Measuring Opportunity Cost in Statistics Using Evaluative Space Grid Items: Results from a Pilot Study
Bibliographic record
Abstract
Challenges to measuring students’ attitudes toward statistics remain despite decades of focused research. Measuring the expectancy-value theory (EVT) Cost construct has been especially challenging owing in part to the historical lack of research about it. To measure the EVT Cost construct better, this study asked university students to respond to items using both a Likert-type response and an Evaluative Space Grid (ESG)- type response. ESG items enable bivariate responses in a single item and permit distinguishing among two different types of neutral attitudes: indifferent and ambivalent. This pilot study evaluates the appropriateness of ESG-type items for measuring the EVT Cost construct by analyzing student response patterns to ESG-type items and comparing them with Likert-type items. Validity evidence is documented using descriptive statistics and graphs, correlations among items, and a trinomial hypothesis test. Internal consistency reliability indices are also reported. Friedman’s Test is used to compare the average response times for items of different types. Results indicate that students can meaningfully respond to ESG-type items in ways that are similar to their Likert-type responses, that students respond to ESG-type items quicker with more practice, and that distinguishing among indifferent and ambivalent attitudes seems appropriate for the EVT Cost construct. These findings suggest that ESG-type items may provide new insights not possible with Likert-type items but also that more research should be conducted to better understand their advantages and disadvantages within statistics education.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".