An extensive comparison of CB-SEM and PLS-SEM for reliability and validity
Bibliographic record
Abstract
Structural Equation Modeling (SEM) includes measurement and structural model for hypothesis testing. The results yielded from structural model is unlikely to be valid if a poor loading of an indicator is selected. The impact of these erroneous result on standardized loading is disregard. Thus, knowing how poor loading can affect the validity of measurement model is a crucial issue. This paper attempts to compare the standardized loadings result between two prominent SEM methods (CBSEM and PLS-SEM) using three varied of simula-tion models (TRA, Loyalty and UTAUT model) to investigate their effects on reliability and validity of measurement model. The data for each model were generated using R software by setting the value of standardized loading and the construct correlations (N=50, 100, 200 and 500). The value of standardized loadings was set to 0.60 for each construct in the model while the construct correlations were set in the range between 0.45 to 0.65. Then, the AMOS 21.0 and ADANCO 2.0 were used to perform the statistical analysis. It shows that good standardized loading can increase the reliability and validity of construct representation. CBSEM is particularly yielded valid and unbiased estimation under confirmatory condition (established theory) compared with PLS-SEM. The results are illustrated with empirical examples. This paper provides updated evidence about CBSEM and PLS-SEM when assessing the measurement model.
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 | 0.039 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".