Coding Practices for LibQUAL+® Open-Ended Comments
Bibliographic record
Abstract
Objective – This paper presents the results of a study of libraries’ practices for coding open-ended comments collected through LibQUAL+® surveys and suggests practical steps for facilitating this qualitative analysis. Methods – In the fall of 2009, survey invitations were sent to contacts at 641 institutions that had participated in the LibQUAL+® survey from 2003 to 2009. Of those invited, there were 154 respondents, for an overall response rate of 24.0%. Results – Nearly 87% of the respondents indicated that their library had performed a qualitative analysis of the comments from their most recent LibQUAL+® survey. Of these, over 65% used computer software to organize, code, sort, or analyze their comments, while 33.6% hand-coded their comments on paper. Of the 76 respondents who provided information on software, 73.7% used Excel, 18.4% used Atlas.ti, and 7.9% used NVivo. Most institutions (55.8%) had only 1 person coding the comments; 26.9% had 2 coders, and very few had 3 or more. Of those who performed some type of analysis on their comments, nearly all (91.9%) indicated that they developed keywords and topics from reading through the comments (emergent keywords). Another common approach was to code the comments according to the LibQUAL+® dimensions; 55.0% of respondents used this strategy. Nearly all of the institutions (92.7%) reported using their LibQUAL+® comments internally to improve library operations. Libraries also typically incorporated the comments into local university reports (75.5%) and used the comments in outreach communications to the university community (60.9%). Conclusion – Comments obtained from the LibQUAL+® survey can be useful for strategic planning, understanding users, identifying areas for improvement, and prioritizing needs. A key suggestion raised by respondents to this survey was for practitioners to consider sharing the fruits of their labor more widely, including coding taxonomies and strategies, as well as broader discussion of qualitative analysis methods and practices.
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.
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 | 0.157 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".