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Record W2043951545 · doi:10.5539/ijps.v3n1p78

Moderating Effect of Gender and Age on the Relationship between Emotional Intelligence with Social and Academic Adjustment among First Year University Students

2011· article· en· W2043951545 on OpenAlexvenueno aff
Ishak Noor-Azniza, T. Jdaitawi Malek, T. Mustafa Farid

Bibliographic record

VenueInternational Journal of Psychological Studies · 2011
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEmotional intelligenceDevelopmental psychologyStructural equation modelingModerationSocial psychologyStatistics

Abstract

fetched live from OpenAlex

This study examined whether emotional intelligence is significantly correlated with social adjustment andacademic adjustment. It also explored the moderating effects of gender and age factors and their linked betweenemotional intelligence and social adjustment as well as academic adjustment among first year university students.289 first year university students (148 males and 141 females) at the Irbid Govern Orate, North of Jordan,participate in the study and were categorized based on two age groups, younger students between the age of 18 –25 and older students between the range of 26 and above. Two valid and reliable instruments were used to assessstudent’s emotional intelligence, social adjustment and academic adjustment. Correlation and multi-groupanalysis using structural equation model were used to analyse these data. The result shows no significantrelationship between emotional intelligence and of both social adjustment and academic adjustment. In addition,the moderating effect of gender was not found. However, the moderating effect of age on the relationshipbetween emotional intelligence with social adjustment and academic adjustment were established.

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 score0.019
Threshold uncertainty score0.250

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 head0.364
GPT teacher head0.454
Teacher spread0.090 · 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

Citations26
Published2011
Admission routes1
Has abstractyes

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