To Thine Own Self, be True: Examining Change in Self-Reported Alcohol Measures over Time as Related to Socially Desirable Responding Bias among People with Unhealthy Alcohol Use
Bibliographic record
Abstract
Background Due to a conscious or unconscious desire to be perceived favorably by others, some participants may under or overexaggerate when reporting sensitive behaviors or attitudes, including those related to addictions. This socially desirable responding bias should be considered when using self-reports in predictive models since it introduces error. Methods A total of 1711 participants were recruited using Amazon's Mechanical Turk crowdsourcing platform for two randomized controlled trials investigating the effectiveness of brief online interventions for hazardous alcohol use. At baseline, participants completed the Balanced Inventory of Desirable Responding (BIDR). Four measures of alcohol use were collected at baseline and follow-up: number of drinks consumed in a typical week, on one occasion, consequences experienced, and amount of perceived risk of injury or illness from alcohol use. Results As expected, individuals scoring high on the BIDR subscales reported less alcohol use and related behaviors ( p < 0.05); however, repeating the analyses for each gender showed no difference for females asked direct questions about the frequency of their alcohol use. Mixed-effects models investigating the interaction of socially desirable responding bias over time on alcohol-related measures showed some significant differences in the amount of change reported depending on BIDR scores. Participants with higher self-deceptive enhancement scores reported less change over time in their ratings of risk of illness or injury ( p = 0.001) compared to lower-scoring participants. Likewise, high-scoring participants reported less change in the number of consequences experienced due to alcohol use over time on both BIDR subscales. Neither direct measure of alcohol use seemed affected by BIDR scores over time. A different pattern was found in males and females analyzed separately. Conclusions These findings suggest that researchers should consider including measures of socially desirable responding bias in longitudinal studies involving self-reported alcohol use, particularly when modeling alcohol-related measures using rating scales across time. In addition, separate gender analyses may be appropriate. Trial registration : ClinicalTrials.gov NCT03008928. Registered 30 December 2016; ClinicalTrials.gov NCT03060135. Registered 17 February 2017
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".