The 2019 Canadian Election Study: A Mode Comparison in Electoral Studies
Bibliographic record
Abstract
Election surveys provide information about voters' attitudes and behaviours during a given pre- and postelectoral campaign. Despite years of research about the benefits and downfalls of utilizing online, nonprobability panels rather than more traditional probability methods, the question of which method is most appropriate for cost-conscious, rigorous research still needs to be answered. Online methods have clear advantages, including lower costs and the ability to interview more respondents quickly. However, there is still a hesitation to trust the accuracy of estimates returned from data gathered through non-probability methods. In this paper, we explore this question in detail by comparing a medium-sized probability telephone survey (n = 4,021) with a large-scale, online non-probability sample survey (n = 37,822), both conducted as part of the 2019 Canadian Election Study (CES). We focus on assessing which survey returns a more accurate estimate of Canadians’ characteristics and attitudes, taking into account mode and sample size considerations. To facilitate fair and targeted comparisons, we use two techniques— propensity score matching and bootstrapping—to evaluate the issues of sampling design and mode separately. We then consider whether size alone is a valuable benefit in favour of the online mode. Our findings reveal that when considering the effect of mode alone, estimations from the online survey are more accurate in predicting intentions to participate and vote choice. When considering the effect of sampling, the results confirm the precision of the online estimates in a Canadian election study. We also find that, regarding attitudes, the online sample is more consistently unbiased when predicting support for immigration. We ultimately conclude that adopting an Internet-only survey for Canadian electoral studies is a wise choice.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".