How Political Efficacy Relates to Online and Offline Political Participation: A Multilevel Meta-analysis
Bibliographic record
Abstract
The rapid rise of digital media use for political participation has coincided with an increase in concerns about citizens’ sense of their capacity to impact political processes. These dual trends raise the important question of how people’s online political participation is connected to perceptions of their own capacity to participate in and influence politics. The current study overcomes the limitation of scarce high-quality cross-national and over-time data on these topics by conducting a meta-analysis of all extant studies that analyze how political efficacy relates to both online and offline political participation using data sources in which all variables were measured simultaneously. We identified and coded 48 relevant studies (with 184 effects) representing 51,860 respondents from 28 countries based on surveys conducted between 2000 and 2016. We conducted a multilevel random effects meta-analysis to test the main hypothesis of whether political efficacy has a weaker relationship with online political participation than offline political participation. The findings show positive relationships between efficacy and both forms of participation, with no distinction in the magnitude of the two associations. In addition, we tested hypotheses about the expected variation across time and democratic contexts, and the results suggest contextual variation for offline participation but cross-national stability for online participation. The findings provide the most comprehensive evidence to date that online participation is as highly associated with political efficacy as offline participation, and that the strength of this association for online political participation is stable over time and across diverse country contexts.
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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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".