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Record W2010221119 · doi:10.1080/1091367x.2010.495539

Evaluation of Social Cognitive Scaling Response Options in the Physical Activity Domain

2010· article· en· W2010221119 on OpenAlexafffund
Ryan E. Rhodes, Deborah Hunt Matheson, Rachel Mark

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2010
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsVancouver Island UniversityUniversity of Victoria
FundersCanadian Institutes of Health ResearchVancouver Island UniversityCanadian Diabetes Association
KeywordsLikert scaleSemantic differentialPsychologyCognitionReliability (semiconductor)Scale (ratio)ValidityPoint (geometry)PsychometricsClinical psychologySocial psychologyMathematicsDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the reliability, variability, and predictive validity of two common scaling response formats (semantic differential, Likert-type) and two numbers of response options (5-point, 7-point) in the physical activity domain. Constructs of the theory of planned behavior were chosen in this analysis based on its high frequency of application in exercise and physical activity. The participants were 412 undergraduate students who completed measures of the theory of planned behavior and self-reported physical activity two weeks later. One of four questionnaires, each containing a scaling response format, were distributed randomly and formed four groups of approximately n = 100 for comparisons (5-point Likert, 5-point semantic differential, 7-point Likert, 7-point semantic differential). Results showed that the 7-point options had greater variability than the 5-point options and that the 7-point Likert scale had the highest overall reliability. These differences, however, did not translate into predictive validity of behavior. The findings support the use of all of these types of scales with physical activity research because of their relatively equivalent outcomes.

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 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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.149
GPT teacher head0.486
Teacher spread0.337 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2010
Admission routes2
Has abstractyes

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