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Record W4388268281 · doi:10.1002/icd.2473

An evaluation of the psychometric properties of the strengths and difficulties scale in Turkey: Implications for other <scp>non‐WEIRD</scp> countries

2023· article· en· W4388268281 on OpenAlexaff
Ted Ruffman, Bilge Selçuk, H. Melis Yavus‐Muren, Kubra Arikan, İ̇pek Tuncay

Bibliographic record

VenueInfant and Child Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsAlgoma UniversityUniversity of Toronto
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuUniversity of Otago
KeywordsStrengths and Difficulties QuestionnairePsychologyScale (ratio)Developed countryDemocracyDeveloping countryDevelopmental psychologyDemographyPsychiatryGeographyPolitical scienceEconomic growthSociologyMental healthPopulation

Abstract

fetched live from OpenAlex

Abstract The Strengths and Difficulties Questionnaire (SDQ) is a very widely used scale in which parents, teachers or the child rate various aspects of the child's well‐being. It is widely used in the Western world and is translated into 80+ languages. It is also used in countries that do not classify as WEIRD (Western, educated, industrialized, rich and democratic). However, unlike WEIRD countries, some studies indicate that the psychometric properties of the SDQ when used in non‐WEIRD countries are questionable. Therefore, we gave the SDQ to the mothers and teachers of 310 3‐ to 5‐year‐olds in urban centres of Turkey and examined its psychometric properties. Turkey is not a WEIRD country because it is not Western, although the participants in our study were well educated, living in an industrialized area, rich relative to others in Turkey (although poor relative to Westerners) and democratic. As such, it is not drastically different from WEIRD countries and our question was whether even relatively small deviations from standard WEIRD criteria could result in questionable psychometric properties for the SDQ.

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 score0.192
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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
Published2023
Admission routes1
Has abstractyes

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