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Record W4387397390 · doi:10.1177/08445621231204296

Notes to Factor Analysis Techniques for Construct Validity

2023· article· en· W4387397390 on OpenAlexvenueno aff
Mousa Alavi, Erik Biroš, Michelle Cleary

Bibliographic record

VenueCanadian Journal of Nursing Research · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct validityConstruct (python library)Computer scienceData scienceManagement scienceEngineeringMathematicsStatisticsPsychometricsProgramming language

Abstract

fetched live from OpenAlex

This paper introduces and discusses factor analysis techniques for construct validity, including some suggestions for reporting using the evidence to support the construct validity from exploratory and confirmatory factor analysis techniques. Construct validity is a vital part of psychological testing and a prerequisite to every measurement instrument, including aptitude, achievement, and interests. Research, particularly in nursing and the health sciences, depends on reliable and valid measurements. Therefore, a growing emphasis is on assessing validity regarding the structure of test variables commonly estimated by factor analysis techniques. However, it is not always clear how to report the analysis and use it to support the construct validity. Both exploratory and confirmatory factor analysis techniques provide vital evidence to support the construct validity. However, these are not the only available evidence for construct validity, and the researcher should always consider other sources of evidence to develop and support the construct validity of their intended measures. In addition, the collection and presentation of this evidence are not limited to a time, but the validity of constructs is a continuous process that leads to validating the underlying theories from which constructs have emerged.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.054
metaresearch head score (Gemma)0.189
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.946
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.189
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0080.012
Science and technology studies0.0040.006
Scholarly communication0.0050.007
Open science0.0020.004
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0330.014

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 head0.912
GPT teacher head0.665
Teacher spread0.247 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreMethods · Commentary

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

Citations25
Published2023
Admission routes1
Has abstractyes

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