Invalid responding in questionnaire-based research: Implications for the study of schizotypy.
Bibliographic record
Abstract
Data collected through self-report questionnaires are particularly susceptible to inappropriate or random responding, and such invalid data increase noise and attenuate true statistical relationships. While many researchers studying schizotypy have employed infrequency measures to exclude participants, such measures are not universally employed. Moreover, some researchers have even outright questioned whether validity scales are warranted. Here, we show the effect of invalid responses on the relationship between schizotypy and hedonic reaction. For valid responders, negative schizotypal traits were inversely related to both anticipatory and consummatory pleasure (p < .01). Invalid responses were found for 23% of respondents, and within these subjects, no relationship was found between any of the measures. When the valid and invalid respondents were pooled, the relationship was dampened. Furthermore, linear multiple regression modeling showed that validity trended toward moderating the relationship between the variables of interest. These data highlight the importance of screening for, and excluding, invalid responses in schizotypy research. Our results also affirm that screening for random responding is effective and warranted. Implications for future studies employing questionnaire-based methods are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.348 | 0.561 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".