Mood State, Issue Involvement, and Argument Strength on Responses to Persuasive Appeals
Bibliographic record
Abstract
This study investigated the effects of mood state, issue involvement, and argument strength on responses to persuasive appeals. Through an unrelated second study paradigm, 144 introductory psychology students were randomly assigned to High or Low Issue Involvement, Happy or Sad Mood Inductions, and Strong or Weak Argument conditions. Attitudes, measured on 9-point Likert-type scales, and cognitive responses, measured through a thought listing, were assessed. On attitudes, people in the Happy Induction condition were equally persuaded by Strong and Weak Arguments, whereas people in the Sad Induction condition were persuaded by Strong, but not Weak, Arguments. Involvement had no effect. On the thought-listing measures, people in the Happy Induction condition showed modest elaboration. A stronger pattern of effects, consistent with high elaboration, was noted on the thought listings of people in the Sad Induction condition and who were in the High Involvement group. Interestingly, people in the Sad Induction condition who were in the Low Involvement group showed mood-congruency on thoughts. The data suggest that the effects of mood state are not moderated by the effects of issue Involvement on this measure of attitudes but that there may be some moderation on measures of elaboration. Implications and directions for research are discussed.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".