Getting More Value from the LibQUAL+® Survey: The Merits of Qualitative Analysis and Importance-Satisfaction Matrices in Assessing Library Patron Comments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the merit of conducting a qualitative analysis of LibQUAL+® survey comments as a means of leveraging quantitative LibQUAL+ results, and using importance-satisfaction matrices to present and assess qualitative findings. Comments collected from the authors’ institution’s LibQUAL+ survey were analyzed using a codebook based on theoretical insights of customer satisfaction with library features. Qualitative findings extended the quantitative results and yielded key recommendations that were new or unclear from the quantitative results alone. Importance-satisfaction matrices were beneficial in pinpointing primary and secondary opportunities for improvement, areas to place continued emphasis, and areas where expectations were exceeded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it