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Record W4393217300 · doi:10.1080/1068316x.2024.2332789

Brief multidimensional self-control scale: psychometric properties and cross-gender measurement invariance of the Portuguese version

2024· article· en· W4393217300 on OpenAlexaff
Pedro Pechorro, Bruno Bonfá-Araújo, Matt DeLisi, Mário R. Simões

Bibliographic record

VenuePsychology Crime and Law · 2024
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsMeasurement invariancePortugueseScale (ratio)PsychologyBrazilian PortugueseClinical psychologyConfirmatory factor analysisStatisticsMathematicsStructural equation modelingGeographyCartography

Abstract

fetched live from OpenAlex

Self-control has been traditionally defined as the capacity to inhibit or overrule immediate urges in order to attain long-term goals, and it is considered an important topic of research in criminology and forensic psychology. The aim of the present study is to examine the psychometric properties of the Portuguese version of the Brief Multidimensional Self-Control Scale (BMSCS). Our sample consisted of 242 male and female participants (M = 30.19 years, SD = 12.78, range = 16–77 years) from Portugal. The one-factor model and the two-factor second-order model obtained adequate fits. Internal consistency/reliability, as measured by the alpha and omega coefficients, was adequate when considering the BMSCS total but the Inhibition and Initiation factors tended to present lower values. Convergent validity (with other self-control measures), divergent validity, and criterion-related validity (with justice-involvement and alcohol/drug abuse variables) were demonstrated. Measurement invariance across gender was established, with no significant differences being found when comparing male and female participants. Our findings add novel information and mostly support the use of the recently developed BMSCS as a short, valid, and reliable measure of self-control.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.114
GPT teacher head0.377
Teacher spread0.263 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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