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Record W2784930664 · doi:10.5539/ies.v11n2p27

A Survey on Chinese Scholars’ Adoption of Mixed Methods

2018· article· en· W2784930664 on OpenAlexvenueno aff
Yuchun Zhou

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMultimethodologyContext (archaeology)ChinaStructural equation modelingAdaptabilityQualitative propertyQualitative researchPerceptionPsychologySocial scienceSociologyPolitical scienceGeographyMathematicsStatisticsManagement

Abstract

fetched live from OpenAlex

Since the 1980s when mixed methods emerged as “the third research methodology”, it was widely adopted in Western countries. However, inadequate literature revealed how this methodology was accepted by scholars in Asian countries, such as China. Therefore, this paper used a quantitative survey to investigate Chinese scholars’ perceptions and adoption of mixed methods in China.The data of the study were obtained from 247 Chinese scholars in higher education. Structural equation modelling was used to examine the relationship between participants’ perceptions and use of mixed methods. The results revealed that Chinese scholars’ research expertise of using quantitative and qualitative methods as well as their perceived advantage of using mixed methods has significantly influenced their adoption of mixed methods. This paper advanced the literature of the evolution of mixed methods by investigating the expansion and adaptability of mixed methods in an Asian context.

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)0.018
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.267
GPT teacher head0.572
Teacher spread0.304 · 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.

Study designObservational
DomainMethods
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

Citations1
Published2018
Admission routes1
Has abstractyes

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