underestimated and sometimes substantially underestimated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.437 | 0.811 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".