Sources of Response Bias in Cognitive Self-Report Items: “Which Memory Are You Talking About?”
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Self-reported cognitive difficulties are common in the years before an Alzheimer's disease (AD) diagnosis. Understanding how older adults interpret and respond to questions about their cognition is critical to recognize response biases that may limit the accuracy of cognitive self-reports in identifying AD risk. Cognitive interviewing is a systematic approach for examining sources of response bias that influence individuals' answers to specific questions. The purpose of this study was to identify features of common cognitive self-report items that contribute to (a) differing interpretations among respondents and (b) older adults' decisional processes when responding. RESEARCH DESIGN AND METHODS: A convenience sample of community-dwelling older adults (n = 49; Mage = 74.5 years; 36.7% male) without dementia completed a demographic questionnaire, the Montreal Cognitive Assessment, and an audio-recorded cognitive interview. Twenty commonly used cognitive self-report items were evaluated using cognitive interviewing techniques. The Question Appraisal System was used to guide the analysis of interview data and identify sources of response bias within and across cognitive self-report items. RESULTS: The most common sources of inconsistency in item interpretation and decisional processes were vague item wording, incorrect assumptions regarding consistency of cognitive problems across situations, and provocation of an emotional reaction that influenced responses. DISCUSSION AND IMPLICATIONS: Assessment of self-reported cognition is critical to facilitate research on early AD symptoms. Findings from this study identify modifiable sources of response bias that may influence the measurement properties of currently used cognitive self-report items and can inform refinement of measures.
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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 | 0.062 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".