What Do We Measure? Methodological Versus Institutional Validity in Student Surveys
Bibliographic record
Abstract
This paper examines the tension in the process of designing student surveys between the methodological requirements of good survey design and the institutional needs for survey data. Building on the commonly used argumentative approach to construct validity, I build an interpretive argument for student opinion surveys that allows assessment of the inferences and assumptions underlying the claim that an opinion survey item is a valid measure of a construct. I then evaluate the content validity of measures of program satisfaction and academic growth in Utah Valley University’s 2010 Graduated Alumni Survey and 2009 Graduating Student Survey, surveys designed to maximize conformity with existing and legacy institutional priorities and demands. Using analytical assessment and empirical tests including response change across surveys, interitem correlation, and factor analysis I show that UVU’s surveys—and by implication, all surveys developed with primarily institutional validity in mind—are subject to grave challenges to the construct validity. I conclude by suggesting that effective operationalization of institutional priorities can bring together construct and institutional validity. jeffrey.johnson@uvu.edu http://johnsonanalytical.com 800 West University Parkway Orem, Utah 84058 (801) 863-8993 AIR 2011 Forum, Toronto, Ontario, Canada Table of
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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 | 0.454 | 0.753 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".