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Record W2806165465 · doi:10.5539/ies.v11n6p92

Developing Scale for Determining the Social Participation Skills for Children and Analyzing Its Psychometric Characteristics

2018· article· en· W2806165465 on OpenAlexvenueno aff
Osman Samancı, Ebru Ocakcı, İsmail Seçer

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyScale (ratio)Reliability (semiconductor)Confirmatory factor analysisExploratory factor analysisContext (archaeology)Social skillsPsychometricsTest validityValidityApplied psychologySocial psychologyDevelopmental psychologyStructural equation modelingStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this research is to conduct validity and reliability studies of the Scale for the Determining Social Participation for Children, developed to measure social participation skills of children aged 7-10 years. During the development of the scale, pilot schemes, validity analyzes, and reliability analyzes were conducted. In this context, the research was carried out with a total of 472 elementary school students in the ages of 7-10 years using the descriptive survey model. Exploratory and confirmatory factor analyses were performed to examine the factor structure of the scale and it was determined that the scale had a structure consisting of 16 items and one dimension and that this model had a good level of model fit. In order to examine the reliability of the scale, internal consistency and split-half reliability analyzes were performed and it was found that the scale had sufficient reliability. It can be said that the Scale for the Determining Social Participation for Children is a reliable and valid measurement tool that can be used to measure the social participation skills of students aged 7-10 years.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.191
GPT teacher head0.463
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2018
Admission routes1
Has abstractyes

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