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Record W2889192100 · doi:10.25336/csp29397

Review essay on Rex B. Kline’s Principles and Practice of Structural Equation Modeling: Encouraging a fifth edition

2018· article· en· W2889192100 on OpenAlexaffvenue
Leslie A. Hayduk

Bibliographic record

VenueCanadian Studies in Population · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStructural equation modelingCovarianceComputer scienceStructuringEpistemologyPsychologySociologyManagement scienceMathematicsStatisticsPolitical scienceLawPhilosophyMachine learningEconomics

Abstract

fetched live from OpenAlex

Principles and Practice of Structural Equation Modeling, 4th editionRex B. KlineNew York: The Guilford Press 2016ISBN 978-1-4625-2334-4Softcover, US$65, 534 pp.Kline’s fourth edition is reasonably strong but improvable. The text aims to introduce newcomers to fundamental structural equation modeling (SEM) principles, but tends to confuse “Principles” with “Rules.” Rules having insufficient grounding in principles leave readers ill-prepared for understanding and responding to changes in previously traditional “rules”—such as those concerning model testing, and latents having single indicators. SEM’s foundations would be clearer if Kline began by presenting structural equation models as striving to represent causal effects—a commitment that differentiates structural equation models from regression and encourages model testing. I begin this review by summarizing the covariance/correlation implications of three simple causal structures, which pinpoints multiple text improvements and underpins the discussions of measurement and model testing that follow. Causal structuring also grounds my later comments regarding modelling means/intercepts and interactions. A file of Supplementary Sections expands on several points and lists multiple editorial corrections you might pencil into your copy of Kline’s text.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.365
GPT teacher head0.526
Teacher spread0.162 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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