Your output. Simplified.
A check of Experiment 2 from Hughes, Cummins & Hussey, 2023.
| Hypothesis | Study Feature | Information in paper | Preregistered Protocol |
Match**
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|---|---|---|---|---|
| M1: AMP effect will be demonstrated | Sample size before exclusions | 214 | 150 | TRUE |
| M1: AMP effect will be demonstrated | Sample size after exclusions | 147 | 150 | FALSE |
| M1: AMP effect will be demonstrated | Planned variable(s) to measure/test | Valence Ratings (pleasant or unpleasant) of the target stimulus | Evaluations within the AMP as pleasant or unpleasant | TRUE |
| M1: AMP effect will be demonstrated | Planned processing/scoring | Model acknowledges non-independence of multiple data points; modeled trial type as random slope within random intercept for participant, modeling prime identity as random intercept | Frequentist logistic mixed-effects model; include subject ID as random intercept | TRUE |
| M1: AMP effect will be demonstrated | Planned statistical model | logistic mixed-effects model: Valence Ratings ~ Prime Valence + (1 | Participant) | logistic mixed-effects model: valence_rating ~ prime_valence + (1 | subject) | TRUE |
| H1: Influence of prime valence moderated by trials influenced by prime stimulus | Planned variable(s) to measure/test | Interaction: prime valence and influence awareness (influence-aware vs. non-influence-aware) | Interaction: prime valence and reported influence | TRUE |
| H1: Influence of prime valence moderated by trials influenced by prime stimulus | Planned processing/scoring | Extended model by adding influence awareness as fixed effect | Reported influence + prime valence interaction | TRUE |
| H1: Influence of prime valence moderated by trials influenced by prime stimulus | Planned statistical model | logistic mixed-effects model: Valence Ratings ~ Prime Valence * Influence Awareness + (1 | Participant) | logistic mixed-effects model: valence_rating ~ prime_valence * reported_influence + (1 | subject) | TRUE |
| H2: Magnitude of AMP effect predicted by proportion of influenced trials to non-influenced trials | Planned variable(s) to measure/test | AMP effect size and proportion of influenced trials | AMP effect size and proportion of influenced trials | TRUE |
| H2: Magnitude of AMP effect predicted by proportion of influenced trials to non-influenced trials | Planned processing/scoring | Proportion of influenced trials computed as influenced trials divided by total trials | Proportion of influenced trials to uninfluenced trials | FALSE |
| H2: Magnitude of AMP effect predicted by proportion of influenced trials to non-influenced trials | Planned statistical model | linear regression: AMP effect size ~ influence-awareness rate | linear regression: AMP_effect_size ~ proportion_influenced | TRUE |
| H3: Online and offline measures of influence correlate | Planned variable(s) to measure/test | Correlation between IA-AMP and post hoc awareness measures | Correlation between online and offline measures | TRUE |
| H3: Online and offline measures of influence correlate | Planned processing/scoring | Simple correlations | Standard correlation analysis | TRUE |
| H3: Online and offline measures of influence correlate | Planned statistical model | correlation analysis | correlation analysis | TRUE |
| H4: Online measure of influence predicts AMP effect better than offline measure | Planned variable(s) to measure/test | Regression: AMP effect size ~ IA-AMP + General Influence | Regression: AMP_effect_size ~ proportion_influenced + general_influence | TRUE |
| H4: Online measure of influence predicts AMP effect better than offline measure | Planned processing/scoring | Only awareness assessed during IA-AMP predicted AMP effect sizes | Online measure predicted AMP effect size more greatly than offline measure | TRUE |
| H4: Online measure of influence predicts AMP effect better than offline measure | Planned statistical model | Regression: AMP effect size ~ IA-AMP + Post hoc self-report | Regression: AMP_effect_size ~ proportion_influenced + general_influence | TRUE |