A Mixed Method Investigation of Social Science Graduate Students’ Statistics Anxiety Conditions Before and After the Introductory Statistics Course
Bibliographic record
Abstract
Research frequently uses quantitative approach to explore undergraduates’ statistics anxiety conditions. However, few studies of adults’ statistics anxiety use qualitative method or focus solely on graduate students. Moreover, even less studies focus on comparing adults’ anxiety levels before and after the introductory statistics course. This line of study is important to pursue since the introductory statistics course should play the very important roles of both preparing students’ the foundation knowledge of higher level statistics course, and inspiring students’ interests for higher level course. In addition, graduate students tend to have different backgrounds, learning motivations, and learning habits compared to their undergraduate counterparts. Overall, limited mixed research method is available on social sciences graduate students’ (1) statistics anxiety before and after the introductory statistics course and (2) actions taken to decrease the anxiety. This study seeks to fill this gap by incorporating a mixed research method to explore social sciences graduate students’ statistics learning processes. Findings suggest that the social sciences graduate students’ anxiety levels diminished after the introductory statistics course, even though they also experienced severe statistics anxiety at the very beginning. These findings became essential for institutions, higher education instructors, and social sciences statistics learners to consider.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".