Cognitive, Affective and Behavioral Components That Explain Attitude toward Statistics
Bibliographic record
Abstract
The purpose of this research is to measure the students' attitude towards statistics. It was necessary to define a certain type of research subjects, so we chose higher education students from the economic-administrative and engineering areas that were taking statistics as a subject in both, public and private universities located in Veracruz - Boca del Rio metropolitan area. The instrument used was the Survey of Attitude towards Statistics (SATS), and we applied it to a sample of 116 students. The statistical technique used was an exploratory factorial analysis with an extracted principal component. The Statistics Hypothesis: $Ho: \rho = 0$ has no correlation, while $Ha: \rho \neq0$ does. Statistics test to prove: $\chi^{2}$, Bartlett's test of sphericity, KMO (Kaiser-Meyer-Olkin), Measure of sampling adequacy (MSA) with a significance level: $\alpha=0.01$; $ p<0.01$ Decision rule: Reject $Ho$ if $\chi^{2}$ calculated $>\chi^{2}$ tabulated. The results obtained from the Bartlett's test of sphericity, KMO (0.600), Chi square $\chi^{2}$ 74.146 $>$ $\chi^{2}$ tabulated, Sig. $0.00 < p 0.01$, MSA (USF 0.673; ANX 0.521; CNF 0.624; LIK 0.613 and MTV 0.523) provide evidence to reject $Ho$. Global results point out that usefulness and anxiety are the most significant components in measuring students' perception towards statistics. Evidence obtained was enough to reject the null hypothesis, thus we can infer that attitude can be measured based on the cognitive, affective and behavioral components.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".