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Record W1983604458 · doi:10.1177/1094428115571894

How Careless Responding and Acquiescence Response Bias Can Influence Construct Dimensionality

2015· article· en· W1983604458 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganizational Research Methods · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsWestern University
Fundersnot available
KeywordsAcquiescenceConstruct (python library)PsychologySocial psychologyCurse of dimensionalityJob satisfactionResponse biasConstruct validityPsychometricsDevelopmental psychologyStatisticsPolitical scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

We investigated the effects that careless responding and acquiescence response bias have on analyses conducted to assess construct dimensionality. Using job satisfaction/dissatisfaction as the focal construct, we measured and controlled for careless responding and acquiescence bias in data obtained from an online survey of employees ( N = 666) from different organizations and occupational groups. We found that the negative correlation between factors defined by job satisfaction and dissatisfaction items, respectively, was attenuated by careless responding and acquiescence bias, and was not significantly different from –1.0 when both were controlled. Moreover, the correlations between the two factors and measures of other constructs were similar when careless responding and acquiescence were controlled. These findings challenge recent research purporting to demonstrate that job satisfaction and dissatisfaction are distinct constructs. Recommendations for future investigations of construct dimensionality are provided.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.496
GPT teacher head0.579
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it