Review essay on Rex B. Kline’s Principles and Practice of Structural Equation Modeling: Encouraging a fifth edition
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".